AI reflection
Prof. Yucheng Zhang
2 ILLUSTRATION-FULL MODEL: M ON X
2.2 Study 1
基于原始值的调节分析
\[Y=i_Y+b_1X+b_2W+b_3XW+e_Y\]
\[Y=i_y+(b_1+b_3W)X+b_2W+e_Y\]
判断标准：同号同向加强（++更正/–更负）；异号反向削弱（+-不那么正/-+不那么负）
可以写成：
\[Y=i_y+f(W)X+b_2W+e_Y\]
S1=PROCESS(data1, y="WP.SystemPerformanceImprovementBehaviorV", x="W.X", mods="BA.AIOnlineCommunicationSkillsV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.SystemPerformanceImprovementBehaviorV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.AIOnlineCommunicationSkillsV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.SystemPerformanceImprovementBehaviorV ~ W.X*BA.AIOnlineCommunicationSkillsV + (W.X|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.SystemPerformanceImprovementBehaviorV (2) WP.SystemPerformanceImprovementBehaviorV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.449 *** 2.447 ***
## (0.100) (0.390)
## W.X -0.121 0.530
## (0.082) (0.321)
## BA.AIOnlineCommunicationSkillsV 0.235 **
## (0.089)
## W.X:BA.AIOnlineCommunicationSkillsV -0.153 *
## (0.073)
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.019
## Conditional R^2 0.496 0.502
## AIC 2229.463 2231.960
## BIC 2256.453 2267.947
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.119 1.068
## Var: B.ID W.X 0.001 0.000
## Cov: B.ID (Intercept) W.X -0.037 -0.008
## Var: Residual 1.101 1.094
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ─────────────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────────────
## W.X * BA.AIOnlineCommunicationSkillsV 4.39 1 496 .037 *
## ─────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ───────────────────────────────────────────────────────────────────────────────────
## "BA.AIOnlineCommunicationSkillsV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────────────────
## 3.146 (- SD) 0.049 (0.115) 0.428 .669 [-0.176, 0.274]
## 4.260 (Mean) -0.121 (0.081) -1.491 .137 [-0.280, 0.038]
## 5.374 (+ SD) -0.291 (0.115) -2.536 .012 * [-0.516, -0.066]
## ───────────────────────────────────────────────────────────────────────────────────interact_plot(S1$model.y, W.X, BA.AIOnlineCommunicationSkillsV,modx.values = "plus-minus")+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
2.3 Study 2
表10.1包含了两个模型的同归系数并总了统计量。前四行构建了指示符代码和两个乘积，以及剩余的行估计出：
\[\hat{Y}=i_Y+b_1 D_1+b_2 D_2+b_3\] \[\hat{Y}=i_Y+b_1 D_1+b_2 D_2+b_3 W+b_4 D_1 W+b_5 D_2 W\] 公式可改写为：
\[\hat{Y}=i_Y+(b_1 +b_4 W)D_1+(b_2+b_5 W)D_2 +b_3 W\]
data2=import("AIReflectionBW.NoMissingS2.sav")%>%data.table #%>%
data2$W.dX=as.factor(data2$W.X)
data2 <- data2 %>%
mutate(
W.X10 = ifelse(W.X == 1, 1, 0),
W.X01 = ifelse(W.X == 2, 1, 0)
)
data2=data2[, W.CheckDummyX := paste(W.Intervention_new,W.X,W.X10,W.X01, sep = "_")]
Freq(data2$W.CheckDummyX)## Frequency Statistics:
## ───────────────────────────
## N %
## ───────────────────────────
## AI_0_0_0 328 33.3
## No_1_1_0 328 33.3
## Traditional_2_0_1 328 33.3
## ───────────────────────────
## Total N = 984data2=added(data2,{
W.X10BA.AIOnlineCommunicationSkillsV=W.X10*BA.AIOnlineCommunicationSkillsV
W.X01BA.AIOnlineCommunicationSkillsV=W.X01*BA.AIOnlineCommunicationSkillsV})
variables <- c(
"BA.AIOnlineCommunicationSkillsV", "BA.StructureV", "BA.WayOfQuestioningV",
"BA.ClarityOfInformationV", "BA.AIInteractionQualityV", "BA.ProblemSolvingConfidenceV",
"BA.NeedForPersonalizationDueToAIV", "BA.ReflectionOnAIUseV", "BA.CapabilityV",
"BA.PositiveReflectionOnAIUseV", "BA.NegativeReflectionOnAIUseV", "BB.AIUsageV",
"BB.AITechnologyAnxietyV", "BB.TrustInAIV", "BA.EffectivenessV",
"BA.QualityV", "BA.PersonalControlV", "BA.AIServiceFailureV",
"BA.AnthropomorphismV"
)
# 使用 lapply 和 := 动态创建交乘项变量
data2=data2[, paste0("W.X10", variables) := lapply(.SD, function(x) W.X10 * x), .SDcols = variables]
data2=data2[, paste0("W.X01", variables) := lapply(.SD, function(x) W.X01 * x), .SDcols = variables]
# 查看结果
#print(names(data2))2.3.1 Treating W.X as moderator
data2$W.X=as.factor(data2$W.X)
S2=PROCESS(data2, y="WP.SystemPerformanceImprovementBehaviorV", x="BA.AIOnlineCommunicationSkillsV", mods="W.X", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.SystemPerformanceImprovementBehaviorV
## - Predictor (X) : BA.AIOnlineCommunicationSkillsV
## - Mediators (M) : -
## - Moderators (W) : W.X
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.SystemPerformanceImprovementBehaviorV ~ BA.AIOnlineCommunicationSkillsV*W.X + (W.X|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.SystemPerformanceImprovementBehaviorV (2) WP.SystemPerformanceImprovementBehaviorV
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 1.956 *** 1.511 ***
## (0.349) (0.375)
## BA.AIOnlineCommunicationSkillsV 0.348 *** 0.447 ***
## (0.079) (0.085)
## W.X1 0.692 **
## (0.259)
## W.X2 0.699 **
## (0.260)
## BA.AIOnlineCommunicationSkillsV:W.X1 -0.154 **
## (0.059)
## BA.AIOnlineCommunicationSkillsV:W.X2 -0.155 **
## (0.059)
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.076 0.077
## Conditional R^2 0.683 0.687
## AIC 2931.920 2944.105
## BIC 2975.890 3007.617
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.291 1.294
## Var: B.ID W.X1 0.078 0.084
## Var: B.ID W.X2 0.084 0.090
## Cov: B.ID (Intercept) W.X1 0.039 0.038
## Cov: B.ID (Intercept) W.X2 -0.011 -0.012
## Cov: B.ID W.X1 W.X2 -0.081 -0.087
## Var: Residual 0.712 0.702
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ─────────────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────────────
## BA.AIOnlineCommunicationSkillsV * W.X 5.15 2 257 .006 **
## ─────────────────────────────────────────────────────────────
##
## Simple Slopes: "BA.AIOnlineCommunicationSkillsV" (X) ==> "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ────────────────────────────────────────────────────
## "W.X" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────
## 0 0.447 (0.085) 5.263 <.001 *** [0.280, 0.613]
## 1 0.293 (0.089) 3.297 .001 ** [0.119, 0.467]
## 2 0.292 (0.087) 3.370 <.001 *** [0.122, 0.461]
## ────────────────────────────────────────────────────2.3.2 Treating W.X as X
S2.i=PROCESS(data2, y="WP.SystemPerformanceImprovementBehaviorV", x="W.X10", mods="BA.AIOnlineCommunicationSkillsV",covs=c("W.X01","W.X01BA.AIOnlineCommunicationSkillsV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.SystemPerformanceImprovementBehaviorV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.AIOnlineCommunicationSkillsV
## - Covariates (C) : W.X01, W.X01BA.AIOnlineCommunicationSkillsV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.SystemPerformanceImprovementBehaviorV ~ W.X01 + W.X01BA.AIOnlineCommunicationSkillsV + W.X10*BA.AIOnlineCommunicationSkillsV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.SystemPerformanceImprovementBehaviorV (2) WP.SystemPerformanceImprovementBehaviorV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.413 *** 1.511 ***
## (0.108) (0.375)
## W.X01 0.212 0.699 **
## (0.239) (0.260)
## W.X01BA.AIOnlineCommunicationSkillsV -0.040 -0.155 **
## (0.054) (0.059)
## W.X10 0.038 0.692 **
## (0.070) (0.259)
## BA.AIOnlineCommunicationSkillsV 0.447 ***
## (0.085)
## W.X10:BA.AIOnlineCommunicationSkillsV -0.154 **
## (0.059)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.077
## Conditional R^2 0.685 0.687
## AIC 2960.196 2944.105
## BIC 3013.937 3007.617
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.552 1.294
## Var: B.ID W.X10 0.087 0.084
## Var: B.ID W.X01 0.087 0.090
## Cov: B.ID (Intercept) W.X10 -0.040 0.038
## Cov: B.ID (Intercept) W.X01 -0.066 -0.012
## Cov: B.ID W.X10 W.X01 -0.083 -0.087
## Var: Residual 0.710 0.702
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ───────────────────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────────────────
## W.X10 * BA.AIOnlineCommunicationSkillsV 6.84 1 340 .009 **
## ───────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ──────────────────────────────────────────────────────────────────────────────────
## "BA.AIOnlineCommunicationSkillsV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────────────────
## 3.075 (- SD) 0.220 (0.095) 2.308 .021 * [ 0.033, 0.406]
## 4.260 (Mean) 0.038 (0.067) 0.565 .572 [-0.094, 0.170]
## 5.444 (+ SD) -0.144 (0.095) -1.510 .132 [-0.330, 0.043]
## ──────────────────────────────────────────────────────────────────────────────────S2.ii=PROCESS(data2, y="WP.SystemPerformanceImprovementBehaviorV", x="W.X01", mods="BA.AIOnlineCommunicationSkillsV",covs=c("W.X10","W.X10BA.AIOnlineCommunicationSkillsV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.SystemPerformanceImprovementBehaviorV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.AIOnlineCommunicationSkillsV
## - Covariates (C) : W.X10, W.X10BA.AIOnlineCommunicationSkillsV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.SystemPerformanceImprovementBehaviorV ~ W.X10 + W.X10BA.AIOnlineCommunicationSkillsV + W.X01*BA.AIOnlineCommunicationSkillsV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.SystemPerformanceImprovementBehaviorV (2) WP.SystemPerformanceImprovementBehaviorV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.413 *** 1.511 ***
## (0.108) (0.375)
## W.X10 0.274 0.692 **
## (0.241) (0.259)
## W.X10BA.AIOnlineCommunicationSkillsV -0.055 -0.154 **
## (0.054) (0.059)
## W.X01 0.039 0.699 **
## (0.070) (0.260)
## BA.AIOnlineCommunicationSkillsV 0.447 ***
## (0.085)
## W.X01:BA.AIOnlineCommunicationSkillsV -0.155 **
## (0.059)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.077
## Conditional R^2 0.686 0.687
## AIC 2959.761 2944.105
## BIC 3013.501 3007.617
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.553 1.294
## Var: B.ID W.X10 0.079 0.084
## Var: B.ID W.X01 0.096 0.090
## Cov: B.ID (Intercept) W.X10 -0.008 0.038
## Cov: B.ID (Intercept) W.X01 -0.092 -0.012
## Cov: B.ID W.X10 W.X01 -0.084 -0.087
## Var: Residual 0.709 0.702
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ───────────────────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────────────────
## W.X01 * BA.AIOnlineCommunicationSkillsV 6.92 1 329 .009 **
## ───────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ──────────────────────────────────────────────────────────────────────────────────
## "BA.AIOnlineCommunicationSkillsV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────────────────
## 3.075 (- SD) 0.223 (0.103) 2.162 .032 * [ 0.021, 0.425]
## 4.260 (Mean) 0.039 (0.073) 0.539 .590 [-0.103, 0.182]
## 5.444 (+ SD) -0.144 (0.103) -1.400 .163 [-0.346, 0.058]
## ──────────────────────────────────────────────────────────────────────────────────interact_plot(S2.i$model.y, W.X10, BA.AIOnlineCommunicationSkillsV,modx.values = "plus-minus",at = list(W.X01 = 0, W.X01BA.AIOnlineCommunicationSkillsV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(S2.ii$model.y, W.X01, BA.AIOnlineCommunicationSkillsV,modx.values = "plus-minus",at = list(W.X10 = 0, W.X10BA.AIOnlineCommunicationSkillsV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
##
## Model Summary
##
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.SystemPerformanceImprovementBehaviorV (2) WP.SystemPerformanceImprovementBehaviorV
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 2.447 *** 1.511 ***
## (0.390) (0.375)
## W.X 0.530
## (0.321)
## BA.AIOnlineCommunicationSkillsV 0.235 ** 0.447 ***
## (0.089) (0.085)
## W.X:BA.AIOnlineCommunicationSkillsV -0.153 *
## (0.073)
## W.X1 0.692 **
## (0.259)
## W.X2 0.699 **
## (0.260)
## BA.AIOnlineCommunicationSkillsV:W.X1 -0.154 **
## (0.059)
## BA.AIOnlineCommunicationSkillsV:W.X2 -0.155 **
## (0.059)
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.019 0.077
## Conditional R^2 0.502 0.687
## AIC 2231.960 2944.105
## BIC 2267.947 3007.617
## Num. obs. 664 978
## Num. groups: B.ID 166 163
## Var: B.ID (Intercept) 1.068 1.294
## Var: B.ID W.X 0.000
## Cov: B.ID (Intercept) W.X -0.008
## Var: Residual 1.094 0.702
## Var: B.ID W.X1 0.084
## Var: B.ID W.X2 0.090
## Cov: B.ID (Intercept) W.X1 0.038
## Cov: B.ID (Intercept) W.X2 -0.012
## Cov: B.ID W.X1 W.X2 -0.087
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.\(\theta_{D_1\rightarrow Y}=b_1+b_4W=-.604+.162W\)：相对于不干预的条件，即那些被AI干预与没有干预的人M的差异。
总体解读：一个正向的估计值反映了在那些被AI干预的人M更高；而负向的估计意味着与被AI干预的人相比，那些没有干预的人M更高。
\(D_1\) 的系数 (\(b_1\))：在性别歧视得分为零时（W=0），相对于不干预，AI干预对M的影响。负向估计值-.604意味着AI干预M更低。
交互项 \(D_1 \times W\) 的系数 (\(b_4\)):
意义：它量化了这个相对条件效应随着W变化一个单位而变化的程度。AI干预与W的交互效应。
b4=.162：说明W每增加一个单位，AI干预对M的影响增加.162个单位。此交互项显著，表明W调节了AI干预的影响。
判断标准：同号同向加强（++更正/–更负）；异号反向削弱（+-不那么正/-+不那么负）
\(\theta_{D_2\rightarrow Y}=b_2+b_5 W\)有类似的解释
3 STUDY 1 USING .05 AS STANDARD
3.1 Analysis
S1.WP.SystemPerformanceImprovementBehaviorVBA.AIOnlineCommunicationSkillsV=PROCESS(data1, y="WP.SystemPerformanceImprovementBehaviorV", x="W.X", mods="BA.AIOnlineCommunicationSkillsV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.SystemPerformanceImprovementBehaviorV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.AIOnlineCommunicationSkillsV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.SystemPerformanceImprovementBehaviorV ~ W.X*BA.AIOnlineCommunicationSkillsV + (W.X|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.SystemPerformanceImprovementBehaviorV (2) WP.SystemPerformanceImprovementBehaviorV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.449 *** 2.447 ***
## (0.100) (0.390)
## W.X -0.121 0.530
## (0.082) (0.321)
## BA.AIOnlineCommunicationSkillsV 0.235 **
## (0.089)
## W.X:BA.AIOnlineCommunicationSkillsV -0.153 *
## (0.073)
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.019
## Conditional R^2 0.496 0.502
## AIC 2229.463 2231.960
## BIC 2256.453 2267.947
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.119 1.068
## Var: B.ID W.X 0.001 0.000
## Cov: B.ID (Intercept) W.X -0.037 -0.008
## Var: Residual 1.101 1.094
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ─────────────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────────────
## W.X * BA.AIOnlineCommunicationSkillsV 4.39 1 496 .037 *
## ─────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ───────────────────────────────────────────────────────────────────────────────────
## "BA.AIOnlineCommunicationSkillsV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────────────────
## 3.146 (- SD) 0.049 (0.115) 0.428 .669 [-0.176, 0.274]
## 4.260 (Mean) -0.121 (0.081) -1.491 .137 [-0.280, 0.038]
## 5.374 (+ SD) -0.291 (0.115) -2.536 .012 * [-0.516, -0.066]
## ───────────────────────────────────────────────────────────────────────────────────S1.WP.SystemPerformanceImprovementBehaviorVBA.StructureV=PROCESS(data1, y="WP.SystemPerformanceImprovementBehaviorV", x="W.X", mods="BA.StructureV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.SystemPerformanceImprovementBehaviorV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.StructureV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.SystemPerformanceImprovementBehaviorV ~ W.X*BA.StructureV + (W.X|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.SystemPerformanceImprovementBehaviorV (2) WP.SystemPerformanceImprovementBehaviorV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.449 *** 2.547 ***
## (0.100) (0.345)
## W.X -0.121 0.443
## (0.082) (0.284)
## BA.StructureV 0.218 **
## (0.080)
## W.X:BA.StructureV -0.136 *
## (0.066)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.020
## Conditional R^2 0.496 0.502
## AIC 2229.463 2232.115
## BIC 2256.453 2268.102
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.119 1.064
## Var: B.ID W.X 0.001 0.000
## Cov: B.ID (Intercept) W.X -0.037 -0.007
## Var: Residual 1.101 1.095
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ───────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────
## W.X * BA.StructureV 4.28 1 496 .039 *
## ───────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ─────────────────────────────────────────────────────────────────
## "BA.StructureV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────
## 2.902 (- SD) 0.047 (0.115) 0.410 .682 [-0.178, 0.272]
## 4.136 (Mean) -0.121 (0.081) -1.491 .137 [-0.280, 0.038]
## 5.369 (+ SD) -0.289 (0.115) -2.517 .012 * [-0.514, -0.064]
## ─────────────────────────────────────────────────────────────────S1.WP.SystemPerformanceImprovementBehaviorVBA.WayOfQuestioningV=PROCESS(data1, y="WP.SystemPerformanceImprovementBehaviorV", x="W.X", mods="BA.WayOfQuestioningV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.SystemPerformanceImprovementBehaviorV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.WayOfQuestioningV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.SystemPerformanceImprovementBehaviorV ~ W.X*BA.WayOfQuestioningV + (W.X|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.SystemPerformanceImprovementBehaviorV (2) WP.SystemPerformanceImprovementBehaviorV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.449 *** 2.660 ***
## (0.100) (0.372)
## W.X -0.121 0.456
## (0.082) (0.304)
## BA.WayOfQuestioningV 0.182 *
## (0.083)
## W.X:BA.WayOfQuestioningV -0.133 *
## (0.068)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.013
## Conditional R^2 0.496 0.501
## AIC 2229.463 2234.244
## BIC 2256.453 2270.230
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.119 1.088
## Var: B.ID W.X 0.001 0.000
## Cov: B.ID (Intercept) W.X -0.037 -0.016
## Var: Residual 1.101 1.095
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ──────────────────────────────────────────────────
## F df1 df2 p
## ──────────────────────────────────────────────────
## W.X * BA.WayOfQuestioningV 3.88 1 496 .049 *
## ──────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ────────────────────────────────────────────────────────────────────────
## "BA.WayOfQuestioningV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────────
## 3.130 (- SD) 0.039 (0.115) 0.340 .734 [-0.186, 0.264]
## 4.331 (Mean) -0.121 (0.081) -1.490 .137 [-0.280, 0.038]
## 5.533 (+ SD) -0.281 (0.115) -2.447 .015 * [-0.506, -0.056]
## ────────────────────────────────────────────────────────────────────────S1.WA.AffectiveRuminationVBA.ClarityOfInformationV=PROCESS(data1, y="WA.AffectiveRuminationV", x="W.X", mods="BA.ClarityOfInformationV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.AffectiveRuminationV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.ClarityOfInformationV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.AffectiveRuminationV ~ W.X*BA.ClarityOfInformationV + (W.X|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────────
## (1) WA.AffectiveRuminationV (2) WA.AffectiveRuminationV
## ──────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.798 *** 4.194 ***
## (0.117) (0.423)
## W.X 0.017 -0.586 *
## (0.074) (0.265)
## BA.ClarityOfInformationV -0.092
## (0.094)
## W.X:BA.ClarityOfInformationV 0.140 *
## (0.059)
## ──────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.003
## Conditional R^2 0.680 0.682
## AIC 2171.687 2176.983
## BIC 2198.676 2212.969
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.858 1.859
## Var: B.ID W.X 0.080 0.055
## Cov: B.ID (Intercept) W.X -0.111 -0.094
## Var: Residual 0.840 0.840
## ──────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.AffectiveRuminationV" (Y)
## ──────────────────────────────────────────────────────
## F df1 df2 p
## ──────────────────────────────────────────────────────
## W.X * BA.ClarityOfInformationV 5.60 1 164 .019 *
## ──────────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WA.AffectiveRuminationV" (Y)
## ───────────────────────────────────────────────────────────────────────────
## "BA.ClarityOfInformationV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────────
## 3.068 (- SD) -0.157 (0.104) -1.512 .132 [-0.361, 0.047]
## 4.313 (Mean) 0.017 (0.073) 0.230 .819 [-0.127, 0.161]
## 5.559 (+ SD) 0.191 (0.104) 1.837 .068 . [-0.013, 0.395]
## ───────────────────────────────────────────────────────────────────────────S1.WP.AdviceThinkingBasedSocialLearningVBA.AIInteractionQualityV=PROCESS(data1, y="WP.AdviceThinkingBasedSocialLearningV", x="W.X", mods="BA.AIInteractionQualityV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.AdviceThinkingBasedSocialLearningV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.AIInteractionQualityV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.AdviceThinkingBasedSocialLearningV ~ W.X*BA.AIInteractionQualityV + (W.X|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.AdviceThinkingBasedSocialLearningV (2) WP.AdviceThinkingBasedSocialLearningV
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.782 *** 1.788 ***
## (0.112) (0.302)
## W.X -0.187 * 0.332
## (0.091) (0.276)
## BA.AIInteractionQualityV 0.500 ***
## (0.072)
## W.X:BA.AIInteractionQualityV -0.130 *
## (0.065)
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.003 0.144
## Conditional R^2 0.483 0.486
## AIC 2364.000 2330.273
## BIC 2390.990 2366.259
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.418 0.953
## Var: B.ID W.X 0.018 0.002
## Cov: B.ID (Intercept) W.X -0.159 -0.048
## Var: Residual 1.365 1.362
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.AdviceThinkingBasedSocialLearningV" (Y)
## ──────────────────────────────────────────────────────
## F df1 df2 p
## ──────────────────────────────────────────────────────
## W.X * BA.AIInteractionQualityV 3.96 1 492 .047 *
## ──────────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.AdviceThinkingBasedSocialLearningV" (Y)
## ────────────────────────────────────────────────────────────────────────────
## "BA.AIInteractionQualityV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────────────
## 2.600 (- SD) -0.006 (0.128) -0.048 .962 [-0.258, 0.245]
## 3.986 (Mean) -0.187 (0.091) -2.060 .040 * [-0.364, -0.009]
## 5.372 (+ SD) -0.367 (0.128) -2.864 .004 ** [-0.619, -0.116]
## ────────────────────────────────────────────────────────────────────────────S1.WA.SelfReflectionForManipulationCheckVBA.ProblemSolvingConfidenceV=PROCESS(data1, y="WA.SelfReflectionForManipulationCheckV", x="W.X", mods="BA.ProblemSolvingConfidenceV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.SelfReflectionForManipulationCheckV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.ProblemSolvingConfidenceV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.SelfReflectionForManipulationCheckV ~ W.X*BA.ProblemSolvingConfidenceV + (W.X|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WA.SelfReflectionForManipulationCheckV (2) WA.SelfReflectionForManipulationCheckV
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.404 *** 1.760 ***
## (0.097) (0.387)
## W.X -0.064 0.601
## (0.072) (0.323)
## BA.ProblemSolvingConfidenceV 0.606 ***
## (0.087)
## W.X:BA.ProblemSolvingConfidenceV -0.153 *
## (0.072)
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.151
## Conditional R^2 0.547 0.549
## AIC 2076.540 2042.771
## BIC 2103.530 2078.757
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.135 0.788
## Var: B.ID W.X 0.015 0.003
## Cov: B.ID (Intercept) W.X -0.132 -0.050
## Var: Residual 0.839 0.837
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.SelfReflectionForManipulationCheckV" (Y)
## ──────────────────────────────────────────────────────────
## F df1 df2 p
## ──────────────────────────────────────────────────────────
## W.X * BA.ProblemSolvingConfidenceV 4.45 1 487 .035 *
## ──────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WA.SelfReflectionForManipulationCheckV" (Y)
## ────────────────────────────────────────────────────────────────────────────────
## "BA.ProblemSolvingConfidenceV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────────────────
## 3.374 (- SD) 0.086 (0.101) 0.856 .392 [-0.111, 0.284]
## 4.359 (Mean) -0.064 (0.071) -0.899 .369 [-0.203, 0.075]
## 5.344 (+ SD) -0.214 (0.101) -2.128 .034 * [-0.412, -0.017]
## ────────────────────────────────────────────────────────────────────────────────S1.WP.AIEnabledInnovationBehaviorVBA.WayOfQuestioningV=PROCESS(data1, y="WP.AIEnabledInnovationBehaviorV", x="W.X", mods="BA.WayOfQuestioningV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.AIEnabledInnovationBehaviorV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.WayOfQuestioningV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.AIEnabledInnovationBehaviorV ~ W.X*BA.WayOfQuestioningV + (W.X|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.AIEnabledInnovationBehaviorV (2) WP.AIEnabledInnovationBehaviorV
## ───────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.437 *** 2.523 ***
## (0.109) (0.404)
## W.X -0.269 ** 0.390
## (0.087) (0.325)
## BA.WayOfQuestioningV 0.211 *
## (0.090)
## W.X:BA.WayOfQuestioningV -0.152 *
## (0.072)
## ───────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.007 0.020
## Conditional R^2 0.514 0.519
## AIC 2325.675 2329.269
## BIC 2352.665 2365.255
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.352 1.309
## Var: B.ID W.X 0.001 0.000
## Cov: B.ID (Intercept) W.X -0.041 -0.012
## Var: Residual 1.259 1.251
## ───────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.AIEnabledInnovationBehaviorV" (Y)
## ──────────────────────────────────────────────────
## F df1 df2 p
## ──────────────────────────────────────────────────
## W.X * BA.WayOfQuestioningV 4.42 1 496 .036 *
## ──────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.AIEnabledInnovationBehaviorV" (Y)
## ────────────────────────────────────────────────────────────────────────
## "BA.WayOfQuestioningV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────────
## 3.130 (- SD) -0.086 (0.123) -0.701 .484 [-0.327, 0.155]
## 4.331 (Mean) -0.269 (0.087) -3.095 .002 ** [-0.439, -0.099]
## 5.533 (+ SD) -0.451 (0.123) -3.675 <.001 *** [-0.692, -0.211]
## ────────────────────────────────────────────────────────────────────────S1.WP.AIEnabledCreativityVBA.NeedForPersonalizationDueToAIV=PROCESS(data1, y="WP.AIEnabledCreativityV", x="W.X", mods="BA.NeedForPersonalizationDueToAIV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.AIEnabledCreativityV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.NeedForPersonalizationDueToAIV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.AIEnabledCreativityV ~ W.X*BA.NeedForPersonalizationDueToAIV + (W.X|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.AIEnabledCreativityV (2) WP.AIEnabledCreativityV
## ───────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.677 *** 2.216 ***
## (0.116) (0.335)
## W.X -0.225 * 0.552 *
## (0.088) (0.264)
## BA.NeedForPersonalizationDueToAIV 0.355 ***
## (0.077)
## W.X:BA.NeedForPersonalizationDueToAIV -0.189 **
## (0.061)
## ───────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.005 0.062
## Conditional R^2 0.536 0.544
## AIC 2336.662 2326.359
## BIC 2363.652 2362.345
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.604 1.380
## Var: B.ID W.X 0.019 0.004
## Cov: B.ID (Intercept) W.X -0.175 -0.073
## Var: Residual 1.256 1.239
## ───────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.AIEnabledCreativityV" (Y)
## ───────────────────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────────────────
## W.X * BA.NeedForPersonalizationDueToAIV 9.70 1 489 .002 **
## ───────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.AIEnabledCreativityV" (Y)
## ─────────────────────────────────────────────────────────────────────────────────────
## "BA.NeedForPersonalizationDueToAIV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────────────────────────
## 2.683 (- SD) 0.045 (0.122) 0.364 .716 [-0.195, 0.284]
## 4.110 (Mean) -0.225 (0.087) -2.602 .010 ** [-0.395, -0.056]
## 5.536 (+ SD) -0.495 (0.122) -4.042 <.001 *** [-0.735, -0.255]
## ─────────────────────────────────────────────────────────────────────────────────────S1.WP.VoiceForSystemImprovmentVBA.NeedForPersonalizationDueToAIV=PROCESS(data1, y="WP.VoiceForSystemImprovmentV", x="W.X", mods="BA.NeedForPersonalizationDueToAIV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.VoiceForSystemImprovmentV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.NeedForPersonalizationDueToAIV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.VoiceForSystemImprovmentV ~ W.X*BA.NeedForPersonalizationDueToAIV + (W.X|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.VoiceForSystemImprovmentV (2) WP.VoiceForSystemImprovmentV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.646 *** 2.593 ***
## (0.107) (0.318)
## W.X -0.156 0.304
## (0.080) (0.242)
## BA.NeedForPersonalizationDueToAIV 0.256 ***
## (0.073)
## W.X:BA.NeedForPersonalizationDueToAIV -0.112 *
## (0.056)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.003 0.039
## Conditional R^2 0.563 0.568
## AIC 2236.569 2235.592
## BIC 2263.558 2271.578
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.391 1.277
## Var: B.ID W.X 0.001 0.000
## Cov: B.ID (Intercept) W.X -0.037 0.006
## Var: Residual 1.054 1.048
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.VoiceForSystemImprovmentV" (Y)
## ───────────────────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────────────────
## W.X * BA.NeedForPersonalizationDueToAIV 4.03 1 496 .045 *
## ───────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.VoiceForSystemImprovmentV" (Y)
## ─────────────────────────────────────────────────────────────────────────────────────
## "BA.NeedForPersonalizationDueToAIV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────────────────────────
## 2.683 (- SD) 0.004 (0.112) 0.031 .975 [-0.217, 0.224]
## 4.110 (Mean) -0.156 (0.079) -1.963 .050 . [-0.312, -0.000]
## 5.536 (+ SD) -0.316 (0.112) -2.807 .005 ** [-0.536, -0.095]
## ─────────────────────────────────────────────────────────────────────────────────────S1.WP.TakingChargeBehaviorsForSystemImprovementVBA.ReflectionOnAIUseV=PROCESS(data1, y="WP.TakingChargeBehaviorsForSystemImprovementV", x="W.X", mods="BA.ReflectionOnAIUseV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.TakingChargeBehaviorsForSystemImprovementV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.ReflectionOnAIUseV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.TakingChargeBehaviorsForSystemImprovementV ~ W.X*BA.ReflectionOnAIUseV + (W.X|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.TakingChargeBehaviorsForSystemImprovementV (2) WP.TakingChargeBehaviorsForSystemImprovementV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.707 *** 2.143 ***
## (0.107) (0.333)
## W.X -0.154 0.357
## (0.082) (0.271)
## BA.ReflectionOnAIUseV 0.390 ***
## (0.079)
## W.X:BA.ReflectionOnAIUseV -0.127 *
## (0.064)
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.077
## Conditional R^2 0.532 0.536
## AIC 2256.283 2244.539
## BIC 2283.273 2280.525
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.352 1.124
## Var: B.ID W.X 0.006 0.001
## Cov: B.ID (Intercept) W.X -0.093 -0.027
## Var: Residual 1.115 1.110
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ───────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────
## W.X * BA.ReflectionOnAIUseV 3.92 1 495 .048 *
## ───────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ─────────────────────────────────────────────────────────────────────────
## "BA.ReflectionOnAIUseV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────────────
## 2.741 (- SD) 0.008 (0.116) 0.072 .942 [-0.218, 0.235]
## 4.013 (Mean) -0.154 (0.082) -1.878 .061 . [-0.314, 0.007]
## 5.286 (+ SD) -0.316 (0.116) -2.727 .007 ** [-0.542, -0.089]
## ─────────────────────────────────────────────────────────────────────────S1.WP.AIEnabledCreativityVBA.CapabilityV=PROCESS(data1, y="WP.AIEnabledCreativityV", x="W.X", mods="BA.CapabilityV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.AIEnabledCreativityV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.CapabilityV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.AIEnabledCreativityV ~ W.X*BA.CapabilityV + (W.X|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────────
## (1) WP.AIEnabledCreativityV (2) WP.AIEnabledCreativityV
## ───────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.677 *** 1.903 ***
## (0.116) (0.331)
## W.X -0.225 * 0.483
## (0.088) (0.269)
## BA.CapabilityV 0.423 ***
## (0.075)
## W.X:BA.CapabilityV -0.169 **
## (0.061)
## ───────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.005 0.096
## Conditional R^2 0.536 0.542
## AIC 2336.662 2318.400
## BIC 2363.652 2354.386
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.604 1.266
## Var: B.ID W.X 0.019 0.003
## Cov: B.ID (Intercept) W.X -0.175 -0.057
## Var: Residual 1.256 1.245
## ───────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.AIEnabledCreativityV" (Y)
## ────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────
## W.X * BA.CapabilityV 7.73 1 492 .006 **
## ────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.AIEnabledCreativityV" (Y)
## ──────────────────────────────────────────────────────────────────
## "BA.CapabilityV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────
## 2.768 (- SD) 0.016 (0.123) 0.131 .896 [-0.224, 0.256]
## 4.197 (Mean) -0.225 (0.087) -2.598 .010 ** [-0.395, -0.055]
## 5.625 (+ SD) -0.466 (0.123) -3.803 <.001 *** [-0.707, -0.226]
## ──────────────────────────────────────────────────────────────────S1.WP.AIEnabledInnovationBehaviorVBA.StructureV=PROCESS(data1, y="WP.AIEnabledInnovationBehaviorV", x="W.X", mods="BA.StructureV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.AIEnabledInnovationBehaviorV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.StructureV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.AIEnabledInnovationBehaviorV ~ W.X*BA.StructureV + (W.X|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.AIEnabledInnovationBehaviorV (2) WP.AIEnabledInnovationBehaviorV
## ───────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.437 *** 2.530 ***
## (0.109) (0.377)
## W.X -0.269 ** 0.419
## (0.087) (0.304)
## BA.StructureV 0.220 *
## (0.087)
## W.X:BA.StructureV -0.166 *
## (0.070)
## ───────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.007 0.022
## Conditional R^2 0.514 0.520
## AIC 2325.675 2328.020
## BIC 2352.665 2364.006
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.352 1.303
## Var: B.ID W.X 0.001 0.000
## Cov: B.ID (Intercept) W.X -0.041 -0.008
## Var: Residual 1.259 1.248
## ───────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.AIEnabledInnovationBehaviorV" (Y)
## ───────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────
## W.X * BA.StructureV 5.59 1 496 .018 *
## ───────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.AIEnabledInnovationBehaviorV" (Y)
## ─────────────────────────────────────────────────────────────────
## "BA.StructureV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────
## 2.902 (- SD) -0.063 (0.123) -0.517 .605 [-0.304, 0.177]
## 4.136 (Mean) -0.269 (0.087) -3.099 .002 ** [-0.439, -0.099]
## 5.369 (+ SD) -0.474 (0.123) -3.863 <.001 *** [-0.714, -0.233]
## ─────────────────────────────────────────────────────────────────S1.WP.AIEnabledInnovationBehaviorVBA.AIOnlineCommunicationSkillsV=PROCESS(data1, y="WP.AIEnabledInnovationBehaviorV", x="W.X", mods="BA.AIOnlineCommunicationSkillsV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.AIEnabledInnovationBehaviorV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.AIOnlineCommunicationSkillsV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.AIEnabledInnovationBehaviorV ~ W.X*BA.AIOnlineCommunicationSkillsV + (W.X|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.AIEnabledInnovationBehaviorV (2) WP.AIEnabledInnovationBehaviorV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.437 *** 2.262 ***
## (0.109) (0.424)
## W.X -0.269 ** 0.436
## (0.087) (0.343)
## BA.AIOnlineCommunicationSkillsV 0.276 **
## (0.096)
## W.X:BA.AIOnlineCommunicationSkillsV -0.165 *
## (0.078)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.007 0.028
## Conditional R^2 0.514 0.519
## AIC 2325.675 2326.829
## BIC 2352.665 2362.815
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.352 1.279
## Var: B.ID W.X 0.001 0.000
## Cov: B.ID (Intercept) W.X -0.041 -0.003
## Var: Residual 1.259 1.250
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.AIEnabledInnovationBehaviorV" (Y)
## ─────────────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────────────
## W.X * BA.AIOnlineCommunicationSkillsV 4.50 1 496 .034 *
## ─────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.AIEnabledInnovationBehaviorV" (Y)
## ───────────────────────────────────────────────────────────────────────────────────
## "BA.AIOnlineCommunicationSkillsV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────────────────
## 3.146 (- SD) -0.084 (0.123) -0.688 .492 [-0.325, 0.156]
## 4.260 (Mean) -0.269 (0.087) -3.096 .002 ** [-0.439, -0.099]
## 5.374 (+ SD) -0.453 (0.123) -3.688 <.001 *** [-0.694, -0.212]
## ───────────────────────────────────────────────────────────────────────────────────3.2 Plot
interact_plot(S1.WP.SystemPerformanceImprovementBehaviorVBA.AIOnlineCommunicationSkillsV$model.y, W.X, BA.AIOnlineCommunicationSkillsV,modx.values = "plus-minus")+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(S1.WP.SystemPerformanceImprovementBehaviorVBA.StructureV$model.y, W.X, BA.StructureV,modx.values = "plus-minus")+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(S1.WP.SystemPerformanceImprovementBehaviorVBA.WayOfQuestioningV$model.y, W.X, BA.WayOfQuestioningV,modx.values = "plus-minus")+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(S1.WA.AffectiveRuminationVBA.ClarityOfInformationV$model.y, W.X, BA.ClarityOfInformationV,modx.values = "plus-minus")+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(S1.WP.AdviceThinkingBasedSocialLearningVBA.AIInteractionQualityV$model.y, W.X, BA.AIInteractionQualityV,modx.values = "plus-minus")+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(S1.WA.SelfReflectionForManipulationCheckVBA.ProblemSolvingConfidenceV$model.y, W.X, BA.ProblemSolvingConfidenceV,modx.values = "plus-minus")+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(S1.WP.AIEnabledInnovationBehaviorVBA.WayOfQuestioningV$model.y, W.X, BA.WayOfQuestioningV,modx.values = "plus-minus")+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(S1.WP.AIEnabledCreativityVBA.NeedForPersonalizationDueToAIV$model.y, W.X, BA.NeedForPersonalizationDueToAIV,modx.values = "plus-minus")+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(S1.WP.VoiceForSystemImprovmentVBA.NeedForPersonalizationDueToAIV$model.y, W.X, BA.NeedForPersonalizationDueToAIV,modx.values = "plus-minus")+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(S1.WP.TakingChargeBehaviorsForSystemImprovementVBA.ReflectionOnAIUseV$model.y, W.X, BA.ReflectionOnAIUseV,modx.values = "plus-minus")+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(S1.WP.AIEnabledCreativityVBA.CapabilityV$model.y, W.X, BA.CapabilityV,modx.values = "plus-minus")+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(S1.WP.AIEnabledInnovationBehaviorVBA.StructureV$model.y, W.X, BA.StructureV,modx.values = "plus-minus")+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(S1.WP.AIEnabledInnovationBehaviorVBA.AIOnlineCommunicationSkillsV$model.y, W.X, BA.AIOnlineCommunicationSkillsV,modx.values = "plus-minus")+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
4 STUDY 1 USING .10 AS STANDARD
4.1 Analysis
S1.WP.AIEnabledInnovationBehaviorVBA.NeedForPersonalizationDueToAIV=PROCESS(data1, y="WP.AIEnabledInnovationBehaviorV", x="W.X", mods="BA.NeedForPersonalizationDueToAIV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.AIEnabledInnovationBehaviorV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.NeedForPersonalizationDueToAIV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.AIEnabledInnovationBehaviorV ~ W.X*BA.NeedForPersonalizationDueToAIV + (W.X|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.AIEnabledInnovationBehaviorV (2) WP.AIEnabledInnovationBehaviorV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.437 *** 2.268 ***
## (0.109) (0.321)
## W.X -0.269 ** 0.195
## (0.087) (0.265)
## BA.NeedForPersonalizationDueToAIV 0.285 ***
## (0.074)
## W.X:BA.NeedForPersonalizationDueToAIV -0.113
## (0.061)
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.007 0.050
## Conditional R^2 0.514 0.518
## AIC 2325.675 2322.542
## BIC 2352.665 2358.528
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.352 1.206
## Var: B.ID W.X 0.001 0.000
## Cov: B.ID (Intercept) W.X -0.041 0.009
## Var: Residual 1.259 1.253
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.AIEnabledInnovationBehaviorV" (Y)
## ───────────────────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────────────────
## W.X * BA.NeedForPersonalizationDueToAIV 3.42 1 496 .065 .
## ───────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.AIEnabledInnovationBehaviorV" (Y)
## ─────────────────────────────────────────────────────────────────────────────────────
## "BA.NeedForPersonalizationDueToAIV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────────────────────────
## 2.683 (- SD) -0.108 (0.123) -0.877 .381 [-0.349, 0.133]
## 4.110 (Mean) -0.269 (0.087) -3.092 .002 ** [-0.439, -0.098]
## 5.536 (+ SD) -0.430 (0.123) -3.495 <.001 *** [-0.671, -0.189]
## ─────────────────────────────────────────────────────────────────────────────────────S1.WP.TakingChargeBehaviorsForSystemImprovementVBA.PositiveReflectionOnAIUseV=PROCESS(data1, y="WP.TakingChargeBehaviorsForSystemImprovementV", x="W.X", mods="BA.PositiveReflectionOnAIUseV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.TakingChargeBehaviorsForSystemImprovementV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.PositiveReflectionOnAIUseV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.TakingChargeBehaviorsForSystemImprovementV ~ W.X*BA.PositiveReflectionOnAIUseV + (W.X|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.TakingChargeBehaviorsForSystemImprovementV (2) WP.TakingChargeBehaviorsForSystemImprovementV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.707 *** 2.070 ***
## (0.107) (0.300)
## W.X -0.154 0.293
## (0.082) (0.250)
## BA.PositiveReflectionOnAIUseV 0.402 ***
## (0.070)
## W.X:BA.PositiveReflectionOnAIUseV -0.110
## (0.058)
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.106
## Conditional R^2 0.532 0.535
## AIC 2256.283 2236.859
## BIC 2283.273 2272.845
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.352 1.046
## Var: B.ID W.X 0.006 0.000
## Cov: B.ID (Intercept) W.X -0.093 -0.019
## Var: Residual 1.115 1.111
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ───────────────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────────────
## W.X * BA.PositiveReflectionOnAIUseV 3.59 1 495 .059 .
## ───────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ─────────────────────────────────────────────────────────────────────────────────
## "BA.PositiveReflectionOnAIUseV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────────────────────
## 2.657 (- SD) 0.001 (0.116) 0.013 .990 [-0.225, 0.228]
## 4.070 (Mean) -0.154 (0.082) -1.877 .061 . [-0.314, 0.007]
## 5.483 (+ SD) -0.309 (0.116) -2.667 .008 ** [-0.536, -0.082]
## ─────────────────────────────────────────────────────────────────────────────────S1.WP.VoiceForSystemImprovmentVBA.AIOnlineCommunicationSkillsV=PROCESS(data1, y="WP.VoiceForSystemImprovmentV", x="W.X", mods="BA.AIOnlineCommunicationSkillsV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.VoiceForSystemImprovmentV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.AIOnlineCommunicationSkillsV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.VoiceForSystemImprovmentV ~ W.X*BA.AIOnlineCommunicationSkillsV + (W.X|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.VoiceForSystemImprovmentV (2) WP.VoiceForSystemImprovmentV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.646 *** 2.725 ***
## (0.107) (0.420)
## W.X -0.156 0.394
## (0.080) (0.314)
## BA.AIOnlineCommunicationSkillsV 0.216 *
## (0.095)
## W.X:BA.AIOnlineCommunicationSkillsV -0.129
## (0.071)
## ───────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.003 0.016
## Conditional R^2 0.563 0.567
## AIC 2236.569 2240.939
## BIC 2263.558 2276.925
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.391 1.350
## Var: B.ID W.X 0.001 0.000
## Cov: B.ID (Intercept) W.X -0.037 -0.014
## Var: Residual 1.054 1.050
## ───────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.VoiceForSystemImprovmentV" (Y)
## ─────────────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────────────
## W.X * BA.AIOnlineCommunicationSkillsV 3.27 1 496 .071 .
## ─────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.VoiceForSystemImprovmentV" (Y)
## ───────────────────────────────────────────────────────────────────────────────────
## "BA.AIOnlineCommunicationSkillsV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────────────────
## 3.146 (- SD) -0.012 (0.113) -0.108 .914 [-0.233, 0.208]
## 4.260 (Mean) -0.156 (0.080) -1.962 .050 . [-0.312, -0.000]
## 5.374 (+ SD) -0.300 (0.113) -2.666 .008 ** [-0.520, -0.079]
## ───────────────────────────────────────────────────────────────────────────────────S1.WP.SystemPerformanceImprovementBehaviorVBA.ClarityOfInformationV=PROCESS(data1, y="WP.SystemPerformanceImprovementBehaviorV", x="W.X", mods="BA.ClarityOfInformationV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.SystemPerformanceImprovementBehaviorV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.ClarityOfInformationV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.SystemPerformanceImprovementBehaviorV ~ W.X*BA.ClarityOfInformationV + (W.X|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.SystemPerformanceImprovementBehaviorV (2) WP.SystemPerformanceImprovementBehaviorV
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.449 *** 2.667 ***
## (0.100) (0.357)
## W.X -0.121 0.349
## (0.082) (0.293)
## BA.ClarityOfInformationV 0.181 *
## (0.080)
## W.X:BA.ClarityOfInformationV -0.109
## (0.065)
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.015
## Conditional R^2 0.496 0.500
## AIC 2229.463 2234.762
## BIC 2256.453 2270.748
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.119 1.082
## Var: B.ID W.X 0.001 0.000
## Cov: B.ID (Intercept) W.X -0.037 -0.016
## Var: Residual 1.101 1.098
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ──────────────────────────────────────────────────────
## F df1 df2 p
## ──────────────────────────────────────────────────────
## W.X * BA.ClarityOfInformationV 2.79 1 495 .096 .
## ──────────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ────────────────────────────────────────────────────────────────────────────
## "BA.ClarityOfInformationV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────────────
## 3.068 (- SD) 0.015 (0.115) 0.128 .898 [-0.211, 0.240]
## 4.313 (Mean) -0.121 (0.081) -1.489 .137 [-0.280, 0.038]
## 5.559 (+ SD) -0.257 (0.115) -2.233 .026 * [-0.482, -0.031]
## ────────────────────────────────────────────────────────────────────────────S1.WP.VoiceForSystemImprovmentVBA.StructureV=PROCESS(data1, y="WP.VoiceForSystemImprovmentV", x="W.X", mods="BA.StructureV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.VoiceForSystemImprovmentV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.StructureV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.VoiceForSystemImprovmentV ~ W.X*BA.StructureV + (W.X|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.VoiceForSystemImprovmentV (2) WP.VoiceForSystemImprovmentV
## ─────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.646 *** 2.808 ***
## (0.107) (0.372)
## W.X -0.156 0.291
## (0.080) (0.279)
## BA.StructureV 0.203 *
## (0.086)
## W.X:BA.StructureV -0.108
## (0.065)
## ─────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.003 0.018
## Conditional R^2 0.563 0.567
## AIC 2236.569 2241.275
## BIC 2263.558 2277.261
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.391 1.344
## Var: B.ID W.X 0.001 0.000
## Cov: B.ID (Intercept) W.X -0.037 -0.014
## Var: Residual 1.054 1.051
## ─────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.VoiceForSystemImprovmentV" (Y)
## ───────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────
## W.X * BA.StructureV 2.81 1 496 .094 .
## ───────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.VoiceForSystemImprovmentV" (Y)
## ─────────────────────────────────────────────────────────────────
## "BA.StructureV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────
## 2.902 (- SD) -0.023 (0.113) -0.200 .841 [-0.243, 0.198]
## 4.136 (Mean) -0.156 (0.080) -1.961 .050 . [-0.312, -0.000]
## 5.369 (+ SD) -0.289 (0.113) -2.572 .010 * [-0.510, -0.069]
## ─────────────────────────────────────────────────────────────────S1.WP.TakingChargeBehaviorsForSystemImprovementVBA.NegativeReflectionOnAIUseV=PROCESS(data1, y="WP.TakingChargeBehaviorsForSystemImprovementV", x="W.X", mods="BA.NegativeReflectionOnAIUseV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.TakingChargeBehaviorsForSystemImprovementV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.NegativeReflectionOnAIUseV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.TakingChargeBehaviorsForSystemImprovementV ~ W.X*BA.NegativeReflectionOnAIUseV + (W.X|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.TakingChargeBehaviorsForSystemImprovementV (2) WP.TakingChargeBehaviorsForSystemImprovementV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.707 *** 2.736 ***
## (0.107) (0.320)
## W.X -0.154 0.254
## (0.082) (0.251)
## BA.NegativeReflectionOnAIUseV 0.245 **
## (0.076)
## W.X:BA.NegativeReflectionOnAIUseV -0.103
## (0.060)
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.034
## Conditional R^2 0.532 0.535
## AIC 2256.283 2257.379
## BIC 2283.273 2293.365
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.352 1.255
## Var: B.ID W.X 0.006 0.003
## Cov: B.ID (Intercept) W.X -0.093 -0.056
## Var: Residual 1.115 1.112
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ───────────────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────────────
## W.X * BA.NegativeReflectionOnAIUseV 2.96 1 491 .086 .
## ───────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ─────────────────────────────────────────────────────────────────────────────────
## "BA.NegativeReflectionOnAIUseV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────────────────────
## 2.587 (- SD) -0.013 (0.116) -0.108 .914 [-0.240, 0.215]
## 3.956 (Mean) -0.154 (0.082) -1.875 .061 . [-0.314, 0.007]
## 5.325 (+ SD) -0.295 (0.116) -2.543 .011 * [-0.522, -0.068]
## ─────────────────────────────────────────────────────────────────────────────────S1.WA.LearningFromErrorsVBB.AIUsageV=PROCESS(data1, y="WA.LearningFromErrorsV", x="W.X", mods="BB.AIUsageV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.LearningFromErrorsV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BB.AIUsageV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.LearningFromErrorsV ~ W.X*BB.AIUsageV + (W.X|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────
## (1) WA.LearningFromErrorsV (2) WA.LearningFromErrorsV
## ─────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.252 *** 4.136 ***
## (0.105) (0.223)
## W.X 0.035 0.301
## (0.074) (0.156)
## BB.AIUsageV 0.037
## (0.063)
## W.X:BB.AIUsageV -0.085
## (0.044)
## ─────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.002
## Conditional R^2 0.618 0.620
## AIC 2163.529 2172.037
## BIC 2190.626 2208.167
## Num. obs. 676 676
## Num. groups: B.ID 169 169
## Var: B.ID (Intercept) 1.440 1.447
## Var: B.ID W.X 0.092 0.078
## Cov: B.ID (Intercept) W.X -0.142 -0.137
## Var: Residual 0.831 0.831
## ─────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 676 (8 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.LearningFromErrorsV" (Y)
## ─────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────
## W.X * BB.AIUsageV 3.75 1 167 .054 .
## ─────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WA.LearningFromErrorsV" (Y)
## ──────────────────────────────────────────────────────────────
## "BB.AIUsageV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────
## 1.457 (- SD) 0.177 (0.104) 1.705 .090 . [-0.026, 0.380]
## 3.124 (Mean) 0.035 (0.073) 0.474 .636 [-0.109, 0.178]
## 4.791 (+ SD) -0.107 (0.104) -1.035 .302 [-0.311, 0.096]
## ──────────────────────────────────────────────────────────────S1.WA.LearningFromErrorsVBA.AIInteractionQualityV=PROCESS(data1, y="WA.LearningFromErrorsV", x="W.X", mods="BA.AIInteractionQualityV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.LearningFromErrorsV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.AIInteractionQualityV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.LearningFromErrorsV ~ W.X*BA.AIInteractionQualityV + (W.X|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ────────────────────────────────────────────────────────────────────────────────────
## (1) WA.LearningFromErrorsV (2) WA.LearningFromErrorsV
## ────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.291 *** 2.688 ***
## (0.104) (0.290)
## W.X 0.031 0.410
## (0.075) (0.226)
## BA.AIInteractionQualityV 0.402 ***
## (0.069)
## W.X:BA.AIInteractionQualityV -0.095
## (0.054)
## ────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.114
## Conditional R^2 0.612 0.613
## AIC 2122.701 2102.870
## BIC 2149.690 2138.856
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.395 1.092
## Var: B.ID W.X 0.094 0.082
## Cov: B.ID (Intercept) W.X -0.131 -0.060
## Var: Residual 0.832 0.832
## ────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.LearningFromErrorsV" (Y)
## ──────────────────────────────────────────────────────
## F df1 df2 p
## ──────────────────────────────────────────────────────
## W.X * BA.AIInteractionQualityV 3.16 1 164 .077 .
## ──────────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WA.LearningFromErrorsV" (Y)
## ───────────────────────────────────────────────────────────────────────────
## "BA.AIInteractionQualityV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────────
## 2.600 (- SD) 0.163 (0.105) 1.552 .123 [-0.043, 0.369]
## 3.986 (Mean) 0.031 (0.074) 0.416 .678 [-0.115, 0.176]
## 5.372 (+ SD) -0.101 (0.105) -0.963 .337 [-0.307, 0.105]
## ───────────────────────────────────────────────────────────────────────────S1.WA.AffectiveRuminationVBA.AIOnlineCommunicationSkillsV=PROCESS(data1, y="WA.AffectiveRuminationV", x="W.X", mods="BA.AIOnlineCommunicationSkillsV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.AffectiveRuminationV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.AIOnlineCommunicationSkillsV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.AffectiveRuminationV ~ W.X*BA.AIOnlineCommunicationSkillsV + (W.X|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────────────
## (1) WA.AffectiveRuminationV (2) WA.AffectiveRuminationV
## ─────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.798 *** 3.959 ***
## (0.117) (0.464)
## W.X 0.017 -0.467
## (0.074) (0.293)
## BA.AIOnlineCommunicationSkillsV -0.038
## (0.105)
## W.X:BA.AIOnlineCommunicationSkillsV 0.113
## (0.066)
## ─────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.002
## Conditional R^2 0.680 0.682
## AIC 2171.687 2179.094
## BIC 2198.676 2215.080
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.858 1.870
## Var: B.ID W.X 0.080 0.070
## Cov: B.ID (Intercept) W.X -0.111 -0.109
## Var: Residual 0.840 0.840
## ─────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.AffectiveRuminationV" (Y)
## ─────────────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────────────
## W.X * BA.AIOnlineCommunicationSkillsV 2.91 1 164 .090 .
## ─────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WA.AffectiveRuminationV" (Y)
## ──────────────────────────────────────────────────────────────────────────────────
## "BA.AIOnlineCommunicationSkillsV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────────────────
## 3.146 (- SD) -0.110 (0.105) -1.046 .297 [-0.315, 0.096]
## 4.260 (Mean) 0.017 (0.074) 0.228 .820 [-0.128, 0.162]
## 5.374 (+ SD) 0.143 (0.105) 1.368 .173 [-0.062, 0.349]
## ──────────────────────────────────────────────────────────────────────────────────S1.WP.VoiceForSystemImprovmentVBA.ClarityOfInformationV=PROCESS(data1, y="WP.VoiceForSystemImprovmentV", x="W.X", mods="BA.ClarityOfInformationV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.VoiceForSystemImprovmentV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.ClarityOfInformationV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.VoiceForSystemImprovmentV ~ W.X*BA.ClarityOfInformationV + (W.X|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.VoiceForSystemImprovmentV (2) WP.VoiceForSystemImprovmentV
## ────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.646 *** 2.942 ***
## (0.107) (0.385)
## W.X -0.156 0.312
## (0.080) (0.287)
## BA.ClarityOfInformationV 0.163
## (0.086)
## W.X:BA.ClarityOfInformationV -0.108
## (0.064)
## ────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.003 0.012
## Conditional R^2 0.563 0.567
## AIC 2236.569 2242.798
## BIC 2263.558 2278.784
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.391 1.366
## Var: B.ID W.X 0.001 0.000
## Cov: B.ID (Intercept) W.X -0.037 -0.020
## Var: Residual 1.054 1.050
## ────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.VoiceForSystemImprovmentV" (Y)
## ──────────────────────────────────────────────────────
## F df1 df2 p
## ──────────────────────────────────────────────────────
## W.X * BA.ClarityOfInformationV 2.88 1 495 .090 .
## ──────────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.VoiceForSystemImprovmentV" (Y)
## ────────────────────────────────────────────────────────────────────────────
## "BA.ClarityOfInformationV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────────────
## 3.068 (- SD) -0.021 (0.113) -0.186 .852 [-0.242, 0.200]
## 4.313 (Mean) -0.156 (0.080) -1.961 .050 . [-0.312, -0.000]
## 5.559 (+ SD) -0.291 (0.113) -2.586 .010 ** [-0.512, -0.070]
## ────────────────────────────────────────────────────────────────────────────S1.WP.LearningBehaviorVBB.AITechnologyAnxietyV=PROCESS(data1, y="WP.LearningBehaviorV", x="W.X", mods="BB.AITechnologyAnxietyV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.LearningBehaviorV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BB.AITechnologyAnxietyV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.LearningBehaviorV ~ W.X*BB.AITechnologyAnxietyV + (W.X|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────
## (1) WP.LearningBehaviorV (2) WP.LearningBehaviorV
## ───────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.621 *** 3.681 ***
## (0.111) (0.236)
## W.X -0.047 -0.385 *
## (0.093) (0.196)
## BB.AITechnologyAnxietyV -0.021
## (0.074)
## W.X:BB.AITechnologyAnxietyV 0.120
## (0.061)
## ───────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.005
## Conditional R^2 0.463 0.468
## AIC 2432.341 2439.308
## BIC 2459.438 2475.438
## Num. obs. 676 676
## Num. groups: B.ID 169 169
## Var: B.ID (Intercept) 1.357 1.376
## Var: B.ID W.X 0.011 0.013
## Cov: B.ID (Intercept) W.X -0.124 -0.135
## Var: Residual 1.439 1.430
## ───────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 676 (8 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.LearningBehaviorV" (Y)
## ─────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────
## W.X * BB.AITechnologyAnxietyV 3.84 1 484 .051 .
## ─────────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.LearningBehaviorV" (Y)
## ──────────────────────────────────────────────────────────────────────────
## "BB.AITechnologyAnxietyV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────────
## 1.306 (- SD) -0.228 (0.131) -1.742 .082 . [-0.484, 0.028]
## 2.811 (Mean) -0.047 (0.092) -0.504 .614 [-0.228, 0.135]
## 4.315 (+ SD) 0.135 (0.131) 1.030 .304 [-0.122, 0.391]
## ──────────────────────────────────────────────────────────────────────────S1.WP.PerceivedWorkGrowthVBB.TrustInAIV=PROCESS(data1, y="WP.PerceivedWorkGrowthV", x="W.X", mods="BB.TrustInAIV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.PerceivedWorkGrowthV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BB.TrustInAIV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.PerceivedWorkGrowthV ~ W.X*BB.TrustInAIV + (W.X|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────────
## (1) WP.PerceivedWorkGrowthV (2) WP.PerceivedWorkGrowthV
## ───────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.996 *** 3.309 ***
## (0.108) (0.336)
## W.X -0.178 * -0.605 *
## (0.083) (0.260)
## BB.TrustInAIV 0.187 *
## (0.087)
## W.X:BB.TrustInAIV 0.117
## (0.067)
## ───────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.003 0.041
## Conditional R^2 0.541 0.545
## AIC 2327.078 2324.758
## BIC 2354.175 2360.887
## Num. obs. 676 676
## Num. groups: B.ID 169 169
## Var: B.ID (Intercept) 1.391 1.352
## Var: B.ID W.X 0.001 0.005
## Cov: B.ID (Intercept) W.X -0.036 -0.080
## Var: Residual 1.158 1.152
## ───────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 676 (8 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.PerceivedWorkGrowthV" (Y)
## ───────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────
## W.X * BB.TrustInAIV 3.01 1 496 .083 .
## ───────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.PerceivedWorkGrowthV" (Y)
## ─────────────────────────────────────────────────────────────────
## "BB.TrustInAIV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────
## 2.437 (- SD) -0.321 (0.117) -2.743 .006 ** [-0.551, -0.092]
## 3.669 (Mean) -0.178 (0.083) -2.146 .032 * [-0.340, -0.015]
## 4.900 (+ SD) -0.034 (0.117) -0.290 .772 [-0.263, 0.195]
## ─────────────────────────────────────────────────────────────────S1.WP.TakingChargeBehaviorsForSystemImprovementVBA.AIOnlineCommunicationSkillsV=PROCESS(data1, y="WP.TakingChargeBehaviorsForSystemImprovementV", x="W.X", mods="BA.AIOnlineCommunicationSkillsV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.TakingChargeBehaviorsForSystemImprovementV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.AIOnlineCommunicationSkillsV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.TakingChargeBehaviorsForSystemImprovementV ~ W.X*BA.AIOnlineCommunicationSkillsV + (W.X|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.TakingChargeBehaviorsForSystemImprovementV (2) WP.TakingChargeBehaviorsForSystemImprovementV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.707 *** 2.785 ***
## (0.107) (0.419)
## W.X -0.154 0.374
## (0.082) (0.324)
## BA.AIOnlineCommunicationSkillsV 0.216 *
## (0.095)
## W.X:BA.AIOnlineCommunicationSkillsV -0.124
## (0.074)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.017
## Conditional R^2 0.532 0.536
## AIC 2256.283 2261.039
## BIC 2283.273 2297.025
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.352 1.309
## Var: B.ID W.X 0.006 0.004
## Cov: B.ID (Intercept) W.X -0.093 -0.070
## Var: Residual 1.115 1.111
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ─────────────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────────────
## W.X * BA.AIOnlineCommunicationSkillsV 2.83 1 489 .093 .
## ─────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ───────────────────────────────────────────────────────────────────────────────────
## "BA.AIOnlineCommunicationSkillsV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────────────────
## 3.146 (- SD) -0.016 (0.116) -0.135 .893 [-0.243, 0.212]
## 4.260 (Mean) -0.154 (0.082) -1.874 .062 . [-0.314, 0.007]
## 5.374 (+ SD) -0.292 (0.116) -2.514 .012 * [-0.519, -0.064]
## ───────────────────────────────────────────────────────────────────────────────────S1.WP.TakingChargeBehaviorsForSystemImprovementVBA.StructureV=PROCESS(data1, y="WP.TakingChargeBehaviorsForSystemImprovementV", x="W.X", mods="BA.StructureV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.TakingChargeBehaviorsForSystemImprovementV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.StructureV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.TakingChargeBehaviorsForSystemImprovementV ~ W.X*BA.StructureV + (W.X|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.TakingChargeBehaviorsForSystemImprovementV (2) WP.TakingChargeBehaviorsForSystemImprovementV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.707 *** 2.820 ***
## (0.107) (0.370)
## W.X -0.154 0.316
## (0.082) (0.287)
## BA.StructureV 0.214 *
## (0.086)
## W.X:BA.StructureV -0.114
## (0.066)
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.020
## Conditional R^2 0.532 0.536
## AIC 2256.283 2260.500
## BIC 2283.273 2296.486
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.352 1.298
## Var: B.ID W.X 0.006 0.003
## Cov: B.ID (Intercept) W.X -0.093 -0.066
## Var: Residual 1.115 1.111
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ───────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────
## W.X * BA.StructureV 2.91 1 490 .088 .
## ───────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ─────────────────────────────────────────────────────────────────
## "BA.StructureV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────
## 2.902 (- SD) -0.014 (0.116) -0.117 .907 [-0.241, 0.214]
## 4.136 (Mean) -0.154 (0.082) -1.875 .061 . [-0.314, 0.007]
## 5.369 (+ SD) -0.294 (0.116) -2.533 .012 * [-0.521, -0.066]
## ─────────────────────────────────────────────────────────────────S1.WP.TakingChargeBehaviorsForSystemImprovementVBA.ClarityOfInformationV=PROCESS(data1, y="WP.TakingChargeBehaviorsForSystemImprovementV", x="W.X", mods="BA.ClarityOfInformationV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.TakingChargeBehaviorsForSystemImprovementV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.ClarityOfInformationV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.TakingChargeBehaviorsForSystemImprovementV ~ W.X*BA.ClarityOfInformationV + (W.X|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.TakingChargeBehaviorsForSystemImprovementV (2) WP.TakingChargeBehaviorsForSystemImprovementV
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.707 *** 2.958 ***
## (0.107) (0.383)
## W.X -0.154 0.362
## (0.082) (0.295)
## BA.ClarityOfInformationV 0.174 *
## (0.085)
## W.X:BA.ClarityOfInformationV -0.120
## (0.066)
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.013
## Conditional R^2 0.532 0.536
## AIC 2256.283 2262.014
## BIC 2283.273 2298.000
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.352 1.322
## Var: B.ID W.X 0.006 0.004
## Cov: B.ID (Intercept) W.X -0.093 -0.072
## Var: Residual 1.115 1.110
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ──────────────────────────────────────────────────────
## F df1 df2 p
## ──────────────────────────────────────────────────────
## W.X * BA.ClarityOfInformationV 3.30 1 489 .070 .
## ──────────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ────────────────────────────────────────────────────────────────────────────
## "BA.ClarityOfInformationV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────────────
## 3.068 (- SD) -0.005 (0.116) -0.040 .968 [-0.232, 0.223]
## 4.313 (Mean) -0.154 (0.082) -1.875 .061 . [-0.314, 0.007]
## 5.559 (+ SD) -0.303 (0.116) -2.611 .009 ** [-0.530, -0.075]
## ────────────────────────────────────────────────────────────────────────────S1.WA.ErrorStrainVBA.EffectivenessV=PROCESS(data1, y="WA.ErrorStrainV", x="W.X", mods="BA.EffectivenessV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.ErrorStrainV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.EffectivenessV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.ErrorStrainV ~ W.X*BA.EffectivenessV + (W.X|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────
## (1) WA.ErrorStrainV (2) WA.ErrorStrainV
## ───────────────────────────────────────────────────────────────────
## (Intercept) 3.428 *** 2.695 ***
## (0.104) (0.308)
## W.X -0.017 0.325
## (0.073) (0.220)
## BA.EffectivenessV 0.187 *
## (0.074)
## W.X:BA.EffectivenessV -0.087
## (0.053)
## ───────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.019
## Conditional R^2 0.598 0.602
## AIC 2139.426 2144.082
## BIC 2166.416 2180.068
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.341 1.289
## Var: B.ID W.X 0.001 0.000
## Cov: B.ID (Intercept) W.X -0.027 -0.003
## Var: Residual 0.882 0.879
## ───────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.ErrorStrainV" (Y)
## ───────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────
## W.X * BA.EffectivenessV 2.73 1 496 .099 .
## ───────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WA.ErrorStrainV" (Y)
## ────────────────────────────────────────────────────────────────────
## "BA.EffectivenessV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────
## 2.544 (- SD) 0.103 (0.103) 0.999 .318 [-0.099, 0.305]
## 3.920 (Mean) -0.017 (0.073) -0.240 .810 [-0.160, 0.125]
## 5.296 (+ SD) -0.138 (0.103) -1.338 .182 [-0.340, 0.064]
## ────────────────────────────────────────────────────────────────────S1.WP.VoiceForSystemImprovmentVBA.QualityV=PROCESS(data1, y="WP.VoiceForSystemImprovmentV", x="W.X", mods="BA.QualityV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.VoiceForSystemImprovmentV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.QualityV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.VoiceForSystemImprovmentV ~ W.X*BA.QualityV + (W.X|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.VoiceForSystemImprovmentV (2) WP.VoiceForSystemImprovmentV
## ─────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.646 *** 2.087 ***
## (0.107) (0.308)
## W.X -0.156 0.236
## (0.080) (0.246)
## BA.QualityV 0.382 ***
## (0.071)
## W.X:BA.QualityV -0.096
## (0.057)
## ─────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.003 0.094
## Conditional R^2 0.563 0.566
## AIC 2236.569 2221.306
## BIC 2263.558 2257.292
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.391 1.123
## Var: B.ID W.X 0.001 0.000
## Cov: B.ID (Intercept) W.X -0.037 0.021
## Var: Residual 1.054 1.050
## ─────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.VoiceForSystemImprovmentV" (Y)
## ─────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────
## W.X * BA.QualityV 2.85 1 495 .092 .
## ─────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.VoiceForSystemImprovmentV" (Y)
## ───────────────────────────────────────────────────────────────
## "BA.QualityV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────
## 2.685 (- SD) -0.022 (0.113) -0.193 .847 [-0.242, 0.199]
## 4.084 (Mean) -0.156 (0.080) -1.961 .050 . [-0.312, -0.000]
## 5.483 (+ SD) -0.290 (0.113) -2.579 .010 * [-0.511, -0.070]
## ───────────────────────────────────────────────────────────────S1.WP.SocialLearningVBA.ProblemSolvingConfidenceV=PROCESS(data1, y="WP.SocialLearningV", x="W.X", mods="BA.ProblemSolvingConfidenceV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.SocialLearningV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.ProblemSolvingConfidenceV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.SocialLearningV ~ W.X*BA.ProblemSolvingConfidenceV + (W.X|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ────────────────────────────────────────────────────────────────────────────────
## (1) WP.SocialLearningV (2) WP.SocialLearningV
## ────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.759 *** 1.473 ***
## (0.104) (0.438)
## W.X -0.178 * 0.412
## (0.079) (0.359)
## BA.ProblemSolvingConfidenceV 0.524 ***
## (0.098)
## W.X:BA.ProblemSolvingConfidenceV -0.136
## (0.080)
## ────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.004 0.096
## Conditional R^2 0.535 0.538
## AIC 2211.535 2194.776
## BIC 2238.525 2230.762
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.282 1.028
## Var: B.ID W.X 0.007 0.001
## Cov: B.ID (Intercept) W.X -0.097 -0.036
## Var: Residual 1.040 1.038
## ────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SocialLearningV" (Y)
## ──────────────────────────────────────────────────────────
## F df1 df2 p
## ──────────────────────────────────────────────────────────
## W.X * BA.ProblemSolvingConfidenceV 2.84 1 493 .092 .
## ──────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.SocialLearningV" (Y)
## ────────────────────────────────────────────────────────────────────────────────
## "BA.ProblemSolvingConfidenceV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────────────────
## 3.374 (- SD) -0.045 (0.112) -0.402 .688 [-0.264, 0.174]
## 4.359 (Mean) -0.178 (0.079) -2.256 .025 * [-0.334, -0.023]
## 5.344 (+ SD) -0.312 (0.112) -2.787 .006 ** [-0.531, -0.093]
## ────────────────────────────────────────────────────────────────────────────────S1.WP.AIUsageForFacilitatingWorkVBA.PersonalControlV=PROCESS(data1, y="WP.AIUsageForFacilitatingWorkV", x="W.X", mods="BA.PersonalControlV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.AIUsageForFacilitatingWorkV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.PersonalControlV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.AIUsageForFacilitatingWorkV ~ W.X*BA.PersonalControlV + (W.X|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.AIUsageForFacilitatingWorkV (2) WP.AIUsageForFacilitatingWorkV
## ─────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 2.693 *** 4.078 ***
## (0.116) (0.373)
## W.X -0.161 -0.652 *
## (0.091) (0.304)
## BA.PersonalControlV -0.340 ***
## (0.087)
## W.X:BA.PersonalControlV 0.121
## (0.071)
## ─────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.052
## Conditional R^2 0.512 0.514
## AIC 2357.490 2353.599
## BIC 2384.480 2389.585
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.586 1.409
## Var: B.ID W.X 0.072 0.056
## Cov: B.ID (Intercept) W.X -0.250 -0.188
## Var: Residual 1.313 1.313
## ─────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.AIUsageForFacilitatingWorkV" (Y)
## ─────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────
## W.X * BA.PersonalControlV 2.88 1 164 .091 .
## ─────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.AIUsageForFacilitatingWorkV" (Y)
## ───────────────────────────────────────────────────────────────────────
## "BA.PersonalControlV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────
## 2.794 (- SD) -0.315 (0.128) -2.451 .015 * [-0.567, -0.063]
## 4.072 (Mean) -0.161 (0.091) -1.769 .079 . [-0.339, 0.017]
## 5.350 (+ SD) -0.006 (0.128) -0.049 .961 [-0.258, 0.245]
## ───────────────────────────────────────────────────────────────────────S1.WP.AIEnabledCreativityVBA.PersonalControlV=PROCESS(data1, y="WP.AIEnabledCreativityV", x="W.X", mods="BA.PersonalControlV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.AIEnabledCreativityV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.PersonalControlV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.AIEnabledCreativityV ~ W.X*BA.PersonalControlV + (W.X|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────────
## (1) WP.AIEnabledCreativityV (2) WP.AIEnabledCreativityV
## ───────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.677 *** 5.365 ***
## (0.116) (0.364)
## W.X -0.225 * -0.703 *
## (0.088) (0.292)
## BA.PersonalControlV -0.415 ***
## (0.085)
## W.X:BA.PersonalControlV 0.117
## (0.068)
## ───────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.005 0.083
## Conditional R^2 0.536 0.538
## AIC 2336.662 2324.944
## BIC 2363.652 2360.930
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.604 1.338
## Var: B.ID W.X 0.019 0.008
## Cov: B.ID (Intercept) W.X -0.175 -0.105
## Var: Residual 1.256 1.254
## ───────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.AIEnabledCreativityV" (Y)
## ─────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────
## W.X * BA.PersonalControlV 2.95 1 482 .087 .
## ─────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.AIEnabledCreativityV" (Y)
## ───────────────────────────────────────────────────────────────────────
## "BA.PersonalControlV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────
## 2.794 (- SD) -0.375 (0.123) -3.039 .003 ** [-0.617, -0.133]
## 4.072 (Mean) -0.225 (0.087) -2.581 .010 * [-0.396, -0.054]
## 5.350 (+ SD) -0.075 (0.123) -0.610 .542 [-0.317, 0.167]
## ───────────────────────────────────────────────────────────────────────S1.WP.SocialLearningVBA.PositiveReflectionOnAIUseV=PROCESS(data1, y="WP.SocialLearningV", x="W.X", mods="BA.PositiveReflectionOnAIUseV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.SocialLearningV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.PositiveReflectionOnAIUseV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.SocialLearningV ~ W.X*BA.PositiveReflectionOnAIUseV + (W.X|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────
## (1) WP.SocialLearningV (2) WP.SocialLearningV
## ─────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.759 *** 1.925 ***
## (0.104) (0.281)
## W.X -0.178 * 0.248
## (0.079) (0.241)
## BA.PositiveReflectionOnAIUseV 0.451 ***
## (0.065)
## W.X:BA.PositiveReflectionOnAIUseV -0.105
## (0.056)
## ─────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.004 0.147
## Conditional R^2 0.535 0.538
## AIC 2211.535 2179.824
## BIC 2238.525 2215.810
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.282 0.890
## Var: B.ID W.X 0.007 0.000
## Cov: B.ID (Intercept) W.X -0.097 -0.014
## Var: Residual 1.040 1.037
## ─────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SocialLearningV" (Y)
## ───────────────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────────────
## W.X * BA.PositiveReflectionOnAIUseV 3.49 1 496 .062 .
## ───────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.SocialLearningV" (Y)
## ─────────────────────────────────────────────────────────────────────────────────
## "BA.PositiveReflectionOnAIUseV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────────────────────
## 2.657 (- SD) -0.031 (0.112) -0.274 .784 [-0.250, 0.189]
## 4.070 (Mean) -0.178 (0.079) -2.258 .024 * [-0.333, -0.024]
## 5.483 (+ SD) -0.326 (0.112) -2.918 .004 ** [-0.545, -0.107]
## ─────────────────────────────────────────────────────────────────────────────────S1.WP.FamilyMemberUndermingVBA.AIServiceFailureV=PROCESS(data1, y="WP.FamilyMemberUndermingV", x="W.X", mods="BA.AIServiceFailureV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.FamilyMemberUndermingV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.AIServiceFailureV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.FamilyMemberUndermingV ~ W.X*BA.AIServiceFailureV + (W.X|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────────────
## (1) WP.FamilyMemberUndermingV (2) WP.FamilyMemberUndermingV
## ───────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 1.640 *** 1.126 ***
## (0.083) (0.237)
## W.X 0.090 -0.198
## (0.063) (0.182)
## BA.AIServiceFailureV 0.145 *
## (0.063)
## W.X:BA.AIServiceFailureV 0.081
## (0.048)
## ───────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.043
## Conditional R^2 0.552 0.556
## AIC 1914.774 1913.454
## BIC 1941.764 1949.440
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 0.811 0.783
## Var: B.ID W.X 0.000 0.001
## Cov: B.ID (Intercept) W.X -0.006 -0.031
## Var: Residual 0.654 0.651
## ───────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.FamilyMemberUndermingV" (Y)
## ──────────────────────────────────────────────────
## F df1 df2 p
## ──────────────────────────────────────────────────
## W.X * BA.AIServiceFailureV 2.85 1 492 .092 .
## ──────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.FamilyMemberUndermingV" (Y)
## ───────────────────────────────────────────────────────────────────────
## "BA.AIServiceFailureV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────
## 2.248 (- SD) -0.016 (0.089) -0.175 .861 [-0.189, 0.158]
## 3.554 (Mean) 0.090 (0.063) 1.441 .150 [-0.033, 0.213]
## 4.861 (+ SD) 0.196 (0.089) 2.213 .027 * [ 0.022, 0.370]
## ───────────────────────────────────────────────────────────────────────S1.WP.FamilyMemberConflictVBA.AIServiceFailureV=PROCESS(data1, y="WP.FamilyMemberConflictV", x="W.X", mods="BA.AIServiceFailureV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.FamilyMemberConflictV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.AIServiceFailureV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.FamilyMemberConflictV ~ W.X*BA.AIServiceFailureV + (W.X|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────
## (1) WP.FamilyMemberConflictV (2) WP.FamilyMemberConflictV
## ─────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 1.532 *** 1.058 ***
## (0.079) (0.227)
## W.X 0.038 -0.281
## (0.060) (0.175)
## BA.AIServiceFailureV 0.134 *
## (0.060)
## W.X:BA.AIServiceFailureV 0.090
## (0.046)
## ─────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.043
## Conditional R^2 0.546 0.551
## AIC 1861.503 1859.000
## BIC 1888.492 1894.986
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 0.739 0.718
## Var: B.ID W.X 0.000 0.002
## Cov: B.ID (Intercept) W.X -0.010 -0.038
## Var: Residual 0.607 0.602
## ─────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.FamilyMemberConflictV" (Y)
## ──────────────────────────────────────────────────
## F df1 df2 p
## ──────────────────────────────────────────────────
## W.X * BA.AIServiceFailureV 3.79 1 488 .052 .
## ──────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.FamilyMemberConflictV" (Y)
## ───────────────────────────────────────────────────────────────────────
## "BA.AIServiceFailureV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────
## 2.248 (- SD) -0.079 (0.085) -0.927 .354 [-0.247, 0.088]
## 3.554 (Mean) 0.038 (0.060) 0.636 .525 [-0.080, 0.157]
## 4.861 (+ SD) 0.156 (0.085) 1.827 .068 . [-0.011, 0.323]
## ───────────────────────────────────────────────────────────────────────S1.WP.FeedbackSeekingForSystemImprovementVBA.AnthropomorphismV=PROCESS(data1, y="WP.FeedbackSeekingForSystemImprovementV", x="W.X", mods="BA.AnthropomorphismV", cluster ="B.ID", hlm.re.y = "(W.X|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.FeedbackSeekingForSystemImprovementV
## - Predictor (X) : W.X
## - Mediators (M) : -
## - Moderators (W) : BA.AnthropomorphismV
## - Covariates (C) : -
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.FeedbackSeekingForSystemImprovementV ~ W.X*BA.AnthropomorphismV + (W.X|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.FeedbackSeekingForSystemImprovementV (2) WP.FeedbackSeekingForSystemImprovementV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.185 *** 2.257 ***
## (0.114) (0.240)
## W.X -0.149 0.155
## (0.086) (0.189)
## BA.AnthropomorphismV 0.326 ***
## (0.075)
## W.X:BA.AnthropomorphismV -0.107
## (0.059)
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.067
## Conditional R^2 0.528 0.529
## AIC 2288.555 2281.765
## BIC 2315.545 2317.751
## Num. obs. 664 664
## Num. groups: B.ID 166 166
## Var: B.ID (Intercept) 1.571 1.361
## Var: B.ID W.X 0.054 0.037
## Cov: B.ID (Intercept) W.X -0.292 -0.225
## Var: Residual 1.175 1.175
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 664 (20 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.FeedbackSeekingForSystemImprovementV" (Y)
## ──────────────────────────────────────────────────
## F df1 df2 p
## ──────────────────────────────────────────────────
## W.X * BA.AnthropomorphismV 3.25 1 433 .072 .
## ──────────────────────────────────────────────────
##
## Simple Slopes: "W.X" (X) ==> "WP.FeedbackSeekingForSystemImprovementV" (Y)
## ────────────────────────────────────────────────────────────────────────
## "BA.AnthropomorphismV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────────
## 1.403 (- SD) 0.005 (0.121) 0.042 .966 [-0.232, 0.242]
## 2.847 (Mean) -0.149 (0.085) -1.745 .082 . [-0.317, 0.018]
## 4.291 (+ SD) -0.303 (0.121) -2.509 .012 * [-0.540, -0.066]
## ────────────────────────────────────────────────────────────────────────4.2 Plot
interact_plot(S1.WP.AIEnabledInnovationBehaviorVBA.NeedForPersonalizationDueToAIV$model.y, W.X, BA.NeedForPersonalizationDueToAIV,modx.values = "plus-minus")+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(S1.WP.TakingChargeBehaviorsForSystemImprovementVBA.PositiveReflectionOnAIUseV$model.y, W.X, BA.PositiveReflectionOnAIUseV,modx.values = "plus-minus")+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(S1.WP.VoiceForSystemImprovmentVBA.AIOnlineCommunicationSkillsV$model.y, W.X, BA.AIOnlineCommunicationSkillsV,modx.values = "plus-minus")+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(S1.WP.SystemPerformanceImprovementBehaviorVBA.ClarityOfInformationV$model.y, W.X, BA.ClarityOfInformationV,modx.values = "plus-minus")+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(S1.WP.VoiceForSystemImprovmentVBA.StructureV$model.y, W.X, BA.StructureV,modx.values = "plus-minus")+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(S1.WP.TakingChargeBehaviorsForSystemImprovementVBA.NegativeReflectionOnAIUseV$model.y, W.X, BA.NegativeReflectionOnAIUseV,modx.values = "plus-minus")+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(S1.WA.LearningFromErrorsVBB.AIUsageV$model.y, W.X, BB.AIUsageV,modx.values = "plus-minus")+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(S1.WA.LearningFromErrorsVBA.AIInteractionQualityV$model.y, W.X, BA.AIInteractionQualityV,modx.values = "plus-minus")+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(S1.WA.AffectiveRuminationVBA.AIOnlineCommunicationSkillsV$model.y, W.X, BA.AIOnlineCommunicationSkillsV,modx.values = "plus-minus")+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(S1.WP.VoiceForSystemImprovmentVBA.ClarityOfInformationV$model.y, W.X, BA.ClarityOfInformationV,modx.values = "plus-minus")+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(S1.WP.LearningBehaviorVBB.AITechnologyAnxietyV$model.y, W.X, BB.AITechnologyAnxietyV,modx.values = "plus-minus")+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(S1.WP.PerceivedWorkGrowthVBB.TrustInAIV$model.y, W.X, BB.TrustInAIV,modx.values = "plus-minus")+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(S1.WP.TakingChargeBehaviorsForSystemImprovementVBA.AIOnlineCommunicationSkillsV$model.y, W.X, BA.AIOnlineCommunicationSkillsV,modx.values = "plus-minus")+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(S1.WP.TakingChargeBehaviorsForSystemImprovementVBA.StructureV$model.y, W.X, BA.StructureV,modx.values = "plus-minus")+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(S1.WP.TakingChargeBehaviorsForSystemImprovementVBA.ClarityOfInformationV$model.y, W.X, BA.ClarityOfInformationV,modx.values = "plus-minus")+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(S1.WA.ErrorStrainVBA.EffectivenessV$model.y, W.X, BA.EffectivenessV,modx.values = "plus-minus")+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(S1.WP.VoiceForSystemImprovmentVBA.QualityV$model.y, W.X, BA.QualityV,modx.values = "plus-minus")+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(S1.WP.SocialLearningVBA.ProblemSolvingConfidenceV$model.y, W.X, BA.ProblemSolvingConfidenceV,modx.values = "plus-minus")+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(S1.WP.AIUsageForFacilitatingWorkVBA.PersonalControlV$model.y, W.X, BA.PersonalControlV,modx.values = "plus-minus")+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(S1.WP.AIEnabledCreativityVBA.PersonalControlV$model.y, W.X, BA.PersonalControlV,modx.values = "plus-minus")+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(S1.WP.SocialLearningVBA.PositiveReflectionOnAIUseV$model.y, W.X, BA.PositiveReflectionOnAIUseV,modx.values = "plus-minus")+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(S1.WP.FamilyMemberUndermingVBA.AIServiceFailureV$model.y, W.X, BA.AIServiceFailureV,modx.values = "plus-minus")+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(S1.WP.FamilyMemberConflictVBA.AIServiceFailureV$model.y, W.X, BA.AIServiceFailureV,modx.values = "plus-minus")+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(S1.WP.FeedbackSeekingForSystemImprovementVBA.AnthropomorphismV$model.y, W.X, BA.AnthropomorphismV,modx.values = "plus-minus")+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
5 STUDY 2 USING .05 AS STANDARD
5.1 Analysis
Sb10.WP.SystemPerformanceImprovementBehaviorVBA.AIOnlineCommunicationSkillsV=PROCESS(data2, y="WP.SystemPerformanceImprovementBehaviorV", x="W.X10", mods="BA.AIOnlineCommunicationSkillsV",covs=c("W.X01","W.X01BA.AIOnlineCommunicationSkillsV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.SystemPerformanceImprovementBehaviorV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.AIOnlineCommunicationSkillsV
## - Covariates (C) : W.X01, W.X01BA.AIOnlineCommunicationSkillsV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.SystemPerformanceImprovementBehaviorV ~ W.X01 + W.X01BA.AIOnlineCommunicationSkillsV + W.X10*BA.AIOnlineCommunicationSkillsV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.SystemPerformanceImprovementBehaviorV (2) WP.SystemPerformanceImprovementBehaviorV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.413 *** 1.511 ***
## (0.108) (0.375)
## W.X01 0.212 0.699 **
## (0.239) (0.260)
## W.X01BA.AIOnlineCommunicationSkillsV -0.040 -0.155 **
## (0.054) (0.059)
## W.X10 0.038 0.692 **
## (0.070) (0.259)
## BA.AIOnlineCommunicationSkillsV 0.447 ***
## (0.085)
## W.X10:BA.AIOnlineCommunicationSkillsV -0.154 **
## (0.059)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.077
## Conditional R^2 0.685 0.687
## AIC 2960.196 2944.105
## BIC 3013.937 3007.617
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.552 1.294
## Var: B.ID W.X10 0.087 0.084
## Var: B.ID W.X01 0.087 0.090
## Cov: B.ID (Intercept) W.X10 -0.040 0.038
## Cov: B.ID (Intercept) W.X01 -0.066 -0.012
## Cov: B.ID W.X10 W.X01 -0.083 -0.087
## Var: Residual 0.710 0.702
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ───────────────────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────────────────
## W.X10 * BA.AIOnlineCommunicationSkillsV 6.84 1 340 .009 **
## ───────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ──────────────────────────────────────────────────────────────────────────────────
## "BA.AIOnlineCommunicationSkillsV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────────────────
## 3.075 (- SD) 0.220 (0.095) 2.308 .021 * [ 0.033, 0.406]
## 4.260 (Mean) 0.038 (0.067) 0.565 .572 [-0.094, 0.170]
## 5.444 (+ SD) -0.144 (0.095) -1.510 .132 [-0.330, 0.043]
## ──────────────────────────────────────────────────────────────────────────────────Sb10.WP.SystemPerformanceImprovementBehaviorVBA.StructureV=PROCESS(data2, y="WP.SystemPerformanceImprovementBehaviorV", x="W.X10", mods="BA.StructureV",covs=c("W.X01","W.X01BA.StructureV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.SystemPerformanceImprovementBehaviorV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.StructureV
## - Covariates (C) : W.X01, W.X01BA.StructureV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.SystemPerformanceImprovementBehaviorV ~ W.X01 + W.X01BA.StructureV + W.X10*BA.StructureV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.SystemPerformanceImprovementBehaviorV (2) WP.SystemPerformanceImprovementBehaviorV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.413 *** 1.591 ***
## (0.108) (0.335)
## W.X01 0.199 0.662 **
## (0.216) (0.235)
## W.X01BA.StructureV -0.039 -0.151 **
## (0.050) (0.055)
## W.X10 0.038 0.659 **
## (0.070) (0.234)
## BA.StructureV 0.443 ***
## (0.078)
## W.X10:BA.StructureV -0.151 **
## (0.054)
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.089
## Conditional R^2 0.685 0.688
## AIC 2960.326 2940.396
## BIC 3014.067 3003.907
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.552 1.256
## Var: B.ID W.X10 0.087 0.085
## Var: B.ID W.X01 0.087 0.090
## Cov: B.ID (Intercept) W.X10 -0.040 0.048
## Cov: B.ID (Intercept) W.X01 -0.064 -0.002
## Cov: B.ID W.X10 W.X01 -0.083 -0.087
## Var: Residual 0.709 0.701
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ─────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────
## W.X10 * BA.StructureV 7.69 1 340 .006 **
## ─────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ────────────────────────────────────────────────────────────────
## "BA.StructureV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────
## 2.839 (- SD) 0.231 (0.098) 2.348 .019 * [ 0.038, 0.423]
## 4.117 (Mean) 0.038 (0.069) 0.548 .584 [-0.098, 0.174]
## 5.394 (+ SD) -0.155 (0.098) -1.574 .117 [-0.347, 0.038]
## ────────────────────────────────────────────────────────────────Sb10.WP.SystemPerformanceImprovementBehaviorVBA.WayOfQuestioningV=PROCESS(data2, y="WP.SystemPerformanceImprovementBehaviorV", x="W.X10", mods="BA.WayOfQuestioningV",covs=c("W.X01","W.X01BA.WayOfQuestioningV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.SystemPerformanceImprovementBehaviorV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.WayOfQuestioningV
## - Covariates (C) : W.X01, W.X01BA.WayOfQuestioningV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.SystemPerformanceImprovementBehaviorV ~ W.X01 + W.X01BA.WayOfQuestioningV + W.X10*BA.WayOfQuestioningV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.SystemPerformanceImprovementBehaviorV (2) WP.SystemPerformanceImprovementBehaviorV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.413 *** 1.840 ***
## (0.108) (0.369)
## W.X01 0.209 0.610 *
## (0.230) (0.251)
## W.X01BA.WayOfQuestioningV -0.039 -0.132 *
## (0.051) (0.056)
## W.X10 0.038 0.562 *
## (0.070) (0.251)
## BA.WayOfQuestioningV 0.365 ***
## (0.082)
## W.X10:BA.WayOfQuestioningV -0.122 *
## (0.056)
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.057
## Conditional R^2 0.684 0.686
## AIC 2960.255 2951.915
## BIC 3013.996 3015.427
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.551 1.362
## Var: B.ID W.X10 0.086 0.084
## Var: B.ID W.X01 0.087 0.088
## Cov: B.ID (Intercept) W.X10 -0.039 0.017
## Cov: B.ID (Intercept) W.X01 -0.069 -0.028
## Cov: B.ID W.X10 W.X01 -0.083 -0.086
## Var: Residual 0.710 0.705
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────
## W.X10 * BA.WayOfQuestioningV 4.74 1 336 .030 *
## ────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ───────────────────────────────────────────────────────────────────────
## "BA.WayOfQuestioningV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────
## 3.061 (- SD) 0.190 (0.095) 1.989 .047 * [ 0.003, 0.377]
## 4.307 (Mean) 0.038 (0.067) 0.564 .573 [-0.094, 0.170]
## 5.552 (+ SD) -0.114 (0.095) -1.191 .234 [-0.300, 0.073]
## ───────────────────────────────────────────────────────────────────────Sb10.WA.AffectiveRuminationVBA.ClarityOfInformationV=PROCESS(data2, y="WA.AffectiveRuminationV", x="W.X10", mods="BA.ClarityOfInformationV",covs=c("W.X01","W.X01BA.ClarityOfInformationV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.AffectiveRuminationV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.ClarityOfInformationV
## - Covariates (C) : W.X01, W.X01BA.ClarityOfInformationV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.AffectiveRuminationV ~ W.X01 + W.X01BA.ClarityOfInformationV + W.X10*BA.ClarityOfInformationV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ────────────────────────────────────────────────────────────────────────────────────────
## (1) WA.AffectiveRuminationV (2) WA.AffectiveRuminationV
## ────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.731 *** 3.029 ***
## (0.114) (0.408)
## W.X01 0.247 0.525 *
## (0.232) (0.259)
## W.X01BA.ClarityOfInformationV -0.044 -0.108
## (0.051) (0.057)
## W.X10 0.093 0.631 *
## (0.072) (0.256)
## BA.ClarityOfInformationV 0.161
## (0.090)
## W.X10:BA.ClarityOfInformationV -0.123 *
## (0.057)
## ────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.007
## Conditional R^2 0.681 0.683
## AIC 3044.486 3049.476
## BIC 3098.226 3112.987
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.714 1.691
## Var: B.ID W.X10 0.030 0.027
## Var: B.ID W.X01 0.042 0.045
## Cov: B.ID (Intercept) W.X10 -0.054 -0.033
## Cov: B.ID (Intercept) W.X01 0.012 0.020
## Cov: B.ID W.X10 W.X01 -0.035 -0.035
## Var: Residual 0.803 0.799
## ────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.AffectiveRuminationV" (Y)
## ────────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────────
## W.X10 * BA.ClarityOfInformationV 4.77 1 454 .029 *
## ────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WA.AffectiveRuminationV" (Y)
## ───────────────────────────────────────────────────────────────────────────
## "BA.ClarityOfInformationV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────────
## 3.095 (- SD) 0.249 (0.101) 2.471 .014 * [ 0.051, 0.446]
## 4.356 (Mean) 0.093 (0.071) 1.310 .191 [-0.046, 0.233]
## 5.617 (+ SD) -0.062 (0.101) -0.620 .536 [-0.260, 0.135]
## ───────────────────────────────────────────────────────────────────────────Sb10.WP.AdviceThinkingBasedSocialLearningVBA.AIInteractionQualityV=PROCESS(data2, y="WP.AdviceThinkingBasedSocialLearningV", x="W.X10", mods="BA.AIInteractionQualityV",covs=c("W.X01","W.X01BA.AIInteractionQualityV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.AdviceThinkingBasedSocialLearningV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.AIInteractionQualityV
## - Covariates (C) : W.X01, W.X01BA.AIInteractionQualityV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.AdviceThinkingBasedSocialLearningV ~ W.X01 + W.X01BA.AIInteractionQualityV + W.X10*BA.AIInteractionQualityV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.AdviceThinkingBasedSocialLearningV (2) WP.AdviceThinkingBasedSocialLearningV
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.617 *** 1.877 ***
## (0.111) (0.315)
## W.X01 0.062 0.076
## (0.229) (0.260)
## W.X01BA.AIInteractionQualityV 0.001 -0.002
## (0.053) (0.061)
## W.X10 0.058 -0.500 *
## (0.082) (0.250)
## BA.AIInteractionQualityV 0.434 ***
## (0.074)
## W.X10:BA.AIInteractionQualityV 0.139 *
## (0.059)
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.162
## Conditional R^2 0.608 0.608
## AIC 3264.496 3223.892
## BIC 3318.236 3287.404
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.489 1.153
## Var: B.ID W.X10 0.038 0.000
## Var: B.ID W.X01 0.057 0.085
## Cov: B.ID (Intercept) W.X10 0.100 -0.024
## Cov: B.ID (Intercept) W.X01 0.084 0.066
## Cov: B.ID W.X10 W.X01 -0.035 -0.001
## Var: Residual 1.060 1.062
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.AdviceThinkingBasedSocialLearningV" (Y)
## ────────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────────
## W.X10 * BA.AIInteractionQualityV 5.56 1 647 .019 *
## ────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.AdviceThinkingBasedSocialLearningV" (Y)
## ───────────────────────────────────────────────────────────────────────────
## "BA.AIInteractionQualityV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────────
## 2.640 (- SD) -0.132 (0.115) -1.152 .250 [-0.357, 0.093]
## 4.006 (Mean) 0.058 (0.081) 0.719 .473 [-0.101, 0.217]
## 5.372 (+ SD) 0.249 (0.115) 2.169 .031 * [ 0.024, 0.474]
## ───────────────────────────────────────────────────────────────────────────Sb10.WA.SelfReflectionForManipulationCheckVBA.ProblemSolvingConfidenceV=PROCESS(data2, y="WA.SelfReflectionForManipulationCheckV", x="W.X10", mods="BA.ProblemSolvingConfidenceV",covs=c("W.X01","W.X01BA.ProblemSolvingConfidenceV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.SelfReflectionForManipulationCheckV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.ProblemSolvingConfidenceV
## - Covariates (C) : W.X01, W.X01BA.ProblemSolvingConfidenceV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.SelfReflectionForManipulationCheckV ~ W.X01 + W.X01BA.ProblemSolvingConfidenceV + W.X10*BA.ProblemSolvingConfidenceV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WA.SelfReflectionForManipulationCheckV (2) WA.SelfReflectionForManipulationCheckV
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.104 *** 0.534
## (0.110) (0.425)
## W.X01 -0.681 ** 0.322
## (0.263) (0.311)
## W.X01BA.ProblemSolvingConfidenceV 0.157 ** -0.072
## (0.058) (0.069)
## W.X10 0.039 0.600 *
## (0.063) (0.291)
## BA.ProblemSolvingConfidenceV 0.816 ***
## (0.095)
## W.X10:BA.ProblemSolvingConfidenceV -0.128 *
## (0.065)
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.004 0.245
## Conditional R^2 0.692 0.702
## AIC 2838.299 2786.485
## BIC 2892.040 2849.997
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.659 1.047
## Var: B.ID W.X10 0.008 0.001
## Var: B.ID W.X01 0.131 0.091
## Cov: B.ID (Intercept) W.X10 -0.114 -0.023
## Cov: B.ID (Intercept) W.X01 -0.282 -0.115
## Cov: B.ID W.X10 W.X01 0.019 0.003
## Var: Residual 0.644 0.643
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.SelfReflectionForManipulationCheckV" (Y)
## ────────────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────────────
## W.X10 * BA.ProblemSolvingConfidenceV 3.89 1 646 .049 *
## ────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WA.SelfReflectionForManipulationCheckV" (Y)
## ───────────────────────────────────────────────────────────────────────────────
## "BA.ProblemSolvingConfidenceV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────────────
## 3.409 (- SD) 0.163 (0.089) 1.834 .067 . [-0.011, 0.337]
## 4.376 (Mean) 0.039 (0.063) 0.622 .534 [-0.084, 0.162]
## 5.343 (+ SD) -0.085 (0.089) -0.955 .340 [-0.259, 0.089]
## ───────────────────────────────────────────────────────────────────────────────Sb10.WP.AIEnabledInnovationBehaviorVBA.WayOfQuestioningV=PROCESS(data2, y="WP.AIEnabledInnovationBehaviorV", x="W.X10", mods="BA.WayOfQuestioningV",covs=c("W.X01","W.X01BA.WayOfQuestioningV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.AIEnabledInnovationBehaviorV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.WayOfQuestioningV
## - Covariates (C) : W.X01, W.X01BA.WayOfQuestioningV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.AIEnabledInnovationBehaviorV ~ W.X01 + W.X01BA.WayOfQuestioningV + W.X10*BA.WayOfQuestioningV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.AIEnabledInnovationBehaviorV (2) WP.AIEnabledInnovationBehaviorV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.474 *** 2.112 ***
## (0.115) (0.399)
## W.X01 0.362 0.449
## (0.213) (0.245)
## W.X01BA.WayOfQuestioningV -0.096 * -0.116 *
## (0.047) (0.055)
## W.X10 0.099 0.060
## (0.068) (0.245)
## BA.WayOfQuestioningV 0.316 ***
## (0.089)
## W.X10:BA.WayOfQuestioningV 0.009
## (0.055)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.003 0.051
## Conditional R^2 0.706 0.704
## AIC 2976.768 2974.051
## BIC 3030.508 3037.562
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.766 1.621
## Var: B.ID W.X10 0.000 0.000
## Var: B.ID W.X01 0.001 0.002
## Cov: B.ID (Intercept) W.X10 0.015 0.011
## Cov: B.ID (Intercept) W.X01 0.044 0.053
## Cov: B.ID W.X10 W.X01 0.000 0.000
## Var: Residual 0.754 0.754
## ─────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.AIEnabledInnovationBehaviorV" (Y)
## ────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────
## W.X10 * BA.WayOfQuestioningV 0.03 1 810 .870
## ────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.AIEnabledInnovationBehaviorV" (Y)
## ──────────────────────────────────────────────────────────────────────
## "BA.WayOfQuestioningV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────
## 3.061 (- SD) 0.088 (0.096) 0.911 .363 [-0.101, 0.276]
## 4.307 (Mean) 0.099 (0.068) 1.452 .147 [-0.035, 0.232]
## 5.552 (+ SD) 0.110 (0.096) 1.142 .254 [-0.079, 0.299]
## ──────────────────────────────────────────────────────────────────────Sb10.WP.AIEnabledCreativityVBA.NeedForPersonalizationDueToAIV=PROCESS(data2, y="WP.AIEnabledCreativityV", x="W.X10", mods="BA.NeedForPersonalizationDueToAIV",covs=c("W.X01","W.X01BA.NeedForPersonalizationDueToAIV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.AIEnabledCreativityV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.NeedForPersonalizationDueToAIV
## - Covariates (C) : W.X01, W.X01BA.NeedForPersonalizationDueToAIV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.AIEnabledCreativityV ~ W.X01 + W.X01BA.NeedForPersonalizationDueToAIV + W.X10*BA.NeedForPersonalizationDueToAIV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.AIEnabledCreativityV (2) WP.AIEnabledCreativityV
## ─────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.721 *** 1.974 ***
## (0.119) (0.338)
## W.X01 0.098 0.349
## (0.188) (0.212)
## W.X01BA.NeedForPersonalizationDueToAIV -0.040 -0.102 *
## (0.043) (0.049)
## W.X10 0.006 0.203
## (0.068) (0.210)
## BA.NeedForPersonalizationDueToAIV 0.429 ***
## (0.079)
## W.X10:BA.NeedForPersonalizationDueToAIV -0.048
## (0.049)
## ─────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.110
## Conditional R^2 0.712 0.707
## AIC 2988.311 2971.736
## BIC 3042.052 3035.247
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.938 1.587
## Var: B.ID W.X10 0.015 0.003
## Var: B.ID W.X01 0.028 0.018
## Cov: B.ID (Intercept) W.X10 -0.107 -0.070
## Cov: B.ID (Intercept) W.X01 -0.048 -0.002
## Cov: B.ID W.X10 W.X01 -0.013 0.000
## Var: Residual 0.749 0.758
## ─────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.AIEnabledCreativityV" (Y)
## ─────────────────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────────────────
## W.X10 * BA.NeedForPersonalizationDueToAIV 0.98 1 421 .322
## ─────────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.AIEnabledCreativityV" (Y)
## ────────────────────────────────────────────────────────────────────────────────────
## "BA.NeedForPersonalizationDueToAIV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────────────────────
## 2.671 (- SD) 0.074 (0.097) 0.764 .445 [-0.116, 0.263]
## 4.071 (Mean) 0.006 (0.068) 0.090 .929 [-0.128, 0.140]
## 5.470 (+ SD) -0.062 (0.097) -0.637 .524 [-0.251, 0.128]
## ────────────────────────────────────────────────────────────────────────────────────Sb10.WP.VoiceForSystemImprovmentVBA.NeedForPersonalizationDueToAIV=PROCESS(data2, y="WP.VoiceForSystemImprovmentV", x="W.X10", mods="BA.NeedForPersonalizationDueToAIV",covs=c("W.X01","W.X01BA.NeedForPersonalizationDueToAIV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.VoiceForSystemImprovmentV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.NeedForPersonalizationDueToAIV
## - Covariates (C) : W.X01, W.X01BA.NeedForPersonalizationDueToAIV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.VoiceForSystemImprovmentV ~ W.X01 + W.X01BA.NeedForPersonalizationDueToAIV + W.X10*BA.NeedForPersonalizationDueToAIV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.VoiceForSystemImprovmentV (2) WP.VoiceForSystemImprovmentV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.531 *** 1.851 ***
## (0.121) (0.347)
## W.X01 0.062 0.557 *
## (0.213) (0.246)
## W.X01BA.NeedForPersonalizationDueToAIV -0.014 -0.136 *
## (0.048) (0.057)
## W.X10 0.058 0.353
## (0.062) (0.189)
## BA.NeedForPersonalizationDueToAIV 0.413 ***
## (0.081)
## W.X10:BA.NeedForPersonalizationDueToAIV -0.072
## (0.044)
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.093
## Conditional R^2 0.762 0.763
## AIC 2903.274 2890.559
## BIC 2957.015 2954.071
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 2.096 1.775
## Var: B.ID W.X10 0.011 0.008
## Var: B.ID W.X01 0.464 0.439
## Cov: B.ID (Intercept) W.X10 -0.075 -0.021
## Cov: B.ID (Intercept) W.X01 -0.383 -0.290
## Cov: B.ID W.X10 W.X01 0.071 0.059
## Var: Residual 0.608 0.607
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.VoiceForSystemImprovmentV" (Y)
## ─────────────────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────────────────
## W.X10 * BA.NeedForPersonalizationDueToAIV 2.72 1 585 .099 .
## ─────────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.VoiceForSystemImprovmentV" (Y)
## ────────────────────────────────────────────────────────────────────────────────────
## "BA.NeedForPersonalizationDueToAIV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────────────────────
## 2.671 (- SD) 0.160 (0.087) 1.838 .067 . [-0.011, 0.330]
## 4.071 (Mean) 0.058 (0.061) 0.949 .343 [-0.062, 0.179]
## 5.470 (+ SD) -0.043 (0.087) -0.497 .620 [-0.213, 0.127]
## ────────────────────────────────────────────────────────────────────────────────────Sb10.WP.TakingChargeBehaviorsForSystemImprovementVBA.ReflectionOnAIUseV=PROCESS(data2, y="WP.TakingChargeBehaviorsForSystemImprovementV", x="W.X10", mods="BA.ReflectionOnAIUseV",covs=c("W.X01","W.X01BA.ReflectionOnAIUseV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.TakingChargeBehaviorsForSystemImprovementV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.ReflectionOnAIUseV
## - Covariates (C) : W.X01, W.X01BA.ReflectionOnAIUseV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.TakingChargeBehaviorsForSystemImprovementV ~ W.X01 + W.X01BA.ReflectionOnAIUseV + W.X10*BA.ReflectionOnAIUseV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.TakingChargeBehaviorsForSystemImprovementV (2) WP.TakingChargeBehaviorsForSystemImprovementV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.546 *** 1.170 **
## (0.122) (0.369)
## W.X01 -0.001 0.640 *
## (0.214) (0.250)
## W.X01BA.ReflectionOnAIUseV 0.010 -0.149 *
## (0.050) (0.059)
## W.X10 0.103 0.479 *
## (0.065) (0.219)
## BA.ReflectionOnAIUseV 0.590 ***
## (0.087)
## W.X10:BA.ReflectionOnAIUseV -0.093
## (0.052)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.155
## Conditional R^2 0.740 0.742
## AIC 2946.196 2917.085
## BIC 2999.937 2980.596
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 2.106 1.583
## Var: B.ID W.X10 0.005 0.003
## Var: B.ID W.X01 0.240 0.208
## Cov: B.ID (Intercept) W.X10 -0.062 0.015
## Cov: B.ID (Intercept) W.X01 -0.327 -0.190
## Cov: B.ID W.X10 W.X01 0.036 0.022
## Var: Residual 0.678 0.677
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ─────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────
## W.X10 * BA.ReflectionOnAIUseV 3.20 1 625 .074 .
## ─────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ────────────────────────────────────────────────────────────────────────
## "BA.ReflectionOnAIUseV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────────
## 2.788 (- SD) 0.219 (0.091) 2.397 .017 * [ 0.040, 0.398]
## 4.030 (Mean) 0.103 (0.065) 1.599 .110 [-0.023, 0.230]
## 5.272 (+ SD) -0.012 (0.091) -0.136 .892 [-0.191, 0.167]
## ────────────────────────────────────────────────────────────────────────Sb10.WP.AIEnabledCreativityVBA.CapabilityV=PROCESS(data2, y="WP.AIEnabledCreativityV", x="W.X10", mods="BA.CapabilityV",covs=c("W.X01","W.X01BA.CapabilityV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.AIEnabledCreativityV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.CapabilityV
## - Covariates (C) : W.X01, W.X01BA.CapabilityV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.AIEnabledCreativityV ~ W.X01 + W.X01BA.CapabilityV + W.X10*BA.CapabilityV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────
## (1) WP.AIEnabledCreativityV (2) WP.AIEnabledCreativityV
## ─────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.721 *** 1.510 ***
## (0.119) (0.331)
## W.X01 0.134 0.339
## (0.193) (0.218)
## W.X01BA.CapabilityV -0.048 -0.097 *
## (0.043) (0.049)
## W.X10 0.006 0.029
## (0.068) (0.217)
## BA.CapabilityV 0.527 ***
## (0.075)
## W.X10:BA.CapabilityV -0.005
## (0.049)
## ─────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.185
## Conditional R^2 0.713 0.710
## AIC 2988.148 2949.446
## BIC 3041.888 3012.958
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.937 1.402
## Var: B.ID W.X10 0.013 0.013
## Var: B.ID W.X01 0.025 0.020
## Cov: B.ID (Intercept) W.X10 -0.106 -0.101
## Cov: B.ID (Intercept) W.X01 -0.031 0.018
## Cov: B.ID W.X10 W.X01 -0.012 -0.012
## Var: Residual 0.750 0.751
## ─────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.AIEnabledCreativityV" (Y)
## ──────────────────────────────────────────────
## F df1 df2 p
## ──────────────────────────────────────────────
## W.X10 * BA.CapabilityV 0.01 1 593 .911
## ──────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.AIEnabledCreativityV" (Y)
## ─────────────────────────────────────────────────────────────────
## "BA.CapabilityV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────
## 2.799 (- SD) 0.014 (0.097) 0.142 .887 [-0.176, 0.204]
## 4.196 (Mean) 0.006 (0.068) 0.090 .929 [-0.128, 0.140]
## 5.593 (+ SD) -0.002 (0.097) -0.016 .988 [-0.191, 0.188]
## ─────────────────────────────────────────────────────────────────Sb10.WP.AIEnabledInnovationBehaviorVBA.StructureV=PROCESS(data2, y="WP.AIEnabledInnovationBehaviorV", x="W.X10", mods="BA.StructureV",covs=c("W.X01","W.X01BA.StructureV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.AIEnabledInnovationBehaviorV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.StructureV
## - Covariates (C) : W.X01, W.X01BA.StructureV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.AIEnabledInnovationBehaviorV ~ W.X01 + W.X01BA.StructureV + W.X10*BA.StructureV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.AIEnabledInnovationBehaviorV (2) WP.AIEnabledInnovationBehaviorV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.474 *** 1.953 ***
## (0.115) (0.367)
## W.X01 0.254 0.413
## (0.200) (0.230)
## W.X01BA.StructureV -0.074 -0.113 *
## (0.046) (0.053)
## W.X10 0.099 0.185
## (0.068) (0.230)
## BA.StructureV 0.369 ***
## (0.085)
## W.X10:BA.StructureV -0.021
## (0.053)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.003 0.071
## Conditional R^2 0.706 0.704
## AIC 2978.328 2970.918
## BIC 3032.069 3034.429
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.766 1.554
## Var: B.ID W.X10 0.000 0.000
## Var: B.ID W.X01 0.001 0.002
## Cov: B.ID (Intercept) W.X10 0.014 0.025
## Cov: B.ID (Intercept) W.X01 0.042 0.062
## Cov: B.ID W.X10 W.X01 0.000 0.001
## Var: Residual 0.755 0.756
## ─────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.AIEnabledInnovationBehaviorV" (Y)
## ─────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────
## W.X10 * BA.StructureV 0.15 1 807 .694
## ─────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.AIEnabledInnovationBehaviorV" (Y)
## ───────────────────────────────────────────────────────────────
## "BA.StructureV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────
## 2.839 (- SD) 0.126 (0.096) 1.304 .193 [-0.063, 0.314]
## 4.117 (Mean) 0.099 (0.068) 1.450 .147 [-0.035, 0.232]
## 5.394 (+ SD) 0.072 (0.096) 0.747 .455 [-0.117, 0.261]
## ───────────────────────────────────────────────────────────────Sb10.WP.AIEnabledInnovationBehaviorVBA.AIOnlineCommunicationSkillsV=PROCESS(data2, y="WP.AIEnabledInnovationBehaviorV", x="W.X10", mods="BA.AIOnlineCommunicationSkillsV",covs=c("W.X01","W.X01BA.AIOnlineCommunicationSkillsV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.AIEnabledInnovationBehaviorV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.AIOnlineCommunicationSkillsV
## - Covariates (C) : W.X01, W.X01BA.AIOnlineCommunicationSkillsV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.AIEnabledInnovationBehaviorV ~ W.X01 + W.X01BA.AIOnlineCommunicationSkillsV + W.X10*BA.AIOnlineCommunicationSkillsV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.AIEnabledInnovationBehaviorV (2) WP.AIEnabledInnovationBehaviorV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.474 *** 1.804 ***
## (0.115) (0.407)
## W.X01 0.368 0.519 *
## (0.221) (0.255)
## W.X01BA.AIOnlineCommunicationSkillsV -0.098 * -0.134 *
## (0.049) (0.058)
## W.X10 0.099 0.158
## (0.068) (0.254)
## BA.AIOnlineCommunicationSkillsV 0.392 ***
## (0.092)
## W.X10:BA.AIOnlineCommunicationSkillsV -0.014
## (0.057)
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.003 0.068
## Conditional R^2 0.707 0.704
## AIC 2976.955 2969.570
## BIC 3030.696 3033.081
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.767 1.562
## Var: B.ID W.X10 0.000 0.000
## Var: B.ID W.X01 0.001 0.003
## Cov: B.ID (Intercept) W.X10 0.014 0.021
## Cov: B.ID (Intercept) W.X01 0.049 0.066
## Cov: B.ID W.X10 W.X01 0.000 0.001
## Var: Residual 0.754 0.754
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.AIEnabledInnovationBehaviorV" (Y)
## ───────────────────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────────────────
## W.X10 * BA.AIOnlineCommunicationSkillsV 0.06 1 808 .809
## ───────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.AIEnabledInnovationBehaviorV" (Y)
## ─────────────────────────────────────────────────────────────────────────────────
## "BA.AIOnlineCommunicationSkillsV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────────────────────
## 3.075 (- SD) 0.115 (0.096) 1.198 .231 [-0.073, 0.304]
## 4.260 (Mean) 0.099 (0.068) 1.452 .147 [-0.035, 0.232]
## 5.444 (+ SD) 0.082 (0.096) 0.855 .393 [-0.106, 0.271]
## ─────────────────────────────────────────────────────────────────────────────────5.2 Plot
interact_plot(Sb10.WP.SystemPerformanceImprovementBehaviorVBA.AIOnlineCommunicationSkillsV$model.y, W.X10, BA.AIOnlineCommunicationSkillsV,modx.values = "plus-minus",at = list(W.X01 = 0, W.X01BA.AIOnlineCommunicationSkillsV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.SystemPerformanceImprovementBehaviorVBA.StructureV$model.y, W.X10, BA.StructureV,modx.values = "plus-minus",at = list(W.X01 = 0, W.X01BA.StructureV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.SystemPerformanceImprovementBehaviorVBA.WayOfQuestioningV$model.y, W.X10, BA.WayOfQuestioningV,modx.values = "plus-minus",at = list(W.X01 = 0, W.X01BA.WayOfQuestioningV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WA.AffectiveRuminationVBA.ClarityOfInformationV$model.y, W.X10, BA.ClarityOfInformationV,modx.values = "plus-minus",at = list(W.X01 = 0, W.X01BA.ClarityOfInformationV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.AdviceThinkingBasedSocialLearningVBA.AIInteractionQualityV$model.y, W.X10, BA.AIInteractionQualityV,modx.values = "plus-minus",at = list(W.X01 = 0, W.X01BA.AIInteractionQualityV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WA.SelfReflectionForManipulationCheckVBA.ProblemSolvingConfidenceV$model.y, W.X10, BA.ProblemSolvingConfidenceV,modx.values = "plus-minus",at = list(W.X01 = 0, W.X01BA.ProblemSolvingConfidenceV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.AIEnabledInnovationBehaviorVBA.WayOfQuestioningV$model.y, W.X10, BA.WayOfQuestioningV,modx.values = "plus-minus",at = list(W.X01 = 0, W.X01BA.WayOfQuestioningV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.AIEnabledCreativityVBA.NeedForPersonalizationDueToAIV$model.y, W.X10, BA.NeedForPersonalizationDueToAIV,modx.values = "plus-minus",at = list(W.X01 = 0, W.X01BA.NeedForPersonalizationDueToAIV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.VoiceForSystemImprovmentVBA.NeedForPersonalizationDueToAIV$model.y, W.X10, BA.NeedForPersonalizationDueToAIV,modx.values = "plus-minus",at = list(W.X01 = 0, W.X01BA.NeedForPersonalizationDueToAIV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.TakingChargeBehaviorsForSystemImprovementVBA.ReflectionOnAIUseV$model.y, W.X10, BA.ReflectionOnAIUseV,modx.values = "plus-minus",at = list(W.X01 = 0, W.X01BA.ReflectionOnAIUseV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.AIEnabledCreativityVBA.CapabilityV$model.y, W.X10, BA.CapabilityV,modx.values = "plus-minus",at = list(W.X01 = 0, W.X01BA.CapabilityV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.AIEnabledInnovationBehaviorVBA.StructureV$model.y, W.X10, BA.StructureV,modx.values = "plus-minus",at = list(W.X01 = 0, W.X01BA.StructureV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.AIEnabledInnovationBehaviorVBA.AIOnlineCommunicationSkillsV$model.y, W.X10, BA.AIOnlineCommunicationSkillsV,modx.values = "plus-minus",at = list(W.X01 = 0, W.X01BA.AIOnlineCommunicationSkillsV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
6 STUDY 2 USING .10 AS STANDARD
6.1 Analysis
Sb10.WP.AIEnabledInnovationBehaviorVBA.NeedForPersonalizationDueToAIV=PROCESS(data2, y="WP.AIEnabledInnovationBehaviorV", x="W.X10", mods="BA.NeedForPersonalizationDueToAIV",covs=c("W.X01","W.X01BA.NeedForPersonalizationDueToAIV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.AIEnabledInnovationBehaviorV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.NeedForPersonalizationDueToAIV
## - Covariates (C) : W.X01, W.X01BA.NeedForPersonalizationDueToAIV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.AIEnabledInnovationBehaviorV ~ W.X01 + W.X01BA.NeedForPersonalizationDueToAIV + W.X10*BA.NeedForPersonalizationDueToAIV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.AIEnabledInnovationBehaviorV (2) WP.AIEnabledInnovationBehaviorV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.474 *** 1.908 ***
## (0.115) (0.329)
## W.X01 0.213 0.406
## (0.183) (0.210)
## W.X01BA.NeedForPersonalizationDueToAIV -0.065 -0.112 *
## (0.042) (0.049)
## W.X10 0.099 0.256
## (0.068) (0.210)
## BA.NeedForPersonalizationDueToAIV 0.385 ***
## (0.076)
## W.X10:BA.NeedForPersonalizationDueToAIV -0.039
## (0.049)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.003 0.089
## Conditional R^2 0.706 0.704
## AIC 2978.767 2966.314
## BIC 3032.507 3029.826
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.767 1.489
## Var: B.ID W.X10 0.000 0.001
## Var: B.ID W.X01 0.001 0.004
## Cov: B.ID (Intercept) W.X10 0.013 0.039
## Cov: B.ID (Intercept) W.X01 0.044 0.075
## Cov: B.ID W.X10 W.X01 0.000 0.002
## Var: Residual 0.756 0.755
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.AIEnabledInnovationBehaviorV" (Y)
## ─────────────────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────────────────
## W.X10 * BA.NeedForPersonalizationDueToAIV 0.63 1 802 .429
## ─────────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.AIEnabledInnovationBehaviorV" (Y)
## ───────────────────────────────────────────────────────────────────────────────────
## "BA.NeedForPersonalizationDueToAIV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────────────────
## 2.671 (- SD) 0.153 (0.096) 1.585 .113 [-0.036, 0.341]
## 4.071 (Mean) 0.099 (0.068) 1.450 .147 [-0.035, 0.232]
## 5.470 (+ SD) 0.045 (0.096) 0.466 .641 [-0.144, 0.234]
## ───────────────────────────────────────────────────────────────────────────────────Sb10.WP.TakingChargeBehaviorsForSystemImprovementVBA.PositiveReflectionOnAIUseV=PROCESS(data2, y="WP.TakingChargeBehaviorsForSystemImprovementV", x="W.X10", mods="BA.PositiveReflectionOnAIUseV",covs=c("W.X01","W.X01BA.PositiveReflectionOnAIUseV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.TakingChargeBehaviorsForSystemImprovementV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.PositiveReflectionOnAIUseV
## - Covariates (C) : W.X01, W.X01BA.PositiveReflectionOnAIUseV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.TakingChargeBehaviorsForSystemImprovementV ~ W.X01 + W.X01BA.PositiveReflectionOnAIUseV + W.X10*BA.PositiveReflectionOnAIUseV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.TakingChargeBehaviorsForSystemImprovementV (2) WP.TakingChargeBehaviorsForSystemImprovementV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.546 *** 1.300 ***
## (0.122) (0.323)
## W.X01 0.039 0.587 **
## (0.192) (0.223)
## W.X01BA.PositiveReflectionOnAIUseV 0.000 -0.135 **
## (0.044) (0.052)
## W.X10 0.103 0.360
## (0.065) (0.196)
## BA.PositiveReflectionOnAIUseV 0.556 ***
## (0.076)
## W.X10:BA.PositiveReflectionOnAIUseV -0.064
## (0.046)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.185
## Conditional R^2 0.740 0.741
## AIC 2946.889 2910.612
## BIC 3000.630 2974.123
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 2.101 1.503
## Var: B.ID W.X10 0.002 0.003
## Var: B.ID W.X01 0.206 0.203
## Cov: B.ID (Intercept) W.X10 -0.057 0.003
## Cov: B.ID (Intercept) W.X01 -0.302 -0.173
## Cov: B.ID W.X10 W.X01 0.008 0.023
## Var: Residual 0.680 0.678
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ─────────────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────────────
## W.X10 * BA.PositiveReflectionOnAIUseV 1.91 1 625 .167
## ─────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ───────────────────────────────────────────────────────────────────────────────
## "BA.PositiveReflectionOnAIUseV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────────────
## 2.629 (- SD) 0.193 (0.091) 2.108 .035 * [ 0.014, 0.372]
## 4.037 (Mean) 0.103 (0.065) 1.598 .111 [-0.023, 0.230]
## 5.444 (+ SD) 0.014 (0.091) 0.151 .880 [-0.165, 0.193]
## ───────────────────────────────────────────────────────────────────────────────Sb10.WP.VoiceForSystemImprovmentVBA.AIOnlineCommunicationSkillsV=PROCESS(data2, y="WP.VoiceForSystemImprovmentV", x="W.X10", mods="BA.AIOnlineCommunicationSkillsV",covs=c("W.X01","W.X01BA.AIOnlineCommunicationSkillsV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.VoiceForSystemImprovmentV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.AIOnlineCommunicationSkillsV
## - Covariates (C) : W.X01, W.X01BA.AIOnlineCommunicationSkillsV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.VoiceForSystemImprovmentV ~ W.X01 + W.X01BA.AIOnlineCommunicationSkillsV + W.X10*BA.AIOnlineCommunicationSkillsV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.VoiceForSystemImprovmentV (2) WP.VoiceForSystemImprovmentV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.531 *** 1.718 ***
## (0.121) (0.430)
## W.X01 0.089 0.699 *
## (0.257) (0.299)
## W.X01BA.AIOnlineCommunicationSkillsV -0.020 -0.163 *
## (0.057) (0.068)
## W.X10 0.058 0.537 *
## (0.062) (0.229)
## BA.AIOnlineCommunicationSkillsV 0.425 ***
## (0.097)
## W.X10:BA.AIOnlineCommunicationSkillsV -0.112 *
## (0.052)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.064
## Conditional R^2 0.762 0.764
## AIC 2902.907 2894.791
## BIC 2956.647 2958.302
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 2.095 1.858
## Var: B.ID W.X10 0.011 0.008
## Var: B.ID W.X01 0.463 0.441
## Cov: B.ID (Intercept) W.X10 -0.075 -0.016
## Cov: B.ID (Intercept) W.X01 -0.383 -0.305
## Cov: B.ID W.X10 W.X01 0.070 0.057
## Var: Residual 0.608 0.605
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.VoiceForSystemImprovmentV" (Y)
## ───────────────────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────────────────
## W.X10 * BA.AIOnlineCommunicationSkillsV 4.70 1 590 .031 *
## ───────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.VoiceForSystemImprovmentV" (Y)
## ──────────────────────────────────────────────────────────────────────────────────
## "BA.AIOnlineCommunicationSkillsV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────────────────
## 3.075 (- SD) 0.191 (0.087) 2.205 .028 * [ 0.021, 0.361]
## 4.260 (Mean) 0.058 (0.061) 0.951 .342 [-0.062, 0.178]
## 5.444 (+ SD) -0.075 (0.087) -0.862 .389 [-0.245, 0.095]
## ──────────────────────────────────────────────────────────────────────────────────Sb10.WP.SystemPerformanceImprovementBehaviorVBA.ClarityOfInformationV=PROCESS(data2, y="WP.SystemPerformanceImprovementBehaviorV", x="W.X10", mods="BA.ClarityOfInformationV",covs=c("W.X01","W.X01BA.ClarityOfInformationV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.SystemPerformanceImprovementBehaviorV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.ClarityOfInformationV
## - Covariates (C) : W.X01, W.X01BA.ClarityOfInformationV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.SystemPerformanceImprovementBehaviorV ~ W.X01 + W.X01BA.ClarityOfInformationV + W.X10*BA.ClarityOfInformationV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.SystemPerformanceImprovementBehaviorV (2) WP.SystemPerformanceImprovementBehaviorV
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.413 *** 1.797 ***
## (0.108) (0.368)
## W.X01 0.159 0.586 *
## (0.230) (0.251)
## W.X01BA.ClarityOfInformationV -0.028 -0.125 *
## (0.050) (0.055)
## W.X10 0.038 0.616 *
## (0.070) (0.250)
## BA.ClarityOfInformationV 0.371 ***
## (0.081)
## W.X10:BA.ClarityOfInformationV -0.133 *
## (0.055)
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.060
## Conditional R^2 0.684 0.686
## AIC 2960.550 2950.710
## BIC 3014.291 3014.222
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.550 1.351
## Var: B.ID W.X10 0.085 0.082
## Var: B.ID W.X01 0.089 0.090
## Cov: B.ID (Intercept) W.X10 -0.038 0.025
## Cov: B.ID (Intercept) W.X01 -0.073 -0.030
## Cov: B.ID W.X10 W.X01 -0.083 -0.086
## Var: Residual 0.710 0.705
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ────────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────────
## W.X10 * BA.ClarityOfInformationV 5.79 1 340 .017 *
## ────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ───────────────────────────────────────────────────────────────────────────
## "BA.ClarityOfInformationV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────────
## 3.095 (- SD) 0.205 (0.095) 2.155 .031 * [ 0.019, 0.392]
## 4.356 (Mean) 0.038 (0.067) 0.565 .572 [-0.094, 0.170]
## 5.617 (+ SD) -0.129 (0.095) -1.357 .175 [-0.316, 0.057]
## ───────────────────────────────────────────────────────────────────────────Sb10.WP.VoiceForSystemImprovmentVBA.StructureV=PROCESS(data2, y="WP.VoiceForSystemImprovmentV", x="W.X10", mods="BA.StructureV",covs=c("W.X01","W.X01BA.StructureV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.VoiceForSystemImprovmentV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.StructureV
## - Covariates (C) : W.X01, W.X01BA.StructureV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.VoiceForSystemImprovmentV ~ W.X01 + W.X01BA.StructureV + W.X10*BA.StructureV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.VoiceForSystemImprovmentV (2) WP.VoiceForSystemImprovmentV
## ───────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.531 *** 1.835 ***
## (0.121) (0.386)
## W.X01 -0.026 0.474
## (0.233) (0.264)
## W.X01BA.StructureV 0.008 -0.114
## (0.053) (0.061)
## W.X10 0.058 0.387
## (0.061) (0.206)
## BA.StructureV 0.412 ***
## (0.089)
## W.X10:BA.StructureV -0.080
## (0.048)
## ───────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.079
## Conditional R^2 0.760 0.762
## AIC 2904.652 2895.798
## BIC 2958.393 2959.310
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 2.086 1.823
## Var: B.ID W.X10 0.002 0.000
## Var: B.ID W.X01 0.410 0.391
## Cov: B.ID (Intercept) W.X10 -0.064 -0.015
## Cov: B.ID (Intercept) W.X01 -0.364 -0.289
## Cov: B.ID W.X10 W.X01 0.011 0.002
## Var: Residual 0.611 0.610
## ───────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.VoiceForSystemImprovmentV" (Y)
## ─────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────
## W.X10 * BA.StructureV 2.78 1 650 .096 .
## ─────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.VoiceForSystemImprovmentV" (Y)
## ────────────────────────────────────────────────────────────────
## "BA.StructureV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────
## 2.839 (- SD) 0.160 (0.087) 1.843 .066 . [-0.010, 0.331]
## 4.117 (Mean) 0.058 (0.061) 0.948 .343 [-0.062, 0.179]
## 5.394 (+ SD) -0.044 (0.087) -0.502 .616 [-0.214, 0.127]
## ────────────────────────────────────────────────────────────────Sb10.WP.TakingChargeBehaviorsForSystemImprovementVBA.NegativeReflectionOnAIUseV=PROCESS(data2, y="WP.TakingChargeBehaviorsForSystemImprovementV", x="W.X10", mods="BA.NegativeReflectionOnAIUseV",covs=c("W.X01","W.X01BA.NegativeReflectionOnAIUseV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.TakingChargeBehaviorsForSystemImprovementV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.NegativeReflectionOnAIUseV
## - Covariates (C) : W.X01, W.X01BA.NegativeReflectionOnAIUseV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.TakingChargeBehaviorsForSystemImprovementV ~ W.X01 + W.X01BA.NegativeReflectionOnAIUseV + W.X10*BA.NegativeReflectionOnAIUseV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.TakingChargeBehaviorsForSystemImprovementV (2) WP.TakingChargeBehaviorsForSystemImprovementV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.546 *** 1.960 ***
## (0.122) (0.364)
## W.X01 -0.011 0.462 *
## (0.199) (0.230)
## W.X01BA.NegativeReflectionOnAIUseV 0.013 -0.105
## (0.046) (0.054)
## W.X10 0.103 0.460 *
## (0.065) (0.203)
## BA.NegativeReflectionOnAIUseV 0.394 ***
## (0.086)
## W.X10:BA.NegativeReflectionOnAIUseV -0.089
## (0.048)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.077
## Conditional R^2 0.740 0.742
## AIC 2946.323 2937.838
## BIC 3000.064 3001.349
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 2.106 1.834
## Var: B.ID W.X10 0.006 0.000
## Var: B.ID W.X01 0.240 0.193
## Cov: B.ID (Intercept) W.X10 -0.062 -0.001
## Cov: B.ID (Intercept) W.X01 -0.327 -0.234
## Cov: B.ID W.X10 W.X01 0.036 0.000
## Var: Residual 0.678 0.678
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ─────────────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────────────
## W.X10 * BA.NegativeReflectionOnAIUseV 3.44 1 650 .064 .
## ─────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ────────────────────────────────────────────────────────────────────────────────
## "BA.NegativeReflectionOnAIUseV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────────────────
## 2.675 (- SD) 0.223 (0.091) 2.441 .015 * [ 0.044, 0.402]
## 4.022 (Mean) 0.103 (0.065) 1.600 .110 [-0.023, 0.230]
## 5.370 (+ SD) -0.016 (0.091) -0.180 .857 [-0.195, 0.163]
## ────────────────────────────────────────────────────────────────────────────────Sb10.WA.LearningFromErrorsVBB.AIUsageV=PROCESS(data2, y="WA.LearningFromErrorsV", x="W.X10", mods="BB.AIUsageV",covs=c("W.X01","W.X01BB.AIUsageV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.LearningFromErrorsV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BB.AIUsageV
## - Covariates (C) : W.X01, W.X01BB.AIUsageV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.LearningFromErrorsV ~ W.X01 + W.X01BB.AIUsageV + W.X10*BB.AIUsageV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────────
## (1) WA.LearningFromErrorsV (2) WA.LearningFromErrorsV
## ───────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.133 *** 4.099 ***
## (0.120) (0.263)
## W.X01 0.205 0.267
## (0.140) (0.155)
## W.X01BB.AIUsageV -0.057 -0.076
## (0.039) (0.044)
## W.X10 0.051 0.191
## (0.069) (0.151)
## BB.AIUsageV 0.011
## (0.075)
## W.X10:BB.AIUsageV -0.045
## (0.043)
## ───────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.002
## Conditional R^2 0.720 0.722
## AIC 2924.114 2934.877
## BIC 2977.582 2998.066
## Num. obs. 954 954
## Num. groups: B.ID 159 159
## Var: B.ID (Intercept) 1.910 1.924
## Var: B.ID W.X10 0.010 0.009
## Var: B.ID W.X01 0.048 0.049
## Cov: B.ID (Intercept) W.X10 -0.033 -0.035
## Cov: B.ID (Intercept) W.X01 0.013 0.012
## Cov: B.ID W.X10 W.X01 -0.021 -0.021
## Var: Residual 0.746 0.746
## ───────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 954 (30 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.LearningFromErrorsV" (Y)
## ───────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────
## W.X10 * BB.AIUsageV 1.09 1 496 .298
## ───────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WA.LearningFromErrorsV" (Y)
## ──────────────────────────────────────────────────────────────
## "BB.AIUsageV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────
## 1.513 (- SD) 0.123 (0.097) 1.265 .206 [-0.067, 0.313]
## 3.109 (Mean) 0.051 (0.069) 0.744 .457 [-0.084, 0.186]
## 4.705 (+ SD) -0.021 (0.097) -0.214 .831 [-0.211, 0.170]
## ──────────────────────────────────────────────────────────────Sb10.WA.LearningFromErrorsVBA.AIInteractionQualityV=PROCESS(data2, y="WA.LearningFromErrorsV", x="W.X10", mods="BA.AIInteractionQualityV",covs=c("W.X01","W.X01BA.AIInteractionQualityV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.LearningFromErrorsV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.AIInteractionQualityV
## - Covariates (C) : W.X01, W.X01BA.AIInteractionQualityV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.LearningFromErrorsV ~ W.X01 + W.X01BA.AIInteractionQualityV + W.X10*BA.AIInteractionQualityV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────────
## (1) WA.LearningFromErrorsV (2) WA.LearningFromErrorsV
## ──────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.163 *** 3.006 ***
## (0.118) (0.355)
## W.X01 -0.606 ** -0.425 *
## (0.188) (0.214)
## W.X01BA.AIInteractionQualityV 0.157 *** 0.112 *
## (0.044) (0.051)
## W.X10 0.038 0.132
## (0.068) (0.211)
## BA.AIInteractionQualityV 0.289 ***
## (0.084)
## W.X10:BA.AIInteractionQualityV -0.024
## (0.050)
## ──────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.006 0.074
## Conditional R^2 0.712 0.718
## AIC 2985.004 2984.423
## BIC 3038.744 3047.935
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.906 1.762
## Var: B.ID W.X10 0.009 0.009
## Var: B.ID W.X01 0.031 0.028
## Cov: B.ID (Intercept) W.X10 -0.055 -0.043
## Cov: B.ID (Intercept) W.X01 -0.081 -0.059
## Cov: B.ID W.X10 W.X01 -0.012 -0.013
## Var: Residual 0.748 0.748
## ──────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.LearningFromErrorsV" (Y)
## ────────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────────
## W.X10 * BA.AIInteractionQualityV 0.22 1 495 .638
## ────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WA.LearningFromErrorsV" (Y)
## ──────────────────────────────────────────────────────────────────────────
## "BA.AIInteractionQualityV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────────
## 2.640 (- SD) 0.070 (0.096) 0.723 .470 [-0.119, 0.259]
## 4.006 (Mean) 0.038 (0.068) 0.551 .582 [-0.096, 0.171]
## 5.372 (+ SD) 0.005 (0.096) 0.056 .955 [-0.184, 0.194]
## ──────────────────────────────────────────────────────────────────────────Sb10.WA.AffectiveRuminationVBA.AIOnlineCommunicationSkillsV=PROCESS(data2, y="WA.AffectiveRuminationV", x="W.X10", mods="BA.AIOnlineCommunicationSkillsV",covs=c("W.X01","W.X01BA.AIOnlineCommunicationSkillsV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.AffectiveRuminationV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.AIOnlineCommunicationSkillsV
## - Covariates (C) : W.X01, W.X01BA.AIOnlineCommunicationSkillsV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.AffectiveRuminationV ~ W.X01 + W.X01BA.AIOnlineCommunicationSkillsV + W.X10*BA.AIOnlineCommunicationSkillsV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────────────────────
## (1) WA.AffectiveRuminationV (2) WA.AffectiveRuminationV
## ───────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.731 *** 2.810 ***
## (0.114) (0.421)
## W.X01 0.237 0.537 *
## (0.241) (0.269)
## W.X01BA.AIOnlineCommunicationSkillsV -0.043 -0.113
## (0.054) (0.061)
## W.X10 0.093 0.632 *
## (0.071) (0.266)
## BA.AIOnlineCommunicationSkillsV 0.216 *
## (0.095)
## W.X10:BA.AIOnlineCommunicationSkillsV -0.127 *
## (0.060)
## ───────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.013
## Conditional R^2 0.681 0.683
## AIC 3044.495 3048.114
## BIC 3098.236 3111.626
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.714 1.666
## Var: B.ID W.X10 0.030 0.027
## Var: B.ID W.X01 0.042 0.045
## Cov: B.ID (Intercept) W.X10 -0.053 -0.026
## Cov: B.ID (Intercept) W.X01 0.015 0.027
## Cov: B.ID W.X10 W.X01 -0.035 -0.035
## Var: Residual 0.803 0.800
## ───────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.AffectiveRuminationV" (Y)
## ───────────────────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────────────────
## W.X10 * BA.AIOnlineCommunicationSkillsV 4.42 1 452 .036 *
## ───────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WA.AffectiveRuminationV" (Y)
## ──────────────────────────────────────────────────────────────────────────────────
## "BA.AIOnlineCommunicationSkillsV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────────────────
## 3.075 (- SD) 0.243 (0.101) 2.413 .016 * [ 0.046, 0.441]
## 4.260 (Mean) 0.093 (0.071) 1.309 .191 [-0.046, 0.233]
## 5.444 (+ SD) -0.057 (0.101) -0.562 .575 [-0.254, 0.141]
## ──────────────────────────────────────────────────────────────────────────────────Sb10.WP.VoiceForSystemImprovmentVBA.ClarityOfInformationV=PROCESS(data2, y="WP.VoiceForSystemImprovmentV", x="W.X10", mods="BA.ClarityOfInformationV",covs=c("W.X01","W.X01BA.ClarityOfInformationV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.VoiceForSystemImprovmentV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.ClarityOfInformationV
## - Covariates (C) : W.X01, W.X01BA.ClarityOfInformationV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.VoiceForSystemImprovmentV ~ W.X01 + W.X01BA.ClarityOfInformationV + W.X10*BA.ClarityOfInformationV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.VoiceForSystemImprovmentV (2) WP.VoiceForSystemImprovmentV
## ──────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.531 *** 2.018 ***
## (0.121) (0.420)
## W.X01 0.168 0.683 *
## (0.247) (0.281)
## W.X01BA.ClarityOfInformationV -0.037 -0.156 *
## (0.054) (0.062)
## W.X10 0.058 0.482 *
## (0.062) (0.220)
## BA.ClarityOfInformationV 0.347 ***
## (0.093)
## W.X10:BA.ClarityOfInformationV -0.097 *
## (0.048)
## ──────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.046
## Conditional R^2 0.763 0.763
## AIC 2902.694 2900.666
## BIC 2956.435 2964.178
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 2.095 1.912
## Var: B.ID W.X10 0.010 0.000
## Var: B.ID W.X01 0.455 0.383
## Cov: B.ID (Intercept) W.X10 -0.075 -0.019
## Cov: B.ID (Intercept) W.X01 -0.374 -0.285
## Cov: B.ID W.X10 W.X01 0.068 0.003
## Var: Residual 0.608 0.608
## ──────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.VoiceForSystemImprovmentV" (Y)
## ────────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────────
## W.X10 * BA.ClarityOfInformationV 4.02 1 649 .045 *
## ────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.VoiceForSystemImprovmentV" (Y)
## ───────────────────────────────────────────────────────────────────────────
## "BA.ClarityOfInformationV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────────
## 3.095 (- SD) 0.181 (0.087) 2.085 .038 * [ 0.011, 0.351]
## 4.356 (Mean) 0.058 (0.061) 0.950 .342 [-0.062, 0.179]
## 5.617 (+ SD) -0.064 (0.087) -0.742 .459 [-0.234, 0.106]
## ───────────────────────────────────────────────────────────────────────────Sb10.WP.LearningBehaviorVBB.AITechnologyAnxietyV=PROCESS(data2, y="WP.LearningBehaviorV", x="W.X10", mods="BB.AITechnologyAnxietyV",covs=c("W.X01","W.X01BB.AITechnologyAnxietyV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.LearningBehaviorV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BB.AITechnologyAnxietyV
## - Covariates (C) : W.X01, W.X01BB.AITechnologyAnxietyV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.LearningBehaviorV ~ W.X01 + W.X01BB.AITechnologyAnxietyV + W.X10*BB.AITechnologyAnxietyV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────
## (1) WP.LearningBehaviorV (2) WP.LearningBehaviorV
## ─────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.516 *** 3.311 ***
## (0.124) (0.272)
## W.X01 0.277 * 0.278
## (0.135) (0.153)
## W.X01BB.AITechnologyAnxietyV -0.088 * -0.089
## (0.040) (0.047)
## W.X10 0.136 * 0.087
## (0.069) (0.152)
## BB.AITechnologyAnxietyV 0.071
## (0.084)
## W.X10:BB.AITechnologyAnxietyV 0.017
## (0.047)
## ─────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.003 0.005
## Conditional R^2 0.730 0.729
## AIC 2931.811 2942.249
## BIC 2985.279 3005.438
## Num. obs. 954 954
## Num. groups: B.ID 159 159
## Var: B.ID (Intercept) 2.063 2.066
## Var: B.ID W.X10 0.001 0.000
## Var: B.ID W.X01 0.014 0.012
## Cov: B.ID (Intercept) W.X10 0.011 0.008
## Cov: B.ID (Intercept) W.X01 -0.056 -0.057
## Cov: B.ID W.X10 W.X01 -0.003 -0.000
## Var: Residual 0.758 0.761
## ─────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 954 (30 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.LearningBehaviorV" (Y)
## ───────────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────────
## W.X10 * BB.AITechnologyAnxietyV 0.13 1 586 .718
## ───────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.LearningBehaviorV" (Y)
## ─────────────────────────────────────────────────────────────────────────
## "BB.AITechnologyAnxietyV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────────────
## 1.416 (- SD) 0.111 (0.098) 1.135 .257 [-0.081, 0.303]
## 2.899 (Mean) 0.136 (0.069) 1.967 .050 * [ 0.000, 0.272]
## 4.383 (+ SD) 0.161 (0.098) 1.646 .100 [-0.031, 0.353]
## ─────────────────────────────────────────────────────────────────────────Sb10.WP.PerceivedWorkGrowthVBB.TrustInAIV=PROCESS(data2, y="WP.PerceivedWorkGrowthV", x="W.X10", mods="BB.TrustInAIV",covs=c("W.X01","W.X01BB.TrustInAIV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.PerceivedWorkGrowthV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BB.TrustInAIV
## - Covariates (C) : W.X01, W.X01BB.TrustInAIV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.PerceivedWorkGrowthV ~ W.X01 + W.X01BB.TrustInAIV + W.X10*BB.TrustInAIV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────
## (1) WP.PerceivedWorkGrowthV (2) WP.PerceivedWorkGrowthV
## ─────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.894 *** 3.056 ***
## (0.118) (0.363)
## W.X01 -0.184 -0.159
## (0.214) (0.240)
## W.X01BB.TrustInAIV 0.066 0.059
## (0.054) (0.062)
## W.X10 0.126 -0.207
## (0.066) (0.207)
## BB.TrustInAIV 0.227 *
## (0.093)
## W.X10:BB.TrustInAIV 0.090
## (0.053)
## ─────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.048
## Conditional R^2 0.726 0.729
## AIC 2902.193 2901.098
## BIC 2955.661 2964.287
## Num. obs. 954 954
## Num. groups: B.ID 159 159
## Var: B.ID (Intercept) 1.859 1.794
## Var: B.ID W.X10 0.005 0.004
## Var: B.ID W.X01 0.234 0.237
## Cov: B.ID (Intercept) W.X10 0.009 -0.029
## Cov: B.ID (Intercept) W.X01 -0.163 -0.165
## Cov: B.ID W.X10 W.X01 -0.032 -0.027
## Var: Residual 0.696 0.694
## ─────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 954 (30 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.PerceivedWorkGrowthV" (Y)
## ─────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────
## W.X10 * BB.TrustInAIV 2.87 1 600 .091 .
## ─────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.PerceivedWorkGrowthV" (Y)
## ───────────────────────────────────────────────────────────────
## "BB.TrustInAIV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────
## 2.445 (- SD) 0.013 (0.094) 0.143 .886 [-0.170, 0.197]
## 3.689 (Mean) 0.126 (0.066) 1.901 .058 . [-0.004, 0.255]
## 4.932 (+ SD) 0.238 (0.094) 2.545 .011 * [ 0.055, 0.422]
## ───────────────────────────────────────────────────────────────Sb10.WP.TakingChargeBehaviorsForSystemImprovementVBA.AIOnlineCommunicationSkillsV=PROCESS(data2, y="WP.TakingChargeBehaviorsForSystemImprovementV", x="W.X10", mods="BA.AIOnlineCommunicationSkillsV",covs=c("W.X01","W.X01BA.AIOnlineCommunicationSkillsV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.TakingChargeBehaviorsForSystemImprovementV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.AIOnlineCommunicationSkillsV
## - Covariates (C) : W.X01, W.X01BA.AIOnlineCommunicationSkillsV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.TakingChargeBehaviorsForSystemImprovementV ~ W.X01 + W.X01BA.AIOnlineCommunicationSkillsV + W.X10*BA.AIOnlineCommunicationSkillsV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.TakingChargeBehaviorsForSystemImprovementV (2) WP.TakingChargeBehaviorsForSystemImprovementV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.546 *** 1.773 ***
## (0.122) (0.435)
## W.X01 0.372 0.785 **
## (0.233) (0.270)
## W.X01BA.AIOnlineCommunicationSkillsV -0.078 -0.175 **
## (0.052) (0.061)
## W.X10 0.103 0.322
## (0.065) (0.241)
## BA.AIOnlineCommunicationSkillsV 0.416 ***
## (0.098)
## W.X10:BA.AIOnlineCommunicationSkillsV -0.051
## (0.055)
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.066
## Conditional R^2 0.743 0.741
## AIC 2944.121 2938.140
## BIC 2997.862 3001.651
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 2.106 1.871
## Var: B.ID W.X10 0.005 0.000
## Var: B.ID W.X01 0.206 0.170
## Cov: B.ID (Intercept) W.X10 -0.062 -0.029
## Cov: B.ID (Intercept) W.X01 -0.273 -0.206
## Cov: B.ID W.X10 W.X01 0.030 0.003
## Var: Residual 0.678 0.680
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ───────────────────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────────────────
## W.X10 * BA.AIOnlineCommunicationSkillsV 0.89 1 648 .347
## ───────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ─────────────────────────────────────────────────────────────────────────────────
## "BA.AIOnlineCommunicationSkillsV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────────────────────
## 3.075 (- SD) 0.164 (0.092) 1.793 .073 . [-0.015, 0.343]
## 4.260 (Mean) 0.103 (0.065) 1.596 .111 [-0.024, 0.230]
## 5.444 (+ SD) 0.042 (0.092) 0.464 .643 [-0.137, 0.222]
## ─────────────────────────────────────────────────────────────────────────────────Sb10.WP.TakingChargeBehaviorsForSystemImprovementVBA.StructureV=PROCESS(data2, y="WP.TakingChargeBehaviorsForSystemImprovementV", x="W.X10", mods="BA.StructureV",covs=c("W.X01","W.X01BA.StructureV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.TakingChargeBehaviorsForSystemImprovementV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.StructureV
## - Covariates (C) : W.X01, W.X01BA.StructureV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.TakingChargeBehaviorsForSystemImprovementV ~ W.X01 + W.X01BA.StructureV + W.X10*BA.StructureV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.TakingChargeBehaviorsForSystemImprovementV (2) WP.TakingChargeBehaviorsForSystemImprovementV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.546 *** 1.879 ***
## (0.122) (0.391)
## W.X01 0.228 0.590 *
## (0.212) (0.249)
## W.X01BA.StructureV -0.045 -0.133 *
## (0.048) (0.058)
## W.X10 0.103 0.234
## (0.065) (0.219)
## BA.StructureV 0.405 ***
## (0.091)
## W.X10:BA.StructureV -0.032
## (0.051)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.079
## Conditional R^2 0.741 0.741
## AIC 2945.910 2937.265
## BIC 2999.651 3000.777
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 2.101 1.850
## Var: B.ID W.X10 0.002 0.004
## Var: B.ID W.X01 0.191 0.207
## Cov: B.ID (Intercept) W.X10 -0.057 -0.042
## Cov: B.ID (Intercept) W.X01 -0.272 -0.232
## Cov: B.ID W.X10 W.X01 0.007 0.029
## Var: Residual 0.680 0.679
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ─────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────
## W.X10 * BA.StructureV 0.39 1 618 .530
## ─────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ───────────────────────────────────────────────────────────────
## "BA.StructureV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────
## 2.839 (- SD) 0.144 (0.092) 1.572 .116 [-0.036, 0.323]
## 4.117 (Mean) 0.103 (0.065) 1.595 .111 [-0.024, 0.230]
## 5.394 (+ SD) 0.063 (0.092) 0.684 .495 [-0.117, 0.242]
## ───────────────────────────────────────────────────────────────Sb10.WP.TakingChargeBehaviorsForSystemImprovementVBA.ClarityOfInformationV=PROCESS(data2, y="WP.TakingChargeBehaviorsForSystemImprovementV", x="W.X10", mods="BA.ClarityOfInformationV",covs=c("W.X01","W.X01BA.ClarityOfInformationV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.TakingChargeBehaviorsForSystemImprovementV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.ClarityOfInformationV
## - Covariates (C) : W.X01, W.X01BA.ClarityOfInformationV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.TakingChargeBehaviorsForSystemImprovementV ~ W.X01 + W.X01BA.ClarityOfInformationV + W.X10*BA.ClarityOfInformationV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.TakingChargeBehaviorsForSystemImprovementV (2) WP.TakingChargeBehaviorsForSystemImprovementV
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.546 *** 2.009 ***
## (0.122) (0.424)
## W.X01 0.388 0.747 **
## (0.224) (0.263)
## W.X01BA.ClarityOfInformationV -0.080 -0.162 **
## (0.049) (0.058)
## W.X10 0.103 0.306
## (0.065) (0.233)
## BA.ClarityOfInformationV 0.353 ***
## (0.093)
## W.X10:BA.ClarityOfInformationV -0.047
## (0.051)
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.052
## Conditional R^2 0.742 0.741
## AIC 2944.204 2941.190
## BIC 2997.945 3004.702
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 2.101 1.921
## Var: B.ID W.X10 0.002 0.003
## Var: B.ID W.X01 0.180 0.195
## Cov: B.ID (Intercept) W.X10 -0.057 -0.038
## Cov: B.ID (Intercept) W.X01 -0.260 -0.230
## Cov: B.ID W.X10 W.X01 0.007 0.026
## Var: Residual 0.680 0.679
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ────────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────────
## W.X10 * BA.ClarityOfInformationV 0.83 1 624 .364
## ────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ──────────────────────────────────────────────────────────────────────────
## "BA.ClarityOfInformationV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────────
## 3.095 (- SD) 0.162 (0.092) 1.771 .077 . [-0.017, 0.341]
## 4.356 (Mean) 0.103 (0.065) 1.596 .111 [-0.024, 0.230]
## 5.617 (+ SD) 0.044 (0.092) 0.486 .627 [-0.135, 0.224]
## ──────────────────────────────────────────────────────────────────────────Sb10.WA.ErrorStrainVBA.EffectivenessV=PROCESS(data2, y="WA.ErrorStrainV", x="W.X10", mods="BA.EffectivenessV",covs=c("W.X01","W.X01BA.EffectivenessV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.ErrorStrainV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.EffectivenessV
## - Covariates (C) : W.X01, W.X01BA.EffectivenessV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.ErrorStrainV ~ W.X01 + W.X01BA.EffectivenessV + W.X10*BA.EffectivenessV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────
## (1) WA.ErrorStrainV (2) WA.ErrorStrainV
## ─────────────────────────────────────────────────────────────────────
## (Intercept) 3.512 *** 2.342 ***
## (0.117) (0.353)
## W.X01 -0.029 0.282
## (0.205) (0.242)
## W.X01BA.EffectivenessV -0.037 -0.115 *
## (0.048) (0.058)
## W.X10 0.028 0.286
## (0.078) (0.242)
## BA.EffectivenessV 0.294 ***
## (0.084)
## W.X10:BA.EffectivenessV -0.065
## (0.058)
## ─────────────────────────────────────────────────────────────────────
## Marginal R^2 0.004 0.044
## Conditional R^2 0.615 0.615
## AIC 3162.854 3162.018
## BIC 3216.594 3225.529
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.744 1.600
## Var: B.ID W.X10 0.013 0.009
## Var: B.ID W.X01 0.015 0.010
## Cov: B.ID (Intercept) W.X10 -0.151 -0.120
## Cov: B.ID (Intercept) W.X01 -0.164 -0.128
## Cov: B.ID W.X10 W.X01 0.014 0.010
## Var: Residual 0.972 0.972
## ─────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.ErrorStrainV" (Y)
## ─────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────
## W.X10 * BA.EffectivenessV 1.26 1 751 .261
## ─────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WA.ErrorStrainV" (Y)
## ────────────────────────────────────────────────────────────────────
## "BA.EffectivenessV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────
## 2.634 (- SD) 0.115 (0.110) 1.052 .293 [-0.100, 0.331]
## 3.982 (Mean) 0.028 (0.078) 0.364 .716 [-0.124, 0.180]
## 5.329 (+ SD) -0.059 (0.110) -0.538 .591 [-0.274, 0.156]
## ────────────────────────────────────────────────────────────────────Sb10.WP.VoiceForSystemImprovmentVBA.QualityV=PROCESS(data2, y="WP.VoiceForSystemImprovmentV", x="W.X10", mods="BA.QualityV",covs=c("W.X01","W.X01BA.QualityV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.VoiceForSystemImprovmentV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.QualityV
## - Covariates (C) : W.X01, W.X01BA.QualityV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.VoiceForSystemImprovmentV ~ W.X01 + W.X01BA.QualityV + W.X10*BA.QualityV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.VoiceForSystemImprovmentV (2) WP.VoiceForSystemImprovmentV
## ───────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.531 *** 1.257 ***
## (0.121) (0.335)
## W.X01 -0.050 0.529 *
## (0.219) (0.254)
## W.X01BA.QualityV 0.014 -0.128 *
## (0.050) (0.059)
## W.X10 0.058 0.216
## (0.062) (0.195)
## BA.QualityV 0.555 ***
## (0.078)
## W.X10:BA.QualityV -0.038
## (0.045)
## ───────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.185
## Conditional R^2 0.761 0.762
## AIC 2903.240 2868.548
## BIC 2956.980 2932.059
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 2.096 1.528
## Var: B.ID W.X10 0.011 0.010
## Var: B.ID W.X01 0.478 0.441
## Cov: B.ID (Intercept) W.X10 -0.075 -0.036
## Cov: B.ID (Intercept) W.X01 -0.409 -0.264
## Cov: B.ID W.X10 W.X01 0.074 0.065
## Var: Residual 0.608 0.608
## ───────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.VoiceForSystemImprovmentV" (Y)
## ───────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────
## W.X10 * BA.QualityV 0.73 1 575 .394
## ───────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.VoiceForSystemImprovmentV" (Y)
## ─────────────────────────────────────────────────────────────
## "BA.QualityV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────
## 2.728 (- SD) 0.111 (0.087) 1.273 .204 [-0.060, 0.282]
## 4.094 (Mean) 0.058 (0.062) 0.947 .344 [-0.062, 0.179]
## 5.460 (+ SD) 0.006 (0.087) 0.066 .948 [-0.165, 0.176]
## ─────────────────────────────────────────────────────────────Sb10.WP.SocialLearningVBA.ProblemSolvingConfidenceV=PROCESS(data2, y="WP.SocialLearningV", x="W.X10", mods="BA.ProblemSolvingConfidenceV",covs=c("W.X01","W.X01BA.ProblemSolvingConfidenceV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.SocialLearningV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.ProblemSolvingConfidenceV
## - Covariates (C) : W.X01, W.X01BA.ProblemSolvingConfidenceV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.SocialLearningV ~ W.X01 + W.X01BA.ProblemSolvingConfidenceV + W.X10*BA.ProblemSolvingConfidenceV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────
## (1) WP.SocialLearningV (2) WP.SocialLearningV
## ──────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.656 *** 0.875
## (0.109) (0.455)
## W.X01 0.009 0.011
## (0.278) (0.315)
## W.X01BA.ProblemSolvingConfidenceV 0.015 0.014
## (0.062) (0.070)
## W.X10 0.115 -0.694 *
## (0.069) (0.317)
## BA.ProblemSolvingConfidenceV 0.636 ***
## (0.101)
## W.X10:BA.ProblemSolvingConfidenceV 0.185 **
## (0.071)
## ──────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.198
## Conditional R^2 0.687 0.689
## AIC 2964.897 2915.817
## BIC 3018.638 2979.329
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.567 1.198
## Var: B.ID W.X10 0.034 0.021
## Var: B.ID W.X01 0.017 0.014
## Cov: B.ID (Intercept) W.X10 0.043 -0.076
## Cov: B.ID (Intercept) W.X01 0.014 0.013
## Cov: B.ID W.X10 W.X01 -0.023 -0.016
## Var: Residual 0.741 0.740
## ──────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SocialLearningV" (Y)
## ────────────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────────────
## W.X10 * BA.ProblemSolvingConfidenceV 6.84 1 491 .009 **
## ────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.SocialLearningV" (Y)
## ───────────────────────────────────────────────────────────────────────────────
## "BA.ProblemSolvingConfidenceV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────────────
## 3.409 (- SD) -0.064 (0.096) -0.662 .508 [-0.253, 0.125]
## 4.376 (Mean) 0.115 (0.068) 1.688 .092 . [-0.019, 0.249]
## 5.343 (+ SD) 0.294 (0.096) 3.049 .002 ** [ 0.105, 0.483]
## ───────────────────────────────────────────────────────────────────────────────Sb10.WP.AIUsageForFacilitatingWorkVBA.PersonalControlV=PROCESS(data2, y="WP.AIUsageForFacilitatingWorkV", x="W.X10", mods="BA.PersonalControlV",covs=c("W.X01","W.X01BA.PersonalControlV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.AIUsageForFacilitatingWorkV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.PersonalControlV
## - Covariates (C) : W.X01, W.X01BA.PersonalControlV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.AIUsageForFacilitatingWorkV ~ W.X01 + W.X01BA.PersonalControlV + W.X10*BA.PersonalControlV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.AIUsageForFacilitatingWorkV (2) WP.AIUsageForFacilitatingWorkV
## ───────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 2.717 *** 4.383 ***
## (0.129) (0.399)
## W.X01 -0.169 -0.394
## (0.210) (0.237)
## W.X01BA.PersonalControlV 0.049 0.106
## (0.050) (0.057)
## W.X10 0.127 -0.060
## (0.070) (0.225)
## BA.PersonalControlV -0.421 ***
## (0.096)
## W.X10:BA.PersonalControlV 0.047
## (0.054)
## ───────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.077
## Conditional R^2 0.747 0.740
## AIC 3046.378 3040.853
## BIC 3100.119 3104.365
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 2.327 2.054
## Var: B.ID W.X10 0.053 0.007
## Var: B.ID W.X01 0.061 0.094
## Cov: B.ID (Intercept) W.X10 -0.149 -0.117
## Cov: B.ID (Intercept) W.X01 -0.065 -0.055
## Cov: B.ID W.X10 W.X01 -0.047 0.003
## Var: Residual 0.755 0.772
## ───────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.AIUsageForFacilitatingWorkV" (Y)
## ───────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────
## W.X10 * BA.PersonalControlV 0.76 1 611 .385
## ───────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.AIUsageForFacilitatingWorkV" (Y)
## ─────────────────────────────────────────────────────────────────────
## "BA.PersonalControlV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────────
## 2.678 (- SD) 0.067 (0.100) 0.669 .504 [-0.129, 0.262]
## 3.954 (Mean) 0.127 (0.070) 1.801 .073 . [-0.011, 0.265]
## 5.230 (+ SD) 0.187 (0.100) 1.877 .061 . [-0.008, 0.382]
## ─────────────────────────────────────────────────────────────────────Sb10.WP.AIEnabledCreativityVBA.PersonalControlV=PROCESS(data2, y="WP.AIEnabledCreativityV", x="W.X10", mods="BA.PersonalControlV",covs=c("W.X01","W.X01BA.PersonalControlV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.AIEnabledCreativityV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.PersonalControlV
## - Covariates (C) : W.X01, W.X01BA.PersonalControlV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.AIEnabledCreativityV ~ W.X01 + W.X01BA.PersonalControlV + W.X10*BA.PersonalControlV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────
## (1) WP.AIEnabledCreativityV (2) WP.AIEnabledCreativityV
## ─────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.721 *** 5.761 ***
## (0.119) (0.351)
## W.X01 0.253 -0.218
## (0.197) (0.227)
## W.X01BA.PersonalControlV -0.081 0.038
## (0.047) (0.055)
## W.X10 0.006 -0.455 *
## (0.068) (0.222)
## BA.PersonalControlV -0.516 ***
## (0.085)
## W.X10:BA.PersonalControlV 0.117 *
## (0.053)
## ─────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.137
## Conditional R^2 0.705 0.709
## AIC 2986.414 2964.143
## BIC 3040.155 3027.655
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.932 1.516
## Var: B.ID W.X10 0.011 0.000
## Var: B.ID W.X01 0.042 0.034
## Cov: B.ID (Intercept) W.X10 -0.101 -0.016
## Cov: B.ID (Intercept) W.X01 -0.139 -0.056
## Cov: B.ID W.X10 W.X01 -0.006 0.001
## Var: Residual 0.752 0.753
## ─────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.AIEnabledCreativityV" (Y)
## ───────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────
## W.X10 * BA.PersonalControlV 4.78 1 582 .029 *
## ───────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.AIEnabledCreativityV" (Y)
## ──────────────────────────────────────────────────────────────────────
## "BA.PersonalControlV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────
## 2.678 (- SD) -0.143 (0.096) -1.482 .139 [-0.331, 0.046]
## 3.954 (Mean) 0.006 (0.068) 0.090 .928 [-0.127, 0.139]
## 5.230 (+ SD) 0.155 (0.096) 1.610 .108 [-0.034, 0.343]
## ──────────────────────────────────────────────────────────────────────Sb10.WP.SocialLearningVBA.PositiveReflectionOnAIUseV=PROCESS(data2, y="WP.SocialLearningV", x="W.X10", mods="BA.PositiveReflectionOnAIUseV",covs=c("W.X01","W.X01BA.PositiveReflectionOnAIUseV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.SocialLearningV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.PositiveReflectionOnAIUseV
## - Covariates (C) : W.X01, W.X01BA.PositiveReflectionOnAIUseV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.SocialLearningV ~ W.X01 + W.X01BA.PositiveReflectionOnAIUseV + W.X10*BA.PositiveReflectionOnAIUseV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────────
## (1) WP.SocialLearningV (2) WP.SocialLearningV
## ───────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.656 *** 1.971 ***
## (0.109) (0.301)
## W.X01 0.064 0.033
## (0.184) (0.207)
## W.X01BA.PositiveReflectionOnAIUseV 0.002 0.010
## (0.042) (0.048)
## W.X10 0.115 -0.431 *
## (0.069) (0.207)
## BA.PositiveReflectionOnAIUseV 0.417 ***
## (0.070)
## W.X10:BA.PositiveReflectionOnAIUseV 0.135 **
## (0.049)
## ───────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.185
## Conditional R^2 0.687 0.689
## AIC 2965.685 2920.719
## BIC 3019.426 2984.230
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.567 1.232
## Var: B.ID W.X10 0.033 0.020
## Var: B.ID W.X01 0.017 0.014
## Cov: B.ID (Intercept) W.X10 0.044 -0.079
## Cov: B.ID (Intercept) W.X01 0.021 0.013
## Cov: B.ID W.X10 W.X01 -0.022 -0.015
## Var: Residual 0.742 0.740
## ───────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SocialLearningV" (Y)
## ─────────────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────────────
## W.X10 * BA.PositiveReflectionOnAIUseV 7.79 1 507 .005 **
## ─────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.SocialLearningV" (Y)
## ────────────────────────────────────────────────────────────────────────────────
## "BA.PositiveReflectionOnAIUseV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────────────────
## 2.629 (- SD) -0.076 (0.097) -0.783 .434 [-0.265, 0.114]
## 4.037 (Mean) 0.115 (0.068) 1.685 .093 . [-0.019, 0.249]
## 5.444 (+ SD) 0.306 (0.097) 3.165 .002 ** [ 0.116, 0.495]
## ────────────────────────────────────────────────────────────────────────────────Sb10.WP.FamilyMemberUndermingVBA.AIServiceFailureV=PROCESS(data2, y="WP.FamilyMemberUndermingV", x="W.X10", mods="BA.AIServiceFailureV",covs=c("W.X01","W.X01BA.AIServiceFailureV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.FamilyMemberUndermingV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.AIServiceFailureV
## - Covariates (C) : W.X01, W.X01BA.AIServiceFailureV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.FamilyMemberUndermingV ~ W.X01 + W.X01BA.AIServiceFailureV + W.X10*BA.AIServiceFailureV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.FamilyMemberUndermingV (2) WP.FamilyMemberUndermingV
## ─────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 1.525 *** 0.548 *
## (0.086) (0.229)
## W.X01 -0.290 * -0.045
## (0.122) (0.140)
## W.X01BA.AIServiceFailureV 0.075 * 0.007
## (0.031) (0.036)
## W.X10 -0.024 0.333 *
## (0.051) (0.141)
## BA.AIServiceFailureV 0.270 ***
## (0.059)
## W.X10:BA.AIServiceFailureV -0.099 **
## (0.036)
## ─────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.003 0.085
## Conditional R^2 0.691 0.698
## AIC 2352.923 2345.317
## BIC 2406.663 2408.829
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.005 0.877
## Var: B.ID W.X10 0.015 0.007
## Var: B.ID W.X01 0.003 0.001
## Cov: B.ID (Intercept) W.X10 -0.123 -0.080
## Cov: B.ID (Intercept) W.X01 -0.053 -0.024
## Cov: B.ID W.X10 W.X01 0.006 0.002
## Var: Residual 0.402 0.400
## ─────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.FamilyMemberUndermingV" (Y)
## ────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────
## W.X10 * BA.AIServiceFailureV 7.36 1 705 .007 **
## ────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.FamilyMemberUndermingV" (Y)
## ────────────────────────────────────────────────────────────────────────
## "BA.AIServiceFailureV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────────
## 2.249 (- SD) 0.112 (0.071) 1.582 .114 [-0.027, 0.250]
## 3.626 (Mean) -0.024 (0.050) -0.476 .634 [-0.122, 0.074]
## 5.002 (+ SD) -0.159 (0.071) -2.255 .024 * [-0.298, -0.021]
## ────────────────────────────────────────────────────────────────────────Sb10.WP.FamilyMemberConflictVBA.AIServiceFailureV=PROCESS(data2, y="WP.FamilyMemberConflictV", x="W.X10", mods="BA.AIServiceFailureV",covs=c("W.X01","W.X01BA.AIServiceFailureV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.FamilyMemberConflictV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.AIServiceFailureV
## - Covariates (C) : W.X01, W.X01BA.AIServiceFailureV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.FamilyMemberConflictV ~ W.X01 + W.X01BA.AIServiceFailureV + W.X10*BA.AIServiceFailureV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────────────
## (1) WP.FamilyMemberConflictV (2) WP.FamilyMemberConflictV
## ───────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 1.475 *** 0.504 *
## (0.083) (0.221)
## W.X01 -0.254 * -0.018
## (0.114) (0.129)
## W.X01BA.AIServiceFailureV 0.066 * 0.001
## (0.029) (0.033)
## W.X10 -0.048 0.331 *
## (0.047) (0.130)
## BA.AIServiceFailureV 0.268 ***
## (0.057)
## W.X10:BA.AIServiceFailureV -0.104 **
## (0.033)
## ───────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.003 0.088
## Conditional R^2 0.716 0.723
## AIC 2220.087 2210.493
## BIC 2273.828 2274.005
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 0.954 0.828
## Var: B.ID W.X10 0.012 0.005
## Var: B.ID W.X01 0.001 0.000
## Cov: B.ID (Intercept) W.X10 -0.107 -0.062
## Cov: B.ID (Intercept) W.X01 -0.032 -0.005
## Cov: B.ID W.X10 W.X01 0.004 0.000
## Var: Residual 0.344 0.342
## ───────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.FamilyMemberConflictV" (Y)
## ────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────
## W.X10 * BA.AIServiceFailureV 9.70 1 728 .002 **
## ────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.FamilyMemberConflictV" (Y)
## ────────────────────────────────────────────────────────────────────────
## "BA.AIServiceFailureV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────────
## 2.249 (- SD) 0.096 (0.065) 1.473 .141 [-0.032, 0.224]
## 3.626 (Mean) -0.048 (0.046) -1.032 .303 [-0.138, 0.043]
## 5.002 (+ SD) -0.191 (0.065) -2.932 .003 ** [-0.319, -0.063]
## ────────────────────────────────────────────────────────────────────────Sb10.WP.FeedbackSeekingForSystemImprovementVBA.AnthropomorphismV=PROCESS(data2, y="WP.FeedbackSeekingForSystemImprovementV", x="W.X10", mods="BA.AnthropomorphismV",covs=c("W.X01","W.X01BA.AnthropomorphismV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.FeedbackSeekingForSystemImprovementV
## - Predictor (X) : W.X10
## - Mediators (M) : -
## - Moderators (W) : BA.AnthropomorphismV
## - Covariates (C) : W.X01, W.X01BA.AnthropomorphismV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.FeedbackSeekingForSystemImprovementV ~ W.X01 + W.X01BA.AnthropomorphismV + W.X10*BA.AnthropomorphismV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.FeedbackSeekingForSystemImprovementV (2) WP.FeedbackSeekingForSystemImprovementV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.188 *** 1.988 ***
## (0.117) (0.242)
## W.X01 0.204 0.333 *
## (0.136) (0.152)
## W.X01BA.AnthropomorphismV -0.051 -0.096 *
## (0.041) (0.047)
## W.X10 0.106 0.227
## (0.068) (0.152)
## BA.AnthropomorphismV 0.415 ***
## (0.075)
## W.X10:BA.AnthropomorphismV -0.042
## (0.047)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.114
## Conditional R^2 0.709 0.706
## AIC 2958.523 2940.496
## BIC 3012.264 3004.007
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.867 1.522
## Var: B.ID W.X10 0.010 0.006
## Var: B.ID W.X01 0.010 0.007
## Cov: B.ID (Intercept) W.X10 -0.133 -0.099
## Cov: B.ID (Intercept) W.X01 0.002 0.038
## Cov: B.ID W.X10 W.X01 -0.000 -0.002
## Var: Residual 0.736 0.737
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.FeedbackSeekingForSystemImprovementV" (Y)
## ────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────
## W.X10 * BA.AnthropomorphismV 0.79 1 612 .375
## ────────────────────────────────────────────────────
##
## Simple Slopes: "W.X10" (X) ==> "WP.FeedbackSeekingForSystemImprovementV" (Y)
## ──────────────────────────────────────────────────────────────────────
## "BA.AnthropomorphismV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────
## 1.457 (- SD) 0.166 (0.096) 1.736 .083 . [-0.021, 0.353]
## 2.893 (Mean) 0.106 (0.068) 1.567 .118 [-0.026, 0.238]
## 4.330 (+ SD) 0.046 (0.096) 0.480 .632 [-0.141, 0.233]
## ──────────────────────────────────────────────────────────────────────6.2 Plot
interact_plot(Sb10.WP.AIEnabledInnovationBehaviorVBA.NeedForPersonalizationDueToAIV$model.y, W.X10, BA.NeedForPersonalizationDueToAIV,modx.values = "plus-minus",at = list(W.X01 = 0, W.X01BA.NeedForPersonalizationDueToAIV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.TakingChargeBehaviorsForSystemImprovementVBA.PositiveReflectionOnAIUseV$model.y, W.X10, BA.PositiveReflectionOnAIUseV,modx.values = "plus-minus",at = list(W.X01 = 0, W.X01BA.PositiveReflectionOnAIUseV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.VoiceForSystemImprovmentVBA.AIOnlineCommunicationSkillsV$model.y, W.X10, BA.AIOnlineCommunicationSkillsV,modx.values = "plus-minus",at = list(W.X01 = 0, W.X01BA.AIOnlineCommunicationSkillsV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.SystemPerformanceImprovementBehaviorVBA.ClarityOfInformationV$model.y, W.X10, BA.ClarityOfInformationV,modx.values = "plus-minus",at = list(W.X01 = 0, W.X01BA.ClarityOfInformationV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.VoiceForSystemImprovmentVBA.StructureV$model.y, W.X10, BA.StructureV,modx.values = "plus-minus",at = list(W.X01 = 0, W.X01BA.StructureV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.TakingChargeBehaviorsForSystemImprovementVBA.NegativeReflectionOnAIUseV$model.y, W.X10, BA.NegativeReflectionOnAIUseV,modx.values = "plus-minus",at = list(W.X01 = 0, W.X01BA.NegativeReflectionOnAIUseV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WA.LearningFromErrorsVBB.AIUsageV$model.y, W.X10, BB.AIUsageV,modx.values = "plus-minus",at = list(W.X01 = 0, W.X01BB.AIUsageV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WA.LearningFromErrorsVBA.AIInteractionQualityV$model.y, W.X10, BA.AIInteractionQualityV,modx.values = "plus-minus",at = list(W.X01 = 0, W.X01BA.AIInteractionQualityV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WA.AffectiveRuminationVBA.AIOnlineCommunicationSkillsV$model.y, W.X10, BA.AIOnlineCommunicationSkillsV,modx.values = "plus-minus",at = list(W.X01 = 0, W.X01BA.AIOnlineCommunicationSkillsV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.VoiceForSystemImprovmentVBA.ClarityOfInformationV$model.y, W.X10, BA.ClarityOfInformationV,modx.values = "plus-minus",at = list(W.X01 = 0, W.X01BA.ClarityOfInformationV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.LearningBehaviorVBB.AITechnologyAnxietyV$model.y, W.X10, BB.AITechnologyAnxietyV,modx.values = "plus-minus",at = list(W.X01 = 0, W.X01BB.AITechnologyAnxietyV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.PerceivedWorkGrowthVBB.TrustInAIV$model.y, W.X10, BB.TrustInAIV,modx.values = "plus-minus",at = list(W.X01 = 0, W.X01BB.TrustInAIV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.TakingChargeBehaviorsForSystemImprovementVBA.AIOnlineCommunicationSkillsV$model.y, W.X10, BA.AIOnlineCommunicationSkillsV,modx.values = "plus-minus",at = list(W.X01 = 0, W.X01BA.AIOnlineCommunicationSkillsV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.TakingChargeBehaviorsForSystemImprovementVBA.StructureV$model.y, W.X10, BA.StructureV,modx.values = "plus-minus",at = list(W.X01 = 0, W.X01BA.StructureV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.TakingChargeBehaviorsForSystemImprovementVBA.ClarityOfInformationV$model.y, W.X10, BA.ClarityOfInformationV,modx.values = "plus-minus",at = list(W.X01 = 0, W.X01BA.ClarityOfInformationV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WA.ErrorStrainVBA.EffectivenessV$model.y, W.X10, BA.EffectivenessV,modx.values = "plus-minus",at = list(W.X01 = 0, W.X01BA.EffectivenessV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.VoiceForSystemImprovmentVBA.QualityV$model.y, W.X10, BA.QualityV,modx.values = "plus-minus",at = list(W.X01 = 0, W.X01BA.QualityV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.SocialLearningVBA.ProblemSolvingConfidenceV$model.y, W.X10, BA.ProblemSolvingConfidenceV,modx.values = "plus-minus",at = list(W.X01 = 0, W.X01BA.ProblemSolvingConfidenceV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.AIUsageForFacilitatingWorkVBA.PersonalControlV$model.y, W.X10, BA.PersonalControlV,modx.values = "plus-minus",at = list(W.X01 = 0, W.X01BA.PersonalControlV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.AIEnabledCreativityVBA.PersonalControlV$model.y, W.X10, BA.PersonalControlV,modx.values = "plus-minus",at = list(W.X01 = 0, W.X01BA.PersonalControlV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.SocialLearningVBA.PositiveReflectionOnAIUseV$model.y, W.X10, BA.PositiveReflectionOnAIUseV,modx.values = "plus-minus",at = list(W.X01 = 0, W.X01BA.PositiveReflectionOnAIUseV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.FamilyMemberUndermingVBA.AIServiceFailureV$model.y, W.X10, BA.AIServiceFailureV,modx.values = "plus-minus",at = list(W.X01 = 0, W.X01BA.AIServiceFailureV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.FamilyMemberConflictVBA.AIServiceFailureV$model.y, W.X10, BA.AIServiceFailureV,modx.values = "plus-minus",at = list(W.X01 = 0, W.X01BA.AIServiceFailureV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.FeedbackSeekingForSystemImprovementVBA.AnthropomorphismV$model.y, W.X10, BA.AnthropomorphismV,modx.values = "plus-minus",at = list(W.X01 = 0, W.X01BA.AnthropomorphismV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
7 STUDY 2 FOR W.X01USING .05 AS STANDARD
7.1 Analysis
Sb10.WP.SystemPerformanceImprovementBehaviorVBA.AIOnlineCommunicationSkillsV=PROCESS(data2, y="WP.SystemPerformanceImprovementBehaviorV", x="W.X01", mods="BA.AIOnlineCommunicationSkillsV",covs=c("W.X10","W.X10BA.AIOnlineCommunicationSkillsV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.SystemPerformanceImprovementBehaviorV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.AIOnlineCommunicationSkillsV
## - Covariates (C) : W.X10, W.X10BA.AIOnlineCommunicationSkillsV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.SystemPerformanceImprovementBehaviorV ~ W.X10 + W.X10BA.AIOnlineCommunicationSkillsV + W.X01*BA.AIOnlineCommunicationSkillsV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.SystemPerformanceImprovementBehaviorV (2) WP.SystemPerformanceImprovementBehaviorV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.413 *** 1.511 ***
## (0.108) (0.375)
## W.X10 0.274 0.692 **
## (0.241) (0.259)
## W.X10BA.AIOnlineCommunicationSkillsV -0.055 -0.154 **
## (0.054) (0.059)
## W.X01 0.039 0.699 **
## (0.070) (0.260)
## BA.AIOnlineCommunicationSkillsV 0.447 ***
## (0.085)
## W.X01:BA.AIOnlineCommunicationSkillsV -0.155 **
## (0.059)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.077
## Conditional R^2 0.686 0.687
## AIC 2959.761 2944.105
## BIC 3013.501 3007.617
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.553 1.294
## Var: B.ID W.X10 0.079 0.084
## Var: B.ID W.X01 0.096 0.090
## Cov: B.ID (Intercept) W.X10 -0.008 0.038
## Cov: B.ID (Intercept) W.X01 -0.092 -0.012
## Cov: B.ID W.X10 W.X01 -0.084 -0.087
## Var: Residual 0.709 0.702
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ───────────────────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────────────────
## W.X01 * BA.AIOnlineCommunicationSkillsV 6.92 1 329 .009 **
## ───────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ──────────────────────────────────────────────────────────────────────────────────
## "BA.AIOnlineCommunicationSkillsV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────────────────
## 3.075 (- SD) 0.223 (0.103) 2.162 .032 * [ 0.021, 0.425]
## 4.260 (Mean) 0.039 (0.073) 0.539 .590 [-0.103, 0.182]
## 5.444 (+ SD) -0.144 (0.103) -1.400 .163 [-0.346, 0.058]
## ──────────────────────────────────────────────────────────────────────────────────Sb10.WP.SystemPerformanceImprovementBehaviorVBA.StructureV=PROCESS(data2, y="WP.SystemPerformanceImprovementBehaviorV", x="W.X01", mods="BA.StructureV",covs=c("W.X10","W.X10BA.StructureV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.SystemPerformanceImprovementBehaviorV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.StructureV
## - Covariates (C) : W.X10, W.X10BA.StructureV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.SystemPerformanceImprovementBehaviorV ~ W.X10 + W.X10BA.StructureV + W.X01*BA.StructureV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.SystemPerformanceImprovementBehaviorV (2) WP.SystemPerformanceImprovementBehaviorV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.413 *** 1.591 ***
## (0.108) (0.335)
## W.X10 0.265 0.659 **
## (0.218) (0.234)
## W.X10BA.StructureV -0.055 -0.151 **
## (0.050) (0.054)
## W.X01 0.039 0.662 **
## (0.070) (0.235)
## BA.StructureV 0.443 ***
## (0.078)
## W.X01:BA.StructureV -0.151 **
## (0.055)
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.089
## Conditional R^2 0.686 0.688
## AIC 2959.794 2940.396
## BIC 3013.534 3003.907
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.554 1.256
## Var: B.ID W.X10 0.078 0.085
## Var: B.ID W.X01 0.097 0.090
## Cov: B.ID (Intercept) W.X10 -0.004 0.048
## Cov: B.ID (Intercept) W.X01 -0.093 -0.002
## Cov: B.ID W.X10 W.X01 -0.084 -0.087
## Var: Residual 0.708 0.701
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ─────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────
## W.X01 * BA.StructureV 7.68 1 330 .006 **
## ─────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ────────────────────────────────────────────────────────────────
## "BA.StructureV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────
## 2.839 (- SD) 0.232 (0.099) 2.359 .019 * [ 0.039, 0.426]
## 4.117 (Mean) 0.039 (0.070) 0.563 .573 [-0.097, 0.176]
## 5.394 (+ SD) -0.154 (0.099) -1.562 .119 [-0.347, 0.039]
## ────────────────────────────────────────────────────────────────Sb10.WP.SystemPerformanceImprovementBehaviorVBA.WayOfQuestioningV=PROCESS(data2, y="WP.SystemPerformanceImprovementBehaviorV", x="W.X01", mods="BA.WayOfQuestioningV",covs=c("W.X10","W.X10BA.WayOfQuestioningV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.SystemPerformanceImprovementBehaviorV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.WayOfQuestioningV
## - Covariates (C) : W.X10, W.X10BA.WayOfQuestioningV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.SystemPerformanceImprovementBehaviorV ~ W.X10 + W.X10BA.WayOfQuestioningV + W.X01*BA.WayOfQuestioningV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.SystemPerformanceImprovementBehaviorV (2) WP.SystemPerformanceImprovementBehaviorV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.413 *** 1.840 ***
## (0.108) (0.369)
## W.X10 0.200 0.562 *
## (0.232) (0.251)
## W.X10BA.WayOfQuestioningV -0.038 -0.122 *
## (0.051) (0.056)
## W.X01 0.039 0.610 *
## (0.070) (0.251)
## BA.WayOfQuestioningV 0.365 ***
## (0.082)
## W.X01:BA.WayOfQuestioningV -0.132 *
## (0.056)
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.057
## Conditional R^2 0.684 0.686
## AIC 2960.288 2951.915
## BIC 3014.029 3015.427
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.551 1.362
## Var: B.ID W.X10 0.081 0.084
## Var: B.ID W.X01 0.094 0.088
## Cov: B.ID (Intercept) W.X10 -0.019 0.017
## Cov: B.ID (Intercept) W.X01 -0.090 -0.028
## Cov: B.ID W.X10 W.X01 -0.083 -0.086
## Var: Residual 0.710 0.705
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────
## W.X01 * BA.WayOfQuestioningV 5.59 1 330 .019 *
## ────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ───────────────────────────────────────────────────────────────────────
## "BA.WayOfQuestioningV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────
## 3.061 (- SD) 0.204 (0.103) 1.981 .049 * [ 0.002, 0.406]
## 4.307 (Mean) 0.039 (0.073) 0.539 .590 [-0.104, 0.182]
## 5.552 (+ SD) -0.126 (0.103) -1.220 .224 [-0.328, 0.076]
## ───────────────────────────────────────────────────────────────────────Sb10.WA.AffectiveRuminationVBA.ClarityOfInformationV=PROCESS(data2, y="WA.AffectiveRuminationV", x="W.X01", mods="BA.ClarityOfInformationV",covs=c("W.X10","W.X10BA.ClarityOfInformationV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.AffectiveRuminationV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.ClarityOfInformationV
## - Covariates (C) : W.X10, W.X10BA.ClarityOfInformationV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.AffectiveRuminationV ~ W.X10 + W.X10BA.ClarityOfInformationV + W.X01*BA.ClarityOfInformationV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ────────────────────────────────────────────────────────────────────────────────────────
## (1) WA.AffectiveRuminationV (2) WA.AffectiveRuminationV
## ────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.731 *** 3.029 ***
## (0.114) (0.408)
## W.X10 0.361 0.631 *
## (0.227) (0.256)
## W.X10BA.ClarityOfInformationV -0.061 -0.123 *
## (0.049) (0.057)
## W.X01 0.055 0.525 *
## (0.072) (0.259)
## BA.ClarityOfInformationV 0.161
## (0.090)
## W.X01:BA.ClarityOfInformationV -0.108
## (0.057)
## ────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.007
## Conditional R^2 0.682 0.683
## AIC 3043.765 3049.476
## BIC 3097.506 3112.987
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.716 1.691
## Var: B.ID W.X10 0.026 0.027
## Var: B.ID W.X01 0.048 0.045
## Cov: B.ID (Intercept) W.X10 -0.042 -0.033
## Cov: B.ID (Intercept) W.X01 0.001 0.020
## Cov: B.ID W.X10 W.X01 -0.034 -0.035
## Var: Residual 0.802 0.799
## ────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.AffectiveRuminationV" (Y)
## ────────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────────
## W.X01 * BA.ClarityOfInformationV 3.58 1 366 .059 .
## ────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WA.AffectiveRuminationV" (Y)
## ───────────────────────────────────────────────────────────────────────────
## "BA.ClarityOfInformationV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────────
## 3.095 (- SD) 0.191 (0.102) 1.874 .062 . [-0.009, 0.390]
## 4.356 (Mean) 0.055 (0.072) 0.759 .449 [-0.086, 0.196]
## 5.617 (+ SD) -0.082 (0.102) -0.802 .423 [-0.281, 0.118]
## ───────────────────────────────────────────────────────────────────────────Sb10.WP.AdviceThinkingBasedSocialLearningVBA.AIInteractionQualityV=PROCESS(data2, y="WP.AdviceThinkingBasedSocialLearningV", x="W.X01", mods="BA.AIInteractionQualityV",covs=c("W.X10","W.X10BA.AIInteractionQualityV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.AdviceThinkingBasedSocialLearningV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.AIInteractionQualityV
## - Covariates (C) : W.X10, W.X10BA.AIInteractionQualityV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.AdviceThinkingBasedSocialLearningV ~ W.X10 + W.X10BA.AIInteractionQualityV + W.X01*BA.AIInteractionQualityV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.AdviceThinkingBasedSocialLearningV (2) WP.AdviceThinkingBasedSocialLearningV
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.617 *** 1.877 ***
## (0.111) (0.315)
## W.X10 -0.908 *** -0.500 *
## (0.217) (0.250)
## W.X10BA.AIInteractionQualityV 0.241 *** 0.139 *
## (0.050) (0.059)
## W.X01 0.067 0.076
## (0.083) (0.260)
## BA.AIInteractionQualityV 0.434 ***
## (0.074)
## W.X01:BA.AIInteractionQualityV -0.002
## (0.061)
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.014 0.162
## Conditional R^2 0.593 0.608
## AIC 3246.811 3223.892
## BIC 3300.552 3287.404
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.488 1.153
## Var: B.ID W.X10 0.025 0.000
## Var: B.ID W.X01 0.050 0.085
## Cov: B.ID (Intercept) W.X10 -0.096 -0.024
## Cov: B.ID (Intercept) W.X01 0.086 0.066
## Cov: B.ID W.X10 W.X01 -0.034 -0.001
## Var: Residual 1.060 1.062
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.AdviceThinkingBasedSocialLearningV" (Y)
## ────────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────────
## W.X01 * BA.AIInteractionQualityV 0.00 1 262 .971
## ────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.AdviceThinkingBasedSocialLearningV" (Y)
## ──────────────────────────────────────────────────────────────────────────
## "BA.AIInteractionQualityV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────────
## 2.640 (- SD) 0.071 (0.117) 0.602 .548 [-0.159, 0.300]
## 4.006 (Mean) 0.067 (0.083) 0.815 .416 [-0.095, 0.230]
## 5.372 (+ SD) 0.064 (0.117) 0.550 .583 [-0.165, 0.294]
## ──────────────────────────────────────────────────────────────────────────Sb10.WA.SelfReflectionForManipulationCheckVBA.ProblemSolvingConfidenceV=PROCESS(data2, y="WA.SelfReflectionForManipulationCheckV", x="W.X01", mods="BA.ProblemSolvingConfidenceV",covs=c("W.X10","W.X10BA.ProblemSolvingConfidenceV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.SelfReflectionForManipulationCheckV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.ProblemSolvingConfidenceV
## - Covariates (C) : W.X10, W.X10BA.ProblemSolvingConfidenceV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.SelfReflectionForManipulationCheckV ~ W.X10 + W.X10BA.ProblemSolvingConfidenceV + W.X01*BA.ProblemSolvingConfidenceV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WA.SelfReflectionForManipulationCheckV (2) WA.SelfReflectionForManipulationCheckV
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.104 *** 0.534
## (0.110) (0.425)
## W.X10 0.009 0.600 *
## (0.252) (0.291)
## W.X10BA.ProblemSolvingConfidenceV 0.007 -0.128 *
## (0.056) (0.065)
## W.X01 0.007 0.322
## (0.067) (0.311)
## BA.ProblemSolvingConfidenceV 0.816 ***
## (0.095)
## W.X01:BA.ProblemSolvingConfidenceV -0.072
## (0.069)
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.245
## Conditional R^2 0.700 0.702
## AIC 2843.375 2786.485
## BIC 2897.116 2849.997
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.662 1.047
## Var: B.ID W.X10 0.009 0.001
## Var: B.ID W.X01 0.094 0.091
## Cov: B.ID (Intercept) W.X10 -0.121 -0.023
## Cov: B.ID (Intercept) W.X01 -0.168 -0.115
## Cov: B.ID W.X10 W.X01 0.012 0.003
## Var: Residual 0.645 0.643
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.SelfReflectionForManipulationCheckV" (Y)
## ────────────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────────────
## W.X01 * BA.ProblemSolvingConfidenceV 1.08 1 247 .300
## ────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WA.SelfReflectionForManipulationCheckV" (Y)
## ───────────────────────────────────────────────────────────────────────────────
## "BA.ProblemSolvingConfidenceV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────────────
## 3.409 (- SD) 0.077 (0.095) 0.811 .418 [-0.109, 0.262]
## 4.376 (Mean) 0.007 (0.067) 0.103 .918 [-0.124, 0.138]
## 5.343 (+ SD) -0.063 (0.095) -0.665 .507 [-0.248, 0.122]
## ───────────────────────────────────────────────────────────────────────────────Sb10.WP.AIEnabledInnovationBehaviorVBA.WayOfQuestioningV=PROCESS(data2, y="WP.AIEnabledInnovationBehaviorV", x="W.X01", mods="BA.WayOfQuestioningV",covs=c("W.X10","W.X10BA.WayOfQuestioningV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.AIEnabledInnovationBehaviorV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.WayOfQuestioningV
## - Covariates (C) : W.X10, W.X10BA.WayOfQuestioningV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.AIEnabledInnovationBehaviorV ~ W.X10 + W.X10BA.WayOfQuestioningV + W.X01*BA.WayOfQuestioningV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.AIEnabledInnovationBehaviorV (2) WP.AIEnabledInnovationBehaviorV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.474 *** 2.112 ***
## (0.114) (0.399)
## W.X10 -0.312 0.060
## (0.211) (0.245)
## W.X10BA.WayOfQuestioningV 0.095 * 0.009
## (0.046) (0.055)
## W.X01 -0.050 0.449
## (0.068) (0.245)
## BA.WayOfQuestioningV 0.316 ***
## (0.089)
## W.X01:BA.WayOfQuestioningV -0.116 *
## (0.055)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.003 0.051
## Conditional R^2 0.699 0.704
## AIC 2976.699 2974.051
## BIC 3030.439 3037.562
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.757 1.621
## Var: B.ID W.X10 0.000 0.000
## Var: B.ID W.X01 0.000 0.002
## Cov: B.ID (Intercept) W.X10 -0.021 0.011
## Cov: B.ID (Intercept) W.X01 0.009 0.053
## Cov: B.ID W.X10 W.X01 -0.000 0.000
## Var: Residual 0.758 0.754
## ─────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.AIEnabledInnovationBehaviorV" (Y)
## ────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────
## W.X01 * BA.WayOfQuestioningV 4.49 1 795 .034 *
## ────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.AIEnabledInnovationBehaviorV" (Y)
## ────────────────────────────────────────────────────────────────────────
## "BA.WayOfQuestioningV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────────
## 3.061 (- SD) 0.094 (0.096) 0.976 .329 [-0.095, 0.283]
## 4.307 (Mean) -0.050 (0.068) -0.739 .460 [-0.184, 0.083]
## 5.552 (+ SD) -0.195 (0.096) -2.021 .044 * [-0.384, -0.006]
## ────────────────────────────────────────────────────────────────────────Sb10.WP.AIEnabledCreativityVBA.NeedForPersonalizationDueToAIV=PROCESS(data2, y="WP.AIEnabledCreativityV", x="W.X01", mods="BA.NeedForPersonalizationDueToAIV",covs=c("W.X10","W.X10BA.NeedForPersonalizationDueToAIV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.AIEnabledCreativityV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.NeedForPersonalizationDueToAIV
## - Covariates (C) : W.X10, W.X10BA.NeedForPersonalizationDueToAIV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.AIEnabledCreativityV ~ W.X10 + W.X10BA.NeedForPersonalizationDueToAIV + W.X01*BA.NeedForPersonalizationDueToAIV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.AIEnabledCreativityV (2) WP.AIEnabledCreativityV
## ─────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.721 *** 1.974 ***
## (0.119) (0.338)
## W.X10 -0.219 0.203
## (0.183) (0.210)
## W.X10BA.NeedForPersonalizationDueToAIV 0.055 -0.048
## (0.042) (0.049)
## W.X01 -0.066 0.349
## (0.069) (0.212)
## BA.NeedForPersonalizationDueToAIV 0.429 ***
## (0.079)
## W.X01:BA.NeedForPersonalizationDueToAIV -0.102 *
## (0.049)
## ─────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.110
## Conditional R^2 0.706 0.707
## AIC 2987.601 2971.736
## BIC 3041.342 3035.247
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.931 1.587
## Var: B.ID W.X10 0.019 0.003
## Var: B.ID W.X01 0.027 0.018
## Cov: B.ID (Intercept) W.X10 -0.144 -0.070
## Cov: B.ID (Intercept) W.X01 -0.075 -0.002
## Cov: B.ID W.X10 W.X01 -0.009 0.000
## Var: Residual 0.753 0.758
## ─────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.AIEnabledCreativityV" (Y)
## ─────────────────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────────────────
## W.X01 * BA.NeedForPersonalizationDueToAIV 4.28 1 181 .040 *
## ─────────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.AIEnabledCreativityV" (Y)
## ─────────────────────────────────────────────────────────────────────────────────────
## "BA.NeedForPersonalizationDueToAIV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────────────────────────
## 2.671 (- SD) 0.077 (0.097) 0.787 .432 [-0.114, 0.268]
## 4.071 (Mean) -0.066 (0.069) -0.957 .339 [-0.201, 0.069]
## 5.470 (+ SD) -0.209 (0.097) -2.140 .033 * [-0.400, -0.018]
## ─────────────────────────────────────────────────────────────────────────────────────Sb10.WP.VoiceForSystemImprovmentVBA.NeedForPersonalizationDueToAIV=PROCESS(data2, y="WP.VoiceForSystemImprovmentV", x="W.X01", mods="BA.NeedForPersonalizationDueToAIV",covs=c("W.X10","W.X10BA.NeedForPersonalizationDueToAIV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.VoiceForSystemImprovmentV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.NeedForPersonalizationDueToAIV
## - Covariates (C) : W.X10, W.X10BA.NeedForPersonalizationDueToAIV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.VoiceForSystemImprovmentV ~ W.X10 + W.X10BA.NeedForPersonalizationDueToAIV + W.X01*BA.NeedForPersonalizationDueToAIV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.VoiceForSystemImprovmentV (2) WP.VoiceForSystemImprovmentV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.531 *** 1.851 ***
## (0.121) (0.347)
## W.X10 0.071 0.353
## (0.169) (0.189)
## W.X10BA.NeedForPersonalizationDueToAIV -0.003 -0.072
## (0.039) (0.044)
## W.X01 0.006 0.557 *
## (0.081) (0.246)
## BA.NeedForPersonalizationDueToAIV 0.413 ***
## (0.081)
## W.X01:BA.NeedForPersonalizationDueToAIV -0.136 *
## (0.057)
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.093
## Conditional R^2 0.762 0.763
## AIC 2903.784 2890.559
## BIC 2957.525 2954.071
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 2.095 1.775
## Var: B.ID W.X10 0.011 0.008
## Var: B.ID W.X01 0.465 0.439
## Cov: B.ID (Intercept) W.X10 -0.071 -0.021
## Cov: B.ID (Intercept) W.X01 -0.394 -0.290
## Cov: B.ID W.X10 W.X01 0.071 0.059
## Var: Residual 0.608 0.607
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.VoiceForSystemImprovmentV" (Y)
## ─────────────────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────────────────
## W.X01 * BA.NeedForPersonalizationDueToAIV 5.61 1 169 .019 *
## ─────────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.VoiceForSystemImprovmentV" (Y)
## ────────────────────────────────────────────────────────────────────────────────────
## "BA.NeedForPersonalizationDueToAIV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────────────────────
## 2.671 (- SD) 0.195 (0.113) 1.724 .087 . [-0.027, 0.417]
## 4.071 (Mean) 0.006 (0.080) 0.069 .945 [-0.151, 0.163]
## 5.470 (+ SD) -0.184 (0.113) -1.626 .106 [-0.406, 0.038]
## ────────────────────────────────────────────────────────────────────────────────────Sb10.WP.TakingChargeBehaviorsForSystemImprovementVBA.ReflectionOnAIUseV=PROCESS(data2, y="WP.TakingChargeBehaviorsForSystemImprovementV", x="W.X01", mods="BA.ReflectionOnAIUseV",covs=c("W.X10","W.X10BA.ReflectionOnAIUseV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.TakingChargeBehaviorsForSystemImprovementV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.ReflectionOnAIUseV
## - Covariates (C) : W.X10, W.X10BA.ReflectionOnAIUseV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.TakingChargeBehaviorsForSystemImprovementV ~ W.X10 + W.X10BA.ReflectionOnAIUseV + W.X01*BA.ReflectionOnAIUseV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.TakingChargeBehaviorsForSystemImprovementV (2) WP.TakingChargeBehaviorsForSystemImprovementV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.546 *** 1.170 **
## (0.122) (0.369)
## W.X10 0.102 0.479 *
## (0.197) (0.219)
## W.X10BA.ReflectionOnAIUseV 0.000 -0.093
## (0.046) (0.052)
## W.X01 0.041 0.640 *
## (0.074) (0.250)
## BA.ReflectionOnAIUseV 0.590 ***
## (0.087)
## W.X01:BA.ReflectionOnAIUseV -0.149 *
## (0.059)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.155
## Conditional R^2 0.739 0.742
## AIC 2946.787 2917.085
## BIC 3000.527 2980.596
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 2.100 1.583
## Var: B.ID W.X10 0.001 0.003
## Var: B.ID W.X01 0.202 0.208
## Cov: B.ID (Intercept) W.X10 -0.055 0.015
## Cov: B.ID (Intercept) W.X01 -0.302 -0.190
## Cov: B.ID W.X10 W.X01 0.008 0.022
## Var: Residual 0.681 0.677
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ─────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────
## W.X01 * BA.ReflectionOnAIUseV 6.28 1 182 .013 *
## ─────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ────────────────────────────────────────────────────────────────────────
## "BA.ReflectionOnAIUseV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────────
## 2.788 (- SD) 0.226 (0.104) 2.165 .032 * [ 0.021, 0.430]
## 4.030 (Mean) 0.041 (0.074) 0.555 .579 [-0.103, 0.185]
## 5.272 (+ SD) -0.144 (0.104) -1.380 .169 [-0.348, 0.060]
## ────────────────────────────────────────────────────────────────────────Sb10.WP.AIEnabledCreativityVBA.CapabilityV=PROCESS(data2, y="WP.AIEnabledCreativityV", x="W.X01", mods="BA.CapabilityV",covs=c("W.X10","W.X10BA.CapabilityV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.AIEnabledCreativityV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.CapabilityV
## - Covariates (C) : W.X10, W.X10BA.CapabilityV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.AIEnabledCreativityV ~ W.X10 + W.X10BA.CapabilityV + W.X01*BA.CapabilityV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────
## (1) WP.AIEnabledCreativityV (2) WP.AIEnabledCreativityV
## ─────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.721 *** 1.510 ***
## (0.119) (0.331)
## W.X10 -0.543 ** 0.029
## (0.185) (0.217)
## W.X10BA.CapabilityV 0.131 ** -0.005
## (0.041) (0.049)
## W.X01 -0.066 0.339
## (0.069) (0.218)
## BA.CapabilityV 0.527 ***
## (0.075)
## W.X01:BA.CapabilityV -0.097 *
## (0.049)
## ─────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.005 0.185
## Conditional R^2 0.700 0.710
## AIC 2981.414 2949.446
## BIC 3035.155 3012.958
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.927 1.402
## Var: B.ID W.X10 0.032 0.013
## Var: B.ID W.X01 0.019 0.019
## Cov: B.ID (Intercept) W.X10 -0.228 -0.101
## Cov: B.ID (Intercept) W.X01 -0.069 0.018
## Cov: B.ID W.X10 W.X01 -0.001 -0.012
## Var: Residual 0.755 0.751
## ─────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.AIEnabledCreativityV" (Y)
## ──────────────────────────────────────────────
## F df1 df2 p
## ──────────────────────────────────────────────
## W.X01 * BA.CapabilityV 3.85 1 438 .050 .
## ──────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.AIEnabledCreativityV" (Y)
## ──────────────────────────────────────────────────────────────────
## "BA.CapabilityV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────
## 2.799 (- SD) 0.069 (0.097) 0.709 .479 [-0.122, 0.259]
## 4.196 (Mean) -0.066 (0.069) -0.960 .338 [-0.201, 0.069]
## 5.593 (+ SD) -0.201 (0.097) -2.066 .039 * [-0.391, -0.010]
## ──────────────────────────────────────────────────────────────────Sb10.WP.AIEnabledInnovationBehaviorVBA.StructureV=PROCESS(data2, y="WP.AIEnabledInnovationBehaviorV", x="W.X01", mods="BA.StructureV",covs=c("W.X10","W.X10BA.StructureV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.AIEnabledInnovationBehaviorV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.StructureV
## - Covariates (C) : W.X10, W.X10BA.StructureV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.AIEnabledInnovationBehaviorV ~ W.X10 + W.X10BA.StructureV + W.X01*BA.StructureV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.AIEnabledInnovationBehaviorV (2) WP.AIEnabledInnovationBehaviorV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.474 *** 1.953 ***
## (0.115) (0.367)
## W.X10 -0.186 0.185
## (0.199) (0.230)
## W.X10BA.StructureV 0.069 -0.021
## (0.045) (0.053)
## W.X01 -0.050 0.413
## (0.068) (0.230)
## BA.StructureV 0.369 ***
## (0.085)
## W.X01:BA.StructureV -0.113 *
## (0.053)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.003 0.071
## Conditional R^2 0.698 0.704
## AIC 2978.584 2970.918
## BIC 3032.324 3034.429
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.757 1.554
## Var: B.ID W.X10 0.000 0.000
## Var: B.ID W.X01 0.000 0.002
## Cov: B.ID (Intercept) W.X10 -0.017 0.025
## Cov: B.ID (Intercept) W.X01 0.008 0.062
## Cov: B.ID W.X10 W.X01 -0.000 0.001
## Var: Residual 0.760 0.756
## ─────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.AIEnabledInnovationBehaviorV" (Y)
## ─────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────
## W.X01 * BA.StructureV 4.45 1 789 .035 *
## ─────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.AIEnabledInnovationBehaviorV" (Y)
## ─────────────────────────────────────────────────────────────────
## "BA.StructureV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────
## 2.839 (- SD) 0.094 (0.096) 0.971 .332 [-0.095, 0.283]
## 4.117 (Mean) -0.050 (0.068) -0.738 .461 [-0.184, 0.083]
## 5.394 (+ SD) -0.194 (0.096) -2.014 .044 * [-0.383, -0.005]
## ─────────────────────────────────────────────────────────────────Sb10.WP.AIEnabledInnovationBehaviorVBA.AIOnlineCommunicationSkillsV=PROCESS(data2, y="WP.AIEnabledInnovationBehaviorV", x="W.X01", mods="BA.AIOnlineCommunicationSkillsV",covs=c("W.X10","W.X10BA.AIOnlineCommunicationSkillsV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.AIEnabledInnovationBehaviorV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.AIOnlineCommunicationSkillsV
## - Covariates (C) : W.X10, W.X10BA.AIOnlineCommunicationSkillsV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.AIEnabledInnovationBehaviorV ~ W.X10 + W.X10BA.AIOnlineCommunicationSkillsV + W.X01*BA.AIOnlineCommunicationSkillsV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.AIEnabledInnovationBehaviorV (2) WP.AIEnabledInnovationBehaviorV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.474 *** 1.804 ***
## (0.114) (0.407)
## W.X10 -0.281 0.158
## (0.219) (0.254)
## W.X10BA.AIOnlineCommunicationSkillsV 0.089 -0.014
## (0.049) (0.057)
## W.X01 -0.050 0.519 *
## (0.068) (0.255)
## BA.AIOnlineCommunicationSkillsV 0.392 ***
## (0.092)
## W.X01:BA.AIOnlineCommunicationSkillsV -0.134 *
## (0.058)
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.003 0.068
## Conditional R^2 0.698 0.704
## AIC 2977.512 2969.570
## BIC 3031.253 3033.081
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.756 1.562
## Var: B.ID W.X10 0.000 0.000
## Var: B.ID W.X01 0.000 0.003
## Cov: B.ID (Intercept) W.X10 -0.022 0.021
## Cov: B.ID (Intercept) W.X01 0.009 0.066
## Cov: B.ID W.X10 W.X01 -0.000 0.001
## Var: Residual 0.759 0.754
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.AIEnabledInnovationBehaviorV" (Y)
## ───────────────────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────────────────
## W.X01 * BA.AIOnlineCommunicationSkillsV 5.39 1 786 .020 *
## ───────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.AIEnabledInnovationBehaviorV" (Y)
## ───────────────────────────────────────────────────────────────────────────────────
## "BA.AIOnlineCommunicationSkillsV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────────────────
## 3.075 (- SD) 0.108 (0.096) 1.121 .263 [-0.081, 0.297]
## 4.260 (Mean) -0.050 (0.068) -0.738 .461 [-0.184, 0.083]
## 5.444 (+ SD) -0.209 (0.096) -2.165 .031 * [-0.398, -0.020]
## ───────────────────────────────────────────────────────────────────────────────────7.2 Plot
interact_plot(Sb10.WP.SystemPerformanceImprovementBehaviorVBA.AIOnlineCommunicationSkillsV$model.y, W.X01, BA.AIOnlineCommunicationSkillsV,modx.values = "plus-minus",at = list(W.X10 = 0, W.X10BA.AIOnlineCommunicationSkillsV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.SystemPerformanceImprovementBehaviorVBA.StructureV$model.y, W.X01, BA.StructureV,modx.values = "plus-minus",at = list(W.X10 = 0, W.X10BA.StructureV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.SystemPerformanceImprovementBehaviorVBA.WayOfQuestioningV$model.y, W.X01, BA.WayOfQuestioningV,modx.values = "plus-minus",at = list(W.X10 = 0, W.X10BA.WayOfQuestioningV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WA.AffectiveRuminationVBA.ClarityOfInformationV$model.y, W.X01, BA.ClarityOfInformationV,modx.values = "plus-minus",at = list(W.X10 = 0, W.X10BA.ClarityOfInformationV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.AdviceThinkingBasedSocialLearningVBA.AIInteractionQualityV$model.y, W.X01, BA.AIInteractionQualityV,modx.values = "plus-minus",at = list(W.X10 = 0, W.X10BA.AIInteractionQualityV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WA.SelfReflectionForManipulationCheckVBA.ProblemSolvingConfidenceV$model.y, W.X01, BA.ProblemSolvingConfidenceV,modx.values = "plus-minus",at = list(W.X10 = 0, W.X10BA.ProblemSolvingConfidenceV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.AIEnabledInnovationBehaviorVBA.WayOfQuestioningV$model.y, W.X01, BA.WayOfQuestioningV,modx.values = "plus-minus",at = list(W.X10 = 0, W.X10BA.WayOfQuestioningV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.AIEnabledCreativityVBA.NeedForPersonalizationDueToAIV$model.y, W.X01, BA.NeedForPersonalizationDueToAIV,modx.values = "plus-minus",at = list(W.X10 = 0, W.X10BA.NeedForPersonalizationDueToAIV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.VoiceForSystemImprovmentVBA.NeedForPersonalizationDueToAIV$model.y, W.X01, BA.NeedForPersonalizationDueToAIV,modx.values = "plus-minus",at = list(W.X10 = 0, W.X10BA.NeedForPersonalizationDueToAIV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.TakingChargeBehaviorsForSystemImprovementVBA.ReflectionOnAIUseV$model.y, W.X01, BA.ReflectionOnAIUseV,modx.values = "plus-minus",at = list(W.X10 = 0, W.X10BA.ReflectionOnAIUseV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.AIEnabledCreativityVBA.CapabilityV$model.y, W.X01, BA.CapabilityV,modx.values = "plus-minus",at = list(W.X10 = 0, W.X10BA.CapabilityV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.AIEnabledInnovationBehaviorVBA.StructureV$model.y, W.X01, BA.StructureV,modx.values = "plus-minus",at = list(W.X10 = 0, W.X10BA.StructureV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.AIEnabledInnovationBehaviorVBA.AIOnlineCommunicationSkillsV$model.y, W.X01, BA.AIOnlineCommunicationSkillsV,modx.values = "plus-minus",at = list(W.X10 = 0, W.X10BA.AIOnlineCommunicationSkillsV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
8 STUDY 2 FOR W.X01USING .10 AS STANDARD
8.1 Analysis
Sb10.WP.AIEnabledInnovationBehaviorVBA.NeedForPersonalizationDueToAIV=PROCESS(data2, y="WP.AIEnabledInnovationBehaviorV", x="W.X01", mods="BA.NeedForPersonalizationDueToAIV",covs=c("W.X10","W.X10BA.NeedForPersonalizationDueToAIV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.AIEnabledInnovationBehaviorV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.NeedForPersonalizationDueToAIV
## - Covariates (C) : W.X10, W.X10BA.NeedForPersonalizationDueToAIV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.AIEnabledInnovationBehaviorV ~ W.X10 + W.X10BA.NeedForPersonalizationDueToAIV + W.X01*BA.NeedForPersonalizationDueToAIV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.AIEnabledInnovationBehaviorV (2) WP.AIEnabledInnovationBehaviorV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.474 *** 1.908 ***
## (0.115) (0.329)
## W.X10 -0.116 0.256
## (0.182) (0.210)
## W.X10BA.NeedForPersonalizationDueToAIV 0.053 -0.039
## (0.041) (0.049)
## W.X01 -0.050 0.406
## (0.068) (0.210)
## BA.NeedForPersonalizationDueToAIV 0.385 ***
## (0.076)
## W.X01:BA.NeedForPersonalizationDueToAIV -0.112 *
## (0.049)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.089
## Conditional R^2 0.698 0.704
## AIC 2979.448 2966.314
## BIC 3033.189 3029.826
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.757 1.489
## Var: B.ID W.X10 0.000 0.001
## Var: B.ID W.X01 0.000 0.004
## Cov: B.ID (Intercept) W.X10 -0.015 0.039
## Cov: B.ID (Intercept) W.X01 0.008 0.075
## Cov: B.ID W.X10 W.X01 -0.000 0.002
## Var: Residual 0.760 0.755
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.AIEnabledInnovationBehaviorV" (Y)
## ─────────────────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────────────────
## W.X01 * BA.NeedForPersonalizationDueToAIV 5.29 1 779 .022 *
## ─────────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.AIEnabledInnovationBehaviorV" (Y)
## ─────────────────────────────────────────────────────────────────────────────────────
## "BA.NeedForPersonalizationDueToAIV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────────────────────────
## 2.671 (- SD) 0.107 (0.097) 1.105 .270 [-0.083, 0.296]
## 4.071 (Mean) -0.050 (0.068) -0.737 .461 [-0.184, 0.083]
## 5.470 (+ SD) -0.207 (0.097) -2.147 .032 * [-0.396, -0.018]
## ─────────────────────────────────────────────────────────────────────────────────────Sb10.WP.TakingChargeBehaviorsForSystemImprovementVBA.PositiveReflectionOnAIUseV=PROCESS(data2, y="WP.TakingChargeBehaviorsForSystemImprovementV", x="W.X01", mods="BA.PositiveReflectionOnAIUseV",covs=c("W.X10","W.X10BA.PositiveReflectionOnAIUseV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.TakingChargeBehaviorsForSystemImprovementV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.PositiveReflectionOnAIUseV
## - Covariates (C) : W.X10, W.X10BA.PositiveReflectionOnAIUseV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.TakingChargeBehaviorsForSystemImprovementV ~ W.X10 + W.X10BA.PositiveReflectionOnAIUseV + W.X01*BA.PositiveReflectionOnAIUseV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.TakingChargeBehaviorsForSystemImprovementV (2) WP.TakingChargeBehaviorsForSystemImprovementV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.546 *** 1.300 ***
## (0.122) (0.323)
## W.X10 -0.017 0.360
## (0.176) (0.196)
## W.X10BA.PositiveReflectionOnAIUseV 0.030 -0.064
## (0.041) (0.046)
## W.X01 0.041 0.587 **
## (0.074) (0.223)
## BA.PositiveReflectionOnAIUseV 0.556 ***
## (0.076)
## W.X01:BA.PositiveReflectionOnAIUseV -0.135 **
## (0.052)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.185
## Conditional R^2 0.737 0.741
## AIC 2946.624 2910.612
## BIC 3000.364 2974.123
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 2.097 1.503
## Var: B.ID W.X10 0.003 0.003
## Var: B.ID W.X01 0.199 0.203
## Cov: B.ID (Intercept) W.X10 -0.084 0.003
## Cov: B.ID (Intercept) W.X01 -0.299 -0.173
## Cov: B.ID W.X10 W.X01 0.012 0.023
## Var: Residual 0.682 0.678
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ─────────────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────────────
## W.X01 * BA.PositiveReflectionOnAIUseV 6.72 1 181 .010 *
## ─────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ────────────────────────────────────────────────────────────────────────────────
## "BA.PositiveReflectionOnAIUseV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────────────────
## 2.629 (- SD) 0.232 (0.104) 2.226 .027 * [ 0.028, 0.435]
## 4.037 (Mean) 0.041 (0.074) 0.556 .579 [-0.103, 0.185]
## 5.444 (+ SD) -0.150 (0.104) -1.440 .152 [-0.353, 0.054]
## ────────────────────────────────────────────────────────────────────────────────Sb10.WP.VoiceForSystemImprovmentVBA.AIOnlineCommunicationSkillsV=PROCESS(data2, y="WP.VoiceForSystemImprovmentV", x="W.X01", mods="BA.AIOnlineCommunicationSkillsV",covs=c("W.X10","W.X10BA.AIOnlineCommunicationSkillsV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.VoiceForSystemImprovmentV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.AIOnlineCommunicationSkillsV
## - Covariates (C) : W.X10, W.X10BA.AIOnlineCommunicationSkillsV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.VoiceForSystemImprovmentV ~ W.X10 + W.X10BA.AIOnlineCommunicationSkillsV + W.X01*BA.AIOnlineCommunicationSkillsV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.VoiceForSystemImprovmentV (2) WP.VoiceForSystemImprovmentV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.531 *** 1.718 ***
## (0.121) (0.430)
## W.X10 0.224 0.537 *
## (0.209) (0.229)
## W.X10BA.AIOnlineCommunicationSkillsV -0.039 -0.112 *
## (0.047) (0.052)
## W.X01 0.006 0.699 *
## (0.079) (0.299)
## BA.AIOnlineCommunicationSkillsV 0.425 ***
## (0.097)
## W.X01:BA.AIOnlineCommunicationSkillsV -0.163 *
## (0.068)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.064
## Conditional R^2 0.762 0.764
## AIC 2904.298 2894.791
## BIC 2958.039 2958.302
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 2.089 1.858
## Var: B.ID W.X10 0.001 0.008
## Var: B.ID W.X01 0.408 0.441
## Cov: B.ID (Intercept) W.X10 -0.044 -0.016
## Cov: B.ID (Intercept) W.X01 -0.363 -0.305
## Cov: B.ID W.X10 W.X01 0.008 0.057
## Var: Residual 0.609 0.605
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.VoiceForSystemImprovmentV" (Y)
## ───────────────────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────────────────
## W.X01 * BA.AIOnlineCommunicationSkillsV 5.79 1 170 .017 *
## ───────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.VoiceForSystemImprovmentV" (Y)
## ──────────────────────────────────────────────────────────────────────────────────
## "BA.AIOnlineCommunicationSkillsV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────────────────
## 3.075 (- SD) 0.198 (0.113) 1.750 .082 . [-0.024, 0.420]
## 4.260 (Mean) 0.006 (0.080) 0.069 .945 [-0.152, 0.163]
## 5.444 (+ SD) -0.187 (0.113) -1.652 .100 [-0.409, 0.035]
## ──────────────────────────────────────────────────────────────────────────────────Sb10.WP.SystemPerformanceImprovementBehaviorVBA.ClarityOfInformationV=PROCESS(data2, y="WP.SystemPerformanceImprovementBehaviorV", x="W.X01", mods="BA.ClarityOfInformationV",covs=c("W.X10","W.X10BA.ClarityOfInformationV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.SystemPerformanceImprovementBehaviorV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.ClarityOfInformationV
## - Covariates (C) : W.X10, W.X10BA.ClarityOfInformationV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.SystemPerformanceImprovementBehaviorV ~ W.X10 + W.X10BA.ClarityOfInformationV + W.X01*BA.ClarityOfInformationV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.SystemPerformanceImprovementBehaviorV (2) WP.SystemPerformanceImprovementBehaviorV
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.413 *** 1.797 ***
## (0.108) (0.368)
## W.X10 0.261 0.616 *
## (0.232) (0.250)
## W.X10BA.ClarityOfInformationV -0.051 -0.133 *
## (0.051) (0.055)
## W.X01 0.039 0.586 *
## (0.070) (0.251)
## BA.ClarityOfInformationV 0.371 ***
## (0.081)
## W.X01:BA.ClarityOfInformationV -0.125 *
## (0.055)
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.060
## Conditional R^2 0.685 0.686
## AIC 2959.883 2950.710
## BIC 3013.623 3014.222
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.553 1.351
## Var: B.ID W.X10 0.079 0.082
## Var: B.ID W.X01 0.095 0.090
## Cov: B.ID (Intercept) W.X10 -0.012 0.025
## Cov: B.ID (Intercept) W.X01 -0.091 -0.030
## Cov: B.ID W.X10 W.X01 -0.084 -0.086
## Var: Residual 0.709 0.705
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ────────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────────
## W.X01 * BA.ClarityOfInformationV 5.12 1 326 .024 *
## ────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.SystemPerformanceImprovementBehaviorV" (Y)
## ───────────────────────────────────────────────────────────────────────────
## "BA.ClarityOfInformationV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────────
## 3.095 (- SD) 0.197 (0.103) 1.913 .057 . [-0.005, 0.400]
## 4.356 (Mean) 0.039 (0.073) 0.538 .591 [-0.104, 0.182]
## 5.617 (+ SD) -0.119 (0.103) -1.152 .250 [-0.321, 0.083]
## ───────────────────────────────────────────────────────────────────────────Sb10.WP.VoiceForSystemImprovmentVBA.StructureV=PROCESS(data2, y="WP.VoiceForSystemImprovmentV", x="W.X01", mods="BA.StructureV",covs=c("W.X10","W.X10BA.StructureV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.VoiceForSystemImprovmentV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.StructureV
## - Covariates (C) : W.X10, W.X10BA.StructureV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.VoiceForSystemImprovmentV ~ W.X10 + W.X10BA.StructureV + W.X01*BA.StructureV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.VoiceForSystemImprovmentV (2) WP.VoiceForSystemImprovmentV
## ───────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.531 *** 1.835 ***
## (0.121) (0.386)
## W.X10 0.135 0.387
## (0.185) (0.206)
## W.X10BA.StructureV -0.019 -0.080
## (0.043) (0.048)
## W.X01 0.006 0.474
## (0.081) (0.264)
## BA.StructureV 0.412 ***
## (0.089)
## W.X01:BA.StructureV -0.114
## (0.061)
## ───────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.079
## Conditional R^2 0.763 0.762
## AIC 2903.433 2895.798
## BIC 2957.173 2959.310
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 2.096 1.823
## Var: B.ID W.X10 0.010 0.000
## Var: B.ID W.X01 0.467 0.391
## Cov: B.ID (Intercept) W.X10 -0.063 -0.015
## Cov: B.ID (Intercept) W.X01 -0.395 -0.289
## Cov: B.ID W.X10 W.X01 0.070 0.002
## Var: Residual 0.608 0.610
## ───────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.VoiceForSystemImprovmentV" (Y)
## ─────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────
## W.X01 * BA.StructureV 3.44 1 216 .065 .
## ─────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.VoiceForSystemImprovmentV" (Y)
## ────────────────────────────────────────────────────────────────
## "BA.StructureV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────
## 2.839 (- SD) 0.151 (0.114) 1.323 .188 [-0.073, 0.375]
## 4.117 (Mean) 0.006 (0.081) 0.068 .946 [-0.153, 0.164]
## 5.394 (+ SD) -0.140 (0.114) -1.226 .222 [-0.364, 0.084]
## ────────────────────────────────────────────────────────────────Sb10.WP.TakingChargeBehaviorsForSystemImprovementVBA.NegativeReflectionOnAIUseV=PROCESS(data2, y="WP.TakingChargeBehaviorsForSystemImprovementV", x="W.X01", mods="BA.NegativeReflectionOnAIUseV",covs=c("W.X10","W.X10BA.NegativeReflectionOnAIUseV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.TakingChargeBehaviorsForSystemImprovementV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.NegativeReflectionOnAIUseV
## - Covariates (C) : W.X10, W.X10BA.NegativeReflectionOnAIUseV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.TakingChargeBehaviorsForSystemImprovementV ~ W.X10 + W.X10BA.NegativeReflectionOnAIUseV + W.X01*BA.NegativeReflectionOnAIUseV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.TakingChargeBehaviorsForSystemImprovementV (2) WP.TakingChargeBehaviorsForSystemImprovementV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.546 *** 1.960 ***
## (0.122) (0.364)
## W.X10 0.213 0.460 *
## (0.183) (0.203)
## W.X10BA.NegativeReflectionOnAIUseV -0.027 -0.089
## (0.043) (0.048)
## W.X01 0.041 0.462 *
## (0.074) (0.230)
## BA.NegativeReflectionOnAIUseV 0.394 ***
## (0.086)
## W.X01:BA.NegativeReflectionOnAIUseV -0.105
## (0.054)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.077
## Conditional R^2 0.741 0.742
## AIC 2946.579 2937.838
## BIC 3000.320 3001.349
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 2.102 1.834
## Var: B.ID W.X10 0.001 0.000
## Var: B.ID W.X01 0.205 0.193
## Cov: B.ID (Intercept) W.X10 -0.039 -0.001
## Cov: B.ID (Intercept) W.X01 -0.303 -0.234
## Cov: B.ID W.X10 W.X01 0.006 0.000
## Var: Residual 0.679 0.678
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ─────────────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────────────
## W.X01 * BA.NegativeReflectionOnAIUseV 3.73 1 229 .055 .
## ─────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ────────────────────────────────────────────────────────────────────────────────
## "BA.NegativeReflectionOnAIUseV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────────────────────
## 2.675 (- SD) 0.182 (0.105) 1.735 .085 . [-0.024, 0.388]
## 4.022 (Mean) 0.041 (0.074) 0.551 .582 [-0.105, 0.186]
## 5.370 (+ SD) -0.100 (0.105) -0.955 .341 [-0.306, 0.105]
## ────────────────────────────────────────────────────────────────────────────────Sb10.WA.LearningFromErrorsVBB.AIUsageV=PROCESS(data2, y="WA.LearningFromErrorsV", x="W.X01", mods="BB.AIUsageV",covs=c("W.X10","W.X10BB.AIUsageV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.LearningFromErrorsV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BB.AIUsageV
## - Covariates (C) : W.X10, W.X10BB.AIUsageV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.LearningFromErrorsV ~ W.X10 + W.X10BB.AIUsageV + W.X01*BB.AIUsageV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────────
## (1) WA.LearningFromErrorsV (2) WA.LearningFromErrorsV
## ───────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.133 *** 4.099 ***
## (0.120) (0.263)
## W.X10 0.095 0.191
## (0.136) (0.151)
## W.X10BB.AIUsageV -0.014 -0.045
## (0.038) (0.043)
## W.X01 0.029 0.267
## (0.071) (0.155)
## BB.AIUsageV 0.011
## (0.075)
## W.X01:BB.AIUsageV -0.076
## (0.044)
## ───────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.002
## Conditional R^2 0.720 0.722
## AIC 2926.152 2934.877
## BIC 2979.619 2998.066
## Num. obs. 954 954
## Num. groups: B.ID 159 159
## Var: B.ID (Intercept) 1.908 1.924
## Var: B.ID W.X10 0.008 0.009
## Var: B.ID W.X01 0.054 0.049
## Cov: B.ID (Intercept) W.X10 -0.031 -0.035
## Cov: B.ID (Intercept) W.X01 0.014 0.012
## Cov: B.ID W.X10 W.X01 -0.021 -0.021
## Var: Residual 0.747 0.746
## ───────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 954 (30 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.LearningFromErrorsV" (Y)
## ───────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────
## W.X01 * BB.AIUsageV 2.98 1 256 .085 .
## ───────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WA.LearningFromErrorsV" (Y)
## ──────────────────────────────────────────────────────────────
## "BB.AIUsageV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────
## 1.513 (- SD) 0.151 (0.101) 1.494 .136 [-0.047, 0.350]
## 3.109 (Mean) 0.029 (0.072) 0.407 .685 [-0.111, 0.169]
## 4.705 (+ SD) -0.093 (0.101) -0.919 .359 [-0.291, 0.105]
## ──────────────────────────────────────────────────────────────Sb10.WA.LearningFromErrorsVBA.AIInteractionQualityV=PROCESS(data2, y="WA.LearningFromErrorsV", x="W.X01", mods="BA.AIInteractionQualityV",covs=c("W.X10","W.X10BA.AIInteractionQualityV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.LearningFromErrorsV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.AIInteractionQualityV
## - Covariates (C) : W.X10, W.X10BA.AIInteractionQualityV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.LearningFromErrorsV ~ W.X10 + W.X10BA.AIInteractionQualityV + W.X01*BA.AIInteractionQualityV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────────
## (1) WA.LearningFromErrorsV (2) WA.LearningFromErrorsV
## ──────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.163 *** 3.006 ***
## (0.118) (0.355)
## W.X10 0.178 0.132
## (0.186) (0.211)
## W.X10BA.AIInteractionQualityV -0.035 -0.024
## (0.043) (0.050)
## W.X01 0.022 -0.425 *
## (0.070) (0.214)
## BA.AIInteractionQualityV 0.289 ***
## (0.084)
## W.X01:BA.AIInteractionQualityV 0.112 *
## (0.051)
## ──────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.074
## Conditional R^2 0.718 0.718
## AIC 2996.129 2984.423
## BIC 3049.870 3047.935
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.904 1.762
## Var: B.ID W.X10 0.008 0.009
## Var: B.ID W.X01 0.040 0.028
## Cov: B.ID (Intercept) W.X10 -0.033 -0.043
## Cov: B.ID (Intercept) W.X01 0.008 -0.059
## Cov: B.ID W.X10 W.X01 -0.017 -0.013
## Var: Residual 0.749 0.748
## ──────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.LearningFromErrorsV" (Y)
## ────────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────────
## W.X01 * BA.AIInteractionQualityV 4.88 1 316 .028 *
## ────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WA.LearningFromErrorsV" (Y)
## ───────────────────────────────────────────────────────────────────────────
## "BA.AIInteractionQualityV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────────
## 2.640 (- SD) -0.130 (0.098) -1.334 .183 [-0.322, 0.061]
## 4.006 (Mean) 0.022 (0.069) 0.322 .747 [-0.113, 0.157]
## 5.372 (+ SD) 0.175 (0.098) 1.790 .074 . [-0.017, 0.366]
## ───────────────────────────────────────────────────────────────────────────Sb10.WA.AffectiveRuminationVBA.AIOnlineCommunicationSkillsV=PROCESS(data2, y="WA.AffectiveRuminationV", x="W.X01", mods="BA.AIOnlineCommunicationSkillsV",covs=c("W.X10","W.X10BA.AIOnlineCommunicationSkillsV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.AffectiveRuminationV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.AIOnlineCommunicationSkillsV
## - Covariates (C) : W.X10, W.X10BA.AIOnlineCommunicationSkillsV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.AffectiveRuminationV ~ W.X10 + W.X10BA.AIOnlineCommunicationSkillsV + W.X01*BA.AIOnlineCommunicationSkillsV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────────────────────
## (1) WA.AffectiveRuminationV (2) WA.AffectiveRuminationV
## ───────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.731 *** 2.810 ***
## (0.114) (0.421)
## W.X10 0.327 0.632 *
## (0.235) (0.266)
## W.X10BA.AIOnlineCommunicationSkillsV -0.055 -0.127 *
## (0.053) (0.060)
## W.X01 0.055 0.537 *
## (0.072) (0.269)
## BA.AIOnlineCommunicationSkillsV 0.216 *
## (0.095)
## W.X01:BA.AIOnlineCommunicationSkillsV -0.113
## (0.061)
## ───────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.013
## Conditional R^2 0.681 0.683
## AIC 3044.095 3048.114
## BIC 3097.835 3111.626
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.715 1.666
## Var: B.ID W.X10 0.026 0.027
## Var: B.ID W.X01 0.046 0.045
## Cov: B.ID (Intercept) W.X10 -0.039 -0.026
## Cov: B.ID (Intercept) W.X01 0.002 0.027
## Cov: B.ID W.X10 W.X01 -0.034 -0.035
## Var: Residual 0.802 0.800
## ───────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.AffectiveRuminationV" (Y)
## ───────────────────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────────────────
## W.X01 * BA.AIOnlineCommunicationSkillsV 3.47 1 367 .063 .
## ───────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WA.AffectiveRuminationV" (Y)
## ──────────────────────────────────────────────────────────────────────────────────
## "BA.AIOnlineCommunicationSkillsV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────────────────
## 3.075 (- SD) 0.189 (0.102) 1.853 .065 . [-0.011, 0.388]
## 4.260 (Mean) 0.055 (0.072) 0.759 .449 [-0.086, 0.196]
## 5.444 (+ SD) -0.080 (0.102) -0.781 .435 [-0.279, 0.120]
## ──────────────────────────────────────────────────────────────────────────────────Sb10.WP.VoiceForSystemImprovmentVBA.ClarityOfInformationV=PROCESS(data2, y="WP.VoiceForSystemImprovmentV", x="W.X01", mods="BA.ClarityOfInformationV",covs=c("W.X10","W.X10BA.ClarityOfInformationV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.VoiceForSystemImprovmentV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.ClarityOfInformationV
## - Covariates (C) : W.X10, W.X10BA.ClarityOfInformationV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.VoiceForSystemImprovmentV ~ W.X10 + W.X10BA.ClarityOfInformationV + W.X01*BA.ClarityOfInformationV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.VoiceForSystemImprovmentV (2) WP.VoiceForSystemImprovmentV
## ──────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.531 *** 2.018 ***
## (0.121) (0.420)
## W.X10 0.180 0.482 *
## (0.198) (0.220)
## W.X10BA.ClarityOfInformationV -0.028 -0.097 *
## (0.043) (0.048)
## W.X01 0.006 0.683 *
## (0.081) (0.281)
## BA.ClarityOfInformationV 0.347 ***
## (0.093)
## W.X01:BA.ClarityOfInformationV -0.156 *
## (0.062)
## ──────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.046
## Conditional R^2 0.763 0.763
## AIC 2903.190 2900.666
## BIC 2956.931 2964.178
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 2.097 1.912
## Var: B.ID W.X10 0.010 0.000
## Var: B.ID W.X01 0.468 0.383
## Cov: B.ID (Intercept) W.X10 -0.061 -0.019
## Cov: B.ID (Intercept) W.X01 -0.396 -0.285
## Cov: B.ID W.X10 W.X01 0.068 0.003
## Var: Residual 0.607 0.608
## ──────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.VoiceForSystemImprovmentV" (Y)
## ────────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────────
## W.X01 * BA.ClarityOfInformationV 6.32 1 205 .013 *
## ────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.VoiceForSystemImprovmentV" (Y)
## ───────────────────────────────────────────────────────────────────────────
## "BA.ClarityOfInformationV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────────
## 3.095 (- SD) 0.202 (0.113) 1.781 .077 . [-0.020, 0.424]
## 4.356 (Mean) 0.006 (0.080) 0.069 .945 [-0.151, 0.162]
## 5.617 (+ SD) -0.191 (0.113) -1.683 .094 . [-0.413, 0.031]
## ───────────────────────────────────────────────────────────────────────────Sb10.WP.LearningBehaviorVBB.AITechnologyAnxietyV=PROCESS(data2, y="WP.LearningBehaviorV", x="W.X01", mods="BB.AITechnologyAnxietyV",covs=c("W.X10","W.X10BB.AITechnologyAnxietyV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.LearningBehaviorV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BB.AITechnologyAnxietyV
## - Covariates (C) : W.X10, W.X10BB.AITechnologyAnxietyV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.LearningBehaviorV ~ W.X10 + W.X10BB.AITechnologyAnxietyV + W.X01*BB.AITechnologyAnxietyV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────
## (1) WP.LearningBehaviorV (2) WP.LearningBehaviorV
## ─────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.516 *** 3.311 ***
## (0.124) (0.272)
## W.X10 -0.042 0.087
## (0.136) (0.152)
## W.X10BB.AITechnologyAnxietyV 0.062 0.017
## (0.040) (0.047)
## W.X01 0.021 0.278
## (0.070) (0.153)
## BB.AITechnologyAnxietyV 0.071
## (0.084)
## W.X01:BB.AITechnologyAnxietyV -0.089
## (0.047)
## ─────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.005
## Conditional R^2 0.727 0.729
## AIC 2934.346 2942.249
## BIC 2987.813 3005.438
## Num. obs. 954 954
## Num. groups: B.ID 159 159
## Var: B.ID (Intercept) 2.059 2.066
## Var: B.ID W.X10 0.000 0.000
## Var: B.ID W.X01 0.018 0.012
## Cov: B.ID (Intercept) W.X10 0.007 0.008
## Cov: B.ID (Intercept) W.X01 -0.064 -0.057
## Cov: B.ID W.X10 W.X01 -0.000 -0.000
## Var: Residual 0.762 0.761
## ─────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 954 (30 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.LearningBehaviorV" (Y)
## ───────────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────────
## W.X01 * BB.AITechnologyAnxietyV 3.56 1 255 .060 .
## ───────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.LearningBehaviorV" (Y)
## ──────────────────────────────────────────────────────────────────────────
## "BB.AITechnologyAnxietyV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────────
## 1.416 (- SD) 0.153 (0.099) 1.548 .122 [-0.041, 0.346]
## 2.899 (Mean) 0.021 (0.070) 0.304 .761 [-0.115, 0.158]
## 4.383 (+ SD) -0.110 (0.099) -1.118 .264 [-0.304, 0.083]
## ──────────────────────────────────────────────────────────────────────────Sb10.WP.PerceivedWorkGrowthVBB.TrustInAIV=PROCESS(data2, y="WP.PerceivedWorkGrowthV", x="W.X01", mods="BB.TrustInAIV",covs=c("W.X10","W.X10BB.TrustInAIV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.PerceivedWorkGrowthV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BB.TrustInAIV
## - Covariates (C) : W.X10, W.X10BB.TrustInAIV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.PerceivedWorkGrowthV ~ W.X10 + W.X10BB.TrustInAIV + W.X01*BB.TrustInAIV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────
## (1) WP.PerceivedWorkGrowthV (2) WP.PerceivedWorkGrowthV
## ─────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.894 *** 3.056 ***
## (0.118) (0.363)
## W.X10 -0.223 -0.207
## (0.186) (0.207)
## W.X10BB.TrustInAIV 0.095 * 0.090
## (0.047) (0.053)
## W.X01 0.060 -0.159
## (0.078) (0.240)
## BB.TrustInAIV 0.227 *
## (0.093)
## W.X01:BB.TrustInAIV 0.059
## (0.062)
## ─────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.003 0.048
## Conditional R^2 0.725 0.729
## AIC 2900.411 2901.098
## BIC 2953.878 2964.287
## Num. obs. 954 954
## Num. groups: B.ID 159 159
## Var: B.ID (Intercept) 1.865 1.794
## Var: B.ID W.X10 0.001 0.004
## Var: B.ID W.X01 0.266 0.237
## Cov: B.ID (Intercept) W.X10 -0.032 -0.029
## Cov: B.ID (Intercept) W.X01 -0.159 -0.165
## Cov: B.ID W.X10 W.X01 0.003 -0.027
## Var: Residual 0.695 0.694
## ─────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 954 (30 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.PerceivedWorkGrowthV" (Y)
## ─────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────
## W.X01 * BB.TrustInAIV 0.93 1 190 .336
## ─────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.PerceivedWorkGrowthV" (Y)
## ────────────────────────────────────────────────────────────────
## "BB.TrustInAIV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────
## 2.445 (- SD) -0.014 (0.110) -0.129 .898 [-0.229, 0.201]
## 3.689 (Mean) 0.060 (0.078) 0.769 .443 [-0.092, 0.212]
## 4.932 (+ SD) 0.134 (0.110) 1.216 .225 [-0.082, 0.349]
## ────────────────────────────────────────────────────────────────Sb10.WP.TakingChargeBehaviorsForSystemImprovementVBA.AIOnlineCommunicationSkillsV=PROCESS(data2, y="WP.TakingChargeBehaviorsForSystemImprovementV", x="W.X01", mods="BA.AIOnlineCommunicationSkillsV",covs=c("W.X10","W.X10BA.AIOnlineCommunicationSkillsV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.TakingChargeBehaviorsForSystemImprovementV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.AIOnlineCommunicationSkillsV
## - Covariates (C) : W.X10, W.X10BA.AIOnlineCommunicationSkillsV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.TakingChargeBehaviorsForSystemImprovementV ~ W.X10 + W.X10BA.AIOnlineCommunicationSkillsV + W.X01*BA.AIOnlineCommunicationSkillsV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.TakingChargeBehaviorsForSystemImprovementV (2) WP.TakingChargeBehaviorsForSystemImprovementV
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.546 *** 1.773 ***
## (0.122) (0.435)
## W.X10 -0.082 0.322
## (0.213) (0.241)
## W.X10BA.AIOnlineCommunicationSkillsV 0.043 -0.051
## (0.048) (0.055)
## W.X01 0.041 0.785 **
## (0.075) (0.270)
## BA.AIOnlineCommunicationSkillsV 0.416 ***
## (0.098)
## W.X01:BA.AIOnlineCommunicationSkillsV -0.175 **
## (0.061)
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.066
## Conditional R^2 0.738 0.741
## AIC 2945.554 2938.140
## BIC 2999.294 3001.651
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 2.102 1.871
## Var: B.ID W.X10 0.007 0.000
## Var: B.ID W.X01 0.226 0.170
## Cov: B.ID (Intercept) W.X10 -0.082 -0.029
## Cov: B.ID (Intercept) W.X01 -0.314 -0.206
## Cov: B.ID W.X10 W.X01 0.038 0.003
## Var: Residual 0.681 0.680
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ───────────────────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────────────────
## W.X01 * BA.AIOnlineCommunicationSkillsV 8.19 1 237 .005 **
## ───────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ──────────────────────────────────────────────────────────────────────────────────
## "BA.AIOnlineCommunicationSkillsV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────────────────
## 3.075 (- SD) 0.248 (0.104) 2.392 .018 * [ 0.045, 0.451]
## 4.260 (Mean) 0.041 (0.073) 0.559 .577 [-0.103, 0.184]
## 5.444 (+ SD) -0.166 (0.104) -1.602 .111 [-0.369, 0.037]
## ──────────────────────────────────────────────────────────────────────────────────Sb10.WP.TakingChargeBehaviorsForSystemImprovementVBA.StructureV=PROCESS(data2, y="WP.TakingChargeBehaviorsForSystemImprovementV", x="W.X01", mods="BA.StructureV",covs=c("W.X10","W.X10BA.StructureV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.TakingChargeBehaviorsForSystemImprovementV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.StructureV
## - Covariates (C) : W.X10, W.X10BA.StructureV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.TakingChargeBehaviorsForSystemImprovementV ~ W.X10 + W.X10BA.StructureV + W.X01*BA.StructureV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.TakingChargeBehaviorsForSystemImprovementV (2) WP.TakingChargeBehaviorsForSystemImprovementV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.546 *** 1.879 ***
## (0.122) (0.391)
## W.X10 -0.093 0.234
## (0.195) (0.219)
## W.X10BA.StructureV 0.048 -0.032
## (0.045) (0.051)
## W.X01 0.041 0.590 *
## (0.073) (0.249)
## BA.StructureV 0.405 ***
## (0.091)
## W.X01:BA.StructureV -0.133 *
## (0.058)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.079
## Conditional R^2 0.737 0.741
## AIC 2945.819 2937.265
## BIC 2999.559 3000.777
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 2.097 1.850
## Var: B.ID W.X10 0.003 0.004
## Var: B.ID W.X01 0.197 0.207
## Cov: B.ID (Intercept) W.X10 -0.082 -0.042
## Cov: B.ID (Intercept) W.X01 -0.297 -0.232
## Cov: B.ID W.X10 W.X01 0.012 0.029
## Var: Residual 0.682 0.679
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ─────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────
## W.X01 * BA.StructureV 5.34 1 180 .022 *
## ─────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ────────────────────────────────────────────────────────────────
## "BA.StructureV" Effect S.E. t p [95% CI]
## ────────────────────────────────────────────────────────────────
## 2.839 (- SD) 0.211 (0.104) 2.026 .044 * [ 0.007, 0.416]
## 4.117 (Mean) 0.041 (0.074) 0.555 .580 [-0.104, 0.185]
## 5.394 (+ SD) -0.129 (0.104) -1.242 .216 [-0.334, 0.075]
## ────────────────────────────────────────────────────────────────Sb10.WP.TakingChargeBehaviorsForSystemImprovementVBA.ClarityOfInformationV=PROCESS(data2, y="WP.TakingChargeBehaviorsForSystemImprovementV", x="W.X01", mods="BA.ClarityOfInformationV",covs=c("W.X10","W.X10BA.ClarityOfInformationV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.TakingChargeBehaviorsForSystemImprovementV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.ClarityOfInformationV
## - Covariates (C) : W.X10, W.X10BA.ClarityOfInformationV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.TakingChargeBehaviorsForSystemImprovementV ~ W.X10 + W.X10BA.ClarityOfInformationV + W.X01*BA.ClarityOfInformationV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.TakingChargeBehaviorsForSystemImprovementV (2) WP.TakingChargeBehaviorsForSystemImprovementV
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.546 *** 2.009 ***
## (0.122) (0.424)
## W.X10 -0.057 0.306
## (0.208) (0.233)
## W.X10BA.ClarityOfInformationV 0.037 -0.047
## (0.045) (0.051)
## W.X01 0.041 0.747 **
## (0.073) (0.263)
## BA.ClarityOfInformationV 0.353 ***
## (0.093)
## W.X01:BA.ClarityOfInformationV -0.162 **
## (0.058)
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.052
## Conditional R^2 0.738 0.741
## AIC 2946.203 2941.190
## BIC 2999.944 3004.702
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 2.097 1.921
## Var: B.ID W.X10 0.002 0.003
## Var: B.ID W.X01 0.196 0.195
## Cov: B.ID (Intercept) W.X10 -0.071 -0.038
## Cov: B.ID (Intercept) W.X01 -0.297 -0.230
## Cov: B.ID W.X10 W.X01 0.010 0.026
## Var: Residual 0.682 0.679
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ────────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────────
## W.X01 * BA.ClarityOfInformationV 7.78 1 182 .006 **
## ────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.TakingChargeBehaviorsForSystemImprovementV" (Y)
## ───────────────────────────────────────────────────────────────────────────
## "BA.ClarityOfInformationV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────────
## 3.095 (- SD) 0.245 (0.104) 2.368 .019 * [ 0.042, 0.448]
## 4.356 (Mean) 0.041 (0.073) 0.559 .577 [-0.103, 0.184]
## 5.617 (+ SD) -0.163 (0.104) -1.578 .116 [-0.366, 0.040]
## ───────────────────────────────────────────────────────────────────────────Sb10.WA.ErrorStrainVBA.EffectivenessV=PROCESS(data2, y="WA.ErrorStrainV", x="W.X01", mods="BA.EffectivenessV",covs=c("W.X10","W.X10BA.EffectivenessV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WA.ErrorStrainV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.EffectivenessV
## - Covariates (C) : W.X10, W.X10BA.EffectivenessV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WA.ErrorStrainV ~ W.X10 + W.X10BA.EffectivenessV + W.X01*BA.EffectivenessV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────
## (1) WA.ErrorStrainV (2) WA.ErrorStrainV
## ─────────────────────────────────────────────────────────────────────
## (Intercept) 3.512 *** 2.342 ***
## (0.117) (0.353)
## W.X10 -0.097 0.286
## (0.206) (0.242)
## W.X10BA.EffectivenessV 0.031 -0.065
## (0.048) (0.058)
## W.X01 -0.175 * 0.282
## (0.078) (0.242)
## BA.EffectivenessV 0.294 ***
## (0.084)
## W.X01:BA.EffectivenessV -0.115 *
## (0.058)
## ─────────────────────────────────────────────────────────────────────
## Marginal R^2 0.003 0.044
## Conditional R^2 0.611 0.615
## AIC 3162.994 3162.018
## BIC 3216.735 3225.529
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.740 1.600
## Var: B.ID W.X10 0.015 0.009
## Var: B.ID W.X01 0.018 0.010
## Cov: B.ID (Intercept) W.X10 -0.161 -0.120
## Cov: B.ID (Intercept) W.X01 -0.179 -0.128
## Cov: B.ID W.X10 W.X01 0.017 0.010
## Var: Residual 0.974 0.972
## ─────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WA.ErrorStrainV" (Y)
## ─────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────
## W.X01 * BA.EffectivenessV 3.99 1 742 .046 *
## ─────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WA.ErrorStrainV" (Y)
## ─────────────────────────────────────────────────────────────────────
## "BA.EffectivenessV" Effect S.E. t p [95% CI]
## ─────────────────────────────────────────────────────────────────────
## 2.634 (- SD) -0.020 (0.110) -0.186 .852 [-0.236, 0.195]
## 3.982 (Mean) -0.175 (0.078) -2.261 .024 * [-0.328, -0.023]
## 5.329 (+ SD) -0.330 (0.110) -3.010 .003 ** [-0.546, -0.115]
## ─────────────────────────────────────────────────────────────────────Sb10.WP.VoiceForSystemImprovmentVBA.QualityV=PROCESS(data2, y="WP.VoiceForSystemImprovmentV", x="W.X01", mods="BA.QualityV",covs=c("W.X10","W.X10BA.QualityV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.VoiceForSystemImprovmentV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.QualityV
## - Covariates (C) : W.X10, W.X10BA.QualityV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.VoiceForSystemImprovmentV ~ W.X10 + W.X10BA.QualityV + W.X01*BA.QualityV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.VoiceForSystemImprovmentV (2) WP.VoiceForSystemImprovmentV
## ───────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.531 *** 1.257 ***
## (0.121) (0.335)
## W.X10 -0.148 0.216
## (0.173) (0.195)
## W.X10BA.QualityV 0.050 -0.038
## (0.039) (0.045)
## W.X01 0.006 0.529 *
## (0.081) (0.254)
## BA.QualityV 0.555 ***
## (0.078)
## W.X01:BA.QualityV -0.128 *
## (0.059)
## ───────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.185
## Conditional R^2 0.758 0.762
## AIC 2902.497 2868.548
## BIC 2956.237 2932.059
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 2.091 1.528
## Var: B.ID W.X10 0.015 0.010
## Var: B.ID W.X01 0.461 0.441
## Cov: B.ID (Intercept) W.X10 -0.121 -0.036
## Cov: B.ID (Intercept) W.X01 -0.391 -0.264
## Cov: B.ID W.X10 W.X01 0.079 0.065
## Var: Residual 0.610 0.608
## ───────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.VoiceForSystemImprovmentV" (Y)
## ───────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────
## W.X01 * BA.QualityV 4.74 1 169 .031 *
## ───────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.VoiceForSystemImprovmentV" (Y)
## ──────────────────────────────────────────────────────────────
## "BA.QualityV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────
## 2.728 (- SD) 0.180 (0.114) 1.589 .114 [-0.042, 0.403]
## 4.094 (Mean) 0.006 (0.080) 0.069 .945 [-0.152, 0.163]
## 5.460 (+ SD) -0.169 (0.114) -1.491 .138 [-0.392, 0.053]
## ──────────────────────────────────────────────────────────────Sb10.WP.SocialLearningVBA.ProblemSolvingConfidenceV=PROCESS(data2, y="WP.SocialLearningV", x="W.X01", mods="BA.ProblemSolvingConfidenceV",covs=c("W.X10","W.X10BA.ProblemSolvingConfidenceV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.SocialLearningV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.ProblemSolvingConfidenceV
## - Covariates (C) : W.X10, W.X10BA.ProblemSolvingConfidenceV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.SocialLearningV ~ W.X10 + W.X10BA.ProblemSolvingConfidenceV + W.X01*BA.ProblemSolvingConfidenceV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────
## (1) WP.SocialLearningV (2) WP.SocialLearningV
## ──────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.656 *** 0.875
## (0.109) (0.455)
## W.X10 -1.222 *** -0.694 *
## (0.269) (0.317)
## W.X10BA.ProblemSolvingConfidenceV 0.306 *** 0.185 **
## (0.059) (0.071)
## W.X01 0.073 0.011
## (0.068) (0.315)
## BA.ProblemSolvingConfidenceV 0.636 ***
## (0.101)
## W.X01:BA.ProblemSolvingConfidenceV 0.014
## (0.070)
## ──────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.014 0.198
## Conditional R^2 0.675 0.689
## AIC 2945.376 2915.817
## BIC 2999.116 2979.329
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.569 1.198
## Var: B.ID W.X10 0.032 0.021
## Var: B.ID W.X01 0.014 0.014
## Cov: B.ID (Intercept) W.X10 -0.145 -0.076
## Cov: B.ID (Intercept) W.X01 0.022 0.013
## Cov: B.ID W.X10 W.X01 -0.018 -0.016
## Var: Residual 0.739 0.740
## ──────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SocialLearningV" (Y)
## ────────────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────────────
## W.X01 * BA.ProblemSolvingConfidenceV 0.04 1 531 .840
## ────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.SocialLearningV" (Y)
## ──────────────────────────────────────────────────────────────────────────────
## "BA.ProblemSolvingConfidenceV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────────────
## 3.409 (- SD) 0.059 (0.096) 0.615 .538 [-0.129, 0.247]
## 4.376 (Mean) 0.073 (0.068) 1.072 .284 [-0.060, 0.206]
## 5.343 (+ SD) 0.087 (0.096) 0.901 .368 [-0.102, 0.275]
## ──────────────────────────────────────────────────────────────────────────────Sb10.WP.AIUsageForFacilitatingWorkVBA.PersonalControlV=PROCESS(data2, y="WP.AIUsageForFacilitatingWorkV", x="W.X01", mods="BA.PersonalControlV",covs=c("W.X10","W.X10BA.PersonalControlV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.AIUsageForFacilitatingWorkV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.PersonalControlV
## - Covariates (C) : W.X10, W.X10BA.PersonalControlV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.AIUsageForFacilitatingWorkV ~ W.X10 + W.X10BA.PersonalControlV + W.X01*BA.PersonalControlV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.AIUsageForFacilitatingWorkV (2) WP.AIUsageForFacilitatingWorkV
## ───────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 2.717 *** 4.383 ***
## (0.129) (0.399)
## W.X10 0.325 -0.060
## (0.204) (0.225)
## W.X10BA.PersonalControlV -0.050 0.047
## (0.049) (0.054)
## W.X01 0.026 -0.394
## (0.071) (0.237)
## BA.PersonalControlV -0.421 ***
## (0.096)
## W.X01:BA.PersonalControlV 0.106
## (0.057)
## ───────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.077
## Conditional R^2 0.742 0.740
## AIC 3046.342 3040.853
## BIC 3100.083 3104.365
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 2.322 2.054
## Var: B.ID W.X10 0.057 0.007
## Var: B.ID W.X01 0.061 0.094
## Cov: B.ID (Intercept) W.X10 -0.177 -0.117
## Cov: B.ID (Intercept) W.X01 -0.093 -0.055
## Cov: B.ID W.X10 W.X01 -0.043 0.003
## Var: Residual 0.759 0.772
## ───────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.AIUsageForFacilitatingWorkV" (Y)
## ───────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────
## W.X01 * BA.PersonalControlV 3.45 1 253 .064 .
## ───────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.AIUsageForFacilitatingWorkV" (Y)
## ──────────────────────────────────────────────────────────────────────
## "BA.PersonalControlV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────
## 2.678 (- SD) -0.110 (0.100) -1.100 .272 [-0.306, 0.086]
## 3.954 (Mean) 0.026 (0.071) 0.362 .718 [-0.113, 0.164]
## 5.230 (+ SD) 0.161 (0.100) 1.612 .108 [-0.035, 0.357]
## ──────────────────────────────────────────────────────────────────────Sb10.WP.AIEnabledCreativityVBA.PersonalControlV=PROCESS(data2, y="WP.AIEnabledCreativityV", x="W.X01", mods="BA.PersonalControlV",covs=c("W.X10","W.X10BA.PersonalControlV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.AIEnabledCreativityV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.PersonalControlV
## - Covariates (C) : W.X10, W.X10BA.PersonalControlV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.AIEnabledCreativityV ~ W.X10 + W.X10BA.PersonalControlV + W.X01*BA.PersonalControlV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────
## (1) WP.AIEnabledCreativityV (2) WP.AIEnabledCreativityV
## ─────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.721 *** 5.761 ***
## (0.119) (0.351)
## W.X10 -0.164 -0.455 *
## (0.192) (0.222)
## W.X10BA.PersonalControlV 0.043 0.117 *
## (0.045) (0.053)
## W.X01 -0.066 -0.218
## (0.069) (0.227)
## BA.PersonalControlV -0.516 ***
## (0.085)
## W.X01:BA.PersonalControlV 0.038
## (0.055)
## ─────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.137
## Conditional R^2 0.710 0.709
## AIC 2988.642 2964.143
## BIC 3042.383 3027.655
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.938 1.516
## Var: B.ID W.X10 0.003 0.000
## Var: B.ID W.X01 0.032 0.034
## Cov: B.ID (Intercept) W.X10 -0.074 -0.016
## Cov: B.ID (Intercept) W.X01 -0.085 -0.056
## Cov: B.ID W.X10 W.X01 0.003 0.001
## Var: Residual 0.755 0.753
## ─────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.AIEnabledCreativityV" (Y)
## ───────────────────────────────────────────────────
## F df1 df2 p
## ───────────────────────────────────────────────────
## W.X01 * BA.PersonalControlV 0.49 1 267 .483
## ───────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.AIEnabledCreativityV" (Y)
## ──────────────────────────────────────────────────────────────────────
## "BA.PersonalControlV" Effect S.E. t p [95% CI]
## ──────────────────────────────────────────────────────────────────────
## 2.678 (- SD) -0.115 (0.098) -1.168 .244 [-0.308, 0.078]
## 3.954 (Mean) -0.066 (0.070) -0.948 .344 [-0.202, 0.070]
## 5.230 (+ SD) -0.017 (0.098) -0.173 .863 [-0.210, 0.176]
## ──────────────────────────────────────────────────────────────────────Sb10.WP.SocialLearningVBA.PositiveReflectionOnAIUseV=PROCESS(data2, y="WP.SocialLearningV", x="W.X01", mods="BA.PositiveReflectionOnAIUseV",covs=c("W.X10","W.X10BA.PositiveReflectionOnAIUseV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.SocialLearningV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.PositiveReflectionOnAIUseV
## - Covariates (C) : W.X10, W.X10BA.PositiveReflectionOnAIUseV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.SocialLearningV ~ W.X10 + W.X10BA.PositiveReflectionOnAIUseV + W.X01*BA.PositiveReflectionOnAIUseV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────────
## (1) WP.SocialLearningV (2) WP.SocialLearningV
## ───────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.656 *** 1.971 ***
## (0.109) (0.301)
## W.X10 -0.746 *** -0.431 *
## (0.178) (0.207)
## W.X10BA.PositiveReflectionOnAIUseV 0.213 *** 0.135 **
## (0.041) (0.049)
## W.X01 0.073 0.033
## (0.068) (0.207)
## BA.PositiveReflectionOnAIUseV 0.417 ***
## (0.070)
## W.X01:BA.PositiveReflectionOnAIUseV 0.010
## (0.048)
## ───────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.014 0.185
## Conditional R^2 0.675 0.689
## AIC 2944.987 2920.719
## BIC 2998.728 2984.230
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.569 1.232
## Var: B.ID W.X10 0.029 0.020
## Var: B.ID W.X01 0.014 0.014
## Cov: B.ID (Intercept) W.X10 -0.141 -0.079
## Cov: B.ID (Intercept) W.X01 0.021 0.013
## Cov: B.ID W.X10 W.X01 -0.017 -0.015
## Var: Residual 0.739 0.740
## ───────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.SocialLearningV" (Y)
## ─────────────────────────────────────────────────────────────
## F df1 df2 p
## ─────────────────────────────────────────────────────────────
## W.X01 * BA.PositiveReflectionOnAIUseV 0.04 1 534 .838
## ─────────────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.SocialLearningV" (Y)
## ───────────────────────────────────────────────────────────────────────────────
## "BA.PositiveReflectionOnAIUseV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────────────
## 2.629 (- SD) 0.059 (0.096) 0.613 .540 [-0.130, 0.247]
## 4.037 (Mean) 0.073 (0.068) 1.072 .284 [-0.060, 0.206]
## 5.444 (+ SD) 0.087 (0.096) 0.902 .367 [-0.102, 0.275]
## ───────────────────────────────────────────────────────────────────────────────Sb10.WP.FamilyMemberUndermingVBA.AIServiceFailureV=PROCESS(data2, y="WP.FamilyMemberUndermingV", x="W.X01", mods="BA.AIServiceFailureV",covs=c("W.X10","W.X10BA.AIServiceFailureV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.FamilyMemberUndermingV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.AIServiceFailureV
## - Covariates (C) : W.X10, W.X10BA.AIServiceFailureV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.FamilyMemberUndermingV ~ W.X10 + W.X10BA.AIServiceFailureV + W.X01*BA.AIServiceFailureV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.FamilyMemberUndermingV (2) WP.FamilyMemberUndermingV
## ─────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 1.525 *** 0.548 *
## (0.086) (0.229)
## W.X10 0.174 0.333 *
## (0.119) (0.141)
## W.X10BA.AIServiceFailureV -0.054 -0.099 **
## (0.030) (0.036)
## W.X01 -0.019 -0.045
## (0.050) (0.140)
## BA.AIServiceFailureV 0.270 ***
## (0.059)
## W.X01:BA.AIServiceFailureV 0.007
## (0.036)
## ─────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.001 0.085
## Conditional R^2 0.700 0.698
## AIC 2355.336 2345.317
## BIC 2409.076 2408.829
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.010 0.877
## Var: B.ID W.X10 0.010 0.007
## Var: B.ID W.X01 0.000 0.001
## Cov: B.ID (Intercept) W.X10 -0.101 -0.080
## Cov: B.ID (Intercept) W.X01 -0.020 -0.024
## Cov: B.ID W.X10 W.X01 0.002 0.002
## Var: Residual 0.400 0.400
## ─────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.FamilyMemberUndermingV" (Y)
## ────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────
## W.X01 * BA.AIServiceFailureV 0.04 1 800 .843
## ────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.FamilyMemberUndermingV" (Y)
## ───────────────────────────────────────────────────────────────────────
## "BA.AIServiceFailureV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────
## 2.249 (- SD) -0.029 (0.070) -0.414 .679 [-0.166, 0.108]
## 3.626 (Mean) -0.019 (0.050) -0.387 .699 [-0.116, 0.078]
## 5.002 (+ SD) -0.009 (0.070) -0.133 .894 [-0.147, 0.128]
## ───────────────────────────────────────────────────────────────────────Sb10.WP.FamilyMemberConflictVBA.AIServiceFailureV=PROCESS(data2, y="WP.FamilyMemberConflictV", x="W.X01", mods="BA.AIServiceFailureV",covs=c("W.X10","W.X10BA.AIServiceFailureV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.FamilyMemberConflictV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.AIServiceFailureV
## - Covariates (C) : W.X10, W.X10BA.AIServiceFailureV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.FamilyMemberConflictV ~ W.X10 + W.X10BA.AIServiceFailureV + W.X01*BA.AIServiceFailureV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────────────
## (1) WP.FamilyMemberConflictV (2) WP.FamilyMemberConflictV
## ───────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 1.475 *** 0.504 *
## (0.083) (0.221)
## W.X10 0.177 0.331 *
## (0.110) (0.130)
## W.X10BA.AIServiceFailureV -0.062 * -0.104 **
## (0.028) (0.033)
## W.X01 -0.015 -0.018
## (0.046) (0.129)
## BA.AIServiceFailureV 0.268 ***
## (0.057)
## W.X01:BA.AIServiceFailureV 0.001
## (0.033)
## ───────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.088
## Conditional R^2 0.726 0.723
## AIC 2220.437 2210.493
## BIC 2274.178 2274.005
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 0.959 0.828
## Var: B.ID W.X10 0.007 0.005
## Var: B.ID W.X01 0.000 0.000
## Cov: B.ID (Intercept) W.X10 -0.083 -0.062
## Cov: B.ID (Intercept) W.X01 -0.004 -0.005
## Cov: B.ID W.X10 W.X01 0.000 0.000
## Var: Residual 0.341 0.342
## ───────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.FamilyMemberConflictV" (Y)
## ────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────
## W.X01 * BA.AIServiceFailureV 0.00 1 810 .982
## ────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.FamilyMemberConflictV" (Y)
## ───────────────────────────────────────────────────────────────────────
## "BA.AIServiceFailureV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────
## 2.249 (- SD) -0.016 (0.065) -0.253 .801 [-0.143, 0.111]
## 3.626 (Mean) -0.015 (0.046) -0.335 .738 [-0.105, 0.074]
## 5.002 (+ SD) -0.014 (0.065) -0.221 .825 [-0.141, 0.113]
## ───────────────────────────────────────────────────────────────────────Sb10.WP.FeedbackSeekingForSystemImprovementVBA.AnthropomorphismV=PROCESS(data2, y="WP.FeedbackSeekingForSystemImprovementV", x="W.X01", mods="BA.AnthropomorphismV",covs=c("W.X10","W.X10BA.AnthropomorphismV"), cluster ="B.ID", hlm.re.y = "(W.X10+W.X01|B.ID)", center=FALSE)##
## ****************** PART 1. Regression Model Summary ******************
##
## PROCESS Model Code : 1 (Hayes, 2018; www.guilford.com/p/hayes3)
## PROCESS Model Type : Simple Moderation
## - Outcome (Y) : WP.FeedbackSeekingForSystemImprovementV
## - Predictor (X) : W.X01
## - Mediators (M) : -
## - Moderators (W) : BA.AnthropomorphismV
## - Covariates (C) : W.X10, W.X10BA.AnthropomorphismV
## - HLM Clusters : B.ID
##
## Formula of Outcome:
## - WP.FeedbackSeekingForSystemImprovementV ~ W.X10 + W.X10BA.AnthropomorphismV + W.X01*BA.AnthropomorphismV + (W.X10+W.X01|B.ID)
##
## CAUTION:
## Fixed effect (coef.) of a predictor involved in an interaction
## denotes its "simple effect/slope" at the other predictor = 0.
## Only when all predictors in an interaction are mean-centered
## can the fixed effect denote the "main effect"!
##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) WP.FeedbackSeekingForSystemImprovementV (2) WP.FeedbackSeekingForSystemImprovementV
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.188 *** 1.988 ***
## (0.117) (0.242)
## W.X10 -0.095 0.227
## (0.132) (0.152)
## W.X10BA.AnthropomorphismV 0.069 -0.042
## (0.039) (0.047)
## W.X01 0.057 0.333 *
## (0.068) (0.152)
## BA.AnthropomorphismV 0.415 ***
## (0.075)
## W.X01:BA.AnthropomorphismV -0.096 *
## (0.047)
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.114
## Conditional R^2 0.700 0.706
## AIC 2957.219 2940.496
## BIC 3010.960 3004.007
## Num. obs. 978 978
## Num. groups: B.ID 163 163
## Var: B.ID (Intercept) 1.859 1.522
## Var: B.ID W.X10 0.018 0.006
## Var: B.ID W.X01 0.007 0.007
## Cov: B.ID (Intercept) W.X10 -0.181 -0.099
## Cov: B.ID (Intercept) W.X01 -0.031 0.038
## Cov: B.ID W.X10 W.X01 0.003 -0.002
## Var: Residual 0.741 0.737
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
##
## ************ PART 2. Mediation/Moderation Effect Estimate ************
##
## Package Use : ‘interactions’ (v1.1.5)
## Effect Type : Simple Moderation (Model 1)
## Sample Size : 978 (6 missing observations deleted)
## Random Seed : -
## Simulations : -
##
## Interaction Effect on "WP.FeedbackSeekingForSystemImprovementV" (Y)
## ────────────────────────────────────────────────────
## F df1 df2 p
## ────────────────────────────────────────────────────
## W.X01 * BA.AnthropomorphismV 4.13 1 265 .043 *
## ────────────────────────────────────────────────────
##
## Simple Slopes: "W.X01" (X) ==> "WP.FeedbackSeekingForSystemImprovementV" (Y)
## ───────────────────────────────────────────────────────────────────────
## "BA.AnthropomorphismV" Effect S.E. t p [95% CI]
## ───────────────────────────────────────────────────────────────────────
## 1.457 (- SD) 0.194 (0.096) 2.028 .044 * [ 0.007, 0.382]
## 2.893 (Mean) 0.057 (0.068) 0.839 .402 [-0.076, 0.189]
## 4.330 (+ SD) -0.081 (0.096) -0.842 .401 [-0.268, 0.107]
## ───────────────────────────────────────────────────────────────────────8.2 Plot
interact_plot(Sb10.WP.AIEnabledInnovationBehaviorVBA.NeedForPersonalizationDueToAIV$model.y, W.X01, BA.NeedForPersonalizationDueToAIV,modx.values = "plus-minus",at = list(W.X10 = 0, W.X10BA.NeedForPersonalizationDueToAIV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.TakingChargeBehaviorsForSystemImprovementVBA.PositiveReflectionOnAIUseV$model.y, W.X01, BA.PositiveReflectionOnAIUseV,modx.values = "plus-minus",at = list(W.X10 = 0, W.X10BA.PositiveReflectionOnAIUseV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.VoiceForSystemImprovmentVBA.AIOnlineCommunicationSkillsV$model.y, W.X01, BA.AIOnlineCommunicationSkillsV,modx.values = "plus-minus",at = list(W.X10 = 0, W.X10BA.AIOnlineCommunicationSkillsV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.SystemPerformanceImprovementBehaviorVBA.ClarityOfInformationV$model.y, W.X01, BA.ClarityOfInformationV,modx.values = "plus-minus",at = list(W.X10 = 0, W.X10BA.ClarityOfInformationV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.VoiceForSystemImprovmentVBA.StructureV$model.y, W.X01, BA.StructureV,modx.values = "plus-minus",at = list(W.X10 = 0, W.X10BA.StructureV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.TakingChargeBehaviorsForSystemImprovementVBA.NegativeReflectionOnAIUseV$model.y, W.X01, BA.NegativeReflectionOnAIUseV,modx.values = "plus-minus",at = list(W.X10 = 0, W.X10BA.NegativeReflectionOnAIUseV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WA.LearningFromErrorsVBB.AIUsageV$model.y, W.X01, BB.AIUsageV,modx.values = "plus-minus",at = list(W.X10 = 0, W.X10BB.AIUsageV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WA.LearningFromErrorsVBA.AIInteractionQualityV$model.y, W.X01, BA.AIInteractionQualityV,modx.values = "plus-minus",at = list(W.X10 = 0, W.X10BA.AIInteractionQualityV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WA.AffectiveRuminationVBA.AIOnlineCommunicationSkillsV$model.y, W.X01, BA.AIOnlineCommunicationSkillsV,modx.values = "plus-minus",at = list(W.X10 = 0, W.X10BA.AIOnlineCommunicationSkillsV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.VoiceForSystemImprovmentVBA.ClarityOfInformationV$model.y, W.X01, BA.ClarityOfInformationV,modx.values = "plus-minus",at = list(W.X10 = 0, W.X10BA.ClarityOfInformationV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.LearningBehaviorVBB.AITechnologyAnxietyV$model.y, W.X01, BB.AITechnologyAnxietyV,modx.values = "plus-minus",at = list(W.X10 = 0, W.X10BB.AITechnologyAnxietyV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.PerceivedWorkGrowthVBB.TrustInAIV$model.y, W.X01, BB.TrustInAIV,modx.values = "plus-minus",at = list(W.X10 = 0, W.X10BB.TrustInAIV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.TakingChargeBehaviorsForSystemImprovementVBA.AIOnlineCommunicationSkillsV$model.y, W.X01, BA.AIOnlineCommunicationSkillsV,modx.values = "plus-minus",at = list(W.X10 = 0, W.X10BA.AIOnlineCommunicationSkillsV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.TakingChargeBehaviorsForSystemImprovementVBA.StructureV$model.y, W.X01, BA.StructureV,modx.values = "plus-minus",at = list(W.X10 = 0, W.X10BA.StructureV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.TakingChargeBehaviorsForSystemImprovementVBA.ClarityOfInformationV$model.y, W.X01, BA.ClarityOfInformationV,modx.values = "plus-minus",at = list(W.X10 = 0, W.X10BA.ClarityOfInformationV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WA.ErrorStrainVBA.EffectivenessV$model.y, W.X01, BA.EffectivenessV,modx.values = "plus-minus",at = list(W.X10 = 0, W.X10BA.EffectivenessV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.VoiceForSystemImprovmentVBA.QualityV$model.y, W.X01, BA.QualityV,modx.values = "plus-minus",at = list(W.X10 = 0, W.X10BA.QualityV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.SocialLearningVBA.ProblemSolvingConfidenceV$model.y, W.X01, BA.ProblemSolvingConfidenceV,modx.values = "plus-minus",at = list(W.X10 = 0, W.X10BA.ProblemSolvingConfidenceV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.AIUsageForFacilitatingWorkVBA.PersonalControlV$model.y, W.X01, BA.PersonalControlV,modx.values = "plus-minus",at = list(W.X10 = 0, W.X10BA.PersonalControlV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.AIEnabledCreativityVBA.PersonalControlV$model.y, W.X01, BA.PersonalControlV,modx.values = "plus-minus",at = list(W.X10 = 0, W.X10BA.PersonalControlV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.SocialLearningVBA.PositiveReflectionOnAIUseV$model.y, W.X01, BA.PositiveReflectionOnAIUseV,modx.values = "plus-minus",at = list(W.X10 = 0, W.X10BA.PositiveReflectionOnAIUseV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.FamilyMemberUndermingVBA.AIServiceFailureV$model.y, W.X01, BA.AIServiceFailureV,modx.values = "plus-minus",at = list(W.X10 = 0, W.X10BA.AIServiceFailureV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.FamilyMemberConflictVBA.AIServiceFailureV$model.y, W.X01, BA.AIServiceFailureV,modx.values = "plus-minus",at = list(W.X10 = 0, W.X10BA.AIServiceFailureV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))
interact_plot(Sb10.WP.FeedbackSeekingForSystemImprovementVBA.AnthropomorphismV$model.y, W.X01, BA.AnthropomorphismV,modx.values = "plus-minus",at = list(W.X10 = 0, W.X10BA.AnthropomorphismV = 0))+ scale_y_continuous(breaks = seq(0, 10, by = 0.1))