Analisis Multivariat Modul
5
Implementasi Structural Equation Modeling–Partial Least Squares
(SEM-PLS) untuk Analisis Pengaruh Digital Literacy terhadap Penerimaan
Teknologi
Aisyah Imani Khoirunnisa (020), Elvira Rizki Berliananda.P (062)
2026-05-17
library(seminr)
library(readxl)
library(corrplot)## corrplot 0.95 loadedlibrary(psych)Load Dataset
data <- read_excel("Data Siap SmartPLS dari Excel Asli.xlsx")Exploratory Data Analysis
data_sem <- data[, c(
"DL1","DL2","DL3","DL4","DL5",
"SE1","SE2","SE3",
"PI1","PI2","PI3","PI4","PI5",
"PU1","PU2","PU3",
"PEOU1","PEOU2","PEOU3",
"AT1","AT2","AT3",
"BI1","BI2","BI3","BI4","BI5",
"AU1","AU2","AU3","AU4","AU5"
)]
cor_matrix <- cor(data_sem)corrplot(
cor_matrix,
method = "color",
type = "upper",
addCoef.col = NA,
tl.col = "black",
tl.srt = 45,
tl.cex = 0.6,
number.cex = 0.5,
cl.cex = 0.8,
mar = c(0, 0, 2, 0)
)
summary(data_sem)## DL1 DL2 DL3 DL4
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000
## Median :4.000 Median :4.000 Median :4.000 Median :4.000
## Mean :4.299 Mean :4.317 Mean :4.352 Mean :4.287
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## DL5 SE1 SE2 SE3
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000
## Median :4.000 Median :4.000 Median :4.000 Median :4.000
## Mean :4.284 Mean :4.282 Mean :4.282 Mean :4.317
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## PI1 PI2 PI3 PI4
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000
## Median :4.000 Median :4.000 Median :4.000 Median :4.000
## Mean :4.329 Mean :4.262 Mean :4.175 Mean :4.227
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## PI5 PU1 PU2 PU3
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000
## Median :4.000 Median :4.000 Median :4.000 Median :4.000
## Mean :4.292 Mean :4.374 Mean :4.319 Mean :4.352
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## PEOU1 PEOU2 PEOU3 AT1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000
## Median :5.000 Median :4.000 Median :4.000 Median :4.000
## Mean :4.389 Mean :4.274 Mean :4.289 Mean :4.319
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## AT2 AT3 BI1 BI2 BI3
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.00 1st Qu.:4.000
## Median :4.000 Median :4.000 Median :4.000 Median :4.00 Median :4.000
## Mean :4.289 Mean :4.242 Mean :4.172 Mean :4.16 Mean :4.299
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.00 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.00 Max. :5.000
## BI4 BI5 AU1 AU2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000
## Median :4.000 Median :4.000 Median :4.000 Median :5.000
## Mean :4.229 Mean :4.317 Mean :4.322 Mean :4.364
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## AU3 AU4 AU5
## Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000
## Median :4.000 Median :4.000 Median :4.000
## Mean :4.142 Mean :4.272 Mean :4.294
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000Model Pengukuran (Outer Model)
measurement_model <- constructs(
composite("DL",
multi_items("DL", 1:5)),
composite("SE",
multi_items("SE", 1:3)),
composite("PI",
multi_items("PI", 1:5)),
composite("PU",
multi_items("PU", 1:3)),
composite("PEOU",
multi_items("PEOU", 1:3)),
composite("AT",
multi_items("AT", 1:3)),
composite("BI",
multi_items("BI", 1:5)),
composite("AU",
multi_items("AU", 1:5))
)Model Struktural (Inner Model)
structural_model <- relationships(
paths(from = "DL",
to = c("SE", "PEOU")),
paths(from = "SE",
to = "PEOU"),
paths(from = "PEOU",
to = c("PU", "AT")),
paths(from = "PU",
to = c("AT", "BI")),
paths(from = "AT",
to = "BI"),
paths(from = "BI",
to = "AU"),
paths(from = "PI",
to = c("PEOU", "BI"))
)The Basic Models
plot(structural_model)SEM PLS analysis
pls_model <- estimate_pls(
data = data,
measurement_model = measurement_model,
structural_model = structural_model
)## Generating the seminr model## All 401 observations are valid.model_summary <- summary(pls_model)
model_summary##
## Results from package seminr (2.4.2)
##
## Path Coefficients:
## SE PEOU PU AT BI AU
## R^2 0.689 0.620 0.539 0.629 0.675 0.729
## AdjR^2 0.688 0.617 0.538 0.627 0.673 0.728
## DL 0.830 0.212 . . . .
## SE . 0.278 . . . .
## PEOU . . 0.734 0.279 . .
## PU . . . 0.565 0.060 .
## AT . . . . 0.199 .
## BI . . . . . 0.854
## PI . 0.348 . . 0.597 .
##
## Reliability:
## alpha rhoA rhoC AVE
## DL 0.771 0.772 0.846 0.523
## SE 0.670 0.675 0.820 0.604
## PEOU 0.701 0.705 0.833 0.625
## PU 0.725 0.730 0.845 0.645
## AT 0.734 0.735 0.849 0.653
## BI 0.814 0.817 0.871 0.574
## PI 0.805 0.808 0.865 0.562
## AU 0.840 0.842 0.886 0.610
##
## Alpha, rhoA, and rhoC should exceed 0.7 while AVE should exceed 0.5The Final Models of SEM PLS
plot(pls_model)Convergent Validity test
Tes akan dianggap valid jika nilai faktor loading > 0,5
model_summary$loadings## DL SE PEOU PU AT BI PI AU
## DL1 0.747 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## DL2 0.704 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## DL3 0.673 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## DL4 0.770 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## DL5 0.719 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## SE1 0.000 0.815 0.000 0.000 0.000 0.000 0.000 0.000
## SE2 0.000 0.795 0.000 0.000 0.000 0.000 0.000 0.000
## SE3 0.000 0.716 0.000 0.000 0.000 0.000 0.000 0.000
## PI1 0.000 0.000 0.000 0.000 0.000 0.000 0.792 0.000
## PI2 0.000 0.000 0.000 0.000 0.000 0.000 0.752 0.000
## PI3 0.000 0.000 0.000 0.000 0.000 0.000 0.697 0.000
## PI4 0.000 0.000 0.000 0.000 0.000 0.000 0.753 0.000
## PI5 0.000 0.000 0.000 0.000 0.000 0.000 0.753 0.000
## PU1 0.000 0.000 0.000 0.836 0.000 0.000 0.000 0.000
## PU2 0.000 0.000 0.000 0.806 0.000 0.000 0.000 0.000
## PU3 0.000 0.000 0.000 0.766 0.000 0.000 0.000 0.000
## PEOU1 0.000 0.000 0.815 0.000 0.000 0.000 0.000 0.000
## PEOU2 0.000 0.000 0.801 0.000 0.000 0.000 0.000 0.000
## PEOU3 0.000 0.000 0.755 0.000 0.000 0.000 0.000 0.000
## AT1 0.000 0.000 0.000 0.000 0.826 0.000 0.000 0.000
## AT2 0.000 0.000 0.000 0.000 0.790 0.000 0.000 0.000
## AT3 0.000 0.000 0.000 0.000 0.806 0.000 0.000 0.000
## BI1 0.000 0.000 0.000 0.000 0.000 0.785 0.000 0.000
## BI2 0.000 0.000 0.000 0.000 0.000 0.760 0.000 0.000
## BI3 0.000 0.000 0.000 0.000 0.000 0.726 0.000 0.000
## BI4 0.000 0.000 0.000 0.000 0.000 0.726 0.000 0.000
## BI5 0.000 0.000 0.000 0.000 0.000 0.790 0.000 0.000
## AU1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.743
## AU2 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.801
## AU3 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.739
## AU4 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.822
## AU5 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.797Composite Reliability test
Uji reliabilitas komposit menunjukkan berdasarkan 2 nilai: 1. Nilai AVE > 0,5 2. Nilai rhoA > 0,7
model_summary$reliability## alpha rhoA rhoC AVE
## DL 0.771 0.772 0.846 0.523
## SE 0.670 0.675 0.820 0.604
## PEOU 0.701 0.705 0.833 0.625
## PU 0.725 0.730 0.845 0.645
## AT 0.734 0.735 0.849 0.653
## BI 0.814 0.817 0.871 0.574
## PI 0.805 0.808 0.865 0.562
## AU 0.840 0.842 0.886 0.610
##
## Alpha, rhoA, and rhoC should exceed 0.7 while AVE should exceed 0.5Discriminant Validity test
Nilai tersebut akan menunjukkan nilai terbesar pada setiap masing-masing variabelnya
model_summary$validity$cross_loadings## DL SE PEOU PU AT BI PI AU
## DL1 0.747 0.571 0.528 0.577 0.607 0.541 0.628 0.588
## DL2 0.704 0.641 0.523 0.543 0.564 0.545 0.606 0.542
## DL3 0.673 0.578 0.505 0.569 0.506 0.454 0.562 0.480
## DL4 0.770 0.598 0.545 0.582 0.593 0.582 0.604 0.593
## DL5 0.719 0.609 0.540 0.579 0.594 0.541 0.585 0.583
## SE1 0.647 0.815 0.580 0.616 0.559 0.543 0.609 0.549
## SE2 0.677 0.795 0.617 0.587 0.594 0.556 0.636 0.587
## SE3 0.609 0.716 0.509 0.583 0.577 0.581 0.630 0.620
## PI1 0.682 0.665 0.676 0.675 0.676 0.629 0.792 0.661
## PI2 0.644 0.621 0.556 0.576 0.678 0.619 0.752 0.633
## PI3 0.537 0.515 0.509 0.492 0.539 0.573 0.697 0.536
## PI4 0.609 0.623 0.582 0.626 0.626 0.592 0.753 0.615
## PI5 0.616 0.578 0.460 0.543 0.634 0.632 0.753 0.628
## PU1 0.652 0.619 0.632 0.836 0.638 0.585 0.645 0.638
## PU2 0.679 0.641 0.600 0.806 0.630 0.567 0.642 0.621
## PU3 0.564 0.585 0.532 0.766 0.586 0.481 0.593 0.562
## PEOU1 0.633 0.638 0.815 0.595 0.601 0.619 0.630 0.636
## PEOU2 0.560 0.573 0.801 0.613 0.536 0.575 0.603 0.591
## PEOU3 0.537 0.526 0.755 0.530 0.504 0.504 0.535 0.586
## AT1 0.676 0.594 0.624 0.640 0.826 0.615 0.695 0.681
## AT2 0.633 0.605 0.519 0.611 0.790 0.621 0.683 0.672
## AT3 0.611 0.599 0.535 0.615 0.806 0.579 0.664 0.623
## BI1 0.497 0.531 0.543 0.442 0.537 0.785 0.581 0.655
## BI2 0.477 0.493 0.529 0.448 0.510 0.760 0.583 0.651
## BI3 0.586 0.539 0.548 0.550 0.563 0.726 0.615 0.609
## BI4 0.586 0.542 0.503 0.515 0.590 0.726 0.598 0.584
## BI5 0.641 0.613 0.594 0.609 0.632 0.790 0.691 0.725
## AU1 0.647 0.608 0.608 0.660 0.654 0.599 0.649 0.743
## AU2 0.661 0.648 0.612 0.679 0.655 0.663 0.652 0.801
## AU3 0.541 0.568 0.557 0.520 0.601 0.680 0.646 0.739
## AU4 0.571 0.577 0.610 0.543 0.639 0.697 0.636 0.822
## AU5 0.602 0.539 0.603 0.567 0.642 0.688 0.627 0.797HTMT Value test
model_summary$validity$htmt## DL SE PEOU PU AT BI PI AU
## DL . . . . . . . .
## SE 1.153 . . . . . . .
## PEOU 0.992 1.066 . . . . . .
## PU 1.052 1.100 1.025 . . . . .
## AT 1.053 1.060 0.963 1.055 . . . .
## BI 0.927 0.974 0.946 0.878 0.967 . . .
## PI 1.046 1.093 0.986 1.016 1.095 1.001 . .
## AU 0.962 1.008 0.998 0.973 1.041 1.028 0.999 .Collinierity test
Nilai VIF harus lebih besar dari 0,2 dan kurang dari 5
model_summary$vif_antecedents## SE :
## DL
## .
##
## PEOU :
## DL SE PI
## 4.176 3.746 3.667
##
## PU :
## PEOU
## .
##
## AT :
## PEOU PU
## 2.168 2.168
##
## BI :
## PU AT PI
## 2.882 3.887 4.055
##
## AU :
## BI
## .Path Coefficient
model_summary$path## SE PEOU PU AT BI AU
## R^2 0.689 0.620 0.539 0.629 0.675 0.729
## AdjR^2 0.688 0.617 0.538 0.627 0.673 0.728
## DL 0.830 0.212 . . . .
## SE . 0.278 . . . .
## PEOU . . 0.734 0.279 . .
## PU . . . 0.565 0.060 .
## AT . . . . 0.199 .
## BI . . . . . 0.854
## PI . 0.348 . . 0.597 .F square test
model_summary$fSquare## DL SE PEOU PU AT BI PI AU
## DL 0.000 2.218 0.028 0.000 0.000 0.000 0.000 0.000
## SE 0.000 0.000 0.055 0.000 0.000 0.000 0.000 0.000
## PEOU 0.000 0.000 0.000 1.168 0.097 0.000 0.000 0.000
## PU 0.000 0.000 0.000 0.000 0.394 0.004 0.000 0.000
## AT 0.000 0.000 0.000 0.000 0.000 0.031 0.000 0.000
## BI 0.000 0.000 0.000 0.000 0.000 0.000 0.000 2.690
## PI 0.000 0.000 0.087 0.000 0.000 0.268 0.000 0.000
## AU 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000Boostrapping Model
boot_seminr_model <- bootstrap_model(seminr_model = pls_model,
nboot = 1000,cores = 2,seed = NULL)## Bootstrapping model using seminr...## SEMinR Model successfully bootstrappedhasil_akhir <- summary(boot_seminr_model)
hasil_akhir##
## Results from Bootstrap resamples: 1000
##
## Bootstrapped Structural Paths:
## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI 97.5% CI
## DL -> SE 0.830 0.828 0.028 29.729 0.766 0.875
## DL -> PEOU 0.212 0.210 0.094 2.261 0.017 0.390
## SE -> PEOU 0.278 0.275 0.096 2.910 0.092 0.461
## PEOU -> PU 0.734 0.729 0.058 12.659 0.605 0.825
## PEOU -> AT 0.279 0.282 0.064 4.382 0.167 0.411
## PU -> AT 0.565 0.561 0.068 8.293 0.418 0.692
## PU -> BI 0.060 0.056 0.077 0.785 -0.093 0.198
## AT -> BI 0.199 0.210 0.079 2.513 0.058 0.359
## BI -> AU 0.854 0.854 0.029 28.956 0.783 0.904
## PI -> PEOU 0.348 0.352 0.082 4.225 0.201 0.512
## PI -> BI 0.597 0.592 0.074 8.053 0.439 0.738
## Bootstrap P Val
## DL -> SE 0.000
## DL -> PEOU 0.040
## SE -> PEOU 0.008
## PEOU -> PU 0.000
## PEOU -> AT 0.000
## PU -> AT 0.000
## PU -> BI 0.470
## AT -> BI 0.002
## BI -> AU 0.000
## PI -> PEOU 0.000
## PI -> BI 0.000
##
## Bootstrapped Weights:
## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI
## DL1 -> DL 0.269 0.271 0.018 15.244 0.241
## DL2 -> DL 0.286 0.290 0.023 12.716 0.255
## DL3 -> DL 0.266 0.266 0.020 13.324 0.231
## DL4 -> DL 0.280 0.282 0.021 13.036 0.245
## DL5 -> DL 0.282 0.284 0.018 15.440 0.253
## SE1 -> SE 0.433 0.435 0.018 23.629 0.405
## SE2 -> SE 0.457 0.459 0.024 18.981 0.418
## SE3 -> SE 0.396 0.397 0.021 18.740 0.360
## PI1 -> PI 0.297 0.299 0.017 17.564 0.272
## PI2 -> PI 0.269 0.270 0.018 14.637 0.240
## PI3 -> PI 0.247 0.248 0.013 18.867 0.226
## PI4 -> PI 0.268 0.269 0.013 19.876 0.244
## PI5 -> PI 0.251 0.252 0.015 17.246 0.228
## PU1 -> PU 0.439 0.442 0.024 18.225 0.404
## PU2 -> PU 0.425 0.428 0.020 21.435 0.396
## PU3 -> PU 0.379 0.379 0.017 21.712 0.348
## PEOU1 -> PEOU 0.451 0.455 0.030 14.942 0.408
## PEOU2 -> PEOU 0.426 0.429 0.025 16.941 0.391
## PEOU3 -> PEOU 0.386 0.384 0.020 19.671 0.347
## AT1 -> AT 0.428 0.430 0.022 19.393 0.393
## AT2 -> AT 0.410 0.413 0.019 21.601 0.383
## AT3 -> AT 0.400 0.401 0.017 23.763 0.374
## BI1 -> BI 0.258 0.261 0.014 18.088 0.237
## BI2 -> BI 0.257 0.259 0.014 18.404 0.236
## BI3 -> BI 0.257 0.257 0.013 19.728 0.233
## BI4 -> BI 0.250 0.250 0.013 18.529 0.226
## BI5 -> BI 0.297 0.299 0.021 13.967 0.265
## AU1 -> AU 0.230 0.230 0.013 17.263 0.204
## AU2 -> AU 0.255 0.256 0.011 22.728 0.237
## AU3 -> AU 0.261 0.263 0.016 16.303 0.238
## AU4 -> AU 0.268 0.269 0.014 18.974 0.247
## AU5 -> AU 0.265 0.266 0.013 19.921 0.243
## 97.5% CI Bootstrap P Val
## DL1 -> DL 0.310 0.000
## DL2 -> DL 0.342 0.000
## DL3 -> DL 0.314 0.000
## DL4 -> DL 0.330 0.000
## DL5 -> DL 0.327 0.000
## SE1 -> SE 0.478 0.000
## SE2 -> SE 0.513 0.000
## SE3 -> SE 0.443 0.000
## PI1 -> PI 0.339 0.000
## PI2 -> PI 0.311 0.000
## PI3 -> PI 0.278 0.000
## PI4 -> PI 0.298 0.000
## PI5 -> PI 0.283 0.000
## PU1 -> PU 0.497 0.000
## PU2 -> PU 0.473 0.000
## PU3 -> PU 0.419 0.000
## PEOU1 -> PEOU 0.523 0.000
## PEOU2 -> PEOU 0.490 0.000
## PEOU3 -> PEOU 0.423 0.000
## AT1 -> AT 0.475 0.000
## AT2 -> AT 0.454 0.000
## AT3 -> AT 0.439 0.000
## BI1 -> BI 0.289 0.000
## BI2 -> BI 0.290 0.000
## BI3 -> BI 0.285 0.000
## BI4 -> BI 0.280 0.000
## BI5 -> BI 0.350 0.000
## AU1 -> AU 0.257 0.000
## AU2 -> AU 0.280 0.000
## AU3 -> AU 0.302 0.000
## AU4 -> AU 0.301 0.000
## AU5 -> AU 0.295 0.000
##
## Bootstrapped Loadings:
## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI
## DL1 -> DL 0.747 0.744 0.038 19.688 0.660
## DL2 -> DL 0.704 0.702 0.036 19.348 0.623
## DL3 -> DL 0.673 0.664 0.057 11.904 0.539
## DL4 -> DL 0.770 0.766 0.040 19.089 0.670
## DL5 -> DL 0.719 0.714 0.046 15.735 0.617
## SE1 -> SE 0.815 0.813 0.029 28.075 0.745
## SE2 -> SE 0.795 0.792 0.027 29.776 0.734
## SE3 -> SE 0.716 0.712 0.041 17.356 0.623
## PI1 -> PI 0.792 0.789 0.030 26.033 0.719
## PI2 -> PI 0.752 0.749 0.031 24.422 0.682
## PI3 -> PI 0.697 0.693 0.039 17.984 0.610
## PI4 -> PI 0.753 0.748 0.036 20.995 0.672
## PI5 -> PI 0.753 0.750 0.033 22.869 0.679
## PU1 -> PU 0.836 0.833 0.026 32.740 0.777
## PU2 -> PU 0.806 0.804 0.029 27.775 0.739
## PU3 -> PU 0.766 0.759 0.043 17.859 0.657
## PEOU1 -> PEOU 0.815 0.815 0.028 29.446 0.752
## PEOU2 -> PEOU 0.801 0.798 0.029 27.147 0.732
## PEOU3 -> PEOU 0.755 0.748 0.046 16.470 0.644
## AT1 -> AT 0.826 0.823 0.029 28.261 0.751
## AT2 -> AT 0.790 0.786 0.032 24.660 0.717
## AT3 -> AT 0.806 0.802 0.030 26.823 0.738
## BI1 -> BI 0.785 0.782 0.030 26.481 0.716
## BI2 -> BI 0.760 0.758 0.031 24.654 0.690
## BI3 -> BI 0.726 0.720 0.043 16.795 0.629
## BI4 -> BI 0.726 0.721 0.041 17.820 0.630
## BI5 -> BI 0.790 0.787 0.030 26.716 0.721
## AU1 -> AU 0.743 0.738 0.047 15.802 0.626
## AU2 -> AU 0.801 0.798 0.030 26.489 0.729
## AU3 -> AU 0.739 0.737 0.033 22.693 0.667
## AU4 -> AU 0.822 0.821 0.025 33.185 0.766
## AU5 -> AU 0.797 0.794 0.031 26.027 0.728
## 97.5% CI Bootstrap P Val
## DL1 -> DL 0.808 0.000
## DL2 -> DL 0.763 0.000
## DL3 -> DL 0.750 0.000
## DL4 -> DL 0.831 0.000
## DL5 -> DL 0.786 0.000
## SE1 -> SE 0.861 0.000
## SE2 -> SE 0.838 0.000
## SE3 -> SE 0.782 0.000
## PI1 -> PI 0.838 0.000
## PI2 -> PI 0.803 0.000
## PI3 -> PI 0.762 0.000
## PI4 -> PI 0.808 0.000
## PI5 -> PI 0.807 0.000
## PU1 -> PU 0.875 0.000
## PU2 -> PU 0.852 0.000
## PU3 -> PU 0.832 0.000
## PEOU1 -> PEOU 0.861 0.000
## PEOU2 -> PEOU 0.846 0.000
## PEOU3 -> PEOU 0.821 0.000
## AT1 -> AT 0.871 0.000
## AT2 -> AT 0.838 0.000
## AT3 -> AT 0.854 0.000
## BI1 -> BI 0.832 0.000
## BI2 -> BI 0.811 0.000
## BI3 -> BI 0.789 0.000
## BI4 -> BI 0.791 0.000
## BI5 -> BI 0.837 0.000
## AU1 -> AU 0.812 0.000
## AU2 -> AU 0.849 0.000
## AU3 -> AU 0.789 0.000
## AU4 -> AU 0.862 0.000
## AU5 -> AU 0.845 0.000
##
## Bootstrapped HTMT:
## Original Est. Bootstrap Mean Bootstrap SD 2.5% CI 97.5% CI
## DL -> SE 1.153 1.167 0.059 1.083 1.311
## DL -> PEOU 0.992 0.998 0.080 0.839 1.151
## DL -> PU 1.052 1.061 0.048 0.986 1.171
## DL -> AT 1.053 1.066 0.056 0.970 1.189
## DL -> BI 0.927 0.934 0.052 0.832 1.036
## DL -> PI 1.046 1.051 0.034 0.993 1.134
## DL -> AU 0.962 0.966 0.049 0.871 1.065
## SE -> PEOU 1.066 1.074 0.075 0.936 1.228
## SE -> PU 1.100 1.111 0.062 1.010 1.255
## SE -> AT 1.060 1.071 0.066 0.966 1.219
## SE -> BI 0.974 0.982 0.060 0.868 1.101
## SE -> PI 1.093 1.101 0.047 1.025 1.208
## SE -> AU 1.008 1.013 0.056 0.910 1.128
## PEOU -> PU 1.025 1.031 0.066 0.903 1.163
## PEOU -> AT 0.963 0.972 0.084 0.810 1.136
## PEOU -> BI 0.946 0.955 0.069 0.824 1.099
## PEOU -> PI 0.986 0.991 0.071 0.848 1.121
## PEOU -> AU 0.998 1.004 0.048 0.916 1.096
## PU -> AT 1.055 1.069 0.078 0.930 1.237
## PU -> BI 0.878 0.884 0.065 0.754 1.006
## PU -> PI 1.016 1.021 0.037 0.953 1.099
## PU -> AU 0.973 0.978 0.046 0.890 1.074
## AT -> BI 0.967 0.977 0.065 0.850 1.104
## AT -> PI 1.095 1.105 0.047 1.030 1.214
## AT -> AU 1.041 1.048 0.050 0.957 1.149
## BI -> PI 1.001 1.006 0.042 0.921 1.086
## BI -> AU 1.028 1.033 0.034 0.972 1.104
## PI -> AU 0.999 1.000 0.034 0.928 1.060
## Bootstrap P Val
## DL -> SE 0.000
## DL -> PEOU 0.992
## DL -> PU 0.132
## DL -> AT 0.222
## DL -> BI 0.190
## DL -> PI 0.092
## DL -> AU 0.468
## SE -> PEOU 0.310
## SE -> PU 0.028
## SE -> AT 0.232
## SE -> BI 0.740
## SE -> PI 0.004
## SE -> AU 0.822
## PEOU -> PU 0.616
## PEOU -> AT 0.716
## PEOU -> BI 0.476
## PEOU -> PI 0.922
## PEOU -> AU 0.942
## PU -> AT 0.346
## PU -> BI 0.070
## PU -> PI 0.580
## PU -> AU 0.602
## AT -> BI 0.690
## AT -> PI 0.000
## AT -> AU 0.320
## BI -> PI 0.896
## BI -> AU 0.350
## PI -> AU 0.954
##
## Bootstrapped Total Paths:
## Original Est. Bootstrap Mean Bootstrap SD 2.5% CI 97.5% CI
## DL -> SE 0.830 0.828 0.028 0.766 0.875
## DL -> PEOU 0.444 0.437 0.086 0.270 0.607
## DL -> PU 0.326 0.321 0.076 0.177 0.473
## DL -> AT 0.308 0.304 0.073 0.168 0.448
## DL -> BI 0.081 0.083 0.037 0.016 0.162
## DL -> AU 0.069 0.071 0.033 0.014 0.140
## SE -> PEOU 0.278 0.275 0.096 0.092 0.461
## SE -> PU 0.204 0.201 0.074 0.061 0.350
## SE -> AT 0.193 0.190 0.068 0.058 0.326
## SE -> BI 0.051 0.052 0.028 0.008 0.111
## SE -> AU 0.043 0.045 0.025 0.006 0.098
## PEOU -> PU 0.734 0.729 0.058 0.605 0.825
## PEOU -> AT 0.694 0.691 0.064 0.549 0.809
## PEOU -> BI 0.182 0.187 0.070 0.046 0.326
## PEOU -> AU 0.156 0.161 0.063 0.037 0.289
## PU -> AT 0.565 0.561 0.068 0.418 0.692
## PU -> BI 0.173 0.175 0.079 0.018 0.326
## PU -> AU 0.147 0.150 0.069 0.015 0.285
## AT -> BI 0.199 0.210 0.079 0.058 0.359
## AT -> AU 0.170 0.180 0.069 0.050 0.313
## BI -> AU 0.854 0.854 0.029 0.783 0.904
## PI -> PEOU 0.348 0.352 0.082 0.201 0.512
## PI -> PU 0.256 0.257 0.066 0.139 0.394
## PI -> AT 0.242 0.244 0.066 0.125 0.386
## PI -> BI 0.661 0.658 0.059 0.533 0.762
## PI -> AU 0.564 0.562 0.055 0.451 0.665Plotting Model after boostrapping
plot(boot_seminr_model)