MODUL 5 ANMUL
Devita Nur Aisyah(24031554125) & Meisya Natasafira(24031554174)
2026-05-14
Analisis Faktor yang Mempengaruhi Penerimaan Pembelajaran Daring Menggunakan Technology Acceptance Model (TAM) dengan Metode SEM-CB
Library
library(readxl)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(psych)
library(lavaan)## This is lavaan 0.6-21
## lavaan is FREE software! Please report any bugs.##
## Attaching package: 'lavaan'## The following object is masked from 'package:psych':
##
## cor2covlibrary(semPlot)
library(car)## Loading required package: carData## Registered S3 method overwritten by 'car':
## method from
## na.action.merMod lme4##
## Attaching package: 'car'## The following object is masked from 'package:psych':
##
## logit## The following object is masked from 'package:dplyr':
##
## recodeImport data
data <- read_excel("VCT 277 records (2).xlsx")
str(data)## tibble [277 × 29] (S3: tbl_df/tbl/data.frame)
## $ DEM1: num [1:277] 3 4 4 2 4 3 3 3 4 3 ...
## $ DEM2: chr [1:277] "Nông thôn" "Thà nh thị" "Thà nh thị" "Thà nh thị" ...
## $ DEM3: chr [1:277] "Zoom" "Google Meet" "Google Meet" "Google Meet" ...
## $ OQ1 : num [1:277] 3 3 3 3 4 3 2 2 4 3 ...
## $ OQ2 : num [1:277] 3 3 3 4 4 3 2 2 4 3 ...
## $ SN1 : num [1:277] 2 5 3 4 4 4 3 2 3 4 ...
## $ SN2 : num [1:277] 3 5 3 4 4 4 3 2 3 4 ...
## $ PU1 : num [1:277] 1 3 3 4 4 4 2 2 4 3 ...
## $ PU2 : num [1:277] 3 3 3 4 4 4 2 2 4 3 ...
## $ PU3 : num [1:277] 2 3 3 4 4 4 2 2 4 2 ...
## $ PU4 : num [1:277] 2 4 3 4 4 4 2 3 4 3 ...
## $ ATT1: num [1:277] 1 4 3 4 4 4 2 3 4 3 ...
## $ ATT2: num [1:277] 1 3 4 4 4 4 2 2 4 4 ...
## $ ATT3: num [1:277] 2 3 3 4 4 4 2 2 4 2 ...
## $ ATT4: num [1:277] 1 3 3 4 4 4 2 2 4 1 ...
## $ BI1 : num [1:277] 3 3 3 4 2 4 3 2 4 3 ...
## $ BI2 : num [1:277] 3 3 3 4 2 4 3 2 4 3 ...
## $ BI3 : num [1:277] 2 3 3 4 2 4 3 3 4 3 ...
## $ BI4 : num [1:277] 3 3 3 4 2 4 3 2 4 2 ...
## $ ASU1: num [1:277] 2 3 3 4 3 4 3 3 4 2 ...
## $ ASU2: num [1:277] 2 3 3 4 2 4 3 4 4 2 ...
## $ CP1 : num [1:277] 3 3 4 4 3 4 2 3 4 2 ...
## $ CP2 : num [1:277] 4 3 3 4 3 4 2 2 4 2 ...
## $ CP3 : num [1:277] 4 3 3 4 3 4 2 3 4 3 ...
## $ CP4 : num [1:277] 4 3 3 4 3 4 2 2 4 3 ...
## $ PEU1: num [1:277] 2 5 3 4 4 4 2 3 4 2 ...
## $ PEU2: num [1:277] 1 5 4 4 4 4 3 3 4 4 ...
## $ PEU3: num [1:277] 4 5 3 4 4 4 3 2 4 3 ...
## $ PEU4: num [1:277] 3 5 3 4 4 4 2 3 4 3 ...summary(data)## DEM1 DEM2 DEM3 OQ1
## Min. :1.000 Length :277 Length :277 Min. :1.000
## 1st Qu.:1.000 N.unique : 2 N.unique : 29 1st Qu.:2.000
## Median :2.000 N.blank : 0 N.blank : 0 Median :3.000
## Mean :2.083 Min.nchar: 9 Min.nchar: 3 Mean :3.029
## 3rd Qu.:3.000 Max.nchar: 9 Max.nchar: 45 3rd Qu.:4.000
## Max. :5.000 Max. :5.000
## OQ2 SN1 SN2 PU1 PU2
## Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:3.000 1st Qu.:3.00 1st Qu.:2.000 1st Qu.:2.000
## Median :3.000 Median :4.000 Median :3.00 Median :3.000 Median :3.000
## Mean :3.036 Mean :3.578 Mean :3.48 Mean :2.809 Mean :2.801
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.00 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :5.000 Max. :5.000 Max. :5.00 Max. :5.000 Max. :5.000
## PU3 PU4 ATT1 ATT2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:2.000
## Median :3.000 Median :3.000 Median :3.000 Median :3.000
## Mean :2.751 Mean :3.072 Mean :3.199 Mean :2.975
## 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## ATT3 ATT4 BI1 BI2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:3.000 1st Qu.:3.000
## Median :3.000 Median :3.000 Median :3.000 Median :3.000
## Mean :2.957 Mean :2.906 Mean :3.235 Mean :3.336
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## BI3 BI4 ASU1 ASU2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:2.000
## Median :3.000 Median :3.000 Median :3.000 Median :3.000
## Mean :3.202 Mean :3.097 Mean :3.112 Mean :2.978
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## CP1 CP2 CP3 CP4 PEU1
## Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.00 Min. :1.000
## 1st Qu.:3.000 1st Qu.:2.00 1st Qu.:2.000 1st Qu.:2.00 1st Qu.:3.000
## Median :3.000 Median :3.00 Median :3.000 Median :3.00 Median :3.000
## Mean :3.126 Mean :2.96 Mean :2.939 Mean :3.04 Mean :3.191
## 3rd Qu.:4.000 3rd Qu.:4.00 3rd Qu.:4.000 3rd Qu.:4.00 3rd Qu.:4.000
## Max. :5.000 Max. :5.00 Max. :5.000 Max. :5.00 Max. :5.000
## PEU2 PEU3 PEU4
## Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000
## Median :4.000 Median :3.000 Median :3.000
## Mean :3.617 Mean :3.307 Mean :3.451
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000 Max. :5.000Preprocessing
Mengecek missing value
sum(is.na(data))## [1] 0colSums(is.na(data))## DEM1 DEM2 DEM3 OQ1 OQ2 SN1 SN2 PU1 PU2 PU3 PU4 ATT1 ATT2 ATT3 ATT4 BI1
## 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## BI2 BI3 BI4 ASU1 ASU2 CP1 CP2 CP3 CP4 PEU1 PEU2 PEU3 PEU4
## 0 0 0 0 0 0 0 0 0 0 0 0 0Mengecek data duplikat
sum(duplicated(data))## [1] 0Mengambil Indikator SEM
data_sem <- data %>%
select(OQ1, OQ2,
SN1, SN2,
PU1, PU2, PU3, PU4,
ATT1, ATT2, ATT3, ATT4,
BI1, BI2, BI3, BI4,
ASU1, ASU2,
CP1, CP2, CP3, CP4,
PEU1, PEU2, PEU3, PEU4)Ubah data menjadi numerik
data_sem <- as.data.frame(lapply(data_sem, as.numeric))Ringkasan
summary(data_sem)## OQ1 OQ2 SN1 SN2 PU1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:3.000 1st Qu.:3.00 1st Qu.:2.000
## Median :3.000 Median :3.000 Median :4.000 Median :3.00 Median :3.000
## Mean :3.029 Mean :3.036 Mean :3.578 Mean :3.48 Mean :2.809
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.00 3rd Qu.:3.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.00 Max. :5.000
## PU2 PU3 PU4 ATT1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:3.000 1st Qu.:3.000
## Median :3.000 Median :3.000 Median :3.000 Median :3.000
## Mean :2.801 Mean :2.751 Mean :3.072 Mean :3.199
## 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## ATT2 ATT3 ATT4 BI1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:3.000
## Median :3.000 Median :3.000 Median :3.000 Median :3.000
## Mean :2.975 Mean :2.957 Mean :2.906 Mean :3.235
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## BI2 BI3 BI4 ASU1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000
## Median :3.000 Median :3.000 Median :3.000 Median :3.000
## Mean :3.336 Mean :3.202 Mean :3.097 Mean :3.112
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## ASU2 CP1 CP2 CP3 CP4
## Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.00
## 1st Qu.:2.000 1st Qu.:3.000 1st Qu.:2.00 1st Qu.:2.000 1st Qu.:2.00
## Median :3.000 Median :3.000 Median :3.00 Median :3.000 Median :3.00
## Mean :2.978 Mean :3.126 Mean :2.96 Mean :2.939 Mean :3.04
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.00 3rd Qu.:4.000 3rd Qu.:4.00
## Max. :5.000 Max. :5.000 Max. :5.00 Max. :5.000 Max. :5.00
## PEU1 PEU2 PEU3 PEU4
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000
## Median :3.000 Median :4.000 Median :3.000 Median :3.000
## Mean :3.191 Mean :3.617 Mean :3.307 Mean :3.451
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000Statistika Deskriptif
describe(data_sem)## vars n mean sd median trimmed mad min max range skew kurtosis se
## OQ1 1 277 3.03 0.95 3 3.04 1.48 1 5 4 -0.06 -0.10 0.06
## OQ2 2 277 3.04 1.08 3 3.04 1.48 1 5 4 -0.04 -0.64 0.07
## SN1 3 277 3.58 0.92 4 3.62 1.48 1 5 4 -0.41 0.24 0.06
## SN2 4 277 3.48 0.86 3 3.49 1.48 1 5 4 -0.21 0.25 0.05
## PU1 5 277 2.81 1.00 3 2.83 1.48 1 5 4 0.02 -0.29 0.06
## PU2 6 277 2.80 0.99 3 2.83 1.48 1 5 4 -0.04 -0.35 0.06
## PU3 7 277 2.75 0.98 3 2.76 1.48 1 5 4 0.07 -0.17 0.06
## PU4 8 277 3.07 0.93 3 3.09 1.48 1 5 4 -0.14 -0.13 0.06
## ATT1 9 277 3.20 0.88 3 3.21 1.48 1 5 4 -0.10 0.13 0.05
## ATT2 10 277 2.97 0.96 3 3.00 1.48 1 5 4 -0.10 -0.17 0.06
## ATT3 11 277 2.96 0.94 3 2.98 1.48 1 5 4 -0.12 -0.12 0.06
## ATT4 12 277 2.91 1.00 3 2.93 1.48 1 5 4 -0.05 -0.28 0.06
## BI1 13 277 3.23 0.96 3 3.25 1.48 1 5 4 -0.26 -0.06 0.06
## BI2 14 277 3.34 0.89 3 3.36 1.48 1 5 4 -0.34 0.12 0.05
## BI3 15 277 3.20 0.91 3 3.22 1.48 1 5 4 -0.23 0.11 0.05
## BI4 16 277 3.10 0.91 3 3.12 1.48 1 5 4 -0.19 -0.12 0.05
## ASU1 17 277 3.11 0.97 3 3.13 1.48 1 5 4 -0.20 -0.30 0.06
## ASU2 18 277 2.98 1.00 3 2.99 1.48 1 5 4 -0.02 -0.52 0.06
## CP1 19 277 3.13 0.88 3 3.14 1.48 1 5 4 -0.15 -0.22 0.05
## CP2 20 277 2.96 0.92 3 2.96 1.48 1 5 4 0.02 -0.36 0.06
## CP3 21 277 2.94 0.91 3 2.94 1.48 1 5 4 0.01 -0.20 0.05
## CP4 22 277 3.04 0.91 3 3.04 1.48 1 5 4 -0.02 -0.29 0.05
## PEU1 23 277 3.19 0.88 3 3.21 1.48 1 5 4 -0.16 0.02 0.05
## PEU2 24 277 3.62 0.92 4 3.66 1.48 1 5 4 -0.42 0.20 0.06
## PEU3 25 277 3.31 1.00 3 3.32 1.48 1 5 4 -0.23 -0.27 0.06
## PEU4 26 277 3.45 0.87 3 3.48 1.48 1 5 4 -0.42 0.54 0.05Uji asumsi
Uji normalitas
mardia(data_sem)
## Call: mardia(x = data_sem)
##
## Mardia tests of multivariate skew and kurtosis
## Use describe(x) the to get univariate tests
## n.obs = 277 num.vars = 26
## b1p = 118.99 skew = 5493.46 with probability <= 2.4e-116
## small sample skew = 5557.4 with probability <= 5.2e-122
## b2p = 862.59 kurtosis = 29.35 with probability <= 0KMO
KMO(cor(data_sem))## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = cor(data_sem))
## Overall MSA = 0.94
## MSA for each item =
## OQ1 OQ2 SN1 SN2 PU1 PU2 PU3 PU4 ATT1 ATT2 ATT3 ATT4 BI1 BI2 BI3 BI4
## 0.92 0.90 0.89 0.85 0.96 0.94 0.94 0.97 0.97 0.94 0.97 0.94 0.96 0.94 0.94 0.96
## ASU1 ASU2 CP1 CP2 CP3 CP4 PEU1 PEU2 PEU3 PEU4
## 0.91 0.88 0.97 0.90 0.92 0.96 0.97 0.94 0.90 0.93Multikolinearitas
model_vif <- lm(BI1 ~ OQ1 + OQ2 + SN1 + SN2 + PU1 + PU2 + PU3 + PU4 +
ATT1 + ATT2 + ATT3 + ATT4 + BI2 + BI3 + BI4 +
ASU1 + ASU2 + CP1 + CP2 + CP3 + CP4 +
PEU1 + PEU2 + PEU3 + PEU4,
data = data_sem)
vif(model_vif)## OQ1 OQ2 SN1 SN2 PU1 PU2 PU3 PU4
## 2.661273 2.362601 3.094107 2.866207 4.333685 5.289683 5.154924 3.435621
## ATT1 ATT2 ATT3 ATT4 BI2 BI3 BI4 ASU1
## 4.117024 5.875787 4.273910 5.107940 2.933681 2.828867 3.186198 3.362303
## ASU2 CP1 CP2 CP3 CP4 PEU1 PEU2 PEU3
## 2.326365 2.514771 4.016577 4.073299 2.466888 2.526183 2.994724 2.286784
## PEU4
## 3.541440Confirmatory Factor Analysis (CFA)
model_cfa <- '
OutputQuality =~ OQ1 + OQ2
SubjectiveNorm =~ SN1 + SN2
PerceivedUsefulness =~ PU1 + PU2 + PU3 + PU4
Attitude =~ ATT1 + ATT2 + ATT3 + ATT4
BehavioralIntention =~ BI1 + BI2 + BI3 + BI4
ActualSystemUse =~ ASU1 + ASU2
ComputerPlayfulness =~ CP1 + CP2 + CP3 + CP4
PerceivedEaseOfUse =~ PEU1 + PEU2 + PEU3 + PEU4
'
fit_cfa <- cfa(model_cfa, data = data_sem, std.lv = TRUE)
summary(fit_cfa, fit.measures = TRUE, standardized = TRUE)## lavaan 0.6-21 ended normally after 50 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 80
##
## Number of observations 277
##
## Model Test User Model:
##
## Test statistic 572.645
## Degrees of freedom 271
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 6326.702
## Degrees of freedom 325
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.950
## Tucker-Lewis Index (TLI) 0.940
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -6867.466
## Loglikelihood unrestricted model (H1) -6581.143
##
## Akaike (AIC) 13894.932
## Bayesian (BIC) 14184.853
## Sample-size adjusted Bayesian (SABIC) 13931.184
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.063
## 90 Percent confidence interval - lower 0.056
## 90 Percent confidence interval - upper 0.071
## P-value H_0: RMSEA <= 0.050 0.001
## P-value H_0: RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.059
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## OutputQuality =~
## OQ1 0.860 0.051 16.924 0.000 0.860 0.906
## OQ2 0.876 0.059 14.760 0.000 0.876 0.810
## SubjectiveNorm =~
## SN1 0.861 0.048 18.025 0.000 0.861 0.938
## SN2 0.714 0.046 15.388 0.000 0.714 0.829
## PerceivedUsefulness =~
## PU1 0.892 0.047 19.017 0.000 0.892 0.896
## PU2 0.908 0.046 19.775 0.000 0.908 0.917
## PU3 0.898 0.045 19.910 0.000 0.898 0.920
## PU4 0.759 0.046 16.332 0.000 0.759 0.815
## Attitude =~
## ATT1 0.753 0.042 17.813 0.000 0.753 0.861
## ATT2 0.882 0.044 20.052 0.000 0.882 0.923
## ATT3 0.828 0.045 18.603 0.000 0.828 0.884
## ATT4 0.900 0.047 19.264 0.000 0.900 0.902
## BehavioralIntention =~
## BI1 0.784 0.048 16.274 0.000 0.784 0.819
## BI2 0.747 0.044 16.881 0.000 0.747 0.839
## BI3 0.727 0.046 15.803 0.000 0.727 0.803
## BI4 0.754 0.045 16.628 0.000 0.754 0.830
## ActualSystemUse =~
## ASU1 0.963 0.049 19.474 0.000 0.963 0.995
## ASU2 0.711 0.056 12.739 0.000 0.711 0.710
## ComputerPlayfulness =~
## CP1 0.640 0.047 13.667 0.000 0.640 0.727
## CP2 0.804 0.044 18.075 0.000 0.804 0.878
## CP3 0.819 0.044 18.805 0.000 0.819 0.899
## CP4 0.702 0.047 14.896 0.000 0.702 0.773
## PerceivedEaseOfUse =~
## PEU1 0.653 0.047 13.991 0.000 0.653 0.744
## PEU2 0.758 0.046 16.387 0.000 0.758 0.830
## PEU3 0.719 0.054 13.342 0.000 0.719 0.719
## PEU4 0.765 0.043 17.852 0.000 0.765 0.877
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## OutputQuality ~~
## SubjectiveNorm 0.473 0.055 8.531 0.000 0.473 0.473
## PercevdUsflnss 0.595 0.046 12.918 0.000 0.595 0.595
## Attitude 0.608 0.045 13.454 0.000 0.608 0.608
## BehavirlIntntn 0.562 0.050 11.219 0.000 0.562 0.562
## ActualSystemUs 0.435 0.055 7.880 0.000 0.435 0.435
## ComptrPlyflnss 0.530 0.051 10.294 0.000 0.530 0.530
## PerceivdEsOfUs 0.565 0.050 11.253 0.000 0.565 0.565
## SubjectiveNorm ~~
## PercevdUsflnss 0.378 0.057 6.662 0.000 0.378 0.378
## Attitude 0.483 0.052 9.352 0.000 0.483 0.483
## BehavirlIntntn 0.545 0.050 10.917 0.000 0.545 0.545
## ActualSystemUs 0.258 0.060 4.306 0.000 0.258 0.258
## ComptrPlyflnss 0.437 0.055 7.918 0.000 0.437 0.437
## PerceivdEsOfUs 0.604 0.046 13.023 0.000 0.604 0.604
## PerceivedUsefulness ~~
## Attitude 0.806 0.025 32.314 0.000 0.806 0.806
## BehavirlIntntn 0.666 0.039 17.058 0.000 0.666 0.666
## ActualSystemUs 0.506 0.049 10.367 0.000 0.506 0.506
## ComptrPlyflnss 0.674 0.038 17.849 0.000 0.674 0.674
## PerceivdEsOfUs 0.573 0.046 12.431 0.000 0.573 0.573
## Attitude ~~
## BehavirlIntntn 0.765 0.031 24.936 0.000 0.765 0.765
## ActualSystemUs 0.517 0.048 10.715 0.000 0.517 0.517
## ComptrPlyflnss 0.706 0.035 20.110 0.000 0.706 0.706
## PerceivdEsOfUs 0.722 0.035 20.749 0.000 0.722 0.722
## BehavioralIntention ~~
## ActualSystemUs 0.749 0.037 20.363 0.000 0.749 0.749
## ComptrPlyflnss 0.722 0.036 20.180 0.000 0.722 0.722
## PerceivdEsOfUs 0.644 0.043 15.013 0.000 0.644 0.644
## ActualSystemUse ~~
## ComptrPlyflnss 0.526 0.049 10.766 0.000 0.526 0.526
## PerceivdEsOfUs 0.449 0.054 8.385 0.000 0.449 0.449
## ComputerPlayfulness ~~
## PerceivdEsOfUs 0.523 0.051 10.329 0.000 0.523 0.523
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .OQ1 0.162 0.046 3.485 0.000 0.162 0.179
## .OQ2 0.401 0.057 7.010 0.000 0.401 0.343
## .SN1 0.102 0.042 2.413 0.016 0.102 0.121
## .SN2 0.231 0.035 6.704 0.000 0.231 0.313
## .PU1 0.196 0.021 9.118 0.000 0.196 0.197
## .PU2 0.157 0.019 8.321 0.000 0.157 0.160
## .PU3 0.146 0.018 8.145 0.000 0.146 0.153
## .PU4 0.292 0.028 10.507 0.000 0.292 0.336
## .ATT1 0.199 0.020 10.039 0.000 0.199 0.259
## .ATT2 0.135 0.016 8.232 0.000 0.135 0.148
## .ATT3 0.193 0.020 9.599 0.000 0.193 0.219
## .ATT4 0.185 0.020 9.092 0.000 0.185 0.186
## .BI1 0.302 0.031 9.768 0.000 0.302 0.329
## .BI2 0.236 0.025 9.430 0.000 0.236 0.297
## .BI3 0.291 0.029 9.988 0.000 0.291 0.355
## .BI4 0.256 0.027 9.580 0.000 0.256 0.310
## .ASU1 0.009 0.052 0.166 0.868 0.009 0.009
## .ASU2 0.498 0.051 9.757 0.000 0.498 0.496
## .CP1 0.365 0.034 10.601 0.000 0.365 0.471
## .CP2 0.193 0.024 8.118 0.000 0.193 0.230
## .CP3 0.159 0.022 7.226 0.000 0.159 0.192
## .CP4 0.332 0.033 10.214 0.000 0.332 0.403
## .PEU1 0.343 0.034 10.157 0.000 0.343 0.446
## .PEU2 0.260 0.029 8.834 0.000 0.260 0.311
## .PEU3 0.482 0.046 10.384 0.000 0.482 0.483
## .PEU4 0.175 0.024 7.385 0.000 0.175 0.231
## OutputQuality 1.000 1.000 1.000
## SubjectiveNorm 1.000 1.000 1.000
## PercevdUsflnss 1.000 1.000 1.000
## Attitude 1.000 1.000 1.000
## BehavirlIntntn 1.000 1.000 1.000
## ActualSystemUs 1.000 1.000 1.000
## ComptrPlyflnss 1.000 1.000 1.000
## PerceivdEsOfUs 1.000 1.000 1.000Visualisasi CFA
semPaths(
fit_cfa,
what = "path",
whatLabels = "std",
style = "ram",
layout = "tree",
rotation = 2,
sizeMan = 5,
sizeLat = 5,
edge.label.cex = 0.9,
label.cex = 0.9
)
## Ringkasan fit indeks CFA
fitMeasures(fit_cfa, c("chisq", "df", "pvalue", "cfi", "tli", "rmsea", "srmr"))## chisq df pvalue cfi tli rmsea srmr
## 572.645 271.000 0.000 0.950 0.940 0.063 0.059Composite realibiality dan AVE
hitung_CR_AVE <- function(fit) {
std <- standardizedSolution(fit)
loading <- std %>% filter(op == "=~") %>% select(lhs, rhs, est.std)
hasil <- loading %>%
group_by(lhs) %>%
summarise(
CR = (sum(est.std))^2 / ((sum(est.std))^2 + sum(1 - est.std^2)),
AVE = mean(est.std^2)
)
return(hasil)
}
CR_AVE_CFA <- hitung_CR_AVE(fit_cfa)
CR_AVE_CFA## # A tibble: 8 × 3
## lhs CR AVE
## <chr> <dbl> <dbl>
## 1 ActualSystemUse 0.852 0.747
## 2 Attitude 0.940 0.797
## 3 BehavioralIntention 0.893 0.677
## 4 ComputerPlayfulness 0.892 0.676
## 5 OutputQuality 0.849 0.739
## 6 PerceivedEaseOfUse 0.872 0.632
## 7 PerceivedUsefulness 0.937 0.788
## 8 SubjectiveNorm 0.878 0.783SEM
model_sem <- '
# Measurement Model
OutputQuality =~ OQ1 + OQ2
SubjectiveNorm =~ SN1 + SN2
PerceivedUsefulness =~ PU1 + PU2 + PU3 + PU4
Attitude =~ ATT1 + ATT2 + ATT3 + ATT4
BehavioralIntention =~ BI1 + BI2 + BI3 + BI4
ActualSystemUse =~ ASU1 + ASU2
ComputerPlayfulness =~ CP1 + CP2 + CP3 + CP4
PerceivedEaseOfUse =~ PEU1 + PEU2 + PEU3 + PEU4
# Structural Model
PerceivedEaseOfUse ~ ComputerPlayfulness
PerceivedUsefulness ~ OutputQuality + PerceivedEaseOfUse
Attitude ~ PerceivedUsefulness + PerceivedEaseOfUse
BehavioralIntention ~ Attitude + SubjectiveNorm + PerceivedUsefulness
ActualSystemUse ~ BehavioralIntention
PU1 ~~ PU2
PU3 ~~ PU4
ATT1 ~~ ATT2
ATT3 ~~ ATT4
BI1 ~~ BI2
BI3 ~~ BI4
CP1 ~~ CP2
PEU1 ~~ PEU2
PEU3 ~~ PEU4
'
fit_sem <- sem(model_sem, data = data_sem, std.lv = TRUE)
summary(fit_sem, fit.measures = TRUE, standardized = TRUE, rsquare = TRUE)## lavaan 0.6-21 ended normally after 63 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 73
##
## Number of observations 277
##
## Model Test User Model:
##
## Test statistic 719.943
## Degrees of freedom 278
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 6326.702
## Degrees of freedom 325
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.926
## Tucker-Lewis Index (TLI) 0.914
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -6941.115
## Loglikelihood unrestricted model (H1) -6581.143
##
## Akaike (AIC) 14028.230
## Bayesian (BIC) 14292.783
## Sample-size adjusted Bayesian (SABIC) 14061.310
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.076
## 90 Percent confidence interval - lower 0.069
## 90 Percent confidence interval - upper 0.083
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 0.155
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.095
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## OutputQuality =~
## OQ1 0.852 0.051 16.606 0.000 0.852 0.897
## OQ2 0.877 0.060 14.692 0.000 0.877 0.811
## SubjectiveNorm =~
## SN1 0.871 0.051 16.962 0.000 0.871 0.948
## SN2 0.704 0.049 14.350 0.000 0.704 0.818
## PerceivedUsefulness =~
## PU1 0.595 0.037 15.958 0.000 0.842 0.861
## PU2 0.608 0.037 16.555 0.000 0.861 0.885
## PU3 0.629 0.037 17.130 0.000 0.890 0.932
## PU4 0.546 0.036 14.981 0.000 0.772 0.843
## Attitude =~
## ATT1 0.347 0.026 13.332 0.000 0.736 0.853
## ATT2 0.407 0.028 14.291 0.000 0.862 0.917
## ATT3 0.384 0.028 13.738 0.000 0.815 0.882
## ATT4 0.416 0.030 13.987 0.000 0.883 0.899
## BehavioralIntention =~
## BI1 0.465 0.035 13.398 0.000 0.743 0.792
## BI2 0.442 0.032 13.747 0.000 0.706 0.809
## BI3 0.446 0.033 13.339 0.000 0.712 0.803
## BI4 0.464 0.033 13.968 0.000 0.742 0.834
## ActualSystemUse =~
## ASU1 0.617 0.052 11.805 0.000 0.926 0.972
## ASU2 0.476 0.038 12.563 0.000 0.715 0.720
## ComputerPlayfulness =~
## CP1 0.663 0.047 14.048 0.000 0.663 0.754
## CP2 0.790 0.046 17.305 0.000 0.790 0.862
## CP3 0.807 0.044 18.203 0.000 0.807 0.886
## CP4 0.704 0.047 14.911 0.000 0.704 0.775
## PerceivedEaseOfUse =~
## PEU1 0.518 0.039 13.151 0.000 0.693 0.791
## PEU2 0.531 0.041 12.904 0.000 0.712 0.779
## PEU3 0.479 0.046 10.464 0.000 0.642 0.642
## PEU4 0.538 0.038 14.206 0.000 0.721 0.827
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## PerceivedEaseOfUse ~
## ComptrPlyflnss 0.892 0.100 8.886 0.000 0.665 0.665
## PerceivedUsefulness ~
## OutputQuality 0.521 0.088 5.889 0.000 0.368 0.368
## PerceivdEsOfUs 0.505 0.072 7.033 0.000 0.478 0.478
## Attitude ~
## PercevdUsflnss 0.789 0.098 8.037 0.000 0.526 0.526
## PerceivdEsOfUs 0.716 0.105 6.847 0.000 0.452 0.452
## BehavioralIntention ~
## Attitude 0.408 0.074 5.551 0.000 0.541 0.541
## SubjectiveNorm 0.332 0.083 3.994 0.000 0.208 0.208
## PercevdUsflnss 0.192 0.097 1.976 0.048 0.170 0.170
## ActualSystemUse ~
## BehavirlIntntn 0.700 0.084 8.308 0.000 0.746 0.746
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .PU1 ~~
## .PU2 0.064 0.022 2.860 0.004 0.064 0.284
## .PU3 ~~
## .PU4 -0.040 0.020 -1.970 0.049 -0.040 -0.232
## .ATT1 ~~
## .ATT2 0.010 0.016 0.639 0.523 0.010 0.060
## .ATT3 ~~
## .ATT4 -0.004 0.017 -0.243 0.808 -0.004 -0.023
## .BI1 ~~
## .BI2 0.068 0.025 2.666 0.008 0.068 0.229
## .BI3 ~~
## .BI4 0.000 0.023 0.021 0.983 0.000 0.002
## .CP1 ~~
## .CP2 -0.013 0.023 -0.588 0.556 -0.013 -0.050
## .PEU1 ~~
## .PEU2 0.009 0.029 0.320 0.749 0.009 0.030
## .PEU3 ~~
## .PEU4 0.128 0.033 3.859 0.000 0.128 0.341
## OutputQuality ~~
## SubjectiveNorm 0.472 0.056 8.494 0.000 0.472 0.472
## ComptrPlyflnss 0.588 0.048 12.254 0.000 0.588 0.588
## SubjectiveNorm ~~
## ComptrPlyflnss 0.482 0.053 9.085 0.000 0.482 0.482
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .OQ1 0.177 0.047 3.757 0.000 0.177 0.196
## .OQ2 0.399 0.058 6.837 0.000 0.399 0.342
## .SN1 0.085 0.054 1.557 0.120 0.085 0.101
## .SN2 0.245 0.041 5.967 0.000 0.245 0.330
## .PU1 0.247 0.028 8.685 0.000 0.247 0.259
## .PU2 0.205 0.026 7.958 0.000 0.205 0.217
## .PU3 0.120 0.023 5.150 0.000 0.120 0.132
## .PU4 0.242 0.029 8.267 0.000 0.242 0.289
## .ATT1 0.203 0.022 9.182 0.000 0.203 0.272
## .ATT2 0.140 0.019 7.297 0.000 0.140 0.159
## .ATT3 0.189 0.022 8.449 0.000 0.189 0.222
## .ATT4 0.185 0.023 7.937 0.000 0.185 0.192
## .BI1 0.329 0.036 9.146 0.000 0.329 0.373
## .BI2 0.264 0.030 8.869 0.000 0.264 0.346
## .BI3 0.280 0.032 8.620 0.000 0.280 0.356
## .BI4 0.240 0.030 8.008 0.000 0.240 0.304
## .ASU1 0.050 0.053 0.941 0.347 0.050 0.055
## .ASU2 0.475 0.051 9.273 0.000 0.475 0.482
## .CP1 0.335 0.035 9.619 0.000 0.335 0.432
## .CP2 0.216 0.028 7.748 0.000 0.216 0.257
## .CP3 0.179 0.025 7.272 0.000 0.179 0.216
## .CP4 0.329 0.033 10.044 0.000 0.329 0.399
## .PEU1 0.288 0.036 8.077 0.000 0.288 0.374
## .PEU2 0.329 0.040 8.292 0.000 0.329 0.393
## .PEU3 0.588 0.058 10.202 0.000 0.588 0.588
## .PEU4 0.241 0.031 7.714 0.000 0.241 0.317
## OutputQuality 1.000 1.000 1.000
## SubjectiveNorm 1.000 1.000 1.000
## .PercevdUsflnss 1.000 0.499 0.499
## .Attitude 1.000 0.222 0.222
## .BehavirlIntntn 1.000 0.391 0.391
## .ActualSystemUs 1.000 0.444 0.444
## ComptrPlyflnss 1.000 1.000 1.000
## .PerceivdEsOfUs 1.000 0.557 0.557
##
## R-Square:
## Estimate
## OQ1 0.804
## OQ2 0.658
## SN1 0.899
## SN2 0.670
## PU1 0.741
## PU2 0.783
## PU3 0.868
## PU4 0.711
## ATT1 0.728
## ATT2 0.841
## ATT3 0.778
## ATT4 0.808
## BI1 0.627
## BI2 0.654
## BI3 0.644
## BI4 0.696
## ASU1 0.945
## ASU2 0.518
## CP1 0.568
## CP2 0.743
## CP3 0.784
## CP4 0.601
## PEU1 0.626
## PEU2 0.607
## PEU3 0.412
## PEU4 0.683
## PercevdUsflnss 0.501
## Attitude 0.778
## BehavirlIntntn 0.609
## ActualSystemUs 0.556
## PerceivdEsOfUs 0.443Ringkasan fit indeks SEM
fitMeasures(fit_sem, c("chisq", "df", "pvalue", "cfi", "tli", "rmsea", "srmr"))## chisq df pvalue cfi tli rmsea srmr
## 719.943 278.000 0.000 0.926 0.914 0.076 0.095Visualisasi
semPaths(
fit_sem,
what = "path",
whatLabels = "std",
style = "ram",
layout = "tree",
rotation = 2,
sizeMan = 5,
sizeLat = 8,
edge.label.cex = 0.8,
label.cex = 0.9
)