MODUL 5 ANALISIS MULTIVARIAT
AISYAH IMANI KHOIRUNNISA (24031554020) & ELVIRA RIZKI BERLIANANDA PAMUDJI (24031554062)
2026-05-12
Install Package
install.packages(c(
"readxl",
"lavaan",
"semPlot",
"psych",
"MVN",
"car",
"dplyr",
"seminr",
"corrplot"
))## Installing packages into '/cloud/lib/x86_64-pc-linux-gnu-library/4.6'
## (as 'lib' is unspecified)Import Library
library(readxl)
library(lavaan)## This is lavaan 0.6-21
## lavaan is FREE software! Please report any bugs.library(semPlot)
library(psych)##
## Attaching package: 'psych'## The following object is masked from 'package:lavaan':
##
## cor2covlibrary(MVN)## Registered S3 method overwritten by 'car':
## method from
## na.action.merMod lme4##
## Attaching package: 'MVN'## The following object is masked from 'package:psych':
##
## mardialibrary(car)## Loading required package: carData##
## Attaching package: 'car'## The following object is masked from 'package:psych':
##
## logitlibrary(dplyr)##
## Attaching package: 'dplyr'## The following object is masked from 'package:car':
##
## recode## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(seminr)
library(corrplot)## corrplot 0.95 loadedImport Dataset
data <- read_excel("Data Siap SmartPLS dari Excel Asli.xlsx")str(data)## tibble [401 × 68] (S3: tbl_df/tbl/data.frame)
## $ Timestamp : POSIXct[1:401], format: "2025-06-11 12:55:01" "2025-06-11 13:01:35" ...
## $ Email address : chr [1:401] NA NA "dimas.aditya0101@gmail.com" "dirarus@gmail.com" ...
## $ Drop Your Instagram Username : chr [1:401] NA NA NA NA ...
## $ Jenis Kelamin : chr [1:401] "Laki-Laki" "Laki-Laki" "Laki-Laki" "Laki-Laki" ...
## $ Usia : chr [1:401] "< 18 Tahun" "22 - 25 Tahun" "25 > Tahun" "22 - 25 Tahun" ...
## $ Saat ini, Anda sedang menempuh pendidikan pada jenjang apa? : chr [1:401] "SMA" "S1" "S1" "S1" ...
## $ Apakah Anda pernah menggunakan teknologi Artificial Intelligence (AI) dalam proses pembelajaran?: chr [1:401] "Ya" "Ya" "Ya" "Ya" ...
## $ DL1 : num [1:401] 1 1 2 5 4 3 3 3 5 4 ...
## $ DL2 : num [1:401] 2 1 2 5 3 3 3 3 5 4 ...
## $ DL3 : num [1:401] 4 1 1 5 4 4 5 2 4 4 ...
## $ DL4 : num [1:401] 2 1 1 5 3 4 4 3 5 4 ...
## $ DL5 : num [1:401] 4 1 2 5 4 4 4 3 5 4 ...
## $ SE1 : num [1:401] 2 1 3 5 4 4 4 2 5 4 ...
## $ SE2 : num [1:401] 3 1 2 5 3 4 3 2 5 4 ...
## $ SE3 : num [1:401] 2 1 3 5 4 5 4 3 5 4 ...
## $ SE4 : num [1:401] 1 1 1 5 3 3 3 2 5 4 ...
## $ SE5 : num [1:401] 1 1 1 5 4 3 4 4 4 4 ...
## $ SE6 : num [1:401] 3 1 2 5 3 4 4 3 5 4 ...
## $ SE7 : num [1:401] 3 1 3 5 4 4 3 3 5 5 ...
## $ SE8 : num [1:401] 4 1 1 5 3 4 3 3 5 5 ...
## $ SE9 : num [1:401] 3 1 2 5 4 4 3 3 5 5 ...
## $ PI1 : num [1:401] 2 1 1 5 3 5 3 4 4 4 ...
## $ PI2 : num [1:401] 3 1 2 5 4 4 2 4 3 3 ...
## $ PI3 : num [1:401] 1 1 1 5 4 4 2 4 3 3 ...
## $ PI4 : num [1:401] 1 1 2 5 4 4 3 3 5 5 ...
## $ PI5 : num [1:401] 1 2 1 5 4 4 3 3 3 4 ...
## $ PU1 : num [1:401] 1 1 2 5 4 4 4 4 5 5 ...
## $ PU2 : num [1:401] 3 2 1 5 4 4 4 4 5 5 ...
## $ PU3 : num [1:401] 3 3 1 5 4 4 4 5 5 5 ...
## $ PU4 : num [1:401] 2 4 2 5 4 5 4 4 5 5 ...
## $ PU5 : num [1:401] 1 5 1 5 4 4 4 4 5 5 ...
## $ PU6 : num [1:401] 1 5 2 5 4 4 4 3 5 4 ...
## $ PU7 : num [1:401] 2 2 1 5 3 5 4 3 5 5 ...
## $ PU8 : num [1:401] 3 1 1 4 4 5 4 4 4 4 ...
## $ PU9 : num [1:401] 1 4 2 5 3 4 4 4 5 5 ...
## $ PU10 : num [1:401] 1 1 3 5 4 4 3 3 5 4 ...
## $ PEOU1 : num [1:401] 1 1 3 5 4 4 4 4 5 4 ...
## $ PEOU2 : num [1:401] 2 1 1 5 3 3 4 4 5 4 ...
## $ PEOU3 : num [1:401] 4 1 1 5 3 4 3 4 5 5 ...
## $ PEOU4 : num [1:401] 3 5 2 5 3 3 4 3 4 5 ...
## $ PEOU5 : num [1:401] 1 1 1 5 4 4 4 4 5 5 ...
## $ PEOU6 : num [1:401] 2 1 1 5 3 4 4 3 5 5 ...
## $ PEOU7 : num [1:401] 2 5 2 5 5 5 5 3 5 5 ...
## $ PEOU8 : num [1:401] 1 1 1 5 3 4 4 4 4 4 ...
## $ PEOU9 : num [1:401] 1 1 3 5 4 4 4 3 5 4 ...
## $ PEOU10 : num [1:401] 3 1 1 5 4 4 3 4 5 4 ...
## $ PEOU11 : num [1:401] 1 1 1 5 4 3 3 4 5 5 ...
## $ PEOU12 : num [1:401] 1 1 3 5 4 3 4 4 5 5 ...
## $ PEOU13 : num [1:401] 1 1 3 5 3 4 4 3 4 4 ...
## $ PEOU14 : num [1:401] 1 1 1 5 4 3 4 4 5 4 ...
## $ AT1 : num [1:401] 1 1 1 5 4 4 4 3 5 4 ...
## $ AT2 : num [1:401] 2 1 2 5 4 4 4 4 5 3 ...
## $ AT3 : num [1:401] 2 1 1 5 3 4 3 4 5 4 ...
## $ AT4 : num [1:401] 2 2 1 5 4 5 4 4 5 4 ...
## $ AT5 : num [1:401] 1 2 2 5 3 3 4 4 5 4 ...
## $ AT6 : num [1:401] 1 2 1 5 4 4 4 4 5 5 ...
## $ AT7 : num [1:401] 1 2 1 5 4 4 4 4 4 3 ...
## $ AT8 : num [1:401] 1 2 1 5 4 4 4 4 5 5 ...
## $ BI1 : num [1:401] 1 1 2 4 4 3 4 4 5 3 ...
## $ BI2 : num [1:401] 2 1 1 4 4 3 4 4 5 2 ...
## $ BI3 : num [1:401] 1 1 1 5 4 3 4 3 5 5 ...
## $ BI4 : num [1:401] 2 1 2 5 4 4 4 4 5 4 ...
## $ BI5 : num [1:401] 1 1 1 5 4 4 4 3 5 5 ...
## $ AU1 : num [1:401] 3 1 2 5 4 5 4 4 4 4 ...
## $ AU2 : num [1:401] 2 1 1 5 4 4 4 4 5 5 ...
## $ AU3 : num [1:401] 1 1 1 5 4 4 3 3 5 4 ...
## $ AU4 : num [1:401] 1 1 2 5 4 4 4 4 5 4 ...
## $ AU5 : num [1:401] 2 1 1 5 4 3 3 3 5 5 ...colnames(data)## [1] "Timestamp"
## [2] "Email address"
## [3] "Drop Your Instagram Username"
## [4] "Jenis Kelamin"
## [5] "Usia"
## [6] "Saat ini, Anda sedang menempuh pendidikan pada jenjang apa?"
## [7] "Apakah Anda pernah menggunakan teknologi Artificial Intelligence (AI) dalam proses pembelajaran?"
## [8] "DL1"
## [9] "DL2"
## [10] "DL3"
## [11] "DL4"
## [12] "DL5"
## [13] "SE1"
## [14] "SE2"
## [15] "SE3"
## [16] "SE4"
## [17] "SE5"
## [18] "SE6"
## [19] "SE7"
## [20] "SE8"
## [21] "SE9"
## [22] "PI1"
## [23] "PI2"
## [24] "PI3"
## [25] "PI4"
## [26] "PI5"
## [27] "PU1"
## [28] "PU2"
## [29] "PU3"
## [30] "PU4"
## [31] "PU5"
## [32] "PU6"
## [33] "PU7"
## [34] "PU8"
## [35] "PU9"
## [36] "PU10"
## [37] "PEOU1"
## [38] "PEOU2"
## [39] "PEOU3"
## [40] "PEOU4"
## [41] "PEOU5"
## [42] "PEOU6"
## [43] "PEOU7"
## [44] "PEOU8"
## [45] "PEOU9"
## [46] "PEOU10"
## [47] "PEOU11"
## [48] "PEOU12"
## [49] "PEOU13"
## [50] "PEOU14"
## [51] "AT1"
## [52] "AT2"
## [53] "AT3"
## [54] "AT4"
## [55] "AT5"
## [56] "AT6"
## [57] "AT7"
## [58] "AT8"
## [59] "BI1"
## [60] "BI2"
## [61] "BI3"
## [62] "BI4"
## [63] "BI5"
## [64] "AU1"
## [65] "AU2"
## [66] "AU3"
## [67] "AU4"
## [68] "AU5"dim(data)## [1] 401 68head(data)## # A tibble: 6 × 68
## Timestamp `Email address` Drop Your Instagram …¹ `Jenis Kelamin`
## <dttm> <chr> <chr> <chr>
## 1 2025-06-11 12:55:01 <NA> <NA> Laki-Laki
## 2 2025-06-11 13:01:35 <NA> <NA> Laki-Laki
## 3 2025-06-11 13:51:58 dimas.aditya0101@g… <NA> Laki-Laki
## 4 2025-06-11 14:08:46 dirarus@gmail.com <NA> Laki-Laki
## 5 2025-06-11 14:28:59 ikram.sadida18@gma… <NA> Laki-Laki
## 6 2025-06-11 14:31:40 leohosea1902@gmail… <NA> Laki-Laki
## # ℹ abbreviated name: ¹`Drop Your Instagram Username`
## # ℹ 64 more variables: Usia <chr>,
## # `Saat ini, Anda sedang menempuh pendidikan pada jenjang apa?` <chr>,
## # `Apakah Anda pernah menggunakan teknologi Artificial Intelligence (AI) dalam proses pembelajaran?` <chr>,
## # DL1 <dbl>, DL2 <dbl>, DL3 <dbl>, DL4 <dbl>, DL5 <dbl>, SE1 <dbl>,
## # SE2 <dbl>, SE3 <dbl>, SE4 <dbl>, SE5 <dbl>, SE6 <dbl>, SE7 <dbl>,
## # SE8 <dbl>, SE9 <dbl>, PI1 <dbl>, PI2 <dbl>, PI3 <dbl>, PI4 <dbl>, …Memilih Variabel Penelitian
data_sem <- data %>%
select(
DL1, DL2, DL3, DL4,
PU1, PU2, PU3, PU4,
AT1, AT2, AT3, AT4,
BI1, BI2, BI3, BI4
)
head(data_sem)## # A tibble: 6 × 16
## DL1 DL2 DL3 DL4 PU1 PU2 PU3 PU4 AT1 AT2 AT3 AT4 BI1
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 2 4 2 1 3 3 2 1 2 2 2 1
## 2 1 1 1 1 1 2 3 4 1 1 1 2 1
## 3 2 2 1 1 2 1 1 2 1 2 1 1 2
## 4 5 5 5 5 5 5 5 5 5 5 5 5 4
## 5 4 3 4 3 4 4 4 4 4 4 3 4 4
## 6 3 3 4 4 4 4 4 5 4 4 4 5 3
## # ℹ 3 more variables: BI2 <dbl>, BI3 <dbl>, BI4 <dbl>Exploratory Data Analysis
cor_matrix <- cor(data_sem)corrplot(
cor_matrix,
method = "color",
type = "upper",
addCoef.col = "black",
tl.col = "black",
tl.srt = 45,
number.cex = 0.7
)
boxplot(
data_sem,
main = "Boxplot Variabel Numerik",
col = "lightblue",
las = 2,
outline = TRUE
)
Preprocessing
A. Mengecek Missing Value
sum(is.na(data_sem))## [1] 0colMeans(is.na(data_sem))*100## DL1 DL2 DL3 DL4 PU1 PU2 PU3 PU4 AT1 AT2 AT3 AT4 BI1 BI2 BI3 BI4
## 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0B. Mengecek apakah ada data duplikat
sum(duplicated(data_sem))## [1] 10data_sem <- unique(data_sem)C. Konversi ke Numerik
data_sem <- data_sem %>%
mutate(across(everything(), as.numeric))
str(data_sem)## tibble [391 × 16] (S3: tbl_df/tbl/data.frame)
## $ DL1: num [1:391] 1 1 2 5 4 3 3 3 5 4 ...
## $ DL2: num [1:391] 2 1 2 5 3 3 3 3 5 4 ...
## $ DL3: num [1:391] 4 1 1 5 4 4 5 2 4 4 ...
## $ DL4: num [1:391] 2 1 1 5 3 4 4 3 5 4 ...
## $ PU1: num [1:391] 1 1 2 5 4 4 4 4 5 5 ...
## $ PU2: num [1:391] 3 2 1 5 4 4 4 4 5 5 ...
## $ PU3: num [1:391] 3 3 1 5 4 4 4 5 5 5 ...
## $ PU4: num [1:391] 2 4 2 5 4 5 4 4 5 5 ...
## $ AT1: num [1:391] 1 1 1 5 4 4 4 3 5 4 ...
## $ AT2: num [1:391] 2 1 2 5 4 4 4 4 5 3 ...
## $ AT3: num [1:391] 2 1 1 5 3 4 3 4 5 4 ...
## $ AT4: num [1:391] 2 2 1 5 4 5 4 4 5 4 ...
## $ BI1: num [1:391] 1 1 2 4 4 3 4 4 5 3 ...
## $ BI2: num [1:391] 2 1 1 4 4 3 4 4 5 2 ...
## $ BI3: num [1:391] 1 1 1 5 4 3 4 3 5 5 ...
## $ BI4: num [1:391] 2 1 2 5 4 4 4 4 5 4 ...D. Statistik Deskriptif
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 :4.297 Mean :4.309 Mean :4.353 Mean :4.281
## 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
## PU1 PU2 PU3 PU4 AT1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.00
## Median :4.000 Median :4.000 Median :4.000 Median :4.000 Median :4.00
## Mean :4.366 Mean :4.309 Mean :4.353 Mean :4.353 Mean :4.32
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.00
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.00
## AT2 AT3 AT4 BI1 BI2
## Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.00 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000
## Median :4.000 Median :4.00 Median :4.000 Median :4.000 Median :4.000
## Mean :4.279 Mean :4.24 Mean :4.307 Mean :4.164 Mean :4.151
## 3rd Qu.:5.000 3rd Qu.:5.00 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.00 Max. :5.000 Max. :5.000 Max. :5.000
## BI3 BI4
## Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000
## Median :4.000 Median :4.000
## Mean :4.294 Mean :4.223
## 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000E. Normalisasi Z-Score
data_z <- as.data.frame(scale(data_sem))
head(data_z)## DL1 DL2 DL3 DL4 PU1 PU2 PU3
## 1 -4.1886384 -3.0780215 -0.4664402 -2.9385755 -4.3021399 -1.6570171 -1.7129529
## 2 -4.1886384 -4.4108082 -4.4311821 -4.2266730 -4.3021399 -2.9224345 -1.7129529
## 3 -2.9180739 -3.0780215 -4.4311821 -4.2266730 -3.0239205 -4.1878518 -4.2451441
## 4 0.8936195 0.9203386 0.8551404 0.9257172 0.8107376 0.8738176 0.8192383
## 5 -0.3769450 -1.7452348 -0.4664402 -1.6504779 -0.4674818 -0.3915997 -0.4468573
## 6 -1.6475094 -1.7452348 -0.4664402 -0.3623804 -0.4674818 -0.3915997 -0.4468573
## PU4 AT1 AT2 AT3 AT4 BI1 BI2
## 1 -3.1096015 -3.8428649 -2.7664766 -2.7158635 -2.7768196 -3.3323267 -2.2260908
## 2 -0.4664402 -3.8428649 -3.9804972 -3.9280811 -2.7768196 -3.3323267 -3.2610510
## 3 -3.1096015 -3.8428649 -2.7664766 -3.9280811 -3.9805185 -2.2790205 -3.2610510
## 4 0.8551404 0.7875209 0.8755852 0.9207893 0.8342773 -0.1724082 -0.1561705
## 5 -0.4664402 -0.3700756 -0.3384354 -1.5036459 -0.3694217 -0.1724082 -0.1561705
## 6 0.8551404 -0.3700756 -0.3384354 -0.2914283 0.8342773 -1.2257143 -1.1911306
## BI3 BI4
## 1 -3.9799013 -2.7344314
## 2 -3.9799013 -3.9647683
## 3 -3.9799013 -2.7344314
## 4 0.8528360 0.9565790
## 5 -0.3553483 -0.2737578
## 6 -1.5635327 -0.2737578Uji Asumsi SEM-CB
A. Uji Normalitas Multivariat
mardia(data_z)## Test Statistic p.value Method
## 1 Mardia Skewness 3629.59793 0 asymptotic
## 2 Mardia Kurtosis 40.72892 0 asymptoticB. Uji Multikolinearitas
cor_matrix <- cor(data_z)
cor_matrix## DL1 DL2 DL3 DL4 PU1 PU2 PU3
## DL1 1.0000000 0.3434645 0.3920557 0.6310005 0.4896144 0.5074954 0.3962195
## DL2 0.3434645 1.0000000 0.3310235 0.3563776 0.4357129 0.4780884 0.4079878
## DL3 0.3920557 0.3310235 1.0000000 0.4241690 0.4787539 0.4772380 0.4260093
## DL4 0.6310005 0.3563776 0.4241690 1.0000000 0.4718610 0.5389765 0.3896340
## PU1 0.4896144 0.4357129 0.4787539 0.4718610 1.0000000 0.5132259 0.4794020
## PU2 0.5074954 0.4780884 0.4772380 0.5389765 0.5132259 1.0000000 0.4325535
## PU3 0.3962195 0.4079878 0.4260093 0.3896340 0.4794020 0.4325535 1.0000000
## PU4 0.3834447 0.3852200 0.3954162 0.4110742 0.6303550 0.4815261 0.4131382
## AT1 0.5616031 0.4403675 0.4388821 0.5040434 0.5739719 0.5120081 0.4617943
## AT2 0.4693224 0.4616305 0.3888858 0.4784958 0.4899483 0.5131387 0.4829123
## AT3 0.4467077 0.4801748 0.4059476 0.4586472 0.4871783 0.5149016 0.4912233
## AT4 0.4839066 0.4724976 0.4064540 0.4979050 0.4896371 0.5228202 0.4440971
## BI1 0.3878058 0.4146503 0.2227666 0.4070105 0.3783377 0.3800201 0.3091587
## BI2 0.3118742 0.3846057 0.2811898 0.3944749 0.3711667 0.3853134 0.3197828
## BI3 0.4679319 0.3856833 0.3947229 0.4734502 0.4789037 0.4524311 0.4056066
## BI4 0.4376592 0.4081645 0.3556085 0.5141408 0.4322002 0.4354370 0.3806204
## PU4 AT1 AT2 AT3 AT4 BI1 BI2
## DL1 0.3834447 0.5616031 0.4693224 0.4467077 0.4839066 0.3878058 0.3118742
## DL2 0.3852200 0.4403675 0.4616305 0.4801748 0.4724976 0.4146503 0.3846057
## DL3 0.3954162 0.4388821 0.3888858 0.4059476 0.4064540 0.2227666 0.2811898
## DL4 0.4110742 0.5040434 0.4784958 0.4586472 0.4979050 0.4070105 0.3944749
## PU1 0.6303550 0.5739719 0.4899483 0.4871783 0.4896371 0.3783377 0.3711667
## PU2 0.4815261 0.5120081 0.5131387 0.5149016 0.5228202 0.3800201 0.3853134
## PU3 0.4131382 0.4617943 0.4829123 0.4912233 0.4440971 0.3091587 0.3197828
## PU4 1.0000000 0.4231912 0.4711640 0.4593491 0.5043485 0.3548309 0.3758825
## AT1 0.4231912 1.0000000 0.4762085 0.5179423 0.5882168 0.4206273 0.4089956
## AT2 0.4711640 0.4762085 1.0000000 0.4633660 0.5453603 0.5120141 0.4528177
## AT3 0.4593491 0.5179423 0.4633660 1.0000000 0.4906876 0.3785126 0.3757867
## AT4 0.5043485 0.5882168 0.5453603 0.4906876 1.0000000 0.5083094 0.4404715
## BI1 0.3548309 0.4206273 0.5120141 0.3785126 0.5083094 1.0000000 0.5907462
## BI2 0.3758825 0.4089956 0.4528177 0.3757867 0.4404715 0.5907462 1.0000000
## BI3 0.3742522 0.4634543 0.4473288 0.4669875 0.4836667 0.4737175 0.3547579
## BI4 0.3764545 0.4681226 0.4930851 0.4821713 0.5099773 0.5175697 0.3619974
## BI3 BI4
## DL1 0.4679319 0.4376592
## DL2 0.3856833 0.4081645
## DL3 0.3947229 0.3556085
## DL4 0.4734502 0.5141408
## PU1 0.4789037 0.4322002
## PU2 0.4524311 0.4354370
## PU3 0.4056066 0.3806204
## PU4 0.3742522 0.3764545
## AT1 0.4634543 0.4681226
## AT2 0.4473288 0.4930851
## AT3 0.4669875 0.4821713
## AT4 0.4836667 0.5099773
## BI1 0.4737175 0.5175697
## BI2 0.3547579 0.3619974
## BI3 1.0000000 0.4627574
## BI4 0.4627574 1.0000000cov_matrix <- cov(data_z)
det(cov_matrix)## [1] 0.000413839C. Uji Kecukupan Sampel (KMO)
KMO(cor(data_z))## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = cor(data_z))
## Overall MSA = 0.95
## MSA for each item =
## DL1 DL2 DL3 DL4 PU1 PU2 PU3 PU4 AT1 AT2 AT3 AT4 BI1 BI2 BI3 BI4
## 0.94 0.97 0.96 0.94 0.93 0.97 0.97 0.92 0.95 0.97 0.97 0.96 0.89 0.91 0.97 0.95Confirmatory Factor Analysis (CFA)
model_cfa <- '
DigitalLiteracy =~ DL1 + DL2 + DL3 + DL4
PerceivedUsefulness =~ PU1 + PU2 + PU3 + PU4
Attitude =~ AT1 + AT2 + AT3 + AT4
BehavioralIntention =~ BI1 + BI2 + BI3 + BI4
'fit_cfa <- cfa(
model_cfa,
data = data_z,
std.lv = TRUE,
estimator = "mlr"
)## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.summary(
fit_cfa,
fit.measures = TRUE,
standardized = TRUE
)## lavaan 0.6-21 ended normally after 25 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 38
##
## Number of observations 391
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 274.340 237.382
## Degrees of freedom 98 98
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.156
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 3045.903 2575.334
## Degrees of freedom 120 120
## P-value 0.000 0.000
## Scaling correction factor 1.183
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.940 0.943
## Tucker-Lewis Index (TLI) 0.926 0.930
##
## Robust Comparative Fit Index (CFI) 0.945
## Robust Tucker-Lewis Index (TLI) 0.932
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -7483.087 -7483.087
## Scaling correction factor 1.899
## for the MLR correction
## Loglikelihood unrestricted model (H1) -7345.918 -7345.918
## Scaling correction factor 1.363
## for the MLR correction
##
## Akaike (AIC) 15042.175 15042.175
## Bayesian (BIC) 15192.986 15192.986
## Sample-size adjusted Bayesian (SABIC) 15072.414 15072.414
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.068 0.060
## 90 Percent confidence interval - lower 0.058 0.051
## 90 Percent confidence interval - upper 0.077 0.069
## P-value H_0: RMSEA <= 0.050 0.001 0.031
## P-value H_0: RMSEA >= 0.080 0.018 0.000
##
## Robust RMSEA 0.065
## 90 Percent confidence interval - lower 0.054
## 90 Percent confidence interval - upper 0.075
## P-value H_0: Robust RMSEA <= 0.050 0.011
## P-value H_0: Robust RMSEA >= 0.080 0.008
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.042 0.042
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DigitalLiteracy =~
## DL1 0.694 0.087 7.946 0.000 0.694 0.694
## DL2 0.611 0.074 8.247 0.000 0.611 0.612
## DL3 0.591 0.093 6.363 0.000 0.591 0.592
## DL4 0.710 0.095 7.451 0.000 0.710 0.711
## PerceivedUsefulness =~
## PU1 0.755 0.090 8.372 0.000 0.755 0.756
## PU2 0.733 0.079 9.282 0.000 0.733 0.734
## PU3 0.637 0.092 6.915 0.000 0.637 0.638
## PU4 0.682 0.089 7.700 0.000 0.682 0.683
## Attitude =~
## AT1 0.729 0.092 7.955 0.000 0.729 0.730
## AT2 0.712 0.081 8.783 0.000 0.712 0.713
## AT3 0.687 0.083 8.260 0.000 0.687 0.687
## AT4 0.741 0.082 9.022 0.000 0.741 0.742
## BehavioralIntention =~
## BI1 0.714 0.079 9.023 0.000 0.714 0.715
## BI2 0.623 0.075 8.284 0.000 0.623 0.624
## BI3 0.676 0.087 7.754 0.000 0.676 0.677
## BI4 0.705 0.076 9.316 0.000 0.705 0.706
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DigitalLiteracy ~~
## PercevdUsflnss 0.956 0.044 21.544 0.000 0.956 0.956
## Attitude 0.983 0.043 22.987 0.000 0.983 0.983
## BehavirlIntntn 0.882 0.059 14.924 0.000 0.882 0.882
## PerceivedUsefulness ~~
## Attitude 0.965 0.058 16.710 0.000 0.965 0.965
## BehavirlIntntn 0.807 0.079 10.160 0.000 0.807 0.807
## Attitude ~~
## BehavirlIntntn 0.935 0.064 14.557 0.000 0.935 0.935
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .DL1 0.516 0.049 10.587 0.000 0.516 0.518
## .DL2 0.624 0.043 14.452 0.000 0.624 0.625
## .DL3 0.648 0.046 14.018 0.000 0.648 0.650
## .DL4 0.494 0.058 8.543 0.000 0.494 0.495
## .PU1 0.427 0.037 11.560 0.000 0.427 0.428
## .PU2 0.460 0.029 15.702 0.000 0.460 0.461
## .PU3 0.592 0.037 15.928 0.000 0.592 0.593
## .PU4 0.532 0.051 10.383 0.000 0.532 0.533
## .AT1 0.467 0.043 10.859 0.000 0.467 0.468
## .AT2 0.490 0.031 15.961 0.000 0.490 0.491
## .AT3 0.526 0.031 17.212 0.000 0.526 0.527
## .AT4 0.449 0.040 11.103 0.000 0.449 0.450
## .BI1 0.487 0.056 8.762 0.000 0.487 0.489
## .BI2 0.609 0.064 9.448 0.000 0.609 0.611
## .BI3 0.540 0.041 13.196 0.000 0.540 0.542
## .BI4 0.501 0.042 11.924 0.000 0.501 0.502
## DigitalLitercy 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# Fit Index CFA
fitMeasures(
fit_cfa,
c(
"chisq",
"df",
"pvalue",
"cfi",
"tli",
"rmsea",
"srmr"
)
)## chisq df pvalue cfi tli rmsea srmr
## 274.340 98.000 0.000 0.940 0.926 0.068 0.042# Visualisasi CFA
semPaths(
object = fit_cfa,
what = "std",
whatLabels = "std",
style = "ram",
layout = "tree",
rotation = 2,
sizeLat = 8,
sizeMan = 5,
edge.label.cex = 0.7,
residuals = FALSE
)
Composite Reliability (CR)
hitung_CR <- function(fit) {
std <- standardizedSolution(fit)
konstruk <- unique(std$lhs[std$op == "=~"])
hasil <- data.frame()
for(k in konstruk){
lambda <- std$est[
std$lhs == k & std$op == "=~"
]
theta <- 1 - lambda^2
CR <- sum(lambda)^2 /
(sum(lambda)^2 + sum(theta))
hasil <- rbind(
hasil,
data.frame(
Konstruk = k,
CompositeReliability = CR
)
)
}
return(hasil)
}
hitung_CR(fit_cfa)## Konstruk CompositeReliability
## 1 DigitalLiteracy 0.7485324
## 2 PerceivedUsefulness 0.7968265
## 3 Attitude 0.8099377
## 4 BehavioralIntention 0.7755258Average Value Explained (AVE)
hitung_AVE <- function(fit) {
std <- standardizedSolution(fit)
konstruk <- unique(std$lhs[std$op == "=~"])
hasil <- data.frame()
for(k in konstruk){
lambda <- std$est[
std$lhs == k & std$op == "=~"
]
AVE <- sum(lambda^2) / length(lambda)
hasil <- rbind(
hasil,
data.frame(
Konstruk = k,
AVE = AVE
)
)
}
return(hasil)
}
hitung_AVE(fit_cfa)## Konstruk AVE
## 1 DigitalLiteracy 0.4281563
## 2 PerceivedUsefulness 0.4961364
## 3 Attitude 0.5160230
## 4 BehavioralIntention 0.4641126Structual Equation Modeling (SEM-CB)
model_sem <- '
DigitalLiteracy =~ DL1 + DL2 + DL3 + DL4
PerceivedUsefulness =~ PU1 + PU2 + PU3 + PU4
Attitude =~ AT1 + AT2 + AT3 + AT4
BehavioralIntention =~ BI1 + BI2 + BI3 + BI4
PerceivedUsefulness ~ DigitalLiteracy
Attitude ~ PerceivedUsefulness
BehavioralIntention ~ Attitude
'fit_sem <- sem(
model_sem,
data = data_z,
std.lv = TRUE,
estimator = "MLR"
)
summary(
fit_sem,
fit.measures = TRUE,
standardized = TRUE,
rsquare = TRUE
)## lavaan 0.6-21 ended normally after 105 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 35
##
## Number of observations 391
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 288.464 235.229
## Degrees of freedom 101 101
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.226
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 3045.903 2575.334
## Degrees of freedom 120 120
## P-value 0.000 0.000
## Scaling correction factor 1.183
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.936 0.945
## Tucker-Lewis Index (TLI) 0.924 0.935
##
## Robust Comparative Fit Index (CFI) 0.943
## Robust Tucker-Lewis Index (TLI) 0.933
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -7490.150 -7490.150
## Scaling correction factor 1.759
## for the MLR correction
## Loglikelihood unrestricted model (H1) -7345.918 -7345.918
## Scaling correction factor 1.363
## for the MLR correction
##
## Akaike (AIC) 15050.299 15050.299
## Bayesian (BIC) 15189.204 15189.204
## Sample-size adjusted Bayesian (SABIC) 15078.151 15078.151
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.069 0.058
## 90 Percent confidence interval - lower 0.060 0.050
## 90 Percent confidence interval - upper 0.078 0.067
## P-value H_0: RMSEA <= 0.050 0.001 0.059
## P-value H_0: RMSEA >= 0.080 0.025 0.000
##
## Robust RMSEA 0.065
## 90 Percent confidence interval - lower 0.054
## 90 Percent confidence interval - upper 0.075
## P-value H_0: Robust RMSEA <= 0.050 0.014
## P-value H_0: Robust RMSEA >= 0.080 0.009
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.043 0.043
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DigitalLiteracy =~
## DL1 0.692 0.088 7.902 0.000 0.692 0.693
## DL2 0.609 0.074 8.247 0.000 0.609 0.610
## DL3 0.599 0.094 6.375 0.000 0.599 0.599
## DL4 0.707 0.096 7.364 0.000 0.707 0.708
## PerceivedUsefulness =~
## PU1 0.088 0.205 0.430 0.667 0.737 0.738
## PU2 0.087 0.202 0.432 0.666 0.730 0.731
## PU3 0.075 0.173 0.434 0.665 0.628 0.629
## PU4 0.079 0.184 0.430 0.667 0.661 0.661
## Attitude =~
## AT1 0.193 0.078 2.471 0.013 0.741 0.742
## AT2 0.186 0.076 2.438 0.015 0.716 0.717
## AT3 0.180 0.073 2.476 0.013 0.693 0.694
## AT4 0.196 0.081 2.422 0.015 0.753 0.754
## BehavioralIntention =~
## BI1 0.305 0.107 2.850 0.004 0.712 0.713
## BI2 0.268 0.091 2.949 0.003 0.625 0.626
## BI3 0.291 0.088 3.309 0.001 0.680 0.681
## BI4 0.300 0.094 3.189 0.001 0.701 0.702
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## PerceivedUsefulness ~
## DigitalLitercy 8.294 19.401 0.428 0.669 0.993 0.993
## Attitude ~
## PercevdUsflnss 0.444 1.052 0.422 0.673 0.966 0.966
## BehavioralIntention ~
## Attitude 0.549 0.323 1.698 0.089 0.903 0.903
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .DL1 0.519 0.049 10.560 0.000 0.519 0.520
## .DL2 0.626 0.044 14.362 0.000 0.626 0.628
## .DL3 0.639 0.044 14.655 0.000 0.639 0.641
## .DL4 0.498 0.060 8.279 0.000 0.498 0.499
## .PU1 0.454 0.032 14.294 0.000 0.454 0.455
## .PU2 0.464 0.029 15.882 0.000 0.464 0.466
## .PU3 0.603 0.039 15.391 0.000 0.603 0.604
## .PU4 0.561 0.051 10.999 0.000 0.561 0.563
## .AT1 0.449 0.044 10.233 0.000 0.449 0.450
## .AT2 0.485 0.033 14.906 0.000 0.485 0.486
## .AT3 0.517 0.034 15.372 0.000 0.517 0.518
## .AT4 0.430 0.033 12.850 0.000 0.430 0.432
## .BI1 0.491 0.056 8.708 0.000 0.491 0.492
## .BI2 0.607 0.065 9.342 0.000 0.607 0.608
## .BI3 0.535 0.041 12.908 0.000 0.535 0.537
## .BI4 0.506 0.042 12.006 0.000 0.506 0.507
## DigitalLitercy 1.000 1.000 1.000
## .PercevdUsflnss 1.000 0.014 0.014
## .Attitude 1.000 0.068 0.068
## .BehavirlIntntn 1.000 0.184 0.184
##
## R-Square:
## Estimate
## DL1 0.480
## DL2 0.372
## DL3 0.359
## DL4 0.501
## PU1 0.545
## PU2 0.534
## PU3 0.396
## PU4 0.437
## AT1 0.550
## AT2 0.514
## AT3 0.482
## AT4 0.568
## BI1 0.508
## BI2 0.392
## BI3 0.463
## BI4 0.493
## PercevdUsflnss 0.986
## Attitude 0.932
## BehavirlIntntn 0.816lavInspect(fit_sem, "cor.lv")## DgtlLt PrcvdU Attitd BhvrlI
## DigitalLiteracy 1.000
## PerceivedUsefulness 0.993 1.000
## Attitude 0.959 0.966 1.000
## BehavioralIntention 0.866 0.872 0.903 1.000# Fit Index SEM-CB
fitMeasures(
fit_sem,
c(
"chisq",
"df",
"pvalue",
"cfi",
"tli",
"rmsea",
"srmr"
)
)## chisq df pvalue cfi tli rmsea srmr
## 288.464 101.000 0.000 0.936 0.924 0.069 0.043parameterEstimates(
fit_sem,
standardized = TRUE
)## lhs op rhs est se z pvalue
## 1 DigitalLiteracy =~ DL1 0.692 0.088 7.902 0.000
## 2 DigitalLiteracy =~ DL2 0.609 0.074 8.247 0.000
## 3 DigitalLiteracy =~ DL3 0.599 0.094 6.375 0.000
## 4 DigitalLiteracy =~ DL4 0.707 0.096 7.364 0.000
## 5 PerceivedUsefulness =~ PU1 0.088 0.205 0.430 0.667
## 6 PerceivedUsefulness =~ PU2 0.087 0.202 0.432 0.666
## 7 PerceivedUsefulness =~ PU3 0.075 0.173 0.434 0.665
## 8 PerceivedUsefulness =~ PU4 0.079 0.184 0.430 0.667
## 9 Attitude =~ AT1 0.193 0.078 2.471 0.013
## 10 Attitude =~ AT2 0.186 0.076 2.438 0.015
## 11 Attitude =~ AT3 0.180 0.073 2.476 0.013
## 12 Attitude =~ AT4 0.196 0.081 2.422 0.015
## 13 BehavioralIntention =~ BI1 0.305 0.107 2.850 0.004
## 14 BehavioralIntention =~ BI2 0.268 0.091 2.949 0.003
## 15 BehavioralIntention =~ BI3 0.291 0.088 3.309 0.001
## 16 BehavioralIntention =~ BI4 0.300 0.094 3.189 0.001
## 17 PerceivedUsefulness ~ DigitalLiteracy 8.294 19.401 0.428 0.669
## 18 Attitude ~ PerceivedUsefulness 0.444 1.052 0.422 0.673
## 19 BehavioralIntention ~ Attitude 0.549 0.323 1.698 0.089
## 20 DL1 ~~ DL1 0.519 0.049 10.560 0.000
## 21 DL2 ~~ DL2 0.626 0.044 14.362 0.000
## 22 DL3 ~~ DL3 0.639 0.044 14.655 0.000
## 23 DL4 ~~ DL4 0.498 0.060 8.279 0.000
## 24 PU1 ~~ PU1 0.454 0.032 14.294 0.000
## 25 PU2 ~~ PU2 0.464 0.029 15.882 0.000
## 26 PU3 ~~ PU3 0.603 0.039 15.391 0.000
## 27 PU4 ~~ PU4 0.561 0.051 10.999 0.000
## 28 AT1 ~~ AT1 0.449 0.044 10.233 0.000
## 29 AT2 ~~ AT2 0.485 0.033 14.906 0.000
## 30 AT3 ~~ AT3 0.517 0.034 15.372 0.000
## 31 AT4 ~~ AT4 0.430 0.033 12.850 0.000
## 32 BI1 ~~ BI1 0.491 0.056 8.708 0.000
## 33 BI2 ~~ BI2 0.607 0.065 9.342 0.000
## 34 BI3 ~~ BI3 0.535 0.041 12.908 0.000
## 35 BI4 ~~ BI4 0.506 0.042 12.006 0.000
## 36 DigitalLiteracy ~~ DigitalLiteracy 1.000 0.000 NA NA
## 37 PerceivedUsefulness ~~ PerceivedUsefulness 1.000 0.000 NA NA
## 38 Attitude ~~ Attitude 1.000 0.000 NA NA
## 39 BehavioralIntention ~~ BehavioralIntention 1.000 0.000 NA NA
## ci.lower ci.upper std.lv std.all
## 1 0.520 0.863 0.692 0.693
## 2 0.465 0.754 0.609 0.610
## 3 0.415 0.783 0.599 0.599
## 4 0.519 0.895 0.707 0.708
## 5 -0.314 0.490 0.737 0.738
## 6 -0.309 0.484 0.730 0.731
## 7 -0.265 0.415 0.628 0.629
## 8 -0.282 0.440 0.661 0.661
## 9 0.040 0.346 0.741 0.742
## 10 0.037 0.336 0.716 0.717
## 11 0.038 0.323 0.693 0.694
## 12 0.037 0.355 0.753 0.754
## 13 0.095 0.515 0.712 0.713
## 14 0.090 0.446 0.625 0.626
## 15 0.119 0.464 0.680 0.681
## 16 0.116 0.485 0.701 0.702
## 17 -29.732 46.320 0.993 0.993
## 18 -1.617 2.506 0.966 0.966
## 19 -0.084 1.182 0.903 0.903
## 20 0.423 0.615 0.519 0.520
## 21 0.541 0.712 0.626 0.628
## 22 0.554 0.725 0.639 0.641
## 23 0.380 0.616 0.498 0.499
## 24 0.392 0.516 0.454 0.455
## 25 0.407 0.522 0.464 0.466
## 26 0.526 0.680 0.603 0.604
## 27 0.461 0.661 0.561 0.563
## 28 0.363 0.534 0.449 0.450
## 29 0.421 0.549 0.485 0.486
## 30 0.451 0.583 0.517 0.518
## 31 0.365 0.496 0.430 0.432
## 32 0.380 0.601 0.491 0.492
## 33 0.479 0.734 0.607 0.608
## 34 0.454 0.616 0.535 0.537
## 35 0.423 0.589 0.506 0.507
## 36 1.000 1.000 1.000 1.000
## 37 1.000 1.000 0.014 0.014
## 38 1.000 1.000 0.068 0.068
## 39 1.000 1.000 0.184 0.184Visualisasi SEM-CB
semPaths(
object = fit_sem,
what = "std",
whatLabels = "std",
style = "ram",
layout = "tree",
rotation = 2,
sizeLat = 8,
sizeMan = 5,
edge.label.cex = 0.7,
residuals = FALSE
)
SEM-PLS
Mengubah semua data menjadi numerik
data_sem <- as.data.frame(
lapply(data_sem, function(x) as.numeric(as.character(x)))
)measurement_model <- constructs(
composite(
"DigitalLiteracy",
multi_items("DL", 1:4)
),
composite(
"PerceivedUsefulness",
multi_items("PU", 1:4)
),
composite(
"Attitude",
multi_items("AT", 1:4)
),
composite(
"BehavioralIntention",
multi_items("BI", 1:4)
)
)Structual Equation Modeling (SEM-PLS)
structural_model <- relationships(
paths(
from = c(
"DigitalLiteracy"
),
to = "PerceivedUsefulness"
),
paths(
from = "PerceivedUsefulness",
to = "Attitude"
),
paths(
from = "Attitude",
to = "BehavioralIntention"
)
)pls_model <- estimate_pls(
data = data_sem,
measurement_model = measurement_model,
structural_model = structural_model
)## Generating the seminr model## All 391 observations are valid.summary(pls_model)##
## Results from package seminr (2.4.2)
##
## Path Coefficients:
## PerceivedUsefulness Attitude BehavioralIntention
## R^2 0.570 0.613 0.556
## AdjR^2 0.569 0.612 0.555
## DigitalLiteracy 0.755 . .
## PerceivedUsefulness . 0.783 .
## Attitude . . 0.746
##
## Reliability:
## alpha rhoA rhoC AVE
## DigitalLiteracy 0.738 0.740 0.837 0.563
## PerceivedUsefulness 0.795 0.799 0.867 0.620
## Attitude 0.809 0.809 0.875 0.636
## BehavioralIntention 0.773 0.775 0.855 0.596
##
## Alpha, rhoA, and rhoC should exceed 0.7 while AVE should exceed 0.5boot_pls <- bootstrap_model(
seminr_model = pls_model,
nboot = 100,
cores = 1
)## Bootstrapping model using seminr...## SEMinR Model successfully bootstrappedsummary(boot_pls)##
## Results from Bootstrap resamples: 100
##
## Bootstrapped Structural Paths:
## Original Est. Bootstrap Mean
## DigitalLiteracy -> PerceivedUsefulness 0.755 0.756
## PerceivedUsefulness -> Attitude 0.783 0.779
## Attitude -> BehavioralIntention 0.746 0.746
## Bootstrap SD T Stat. 2.5% CI 97.5% CI
## DigitalLiteracy -> PerceivedUsefulness 0.039 19.272 0.667 0.813
## PerceivedUsefulness -> Attitude 0.055 14.356 0.667 0.865
## Attitude -> BehavioralIntention 0.052 14.261 0.637 0.821
## Bootstrap P Val
## DigitalLiteracy -> PerceivedUsefulness 0.000
## PerceivedUsefulness -> Attitude 0.000
## Attitude -> BehavioralIntention 0.000
##
## Bootstrapped Weights:
## Original Est. Bootstrap Mean Bootstrap SD T Stat.
## DL1 -> DigitalLiteracy 0.336 0.339 0.022 15.586
## DL2 -> DigitalLiteracy 0.322 0.324 0.020 15.705
## DL3 -> DigitalLiteracy 0.335 0.334 0.030 11.014
## DL4 -> DigitalLiteracy 0.342 0.344 0.025 13.759
## PU1 -> PerceivedUsefulness 0.333 0.333 0.019 17.410
## PU2 -> PerceivedUsefulness 0.346 0.347 0.023 14.742
## PU3 -> PerceivedUsefulness 0.298 0.297 0.013 22.420
## PU4 -> PerceivedUsefulness 0.292 0.293 0.016 18.673
## AT1 -> Attitude 0.310 0.307 0.015 20.116
## AT2 -> Attitude 0.319 0.321 0.014 22.523
## AT3 -> Attitude 0.303 0.301 0.012 25.976
## AT4 -> Attitude 0.323 0.325 0.016 20.036
## BI1 -> BehavioralIntention 0.323 0.323 0.020 16.395
## BI2 -> BehavioralIntention 0.297 0.298 0.019 15.449
## BI3 -> BehavioralIntention 0.329 0.326 0.019 17.395
## BI4 -> BehavioralIntention 0.346 0.349 0.025 13.613
## 2.5% CI 97.5% CI Bootstrap P Val
## DL1 -> DigitalLiteracy 0.301 0.384 0.000
## DL2 -> DigitalLiteracy 0.290 0.363 0.000
## DL3 -> DigitalLiteracy 0.277 0.400 0.000
## DL4 -> DigitalLiteracy 0.296 0.399 0.000
## PU1 -> PerceivedUsefulness 0.304 0.371 0.000
## PU2 -> PerceivedUsefulness 0.311 0.392 0.000
## PU3 -> PerceivedUsefulness 0.272 0.323 0.000
## PU4 -> PerceivedUsefulness 0.267 0.324 0.000
## AT1 -> Attitude 0.281 0.337 0.000
## AT2 -> Attitude 0.300 0.354 0.000
## AT3 -> Attitude 0.283 0.329 0.000
## AT4 -> Attitude 0.300 0.360 0.000
## BI1 -> BehavioralIntention 0.288 0.362 0.000
## BI2 -> BehavioralIntention 0.270 0.342 0.000
## BI3 -> BehavioralIntention 0.294 0.357 0.000
## BI4 -> BehavioralIntention 0.308 0.406 0.000
##
## Bootstrapped Loadings:
## Original Est. Bootstrap Mean Bootstrap SD T Stat.
## DL1 -> DigitalLiteracy 0.794 0.790 0.033 24.021
## DL2 -> DigitalLiteracy 0.670 0.669 0.047 14.259
## DL3 -> DigitalLiteracy 0.718 0.710 0.045 16.066
## DL4 -> DigitalLiteracy 0.811 0.807 0.033 24.578
## PU1 -> PerceivedUsefulness 0.838 0.838 0.028 30.026
## PU2 -> PerceivedUsefulness 0.787 0.786 0.027 29.613
## PU3 -> PerceivedUsefulness 0.728 0.725 0.049 14.970
## PU4 -> PerceivedUsefulness 0.792 0.793 0.033 24.226
## AT1 -> Attitude 0.808 0.806 0.033 24.755
## AT2 -> Attitude 0.783 0.786 0.029 26.898
## AT3 -> Attitude 0.769 0.769 0.035 22.110
## AT4 -> Attitude 0.827 0.830 0.022 38.141
## BI1 -> BehavioralIntention 0.834 0.831 0.023 36.109
## BI2 -> BehavioralIntention 0.730 0.731 0.034 21.377
## BI3 -> BehavioralIntention 0.748 0.748 0.039 19.289
## BI4 -> BehavioralIntention 0.773 0.773 0.030 25.981
## 2.5% CI 97.5% CI Bootstrap P Val
## DL1 -> DigitalLiteracy 0.717 0.842 0.000
## DL2 -> DigitalLiteracy 0.553 0.747 0.000
## DL3 -> DigitalLiteracy 0.627 0.779 0.000
## DL4 -> DigitalLiteracy 0.736 0.865 0.000
## PU1 -> PerceivedUsefulness 0.775 0.880 0.000
## PU2 -> PerceivedUsefulness 0.731 0.826 0.000
## PU3 -> PerceivedUsefulness 0.620 0.790 0.000
## PU4 -> PerceivedUsefulness 0.736 0.850 0.000
## AT1 -> Attitude 0.740 0.855 0.000
## AT2 -> Attitude 0.716 0.831 0.000
## AT3 -> Attitude 0.683 0.815 0.000
## AT4 -> Attitude 0.786 0.861 0.000
## BI1 -> BehavioralIntention 0.786 0.876 0.000
## BI2 -> BehavioralIntention 0.666 0.794 0.000
## BI3 -> BehavioralIntention 0.664 0.806 0.000
## BI4 -> BehavioralIntention 0.702 0.818 0.000
##
## Bootstrapped HTMT:
## Original Est. Bootstrap Mean
## DigitalLiteracy -> PerceivedUsefulness 0.981 0.988
## DigitalLiteracy -> Attitude 1.004 1.011
## DigitalLiteracy -> BehavioralIntention 0.895 0.896
## PerceivedUsefulness -> Attitude 0.975 0.972
## PerceivedUsefulness -> BehavioralIntention 0.816 0.819
## Attitude -> BehavioralIntention 0.940 0.939
## Bootstrap SD 2.5% CI 97.5% CI
## DigitalLiteracy -> PerceivedUsefulness 0.040 0.919 1.084
## DigitalLiteracy -> Attitude 0.043 0.936 1.114
## DigitalLiteracy -> BehavioralIntention 0.050 0.798 0.996
## PerceivedUsefulness -> Attitude 0.058 0.848 1.057
## PerceivedUsefulness -> BehavioralIntention 0.068 0.664 0.928
## Attitude -> BehavioralIntention 0.060 0.796 1.046
## Bootstrap P Val
## DigitalLiteracy -> PerceivedUsefulness 0.620
## DigitalLiteracy -> Attitude 0.860
## DigitalLiteracy -> BehavioralIntention 0.040
## PerceivedUsefulness -> Attitude 0.700
## PerceivedUsefulness -> BehavioralIntention 0.000
## Attitude -> BehavioralIntention 0.260
##
## Bootstrapped Total Paths:
## Original Est. Bootstrap Mean
## DigitalLiteracy -> PerceivedUsefulness 0.755 0.756
## DigitalLiteracy -> Attitude 0.591 0.591
## DigitalLiteracy -> BehavioralIntention 0.441 0.442
## PerceivedUsefulness -> Attitude 0.783 0.779
## PerceivedUsefulness -> BehavioralIntention 0.584 0.582
## Attitude -> BehavioralIntention 0.746 0.746
## Bootstrap SD 2.5% CI 97.5% CI
## DigitalLiteracy -> PerceivedUsefulness 0.039 0.667 0.813
## DigitalLiteracy -> Attitude 0.064 0.467 0.696
## DigitalLiteracy -> BehavioralIntention 0.067 0.322 0.555
## PerceivedUsefulness -> Attitude 0.055 0.667 0.865
## PerceivedUsefulness -> BehavioralIntention 0.068 0.454 0.691
## Attitude -> BehavioralIntention 0.052 0.637 0.821plot(pls_model)