MODUL 5 PLS-SEM dan CB-SEM: Analisis Penggunaan Chatbot AI pada Layanan E-Commerce Menggunakan Metode PLS-SEM dan CB-SEM
Kevin Foreman Hutahaean (24031554095)
Mutiara Nur Fadhilatun N (24031554116)
Athiyyatus Salisah (24031554223)
2026-05-22
Dataset yang digunakan dalam penelitian ini adalah data survei mengenai penggunaan chatbot AI pada layanan e-commerce.
Link dataset: https://zenodo.org/records/17172457
library(readxl)## Warning: package 'readxl' was built under R version 4.5.3library(dplyr)## Warning: package 'dplyr' was built under R version 4.5.3##
## 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(ggplot2)## Warning: package 'ggplot2' was built under R version 4.5.3library(corrplot)## Warning: package 'corrplot' was built under R version 4.5.3## corrplot 0.95 loadedlibrary(lavaan)## Warning: package 'lavaan' was built under R version 4.5.3## This is lavaan 0.6-21
## lavaan is FREE software! Please report any bugs.library(semPlot)## Warning: package 'semPlot' was built under R version 4.5.3library(readxl)
library(seminr)## Warning: package 'seminr' was built under R version 4.5.3EDA
df<- read.csv("C:/Users/salis/Downloads/The Role of AI Chatbots in Enhancing Customer Satisfaction A Case Study of E-Commerce in Digital Service Transformation (2025) - Form Responses 1.csv")str(df)## 'data.frame': 504 obs. of 33 variables:
## $ Apakah.anda.pernah.menggunakan.fitur.chatbot.AI.saat.menggunakan.layanan.e.commerce. : chr "Pernah" "Pernah" "Tidak pernah" "Pernah" ...
## $ Nama.Responden : chr "Herning" "Melly" "" "Noor" ...
## $ Jenis.Kelamin.Anda : chr "Perempuan" "Perempuan" "" "Perempuan" ...
## $ Berapa.Umur.Anda : chr "46 - 54" "36 - 45" "" "46 - 54" ...
## $ Pendidikan.Terakhir : chr "S1 (Sarjana)" "S1 (Sarjana)" "" "S1 (Sarjana)" ...
## $ Domisili.saat.ini : chr "Depok" "Karawang" "" "Bandung" ...
## $ X1..Chatbot.ini.memberikan.saya.informasi.yang.akurat : int 3 2 NA 1 NA NA NA NA NA NA ...
## $ X2..Saya.senang.menggunakan.chatbot.ini : int 3 2 NA 1 NA NA NA NA NA NA ...
## $ X3..Chatbot.telah.melakukan.pekerjaan.dengan.baik : int 3 2 NA 4 NA NA NA NA NA NA ...
## $ X4..Chatbot.melakukan.apa.yang.saya.harapkan : int 3 1 NA 1 NA NA NA NA NA NA ...
## $ X5..Saya.puas.dengan.pengalaman.mengobrol.dengan.chatbot : int 3 2 NA 1 NA NA NA NA NA NA ...
## $ X6..Saya.merasa.chatbot.ini.bermanfaat.dalam.memberikan.informasi : int 3 2 NA 1 NA NA NA NA NA NA ...
## $ X7..Saya.akan.selalu.mencoba.menggunakan.chatbot.ini.dalam.aktivitas.menggunakan.aplikasi.E.commerce : int 3 2 NA 1 NA NA NA NA NA NA ...
## $ X8..Saya.akan.sangat.merekomendasikan.orang.lain.untuk.menggunakannya : int 3 2 NA 1 NA NA NA NA NA NA ...
## $ X9..Saya.sering.menggunakan.chatbot.ketika.saya.kebingungan.menggunakan.layanan.E.Commerce : int 3 2 NA 3 NA NA NA NA NA NA ...
## $ X10..Saya.pikir.minat.saya.terhadap.chatbot.akan.meningkat.di.masa.depan : int 3 3 NA 3 NA NA NA NA NA NA ...
## $ X11..Chatbot.ini.ramah.pengguna : int 3 3 NA 4 NA NA NA NA NA NA ...
## $ X12..Chatbot.E.Commerce.menyediakan.informasi.dengan.cepat : int 3 2 NA 4 NA NA NA NA NA NA ...
## $ X13..Chatbot.E.Commerce.mudah.dipahami : int 3 3 NA 4 NA NA NA NA NA NA ...
## $ X14..Saya.menemukan.bahwa.Chatbot.ini.mudah.untuk.digunakan : int 3 3 NA 4 NA NA NA NA NA NA ...
## $ X15..Menggunakan.Chatbot.membantu.mengoptimalkan.pengalaman.penggunaan.saya.terhadap.aplikasi.E.Commerce : int 3 2 NA 4 NA NA NA NA NA NA ...
## $ X16..Menggunakan.Chatbot.membantu.saya.mempermudah.memakai.layanan.E.Commerce : int 3 2 NA 3 NA NA NA NA NA NA ...
## $ X17..Menggunakan.Chatbot.membantu.meningkatkan.produktivitas.dalam.mencari.produk.di.E.Commerce : int 3 2 NA 1 NA NA NA NA NA NA ...
## $ X18..Menggunakan.Chatbot.akan.meningkatkan.efektivitas.saya.dalam.menggunakan.E.Commerce : int 3 2 NA 4 NA NA NA NA NA NA ...
## $ X19..Menggunakan.Chatbot.membuat.aktivitas.saya.lebih.mudah.ketika.memakai.layanan.E.Commerce : int 3 2 NA 4 NA NA NA NA NA NA ...
## $ X20..Chatbot.membantu.saya.mengatasi.kesulitan.penggunaan.aplikasi.E.Commerce : int 3 2 NA 2 NA NA NA NA NA NA ...
## $ Shopee : chr "" "" "" "" ...
## $ Tokopedia : chr "" "" "" "" ...
## $ X21..Saya.mempertimbangkan.untuk.menggunakan.chatbot.di.masa.depan. : int 3 3 NA 3 NA NA NA NA NA NA ...
## $ X22..Saya.berusaha.untuk.memanfaatkan.chatbot.sesering.mungkin : int 3 2 NA 3 NA NA NA NA NA NA ...
## $ X23...Saya.berencana.untuk.meningkatkan.pembelian.melalui.platform.e.commerce.di.masa.depan.karena.bantuan.dari.chatbot. : int 3 3 NA 4 NA NA NA NA NA NA ...
## $ X24...Saya.cenderung.mengatakan.hal.hal.positif.tentang.platform.e.commerce.kepada.orang.lain.karena.bantuan.chatbot. : int 3 2 NA 2 NA NA NA NA NA NA ...
## $ X25...Saya.percaya.bahwa.platform.e.commerce.benar.benar.peduli.terhadap.kesejahteraan.pelanggan.mereka..terutama.melalui.penggunaan.chatbot.: int 4 3 NA 3 NA NA NA NA NA NA ...options(repr.plot.width = 16, repr.plot.height = 4)
data_awal <- df
n_cols <- ncol(data_awal)
par(mfrow = c(1, min(4, n_cols)), mar = c(5, 4, 3, 1))
for (col in colnames(data_awal)) {
nama_singkat <- ifelse(
nchar(col) > 15,
paste0(substr(col, 1, 15), "..."),
col
)
if (is.numeric(data_awal[[col]])) {
hist(data_awal[[col]],
main = paste("Histogram of", nama_singkat),
xlab = "Skala Skor",
col = "lightblue",
border = "black")
} else {
counts <- table(data_awal[[col]])
barplot(counts,
main = paste("Barplot of", nama_singkat),
xlab = "",
col = "lightgreen",
border = "black",
las = 2,
cex.names = 0.8)
}
}
par(mfrow = c(1, 1))
Pre-prosessing
# drop kolom shopee dan tokopedia
df_new <- df
df_new$Shopee <- NULL
df_new$Tokopedia <- NULLsum(is.na(df_new))## [1] 1825colMeans(is.na(df_new))## Apakah.anda.pernah.menggunakan.fitur.chatbot.AI.saat.menggunakan.layanan.e.commerce.
## 0.0000000
## Nama.Responden
## 0.0000000
## Jenis.Kelamin.Anda
## 0.0000000
## Berapa.Umur.Anda
## 0.0000000
## Pendidikan.Terakhir
## 0.0000000
## Domisili.saat.ini
## 0.0000000
## X1..Chatbot.ini.memberikan.saya.informasi.yang.akurat
## 0.1448413
## X2..Saya.senang.menggunakan.chatbot.ini
## 0.1448413
## X3..Chatbot.telah.melakukan.pekerjaan.dengan.baik
## 0.1448413
## X4..Chatbot.melakukan.apa.yang.saya.harapkan
## 0.1448413
## X5..Saya.puas.dengan.pengalaman.mengobrol.dengan.chatbot
## 0.1448413
## X6..Saya.merasa.chatbot.ini.bermanfaat.dalam.memberikan.informasi
## 0.1448413
## X7..Saya.akan.selalu.mencoba.menggunakan.chatbot.ini.dalam.aktivitas.menggunakan.aplikasi.E.commerce
## 0.1448413
## X8..Saya.akan.sangat.merekomendasikan.orang.lain.untuk.menggunakannya
## 0.1448413
## X9..Saya.sering.menggunakan.chatbot.ketika.saya.kebingungan.menggunakan.layanan.E.Commerce
## 0.1448413
## X10..Saya.pikir.minat.saya.terhadap.chatbot.akan.meningkat.di.masa.depan
## 0.1448413
## X11..Chatbot.ini.ramah.pengguna
## 0.1448413
## X12..Chatbot.E.Commerce.menyediakan.informasi.dengan.cepat
## 0.1448413
## X13..Chatbot.E.Commerce.mudah.dipahami
## 0.1448413
## X14..Saya.menemukan.bahwa.Chatbot.ini.mudah.untuk.digunakan
## 0.1448413
## X15..Menggunakan.Chatbot.membantu.mengoptimalkan.pengalaman.penggunaan.saya.terhadap.aplikasi.E.Commerce
## 0.1448413
## X16..Menggunakan.Chatbot.membantu.saya.mempermudah.memakai.layanan.E.Commerce
## 0.1448413
## X17..Menggunakan.Chatbot.membantu.meningkatkan.produktivitas.dalam.mencari.produk.di.E.Commerce
## 0.1448413
## X18..Menggunakan.Chatbot.akan.meningkatkan.efektivitas.saya.dalam.menggunakan.E.Commerce
## 0.1448413
## X19..Menggunakan.Chatbot.membuat.aktivitas.saya.lebih.mudah.ketika.memakai.layanan.E.Commerce
## 0.1448413
## X20..Chatbot.membantu.saya.mengatasi.kesulitan.penggunaan.aplikasi.E.Commerce
## 0.1448413
## X21..Saya.mempertimbangkan.untuk.menggunakan.chatbot.di.masa.depan.
## 0.1448413
## X22..Saya.berusaha.untuk.memanfaatkan.chatbot.sesering.mungkin
## 0.1448413
## X23...Saya.berencana.untuk.meningkatkan.pembelian.melalui.platform.e.commerce.di.masa.depan.karena.bantuan.dari.chatbot.
## 0.1448413
## X24...Saya.cenderung.mengatakan.hal.hal.positif.tentang.platform.e.commerce.kepada.orang.lain.karena.bantuan.chatbot.
## 0.1448413
## X25...Saya.percaya.bahwa.platform.e.commerce.benar.benar.peduli.terhadap.kesejahteraan.pelanggan.mereka..terutama.melalui.penggunaan.chatbot.
## 0.1448413df_clean <- na.omit(df_new)nrow(df_new)## [1] 504nrow(df_clean)## [1] 431df_clean <- df_clean[!duplicated(df_clean), ]
dim(df_clean)## [1] 431 31head(df_clean)## Apakah.anda.pernah.menggunakan.fitur.chatbot.AI.saat.menggunakan.layanan.e.commerce.
## 1 Pernah
## 2 Pernah
## 4 Pernah
## 13 Pernah
## 14 Pernah
## 17 Pernah
## Nama.Responden Jenis.Kelamin.Anda Berapa.Umur.Anda
## 1 Herning Perempuan 46 - 54
## 2 Melly Perempuan 36 - 45
## 4 Noor Perempuan 46 - 54
## 13 Ria EJ Perempuan 46 - 54
## 14 Rr. Dian Kartika Dari Perempuan 46 - 54
## 17 Anaa Perempuan 19 - 25
## Pendidikan.Terakhir Domisili.saat.ini
## 1 S1 (Sarjana) Depok
## 2 S1 (Sarjana) Karawang
## 4 S1 (Sarjana) Bandung
## 13 S1 (Sarjana) Depok
## 14 S1 (Sarjana) Depok
## 17 S1 (Sarjana) Depok
## X1..Chatbot.ini.memberikan.saya.informasi.yang.akurat
## 1 3
## 2 2
## 4 1
## 13 3
## 14 1
## 17 3
## X2..Saya.senang.menggunakan.chatbot.ini
## 1 3
## 2 2
## 4 1
## 13 3
## 14 4
## 17 3
## X3..Chatbot.telah.melakukan.pekerjaan.dengan.baik
## 1 3
## 2 2
## 4 4
## 13 3
## 14 4
## 17 2
## X4..Chatbot.melakukan.apa.yang.saya.harapkan
## 1 3
## 2 1
## 4 1
## 13 2
## 14 4
## 17 2
## X5..Saya.puas.dengan.pengalaman.mengobrol.dengan.chatbot
## 1 3
## 2 2
## 4 1
## 13 3
## 14 4
## 17 3
## X6..Saya.merasa.chatbot.ini.bermanfaat.dalam.memberikan.informasi
## 1 3
## 2 2
## 4 1
## 13 3
## 14 4
## 17 3
## X7..Saya.akan.selalu.mencoba.menggunakan.chatbot.ini.dalam.aktivitas.menggunakan.aplikasi.E.commerce
## 1 3
## 2 2
## 4 1
## 13 3
## 14 4
## 17 2
## X8..Saya.akan.sangat.merekomendasikan.orang.lain.untuk.menggunakannya
## 1 3
## 2 2
## 4 1
## 13 3
## 14 4
## 17 3
## X9..Saya.sering.menggunakan.chatbot.ketika.saya.kebingungan.menggunakan.layanan.E.Commerce
## 1 3
## 2 2
## 4 3
## 13 3
## 14 1
## 17 2
## X10..Saya.pikir.minat.saya.terhadap.chatbot.akan.meningkat.di.masa.depan
## 1 3
## 2 3
## 4 3
## 13 3
## 14 4
## 17 2
## X11..Chatbot.ini.ramah.pengguna
## 1 3
## 2 3
## 4 4
## 13 3
## 14 4
## 17 3
## X12..Chatbot.E.Commerce.menyediakan.informasi.dengan.cepat
## 1 3
## 2 2
## 4 4
## 13 3
## 14 4
## 17 3
## X13..Chatbot.E.Commerce.mudah.dipahami
## 1 3
## 2 3
## 4 4
## 13 3
## 14 1
## 17 2
## X14..Saya.menemukan.bahwa.Chatbot.ini.mudah.untuk.digunakan
## 1 3
## 2 3
## 4 4
## 13 3
## 14 1
## 17 4
## X15..Menggunakan.Chatbot.membantu.mengoptimalkan.pengalaman.penggunaan.saya.terhadap.aplikasi.E.Commerce
## 1 3
## 2 2
## 4 4
## 13 3
## 14 4
## 17 3
## X16..Menggunakan.Chatbot.membantu.saya.mempermudah.memakai.layanan.E.Commerce
## 1 3
## 2 2
## 4 3
## 13 3
## 14 4
## 17 2
## X17..Menggunakan.Chatbot.membantu.meningkatkan.produktivitas.dalam.mencari.produk.di.E.Commerce
## 1 3
## 2 2
## 4 1
## 13 3
## 14 4
## 17 3
## X18..Menggunakan.Chatbot.akan.meningkatkan.efektivitas.saya.dalam.menggunakan.E.Commerce
## 1 3
## 2 2
## 4 4
## 13 3
## 14 4
## 17 3
## X19..Menggunakan.Chatbot.membuat.aktivitas.saya.lebih.mudah.ketika.memakai.layanan.E.Commerce
## 1 3
## 2 2
## 4 4
## 13 3
## 14 4
## 17 3
## X20..Chatbot.membantu.saya.mengatasi.kesulitan.penggunaan.aplikasi.E.Commerce
## 1 3
## 2 2
## 4 2
## 13 3
## 14 4
## 17 3
## X21..Saya.mempertimbangkan.untuk.menggunakan.chatbot.di.masa.depan.
## 1 3
## 2 3
## 4 3
## 13 3
## 14 4
## 17 3
## X22..Saya.berusaha.untuk.memanfaatkan.chatbot.sesering.mungkin
## 1 3
## 2 2
## 4 3
## 13 3
## 14 3
## 17 3
## X23...Saya.berencana.untuk.meningkatkan.pembelian.melalui.platform.e.commerce.di.masa.depan.karena.bantuan.dari.chatbot.
## 1 3
## 2 3
## 4 4
## 13 4
## 14 3
## 17 3
## X24...Saya.cenderung.mengatakan.hal.hal.positif.tentang.platform.e.commerce.kepada.orang.lain.karena.bantuan.chatbot.
## 1 3
## 2 2
## 4 2
## 13 3
## 14 3
## 17 3
## X25...Saya.percaya.bahwa.platform.e.commerce.benar.benar.peduli.terhadap.kesejahteraan.pelanggan.mereka..terutama.melalui.penggunaan.chatbot.
## 1 4
## 2 3
## 4 3
## 13 3
## 14 3
## 17 3df_rename <- df_clean %>%
filter(`Apakah.anda.pernah.menggunakan.fitur.chatbot.AI.saat.menggunakan.layanan.e.commerce.` == "Pernah") %>%
rename(
X1 = `X1..Chatbot.ini.memberikan.saya.informasi.yang.akurat`,
X2 = `X2..Saya.senang.menggunakan.chatbot.ini`,
X3 = `X3..Chatbot.telah.melakukan.pekerjaan.dengan.baik`,
X4 = `X4..Chatbot.melakukan.apa.yang.saya.harapkan`,
X5 = `X5..Saya.puas.dengan.pengalaman.mengobrol.dengan.chatbot`,
X6 = `X6..Saya.merasa.chatbot.ini.bermanfaat.dalam.memberikan.informasi`,
X7 = `X7..Saya.akan.selalu.mencoba.menggunakan.chatbot.ini.dalam.aktivitas.menggunakan.aplikasi.E.commerce`,
X8 = `X8..Saya.akan.sangat.merekomendasikan.orang.lain.untuk.menggunakannya`,
X9 = `X9..Saya.sering.menggunakan.chatbot.ketika.saya.kebingungan.menggunakan.layanan.E.Commerce`,
X10 = `X10..Saya.pikir.minat.saya.terhadap.chatbot.akan.meningkat.di.masa.depan`,
X11 = `X11..Chatbot.ini.ramah.pengguna`,
X12 = `X12..Chatbot.E.Commerce.menyediakan.informasi.dengan.cepat`,
X13 = `X13..Chatbot.E.Commerce.mudah.dipahami`,
X14 = `X14..Saya.menemukan.bahwa.Chatbot.ini.mudah.untuk.digunakan`,
X15 = `X15..Menggunakan.Chatbot.membantu.mengoptimalkan.pengalaman.penggunaan.saya.terhadap.aplikasi.E.Commerce`,
X16 = `X16..Menggunakan.Chatbot.membantu.saya.mempermudah.memakai.layanan.E.Commerce`,
X17 = `X17..Menggunakan.Chatbot.membantu.meningkatkan.produktivitas.dalam.mencari.produk.di.E.Commerce`,
X18 = `X18..Menggunakan.Chatbot.akan.meningkatkan.efektivitas.saya.dalam.menggunakan.E.Commerce`,
X19 = `X19..Menggunakan.Chatbot.membuat.aktivitas.saya.lebih.mudah.ketika.memakai.layanan.E.Commerce`,
X20 = `X20..Chatbot.membantu.saya.mengatasi.kesulitan.penggunaan.aplikasi.E.Commerce`,
X21 = `X21..Saya.mempertimbangkan.untuk.menggunakan.chatbot.di.masa.depan.`,
X22 = `X22..Saya.berusaha.untuk.memanfaatkan.chatbot.sesering.mungkin`,
X23 = `X23...Saya.berencana.untuk.meningkatkan.pembelian.melalui.platform.e.commerce.di.masa.depan.karena.bantuan.dari.chatbot.`,
X24 = `X24...Saya.cenderung.mengatakan.hal.hal.positif.tentang.platform.e.commerce.kepada.orang.lain.karena.bantuan.chatbot.`,
X25 = `X25...Saya.percaya.bahwa.platform.e.commerce.benar.benar.peduli.terhadap.kesejahteraan.pelanggan.mereka..terutama.melalui.penggunaan.chatbot.`
)write.csv(df_rename, "data_sem.csv", row.names = FALSE)df_sem <- read.csv("data_sem.csv")data <- df_sem %>%
select(starts_with("X"))
head(data)## X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19 X20 X21
## 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## 2 2 2 2 1 2 2 2 2 2 3 3 2 3 3 2 2 2 2 2 2 3
## 3 1 1 4 1 1 1 1 1 3 3 4 4 4 4 4 3 1 4 4 2 3
## 4 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## 5 1 4 4 4 4 4 4 4 1 4 4 4 1 1 4 4 4 4 4 4 4
## 6 3 3 2 2 3 3 2 3 2 2 3 3 2 4 3 2 3 3 3 3 3
## X22 X23 X24 X25
## 1 3 3 3 4
## 2 2 3 2 3
## 3 3 4 2 3
## 4 3 4 3 3
## 5 3 3 3 3
## 6 3 3 3 3library(psych)##
## Attaching package: 'psych'## The following object is masked from 'package:lavaan':
##
## cor2cov## The following objects are masked from 'package:ggplot2':
##
## %+%, alphadescribe(data)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 431 3.38 0.61 3 3.42 0.00 1 4 3 -0.73 1.11 0.03
## X2 2 431 3.43 0.62 3 3.48 1.48 1 4 3 -0.96 1.57 0.03
## X3 3 431 3.48 0.59 4 3.54 0.00 1 4 3 -0.81 0.36 0.03
## X4 4 431 3.44 0.65 4 3.53 0.00 1 4 3 -0.95 0.66 0.03
## X5 5 431 3.45 0.66 4 3.53 0.00 1 4 3 -1.13 1.54 0.03
## X6 6 431 3.40 0.61 3 3.45 1.48 1 4 3 -0.72 0.83 0.03
## X7 7 431 3.43 0.71 4 3.54 0.00 1 4 3 -1.27 1.73 0.03
## X8 8 431 3.40 0.71 4 3.50 0.00 1 4 3 -1.27 1.96 0.03
## X9 9 431 3.39 0.71 3 3.49 1.48 1 4 3 -1.26 1.93 0.03
## X10 10 431 3.48 0.65 4 3.55 0.00 1 4 3 -1.20 1.80 0.03
## X11 11 431 3.44 0.57 3 3.46 0.00 1 4 3 -0.61 0.70 0.03
## X12 12 431 3.54 0.59 4 3.59 0.00 1 4 3 -1.13 1.65 0.03
## X13 13 431 3.47 0.60 4 3.52 0.00 1 4 3 -0.92 1.09 0.03
## X14 14 431 3.51 0.58 4 3.55 0.00 1 4 3 -0.96 1.46 0.03
## X15 15 431 3.50 0.60 4 3.56 0.00 1 4 3 -0.96 0.88 0.03
## X16 16 431 3.46 0.60 4 3.52 0.00 1 4 3 -0.84 0.67 0.03
## X17 17 431 3.48 0.62 4 3.55 0.00 1 4 3 -1.14 1.78 0.03
## X18 18 431 3.51 0.62 4 3.57 0.00 1 4 3 -1.15 1.63 0.03
## X19 19 431 3.51 0.62 4 3.58 0.00 1 4 3 -1.22 1.94 0.03
## X20 20 431 3.50 0.63 4 3.57 0.00 1 4 3 -1.15 1.49 0.03
## X21 21 431 3.39 0.58 3 3.42 0.00 1 4 3 -0.62 0.98 0.03
## X22 22 431 3.43 0.62 3 3.49 1.48 1 4 3 -0.84 0.78 0.03
## X23 23 431 3.53 0.63 4 3.59 0.00 1 5 4 -0.96 1.68 0.03
## X24 24 431 3.50 0.61 4 3.56 0.00 1 4 3 -0.97 0.85 0.03
## X25 25 431 3.52 0.63 4 3.61 0.00 1 4 3 -1.24 1.69 0.03options(repr.plot.width = 16, repr.plot.height = 4)
after_prepo <- data
n_cols <- ncol(after_prepo)
par(mfrow = c(1, min(4, n_cols)), mar = c(5, 4, 3, 1))
for (col in colnames(after_prepo)) {
nama_singkat <- if(nchar(col) > 15) paste0(substr(col, 1, 15), "...") else col
if (is.numeric(after_prepo[[col]])) {
hist(after_prepo[[col]],
main = paste("Histogram of", nama_singkat),
xlab = "Skala Skor",
col = "lightblue",
border = "black")
} else {
counts <- table(after_prepo[[col]])
barplot(counts,
main = paste("Barplot of", nama_singkat),
xlab = "",
col = "lightgreen",
border = "black",
las = 2,
cex.names = 0.8)
}
}
par(mfrow = c(1, 1))
Uji Asumsi
Uji Normalitas Multivariat
mardia_result <- psych::mardia(data, plot = FALSE)
print(mardia_result)## Call: psych::mardia(x = data, plot = FALSE)
##
## Mardia tests of multivariate skew and kurtosis
## Use describe(x) the to get univariate tests
## n.obs = 431 num.vars = 25
## b1p = 97.09 skew = 6974.4 with probability <= 0
## small sample skew = 7026.7 with probability <= 0
## b2p = 815.72 kurtosis = 39.75 with probability <= 0Berdasarkan hasil pengujian, diperoleh nilai p-value skewness dan kurtosis < 0.05, yang menunjukkan bahwa data penelitian tidak berdistribusi normal multivariat. Oleh karena itu, pengujian model Structural Equation Modeling (SEM) dilanjutkan dengan menggunakan metode estimasi Robust Maximum Likelihood (MLR) untuk mengantisipasi ketidaknormalan data tersebut
Uji Non Multikolinearitas (VIF < 3.3)
library(car)## Loading required package: carData##
## Attaching package: 'car'## The following object is masked from 'package:psych':
##
## logit## The following object is masked from 'package:dplyr':
##
## recodedata_vif <- data
data_vif$Index_Responden <- 1:nrow(data_vif)
model_vif <- lm(Index_Responden ~ ., data = data_vif)
vif_values <- vif(model_vif)
print(vif_values)## X1 X2 X3 X4 X5 X6 X7 X8
## 1.537257 1.543629 1.632535 1.682618 1.832495 1.546942 1.895848 1.715779
## X9 X10 X11 X12 X13 X14 X15 X16
## 1.625913 1.752846 1.583806 1.654214 1.497982 1.499787 1.701813 1.658034
## X17 X18 X19 X20 X21 X22 X23 X24
## 1.704634 1.681170 1.720161 1.759893 1.322402 1.456651 1.616883 1.535395
## X25
## 1.700596Berdasarkan hasil pengujian multikolinearitas, diperoleh bahwa seluruh indikator penelitian (X1 sampai X25) memiliki nilai Variance Inflation Factor (VIF) yang berkisar antara 1.318 hingga 1.895. Nilai tersebut secara keseluruhan berada jauh di bawah batasan teoritis yang disyaratkan, yaitu < 3.3. Sehingga model penelitian ini memenuhi asumsi non-multikolinearitas, estimasi hubungan antar-variabel dalam model SEM dapat dilakukan dengan stabil.
Uji Kecukupan Sampel
r <- cor(data)
KMO(r)## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = r)
## Overall MSA = 0.96
## MSA for each item =
## X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16
## 0.96 0.95 0.96 0.95 0.96 0.94 0.95 0.96 0.97 0.97 0.93 0.96 0.96 0.96 0.96 0.97
## X17 X18 X19 X20 X21 X22 X23 X24 X25
## 0.96 0.97 0.96 0.96 0.95 0.95 0.96 0.95 0.96Berdasarkan hasil pengujian, diperoleh nilai Overall MSA sebesar 0.96. Nilai MSA untuk setiap indikator individu (X1 hingga X25) seluruhnya berada di atas rentang 0.93, jauh melampaui batas minimal disyaratkan yaitu 0.50. Sehingga dapat disimpulkan bahwa dataset memenuhi asumsi dan layak untuk dilanjutkan ke tahap analisis model struktural (SEM).
CB-SEM
model_cfa <- '
PERCEIVED_EASE_OF_USE =~ X11 + X12 + X13 + X14
PERCEIVED_USEFULNESS =~ X6 + X15 + X16 + X17 + X18 + X19 + X20
CUSTOMER_SATISFACTION =~ X1 + X2 + X3 + X4 + X5 + X25
BEHAVIORAL_INTENTION =~ X7 + X8 + X9 + X10 + X21 + X22 + X23 + X24
'fit_cfa <- cfa(
model_cfa,
data = data,
std.lv = TRUE
)## 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 36 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 56
##
## Number of observations 431
##
## Model Test User Model:
##
## Test statistic 480.213
## Degrees of freedom 269
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 3992.444
## Degrees of freedom 300
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.943
## Tucker-Lewis Index (TLI) 0.936
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -8430.076
## Loglikelihood unrestricted model (H1) -8189.970
##
## Akaike (AIC) 16972.153
## Bayesian (BIC) 17199.855
## Sample-size adjusted Bayesian (SABIC) 17022.143
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.043
## 90 Percent confidence interval - lower 0.036
## 90 Percent confidence interval - upper 0.049
## P-value H_0: RMSEA <= 0.050 0.976
## P-value H_0: RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.039
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv
## PERCEIVED_EASE_OF_USE =~
## X11 0.324 0.027 11.999 0.000 0.324
## X12 0.367 0.028 13.299 0.000 0.367
## X13 0.338 0.029 11.702 0.000 0.338
## X14 0.329 0.028 11.953 0.000 0.329
## PERCEIVED_USEFULNESS =~
## X6 0.326 0.028 11.638 0.000 0.326
## X15 0.382 0.027 14.211 0.000 0.382
## X16 0.380 0.027 14.020 0.000 0.380
## X17 0.399 0.028 14.326 0.000 0.399
## X18 0.388 0.028 14.042 0.000 0.388
## X19 0.399 0.028 14.317 0.000 0.399
## X20 0.397 0.028 14.144 0.000 0.397
## CUSTOMER_SATISFACTION =~
## X1 0.339 0.028 12.152 0.000 0.339
## X2 0.345 0.028 12.180 0.000 0.345
## X3 0.360 0.027 13.429 0.000 0.360
## X4 0.391 0.029 13.299 0.000 0.391
## X5 0.424 0.029 14.578 0.000 0.424
## X25 0.388 0.028 13.768 0.000 0.388
## BEHAVIORAL_INTENTION =~
## X7 0.455 0.031 14.568 0.000 0.455
## X8 0.445 0.031 14.157 0.000 0.445
## X9 0.432 0.032 13.468 0.000 0.432
## X10 0.423 0.029 14.838 0.000 0.423
## X21 0.272 0.028 9.879 0.000 0.272
## X22 0.321 0.029 11.076 0.000 0.321
## X23 0.363 0.029 12.613 0.000 0.363
## X24 0.329 0.028 11.813 0.000 0.329
## Std.all
##
## 0.572
## 0.624
## 0.559
## 0.570
##
## 0.539
## 0.636
## 0.629
## 0.640
## 0.630
## 0.639
## 0.633
##
## 0.558
## 0.559
## 0.607
## 0.602
## 0.648
## 0.619
##
## 0.647
## 0.632
## 0.607
## 0.656
## 0.466
## 0.515
## 0.575
## 0.544
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv
## PERCEIVED_EASE_OF_USE ~~
## PERCEIVED_USEF 0.951 0.029 33.109 0.000 0.951
## CUSTOMER_SATIS 0.901 0.034 26.594 0.000 0.901
## BEHAVIORAL_INT 0.939 0.030 31.631 0.000 0.939
## PERCEIVED_USEFULNESS ~~
## CUSTOMER_SATIS 0.980 0.020 49.867 0.000 0.980
## BEHAVIORAL_INT 0.964 0.018 52.168 0.000 0.964
## CUSTOMER_SATISFACTION ~~
## BEHAVIORAL_INT 1.008 0.018 54.801 0.000 1.008
## Std.all
##
## 0.951
## 0.901
## 0.939
##
## 0.980
## 0.964
##
## 1.008
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .X11 0.216 0.016 13.378 0.000 0.216 0.673
## .X12 0.211 0.016 12.868 0.000 0.211 0.610
## .X13 0.251 0.019 13.472 0.000 0.251 0.687
## .X14 0.225 0.017 13.393 0.000 0.225 0.675
## .X6 0.260 0.018 14.110 0.000 0.260 0.709
## .X15 0.215 0.016 13.712 0.000 0.215 0.596
## .X16 0.221 0.016 13.748 0.000 0.221 0.605
## .X17 0.230 0.017 13.689 0.000 0.230 0.591
## .X18 0.229 0.017 13.744 0.000 0.229 0.604
## .X19 0.230 0.017 13.691 0.000 0.230 0.591
## .X20 0.236 0.017 13.724 0.000 0.236 0.599
## .X1 0.255 0.018 14.087 0.000 0.255 0.688
## .X2 0.261 0.019 14.083 0.000 0.261 0.687
## .X3 0.222 0.016 13.887 0.000 0.222 0.632
## .X4 0.270 0.019 13.910 0.000 0.270 0.638
## .X5 0.249 0.018 13.652 0.000 0.249 0.580
## .X25 0.243 0.018 13.824 0.000 0.243 0.617
## .X7 0.289 0.021 13.733 0.000 0.289 0.582
## .X8 0.298 0.022 13.810 0.000 0.298 0.601
## .X9 0.320 0.023 13.926 0.000 0.320 0.631
## .X10 0.237 0.017 13.679 0.000 0.237 0.570
## .X21 0.266 0.019 14.338 0.000 0.266 0.783
## .X22 0.285 0.020 14.228 0.000 0.285 0.735
## .X23 0.266 0.019 14.050 0.000 0.266 0.669
## .X24 0.258 0.018 14.149 0.000 0.258 0.704
## PERCEIVED_EASE 1.000 1.000 1.000
## PERCEIVED_USEF 1.000 1.000 1.000
## CUSTOMER_SATIS 1.000 1.000 1.000
## BEHAVIORAL_INT 1.000 1.000 1.000library(lavaan)
library(semPlot)
library(qgraph)## Warning: package 'qgraph' was built under R version 4.5.3options(repr.plot.width = 18, repr.plot.height = 12)
semPaths(
fit_cfa,
what = "std",
whatLabels = "std",
style = "ram",
layout = "tree",
rotation = 2,
shapeLat = "ellipse",
asymLat = TRUE,
sizeLat2 = 6,
# ukuran node
sizeLat = 9,
sizeMan = 3,
# ukuran teks
edge.label.cex = 0.7,
label.cex = 1.2,
# jarak
nCharNodes = 0,
residuals = FALSE,
covAtResiduals = FALSE,
curvePivot = TRUE,
mar = c(3, 8, 3, 8), # Jarak margin (bawah, kiri, atas, kanan)
# warna
color = list(
lat = c("lightblue", "lightpink", "lightgreen", "lightcoral"),
man = "lightyellow"
),
edge.color = "black"
)## Warning in qgraph::qgraph(Edgelist, labels = nLab, bidirectional = Bidir, : The
## following arguments are not documented and likely not arguments of qgraph and
## thus ignored: asymLat
PLS SEM
measurement_model <- constructs(
composite(
"CUSTOMER_SATISFACTION",
multi_items("X", 1:5, 25)
),
composite(
"PERCEIVED_USEFULNESS",
multi_items("X", c(6, 15:20))
),
composite(
"PERCEIVED_EASE_OF_USE",
multi_items("X", 11:14)
),
composite(
"BEHAVIORAL_INTENTION",
multi_items("X", c(7:10, 21:24))
)
)structural_model <- relationships(
paths(
from = "PERCEIVED_EASE_OF_USE",
to = c("PERCEIVED_USEFULNESS", "CUSTOMER_SATISFACTION")
),
paths(
from = "PERCEIVED_USEFULNESS",
to = c("CUSTOMER_SATISFACTION", "BEHAVIORAL_INTENTION")
),
paths(
from = "CUSTOMER_SATISFACTION",
to = "BEHAVIORAL_INTENTION"
)
)pls_model <- estimate_pls(
data = data,
measurement_model = measurement_model,
structural_model = structural_model
)## Generating the seminr model## All 431 observations are valid.summary(pls_model)##
## Results from package seminr (2.4.2)
##
## Path Coefficients:
## PERCEIVED_USEFULNESS CUSTOMER_SATISFACTION
## R^2 0.499 0.572
## AdjR^2 0.498 0.570
## PERCEIVED_EASE_OF_USE 0.707 0.198
## PERCEIVED_USEFULNESS . 0.603
## CUSTOMER_SATISFACTION . .
## BEHAVIORAL_INTENTION
## R^2 0.686
## AdjR^2 0.684
## PERCEIVED_EASE_OF_USE .
## PERCEIVED_USEFULNESS 0.486
## CUSTOMER_SATISFACTION 0.400
##
## Reliability:
## alpha rhoA rhoC AVE
## PERCEIVED_EASE_OF_USE 0.676 0.675 0.804 0.506
## PERCEIVED_USEFULNESS 0.813 0.814 0.862 0.473
## CUSTOMER_SATISFACTION 0.746 0.749 0.832 0.497
## BEHAVIORAL_INTENTION 0.804 0.812 0.853 0.423
##
## Alpha, rhoA, and rhoC should exceed 0.7 while AVE should exceed 0.5loadings <- pls_model$outer_loadings
print(loadings)## PERCEIVED_EASE_OF_USE PERCEIVED_USEFULNESS CUSTOMER_SATISFACTION
## X1 0.0000000 0.0000000 0.6738877
## X2 0.0000000 0.0000000 0.6592406
## X3 0.0000000 0.0000000 0.7007803
## X4 0.0000000 0.0000000 0.7333107
## X5 0.0000000 0.0000000 0.7546512
## X6 0.0000000 0.5915308 0.0000000
## X15 0.0000000 0.7029160 0.0000000
## X16 0.0000000 0.6923378 0.0000000
## X17 0.0000000 0.7256167 0.0000000
## X18 0.0000000 0.7006082 0.0000000
## X19 0.0000000 0.7125879 0.0000000
## X20 0.0000000 0.6784401 0.0000000
## X11 0.7139242 0.0000000 0.0000000
## X12 0.7037995 0.0000000 0.0000000
## X13 0.7032649 0.0000000 0.0000000
## X14 0.7247511 0.0000000 0.0000000
## X7 0.0000000 0.0000000 0.0000000
## X8 0.0000000 0.0000000 0.0000000
## X9 0.0000000 0.0000000 0.0000000
## X10 0.0000000 0.0000000 0.0000000
## X21 0.0000000 0.0000000 0.0000000
## X22 0.0000000 0.0000000 0.0000000
## X23 0.0000000 0.0000000 0.0000000
## X24 0.0000000 0.0000000 0.0000000
## BEHAVIORAL_INTENTION
## X1 0.0000000
## X2 0.0000000
## X3 0.0000000
## X4 0.0000000
## X5 0.0000000
## X6 0.0000000
## X15 0.0000000
## X16 0.0000000
## X17 0.0000000
## X18 0.0000000
## X19 0.0000000
## X20 0.0000000
## X11 0.0000000
## X12 0.0000000
## X13 0.0000000
## X14 0.0000000
## X7 0.6997759
## X8 0.6924641
## X9 0.6755023
## X10 0.7018349
## X21 0.5532190
## X22 0.5937990
## X23 0.6516317
## X24 0.6176454boot_model <- bootstrap_model(
seminr_model = pls_model,
nboot = 1000,
cores = 1
)## Bootstrapping model using seminr...## SEMinR Model successfully bootstrappedsummary(boot_model)##
## Results from Bootstrap resamples: 1000
##
## Bootstrapped Structural Paths:
## Original Est. Bootstrap Mean
## PERCEIVED_EASE_OF_USE -> PERCEIVED_USEFULNESS 0.707 0.703
## PERCEIVED_EASE_OF_USE -> CUSTOMER_SATISFACTION 0.198 0.201
## PERCEIVED_USEFULNESS -> CUSTOMER_SATISFACTION 0.603 0.600
## PERCEIVED_USEFULNESS -> BEHAVIORAL_INTENTION 0.486 0.484
## CUSTOMER_SATISFACTION -> BEHAVIORAL_INTENTION 0.400 0.404
## Bootstrap SD T Stat. 2.5% CI
## PERCEIVED_EASE_OF_USE -> PERCEIVED_USEFULNESS 0.051 13.913 0.592
## PERCEIVED_EASE_OF_USE -> CUSTOMER_SATISFACTION 0.067 2.947 0.064
## PERCEIVED_USEFULNESS -> CUSTOMER_SATISFACTION 0.052 11.651 0.502
## PERCEIVED_USEFULNESS -> BEHAVIORAL_INTENTION 0.080 6.075 0.326
## CUSTOMER_SATISFACTION -> BEHAVIORAL_INTENTION 0.072 5.593 0.270
## 97.5% CI Bootstrap P Val
## PERCEIVED_EASE_OF_USE -> PERCEIVED_USEFULNESS 0.787 0.000
## PERCEIVED_EASE_OF_USE -> CUSTOMER_SATISFACTION 0.321 0.002
## PERCEIVED_USEFULNESS -> CUSTOMER_SATISFACTION 0.697 0.000
## PERCEIVED_USEFULNESS -> BEHAVIORAL_INTENTION 0.635 0.000
## CUSTOMER_SATISFACTION -> BEHAVIORAL_INTENTION 0.543 0.000
##
## Bootstrapped Weights:
## Original Est. Bootstrap Mean Bootstrap SD
## X1 -> CUSTOMER_SATISFACTION 0.267 0.272 0.032
## X2 -> CUSTOMER_SATISFACTION 0.267 0.267 0.016
## X3 -> CUSTOMER_SATISFACTION 0.291 0.291 0.015
## X4 -> CUSTOMER_SATISFACTION 0.285 0.285 0.015
## X5 -> CUSTOMER_SATISFACTION 0.306 0.306 0.015
## X6 -> PERCEIVED_USEFULNESS 0.193 0.193 0.019
## X15 -> PERCEIVED_USEFULNESS 0.210 0.211 0.013
## X16 -> PERCEIVED_USEFULNESS 0.213 0.214 0.014
## X17 -> PERCEIVED_USEFULNESS 0.207 0.208 0.012
## X18 -> PERCEIVED_USEFULNESS 0.206 0.207 0.011
## X19 -> PERCEIVED_USEFULNESS 0.210 0.211 0.011
## X20 -> PERCEIVED_USEFULNESS 0.216 0.217 0.014
## X11 -> PERCEIVED_EASE_OF_USE 0.344 0.344 0.022
## X12 -> PERCEIVED_EASE_OF_USE 0.390 0.392 0.031
## X13 -> PERCEIVED_EASE_OF_USE 0.340 0.342 0.023
## X14 -> PERCEIVED_EASE_OF_USE 0.333 0.334 0.021
## X7 -> BEHAVIORAL_INTENTION 0.228 0.227 0.022
## X8 -> BEHAVIORAL_INTENTION 0.207 0.207 0.015
## X9 -> BEHAVIORAL_INTENTION 0.198 0.199 0.015
## X10 -> BEHAVIORAL_INTENTION 0.222 0.222 0.016
## X21 -> BEHAVIORAL_INTENTION 0.145 0.144 0.012
## X22 -> BEHAVIORAL_INTENTION 0.164 0.166 0.014
## X23 -> BEHAVIORAL_INTENTION 0.182 0.183 0.014
## X24 -> BEHAVIORAL_INTENTION 0.181 0.182 0.017
## T Stat. 2.5% CI 97.5% CI Bootstrap P Val
## X1 -> CUSTOMER_SATISFACTION 8.275 0.220 0.348 0.000
## X2 -> CUSTOMER_SATISFACTION 17.173 0.237 0.298 0.000
## X3 -> CUSTOMER_SATISFACTION 18.850 0.262 0.322 0.000
## X4 -> CUSTOMER_SATISFACTION 19.027 0.258 0.317 0.000
## X5 -> CUSTOMER_SATISFACTION 20.687 0.277 0.336 0.000
## X6 -> PERCEIVED_USEFULNESS 9.971 0.154 0.234 0.000
## X15 -> PERCEIVED_USEFULNESS 16.353 0.190 0.239 0.000
## X16 -> PERCEIVED_USEFULNESS 15.446 0.192 0.246 0.000
## X17 -> PERCEIVED_USEFULNESS 17.290 0.188 0.234 0.000
## X18 -> PERCEIVED_USEFULNESS 19.450 0.189 0.230 0.000
## X19 -> PERCEIVED_USEFULNESS 18.620 0.192 0.234 0.000
## X20 -> PERCEIVED_USEFULNESS 14.978 0.191 0.247 0.000
## X11 -> PERCEIVED_EASE_OF_USE 15.770 0.305 0.391 0.000
## X12 -> PERCEIVED_EASE_OF_USE 12.760 0.341 0.462 0.000
## X13 -> PERCEIVED_EASE_OF_USE 14.638 0.302 0.393 0.000
## X14 -> PERCEIVED_EASE_OF_USE 16.155 0.297 0.380 0.000
## X7 -> BEHAVIORAL_INTENTION 10.400 0.188 0.277 0.000
## X8 -> BEHAVIORAL_INTENTION 13.709 0.181 0.239 0.000
## X9 -> BEHAVIORAL_INTENTION 13.189 0.174 0.232 0.000
## X10 -> BEHAVIORAL_INTENTION 13.537 0.195 0.258 0.000
## X21 -> BEHAVIORAL_INTENTION 11.782 0.118 0.167 0.000
## X22 -> BEHAVIORAL_INTENTION 11.457 0.141 0.199 0.000
## X23 -> BEHAVIORAL_INTENTION 13.208 0.158 0.212 0.000
## X24 -> BEHAVIORAL_INTENTION 10.369 0.149 0.218 0.000
##
## Bootstrapped Loadings:
## Original Est. Bootstrap Mean Bootstrap SD
## X1 -> CUSTOMER_SATISFACTION 0.674 0.675 0.039
## X2 -> CUSTOMER_SATISFACTION 0.659 0.655 0.043
## X3 -> CUSTOMER_SATISFACTION 0.701 0.697 0.042
## X4 -> CUSTOMER_SATISFACTION 0.733 0.731 0.033
## X5 -> CUSTOMER_SATISFACTION 0.755 0.751 0.031
## X6 -> PERCEIVED_USEFULNESS 0.592 0.588 0.054
## X15 -> PERCEIVED_USEFULNESS 0.703 0.702 0.037
## X16 -> PERCEIVED_USEFULNESS 0.692 0.690 0.036
## X17 -> PERCEIVED_USEFULNESS 0.726 0.723 0.035
## X18 -> PERCEIVED_USEFULNESS 0.701 0.697 0.040
## X19 -> PERCEIVED_USEFULNESS 0.713 0.709 0.039
## X20 -> PERCEIVED_USEFULNESS 0.678 0.675 0.041
## X11 -> PERCEIVED_EASE_OF_USE 0.714 0.710 0.040
## X12 -> PERCEIVED_EASE_OF_USE 0.704 0.702 0.035
## X13 -> PERCEIVED_EASE_OF_USE 0.703 0.701 0.038
## X14 -> PERCEIVED_EASE_OF_USE 0.725 0.721 0.039
## X7 -> BEHAVIORAL_INTENTION 0.700 0.696 0.047
## X8 -> BEHAVIORAL_INTENTION 0.692 0.690 0.034
## X9 -> BEHAVIORAL_INTENTION 0.676 0.674 0.037
## X10 -> BEHAVIORAL_INTENTION 0.702 0.699 0.039
## X21 -> BEHAVIORAL_INTENTION 0.553 0.547 0.061
## X22 -> BEHAVIORAL_INTENTION 0.594 0.593 0.042
## X23 -> BEHAVIORAL_INTENTION 0.652 0.648 0.048
## X24 -> BEHAVIORAL_INTENTION 0.618 0.614 0.055
## T Stat. 2.5% CI 97.5% CI Bootstrap P Val
## X1 -> CUSTOMER_SATISFACTION 17.074 0.592 0.744 0.000
## X2 -> CUSTOMER_SATISFACTION 15.368 0.563 0.731 0.000
## X3 -> CUSTOMER_SATISFACTION 16.710 0.601 0.765 0.000
## X4 -> CUSTOMER_SATISFACTION 22.351 0.662 0.789 0.000
## X5 -> CUSTOMER_SATISFACTION 24.242 0.684 0.806 0.000
## X6 -> PERCEIVED_USEFULNESS 10.951 0.467 0.677 0.000
## X15 -> PERCEIVED_USEFULNESS 18.993 0.623 0.767 0.000
## X16 -> PERCEIVED_USEFULNESS 19.180 0.611 0.753 0.000
## X17 -> PERCEIVED_USEFULNESS 20.964 0.647 0.782 0.000
## X18 -> PERCEIVED_USEFULNESS 17.718 0.611 0.769 0.000
## X19 -> PERCEIVED_USEFULNESS 18.434 0.626 0.773 0.000
## X20 -> PERCEIVED_USEFULNESS 16.424 0.589 0.746 0.000
## X11 -> PERCEIVED_EASE_OF_USE 18.042 0.626 0.778 0.000
## X12 -> PERCEIVED_EASE_OF_USE 20.189 0.626 0.762 0.000
## X13 -> PERCEIVED_EASE_OF_USE 18.341 0.613 0.768 0.000
## X14 -> PERCEIVED_EASE_OF_USE 18.370 0.635 0.786 0.000
## X7 -> BEHAVIORAL_INTENTION 14.864 0.591 0.772 0.000
## X8 -> BEHAVIORAL_INTENTION 20.504 0.616 0.749 0.000
## X9 -> BEHAVIORAL_INTENTION 18.228 0.594 0.741 0.000
## X10 -> BEHAVIORAL_INTENTION 18.190 0.617 0.764 0.000
## X21 -> BEHAVIORAL_INTENTION 9.083 0.415 0.651 0.000
## X22 -> BEHAVIORAL_INTENTION 14.080 0.507 0.667 0.000
## X23 -> BEHAVIORAL_INTENTION 13.513 0.545 0.732 0.000
## X24 -> BEHAVIORAL_INTENTION 11.280 0.491 0.703 0.000
##
## Bootstrapped HTMT:
## Original Est. Bootstrap Mean
## PERCEIVED_EASE_OF_USE -> PERCEIVED_USEFULNESS 0.948 0.949
## PERCEIVED_EASE_OF_USE -> CUSTOMER_SATISFACTION 0.878 0.881
## PERCEIVED_EASE_OF_USE -> BEHAVIORAL_INTENTION 0.936 0.941
## PERCEIVED_USEFULNESS -> CUSTOMER_SATISFACTION 0.954 0.954
## PERCEIVED_USEFULNESS -> BEHAVIORAL_INTENTION 0.961 0.963
## CUSTOMER_SATISFACTION -> BEHAVIORAL_INTENTION 0.969 0.972
## Bootstrap SD 2.5% CI 97.5% CI
## PERCEIVED_EASE_OF_USE -> PERCEIVED_USEFULNESS 0.046 0.855 1.035
## PERCEIVED_EASE_OF_USE -> CUSTOMER_SATISFACTION 0.077 0.721 1.018
## PERCEIVED_EASE_OF_USE -> BEHAVIORAL_INTENTION 0.044 0.860 1.026
## PERCEIVED_USEFULNESS -> CUSTOMER_SATISFACTION 0.037 0.877 1.023
## PERCEIVED_USEFULNESS -> BEHAVIORAL_INTENTION 0.030 0.901 1.018
## CUSTOMER_SATISFACTION -> BEHAVIORAL_INTENTION 0.039 0.895 1.043
## Bootstrap P Val
## PERCEIVED_EASE_OF_USE -> PERCEIVED_USEFULNESS 0.228
## PERCEIVED_EASE_OF_USE -> CUSTOMER_SATISFACTION 0.104
## PERCEIVED_EASE_OF_USE -> BEHAVIORAL_INTENTION 0.194
## PERCEIVED_USEFULNESS -> CUSTOMER_SATISFACTION 0.208
## PERCEIVED_USEFULNESS -> BEHAVIORAL_INTENTION 0.212
## CUSTOMER_SATISFACTION -> BEHAVIORAL_INTENTION 0.454
##
## Bootstrapped Total Paths:
## Original Est. Bootstrap Mean
## PERCEIVED_EASE_OF_USE -> PERCEIVED_USEFULNESS 0.707 0.703
## PERCEIVED_EASE_OF_USE -> CUSTOMER_SATISFACTION 0.625 0.623
## PERCEIVED_EASE_OF_USE -> BEHAVIORAL_INTENTION 0.593 0.594
## PERCEIVED_USEFULNESS -> CUSTOMER_SATISFACTION 0.603 0.600
## PERCEIVED_USEFULNESS -> BEHAVIORAL_INTENTION 0.728 0.725
## CUSTOMER_SATISFACTION -> BEHAVIORAL_INTENTION 0.400 0.404
## Bootstrap SD 2.5% CI 97.5% CI
## PERCEIVED_EASE_OF_USE -> PERCEIVED_USEFULNESS 0.051 0.592 0.787
## PERCEIVED_EASE_OF_USE -> CUSTOMER_SATISFACTION 0.057 0.509 0.729
## PERCEIVED_EASE_OF_USE -> BEHAVIORAL_INTENTION 0.050 0.489 0.688
## PERCEIVED_USEFULNESS -> CUSTOMER_SATISFACTION 0.052 0.502 0.697
## PERCEIVED_USEFULNESS -> BEHAVIORAL_INTENTION 0.049 0.617 0.812
## CUSTOMER_SATISFACTION -> BEHAVIORAL_INTENTION 0.072 0.270 0.543