SEM Analysis untuk Survey Standarisasi Bappenas (Versi Cepat)
yuli and Arifin
1. Package
pkgs <- c("lavaan","semTools","semPlot","tidyverse",
"psych","corrplot","kableExtra")
for (p in pkgs) {
if (!requireNamespace(p, quietly = TRUE)) install.packages(p)
}
suppressPackageStartupMessages({
library(lavaan); library(semTools); library(semPlot)
library(tidyverse); library(psych); library(corrplot)
library(kableExtra)
})
cat("✓ Semua package berhasil dimuat\n")✓ Semua package berhasil dimuat2. Load Data
CSV_PATH <- "Data SEM_Inggris.csv"
data_raw <- read.csv(CSV_PATH,
stringsAsFactors = FALSE,
na.strings = c("", "NA", "N/A"))
cat("Data dimuat:", nrow(data_raw), "baris ×", ncol(data_raw), "kolom\n")Data dimuat: 276 baris × 233 kolom3. Definisi Indikator (3 Item Variance Tertinggi per Konstruk)
Kenapa 3 item?
- Minimum identifikasi CFA = 3 indikator per konstruk
- Dipilih berdasarkan variance tertinggi = indikator paling informatif
- Model jauh lebih ringan: 16 × 3 = 48 parameter vs 140+ sebelumnya
- Estimasi waktu turun dari 6 jam → < 2 menit
# 3 indikator variance tertinggi per konstruk (dipilih dari data aktual)
cols_def <- list(
Importance = c("I2A13", "I2A8", "I2A12"), # Pentingnya Standar
Quality = c("I3A2", "I3A3", "I3A4"), # Peningkatan Kualitas
Efficiency = c("I4A1", "I4A4", "I4A2"), # Efisiensi Operasional
Compliance = c("I5A2", "I5A4", "I5A6"), # Kepatuhan Regulasi
CustSat = c("I6A4", "I6A3", "I6A2"), # Kepuasan Pelanggan
RiskMgt = c("I7A4", "I7A3", "I7A7"), # Manajemen Risiko
Supplier = c("I8A5", "I8A4", "I8A1"), # Manajemen Pemasok
Employee = c("I9A1", "I9A4", "I9A2"), # Keterlibatan Karyawan
Cost = c("I10A1", "I10A4", "I10A2"), # Cost Effectiveness
Innovation = c("I11A8", "I11A5", "I11A7"), # Inovasi
StdImpl = c("I12A7", "I12A6", "I12A9"), # Implementasi Standar
MarketPerf = c("I13A10","I13A9", "I13A3"), # Market Performance
FinancialPerf = c("I14A7", "I14A1", "I14A4"), # Financial Performance
EffPerf = c("I15A5", "I15A6", "I15A4"), # Efficiency Performance
QualComp = c("I16A10","I16A3", "I16A5"), # Quality Competition
GovProgram = c("I17A4", "I17A1", "I17A3") # Government Program
)
# Tampilkan ringkasan
tbl_cols <- tibble(
Konstruk = names(cols_def),
Indikator = sapply(cols_def, paste, collapse = ", ")
)
kable(tbl_cols, caption = "Indikator Terpilih (3 variance tertinggi per konstruk)") %>%
kable_styling(bootstrap_options = c("striped","hover"), full_width = FALSE)| Konstruk | Indikator |
|---|---|
| Importance | I2A13, I2A8, I2A12 |
| Quality | I3A2, I3A3, I3A4 |
| Efficiency | I4A1, I4A4, I4A2 |
| Compliance | I5A2, I5A4, I5A6 |
| CustSat | I6A4, I6A3, I6A2 |
| RiskMgt | I7A4, I7A3, I7A7 |
| Supplier | I8A5, I8A4, I8A1 |
| Employee | I9A1, I9A4, I9A2 |
| Cost | I10A1, I10A4, I10A2 |
| Innovation | I11A8, I11A5, I11A7 |
| StdImpl | I12A7, I12A6, I12A9 |
| MarketPerf | I13A10, I13A9, I13A3 |
| FinancialPerf | I14A7, I14A1, I14A4 |
| EffPerf | I15A5, I15A6, I15A4 |
| QualComp | I16A10, I16A3, I16A5 |
| GovProgram | I17A4, I17A1, I17A3 |
4. Subset, Konversi & Missing Data
all_cols <- unique(unlist(cols_def))
df <- data_raw[, all_cols]
df[] <- lapply(df, function(x) suppressWarnings(as.numeric(as.character(x))))
# Hapus baris >30% missing
n_awal <- nrow(df)
df_clean <- df[rowMeans(is.na(df)) <= 0.30, ]
# Imputasi median
df_imp <- df_clean
for (col in names(df_imp)) {
med <- median(df_imp[[col]], na.rm = TRUE)
if (!is.na(med)) df_imp[[col]][is.na(df_imp[[col]])] <- med
}
cat(sprintf("Baris awal: %d → setelah cleaning: %d\n", n_awal, nrow(df_imp)))Baris awal: 276 → setelah cleaning: 276Missing tersisa: 0 5. Statistik Deskriptif
desc <- psych::describe(df_imp)[, c("n","mean","sd","median","min","max","skew","kurtosis")]
kable(round(desc, 3), caption = "Statistik Deskriptif") %>%
kable_styling(bootstrap_options = c("striped","hover","condensed"),
full_width = FALSE, font_size = 11) %>%
scroll_box(height = "350px")| n | mean | sd | median | min | max | skew | kurtosis | |
|---|---|---|---|---|---|---|---|---|
| I2A13 | 276 | 6.072 | 1.049 | 6 | 1 | 7 | -1.312 | 2.052 |
| I2A8 | 276 | 6.051 | 1.019 | 6 | 2 | 7 | -1.109 | 0.956 |
| I2A12 | 276 | 6.127 | 0.939 | 6 | 1 | 7 | -1.276 | 2.586 |
| I3A2 | 276 | 6.029 | 0.983 | 6 | 2 | 7 | -1.110 | 1.416 |
| I3A3 | 276 | 6.091 | 0.920 | 6 | 3 | 7 | -0.905 | 0.308 |
| I3A4 | 276 | 6.098 | 0.915 | 6 | 1 | 7 | -1.214 | 2.743 |
| I4A1 | 276 | 5.975 | 1.063 | 6 | 1 | 7 | -1.307 | 2.144 |
| I4A4 | 276 | 6.043 | 0.968 | 6 | 1 | 7 | -1.166 | 2.182 |
| I4A2 | 276 | 5.971 | 0.949 | 6 | 3 | 7 | -0.731 | -0.113 |
| I5A2 | 276 | 6.109 | 0.871 | 6 | 3 | 7 | -0.835 | 0.392 |
| I5A4 | 276 | 6.130 | 0.868 | 6 | 3 | 7 | -0.917 | 0.451 |
| I5A6 | 276 | 6.065 | 0.797 | 6 | 3 | 7 | -0.803 | 0.737 |
| I6A4 | 276 | 6.022 | 0.930 | 6 | 3 | 7 | -0.800 | 0.084 |
| I6A3 | 276 | 6.047 | 0.927 | 6 | 3 | 7 | -0.829 | 0.139 |
| I6A2 | 276 | 6.087 | 0.844 | 6 | 3 | 7 | -0.743 | 0.369 |
| I7A4 | 276 | 6.000 | 0.950 | 6 | 3 | 7 | -0.787 | -0.024 |
| I7A3 | 276 | 6.101 | 0.921 | 6 | 3 | 7 | -0.925 | 0.339 |
| I7A7 | 276 | 6.051 | 0.909 | 6 | 3 | 7 | -0.794 | 0.150 |
| I8A5 | 276 | 6.062 | 0.914 | 6 | 3 | 7 | -0.889 | 0.461 |
| I8A4 | 276 | 6.112 | 0.910 | 6 | 1 | 7 | -1.175 | 2.841 |
| I8A1 | 276 | 6.105 | 0.882 | 6 | 4 | 7 | -0.743 | -0.211 |
| I9A1 | 276 | 5.978 | 0.976 | 6 | 3 | 7 | -0.752 | -0.105 |
| I9A4 | 276 | 6.004 | 0.868 | 6 | 3 | 7 | -0.573 | -0.209 |
| I9A2 | 276 | 6.054 | 0.857 | 6 | 4 | 7 | -0.621 | -0.299 |
| I10A1 | 276 | 5.808 | 1.126 | 6 | 3 | 7 | -0.731 | -0.297 |
| I10A4 | 276 | 5.942 | 1.064 | 6 | 2 | 7 | -1.005 | 0.569 |
| I10A2 | 276 | 5.899 | 1.022 | 6 | 3 | 7 | -0.673 | -0.409 |
| I11A8 | 276 | 6.014 | 1.044 | 6 | 1 | 7 | -1.212 | 2.000 |
| I11A5 | 276 | 6.014 | 1.020 | 6 | 2 | 7 | -1.115 | 1.117 |
| I11A7 | 276 | 6.014 | 0.980 | 6 | 3 | 7 | -0.930 | 0.405 |
| I12A7 | 276 | 4.522 | 2.051 | 5 | 1 | 7 | -0.384 | -1.132 |
| I12A6 | 276 | 4.743 | 2.006 | 5 | 1 | 7 | -0.554 | -0.992 |
| I12A9 | 276 | 4.696 | 1.986 | 5 | 1 | 7 | -0.538 | -0.930 |
| I13A10 | 276 | 5.870 | 1.088 | 6 | 1 | 7 | -1.921 | 5.329 |
| I13A9 | 276 | 5.819 | 1.133 | 6 | 1 | 7 | -1.676 | 4.173 |
| I13A3 | 276 | 5.844 | 1.076 | 6 | 2 | 7 | -1.295 | 2.130 |
| I14A7 | 276 | 5.732 | 1.122 | 6 | 2 | 7 | -0.832 | 0.127 |
| I14A1 | 276 | 5.920 | 1.013 | 6 | 2 | 7 | -0.845 | 0.481 |
| I14A4 | 276 | 5.888 | 0.997 | 6 | 2 | 7 | -1.046 | 1.335 |
| I15A5 | 276 | 5.681 | 1.154 | 6 | 1 | 7 | -1.357 | 2.693 |
| I15A6 | 276 | 5.656 | 1.138 | 6 | 1 | 7 | -0.850 | 0.738 |
| I15A4 | 276 | 5.638 | 1.112 | 6 | 1 | 7 | -0.885 | 0.905 |
| I16A10 | 276 | 5.837 | 1.078 | 6 | 1 | 7 | -1.617 | 4.162 |
| I16A3 | 276 | 5.808 | 1.028 | 6 | 2 | 7 | -0.711 | 0.072 |
| I16A5 | 276 | 5.931 | 1.016 | 6 | 1 | 7 | -1.419 | 3.074 |
| I17A4 | 276 | 5.522 | 1.266 | 6 | 1 | 7 | -1.055 | 1.045 |
| I17A1 | 276 | 5.275 | 1.229 | 5 | 1 | 7 | -0.720 | 0.708 |
| I17A3 | 276 | 5.471 | 1.243 | 6 | 1 | 7 | -0.829 | 0.302 |
6. Reliabilitas (Cronbach Alpha)
calc_alpha <- function(data, cols, nama) {
ok <- cols[cols %in% names(data)]
if (length(ok) < 2) return(data.frame(Konstruk=nama, Alpha=NA, N=length(ok), Status="Tidak cukup"))
a <- tryCatch(psych::alpha(data[,ok,drop=FALSE], check.keys=TRUE, warnings=FALSE),
error = function(e) NULL)
if (is.null(a)) return(data.frame(Konstruk=nama, Alpha=NA, N=length(ok), Status="Error"))
data.frame(
Konstruk = nama,
Alpha = round(a$total$raw_alpha, 3),
N = length(ok),
Status = ifelse(a$total$raw_alpha >= 0.70, "Reliabel ✓", "Perlu Review ✗")
)
}
alpha_tbl <- bind_rows(lapply(names(cols_def), function(k)
calc_alpha(df_imp, cols_def[[k]], k)))
kable(alpha_tbl, caption = "Cronbach Alpha per Konstruk") %>%
kable_styling(bootstrap_options = c("striped","hover"), full_width = FALSE) %>%
row_spec(which(alpha_tbl$Status == "Perlu Review ✗"), background = "#ffe0e0")| Konstruk | Alpha | N | Status |
|---|---|---|---|
| Importance | 0.739 | 3 | Reliabel ✓ |
| Quality | 0.797 | 3 | Reliabel ✓ |
| Efficiency | 0.781 | 3 | Reliabel ✓ |
| Compliance | 0.683 | 3 | Perlu Review ✗ |
| CustSat | 0.834 | 3 | Reliabel ✓ |
| RiskMgt | 0.830 | 3 | Reliabel ✓ |
| Supplier | 0.777 | 3 | Reliabel ✓ |
| Employee | 0.850 | 3 | Reliabel ✓ |
| Cost | 0.872 | 3 | Reliabel ✓ |
| Innovation | 0.829 | 3 | Reliabel ✓ |
| StdImpl | 0.953 | 3 | Reliabel ✓ |
| MarketPerf | 0.629 | 3 | Perlu Review ✗ |
| FinancialPerf | 0.816 | 3 | Reliabel ✓ |
| EffPerf | 0.851 | 3 | Reliabel ✓ |
| QualComp | 0.712 | 3 | Reliabel ✓ |
| GovProgram | 0.866 | 3 | Reliabel ✓ |
7. Matriks Korelasi
rmeans <- function(data, cols) rowMeans(data[, cols, drop=FALSE], na.rm=TRUE)
df_scores <- as.data.frame(lapply(cols_def, function(c) rmeans(df_imp, c)))
cor_mat <- cor(df_scores, use = "pairwise.complete.obs")
corrplot::corrplot(cor_mat,
method = "color", type = "upper", order = "hclust",
addCoef.col = "black", number.cex = 0.55, tl.cex = 0.70,
col = colorRampPalette(c("#E84855","white","#2E86AB"))(200),
title = "Korelasi Antar Konstruk", mar = c(0,0,2,0))
8. CFA (Confirmatory Factor Analysis)
Menggunakan MLR — valid untuk Likert 1–7 dengan n > 200 (Rhemtulla et al., 2012). Jauh lebih cepat dari WLSMV.
# Buat syntax CFA otomatis dari cols_def
make_meas <- function(cd) {
paste(mapply(function(k,v) paste0(" ", k, " =~ ", paste(v, collapse="+")),
names(cd), cd), collapse="\n")
}
cfa_syntax <- paste0("# MEASUREMENT MODEL\n", make_meas(cols_def))
cat(cfa_syntax)# MEASUREMENT MODEL
Importance =~ I2A13+I2A8+I2A12
Quality =~ I3A2+I3A3+I3A4
Efficiency =~ I4A1+I4A4+I4A2
Compliance =~ I5A2+I5A4+I5A6
CustSat =~ I6A4+I6A3+I6A2
RiskMgt =~ I7A4+I7A3+I7A7
Supplier =~ I8A5+I8A4+I8A1
Employee =~ I9A1+I9A4+I9A2
Cost =~ I10A1+I10A4+I10A2
Innovation =~ I11A8+I11A5+I11A7
StdImpl =~ I12A7+I12A6+I12A9
MarketPerf =~ I13A10+I13A9+I13A3
FinancialPerf =~ I14A7+I14A1+I14A4
EffPerf =~ I15A5+I15A6+I15A4
QualComp =~ I16A10+I16A3+I16A5
GovProgram =~ I17A4+I17A1+I17A3Menjalankan CFA...fit_cfa <- cfa(
model = cfa_syntax,
data = df_imp,
estimator = "MLR", # cepat, valid untuk Likert 7 poin
std.lv = FALSE
)
summary(fit_cfa, fit.measures = TRUE, standardized = TRUE, rsquare = TRUE)lavaan 0.6-21 ended normally after 216 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 216
Number of observations 276
Model Test User Model:
Standard Scaled
Test Statistic 1743.144 1430.357
Degrees of freedom 960 960
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.219
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 10262.124 7999.105
Degrees of freedom 1128 1128
P-value 0.000 0.000
Scaling correction factor 1.283
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.914 0.932
Tucker-Lewis Index (TLI) 0.899 0.920
Robust Comparative Fit Index (CFI) 0.935
Robust Tucker-Lewis Index (TLI) 0.924
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -15070.999 -15070.999
Scaling correction factor 1.618
for the MLR correction
Loglikelihood unrestricted model (H1) -14199.427 -14199.427
Scaling correction factor 1.292
for the MLR correction
Akaike (AIC) 30573.998 30573.998
Bayesian (BIC) 31356.005 31356.005
Sample-size adjusted Bayesian (SABIC) 30671.105 30671.105
Root Mean Square Error of Approximation:
RMSEA 0.054 0.042
90 Percent confidence interval - lower 0.050 0.038
90 Percent confidence interval - upper 0.058 0.046
P-value H_0: RMSEA <= 0.050 0.039 0.999
P-value H_0: RMSEA >= 0.080 0.000 0.000
Robust RMSEA 0.047
90 Percent confidence interval - lower 0.041
90 Percent confidence interval - upper 0.051
P-value H_0: Robust RMSEA <= 0.050 0.875
P-value H_0: Robust RMSEA >= 0.080 0.000
Standardized Root Mean Square Residual:
SRMR 0.047 0.047
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
Importance =~
I2A13 1.000 0.847 0.809
I2A8 0.931 0.077 12.069 0.000 0.788 0.775
I2A12 0.561 0.087 6.435 0.000 0.475 0.506
Quality =~
I3A2 1.000 0.746 0.760
I3A3 0.935 0.074 12.600 0.000 0.697 0.759
I3A4 0.907 0.080 11.377 0.000 0.676 0.740
Efficiency =~
I4A1 1.000 0.786 0.740
I4A4 0.875 0.089 9.841 0.000 0.687 0.711
I4A2 0.923 0.078 11.764 0.000 0.725 0.765
Compliance =~
I5A2 1.000 0.667 0.766
I5A4 1.027 0.070 14.674 0.000 0.684 0.790
I5A6 0.525 0.087 6.033 0.000 0.350 0.440
CustSat =~
I6A4 1.000 0.752 0.810
I6A3 1.000 0.059 17.045 0.000 0.753 0.813
I6A2 0.841 0.064 13.089 0.000 0.633 0.751
RiskMgt =~
I7A4 1.000 0.725 0.764
I7A3 1.029 0.071 14.453 0.000 0.746 0.811
I7A7 0.988 0.067 14.733 0.000 0.716 0.789
Supplier =~
I8A5 1.000 0.664 0.728
I8A4 1.041 0.086 12.099 0.000 0.691 0.761
I8A1 0.945 0.090 10.464 0.000 0.628 0.713
Employee =~
I9A1 1.000 0.790 0.812
I9A4 0.916 0.057 15.999 0.000 0.724 0.836
I9A2 0.853 0.060 14.311 0.000 0.674 0.787
Cost =~
I10A1 1.000 0.939 0.835
I10A4 0.953 0.051 18.840 0.000 0.895 0.843
I10A2 0.895 0.058 15.436 0.000 0.841 0.824
Innovation =~
I11A8 1.000 0.835 0.801
I11A5 0.940 0.055 16.937 0.000 0.785 0.771
I11A7 0.921 0.063 14.671 0.000 0.769 0.787
StdImpl =~
I12A7 1.000 1.866 0.911
I12A6 1.009 0.038 26.789 0.000 1.882 0.940
I12A9 1.009 0.037 27.292 0.000 1.883 0.950
MarketPerf =~
I13A10 1.000 0.480 0.442
I13A9 1.325 0.248 5.337 0.000 0.636 0.562
I13A3 1.663 0.342 4.859 0.000 0.798 0.743
FinancialPerf =~
I14A7 1.000 0.851 0.760
I14A1 0.933 0.078 12.004 0.000 0.793 0.785
I14A4 0.920 0.082 11.177 0.000 0.783 0.786
EffPerf =~
I15A5 1.000 0.899 0.781
I15A6 1.104 0.060 18.402 0.000 0.993 0.874
I15A4 0.953 0.090 10.604 0.000 0.857 0.772
QualComp =~
I16A10 1.000 0.636 0.591
I16A3 1.182 0.174 6.789 0.000 0.752 0.733
I16A5 1.114 0.136 8.220 0.000 0.709 0.699
GovProgram =~
I17A4 1.000 1.122 0.888
I17A1 0.822 0.063 13.037 0.000 0.922 0.752
I17A3 0.938 0.053 17.777 0.000 1.052 0.848
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Importance ~~
Quality 0.519 0.073 7.105 0.000 0.822 0.822
Efficiency 0.578 0.092 6.253 0.000 0.869 0.869
Compliance 0.419 0.059 7.061 0.000 0.743 0.743
CustSat 0.504 0.068 7.421 0.000 0.790 0.790
RiskMgt 0.483 0.075 6.486 0.000 0.788 0.788
Supplier 0.424 0.065 6.561 0.000 0.754 0.754
Employee 0.457 0.066 6.960 0.000 0.683 0.683
Cost 0.568 0.095 5.965 0.000 0.714 0.714
Innovation 0.555 0.100 5.576 0.000 0.785 0.785
StdImpl 0.033 0.094 0.350 0.726 0.021 0.021
MarketPerf 0.262 0.047 5.545 0.000 0.646 0.646
FinancialPerf 0.470 0.086 5.477 0.000 0.653 0.653
EffPerf 0.403 0.073 5.485 0.000 0.529 0.529
QualComp 0.369 0.070 5.250 0.000 0.685 0.685
GovProgram 0.491 0.095 5.154 0.000 0.517 0.517
Quality ~~
Efficiency 0.506 0.061 8.265 0.000 0.863 0.863
Compliance 0.388 0.056 6.928 0.000 0.779 0.779
CustSat 0.467 0.062 7.554 0.000 0.832 0.832
RiskMgt 0.443 0.059 7.513 0.000 0.819 0.819
Supplier 0.378 0.056 6.742 0.000 0.762 0.762
Employee 0.456 0.056 8.112 0.000 0.773 0.773
Cost 0.520 0.066 7.866 0.000 0.742 0.742
Innovation 0.437 0.058 7.585 0.000 0.701 0.701
StdImpl 0.027 0.085 0.311 0.755 0.019 0.019
MarketPerf 0.266 0.051 5.182 0.000 0.743 0.743
FinancialPerf 0.448 0.072 6.242 0.000 0.705 0.705
EffPerf 0.370 0.066 5.630 0.000 0.551 0.551
QualComp 0.358 0.064 5.579 0.000 0.753 0.753
GovProgram 0.398 0.082 4.872 0.000 0.476 0.476
Efficiency ~~
Compliance 0.439 0.055 8.012 0.000 0.838 0.838
CustSat 0.489 0.057 8.606 0.000 0.827 0.827
RiskMgt 0.479 0.062 7.679 0.000 0.841 0.841
Supplier 0.428 0.054 7.921 0.000 0.821 0.821
Employee 0.505 0.059 8.632 0.000 0.814 0.814
Cost 0.645 0.092 7.018 0.000 0.874 0.874
Innovation 0.562 0.086 6.571 0.000 0.857 0.857
StdImpl 0.058 0.092 0.631 0.528 0.040 0.040
MarketPerf 0.218 0.045 4.790 0.000 0.578 0.578
FinancialPerf 0.458 0.080 5.710 0.000 0.685 0.685
EffPerf 0.434 0.073 5.952 0.000 0.614 0.614
QualComp 0.350 0.053 6.550 0.000 0.700 0.700
GovProgram 0.422 0.076 5.563 0.000 0.478 0.478
Compliance ~~
CustSat 0.469 0.055 8.511 0.000 0.934 0.934
RiskMgt 0.427 0.052 8.149 0.000 0.884 0.884
Supplier 0.380 0.054 7.094 0.000 0.857 0.857
Employee 0.411 0.051 8.059 0.000 0.780 0.780
Cost 0.451 0.053 8.518 0.000 0.721 0.721
Innovation 0.377 0.051 7.438 0.000 0.676 0.676
StdImpl 0.103 0.081 1.274 0.203 0.083 0.083
MarketPerf 0.242 0.049 4.903 0.000 0.758 0.758
FinancialPerf 0.365 0.059 6.187 0.000 0.644 0.644
EffPerf 0.325 0.056 5.814 0.000 0.543 0.543
QualComp 0.298 0.050 5.999 0.000 0.703 0.703
GovProgram 0.312 0.063 4.971 0.000 0.417 0.417
CustSat ~~
RiskMgt 0.517 0.064 8.124 0.000 0.949 0.949
Supplier 0.433 0.057 7.561 0.000 0.867 0.867
Employee 0.512 0.060 8.575 0.000 0.861 0.861
Cost 0.578 0.064 9.049 0.000 0.818 0.818
Innovation 0.509 0.058 8.834 0.000 0.810 0.810
StdImpl 0.157 0.084 1.865 0.062 0.112 0.112
MarketPerf 0.284 0.053 5.377 0.000 0.787 0.787
FinancialPerf 0.454 0.067 6.775 0.000 0.709 0.709
EffPerf 0.415 0.065 6.338 0.000 0.613 0.613
QualComp 0.362 0.057 6.385 0.000 0.757 0.757
GovProgram 0.425 0.075 5.655 0.000 0.504 0.504
RiskMgt ~~
Supplier 0.441 0.058 7.574 0.000 0.916 0.916
Employee 0.490 0.061 8.067 0.000 0.855 0.855
Cost 0.584 0.068 8.596 0.000 0.858 0.858
Innovation 0.488 0.063 7.749 0.000 0.806 0.806
StdImpl 0.111 0.085 1.302 0.193 0.082 0.082
MarketPerf 0.248 0.048 5.209 0.000 0.713 0.713
FinancialPerf 0.466 0.067 6.923 0.000 0.756 0.756
EffPerf 0.414 0.061 6.813 0.000 0.635 0.635
QualComp 0.380 0.058 6.558 0.000 0.824 0.824
GovProgram 0.403 0.073 5.523 0.000 0.496 0.496
Supplier ~~
Employee 0.467 0.057 8.173 0.000 0.890 0.890
Cost 0.481 0.063 7.628 0.000 0.771 0.771
Innovation 0.416 0.057 7.371 0.000 0.751 0.751
StdImpl 0.165 0.075 2.200 0.028 0.133 0.133
MarketPerf 0.248 0.056 4.437 0.000 0.777 0.777
FinancialPerf 0.401 0.067 6.008 0.000 0.709 0.709
EffPerf 0.371 0.061 6.127 0.000 0.622 0.622
QualComp 0.323 0.058 5.538 0.000 0.764 0.764
GovProgram 0.362 0.073 4.985 0.000 0.486 0.486
Employee ~~
Cost 0.623 0.070 8.919 0.000 0.839 0.839
Innovation 0.569 0.057 10.026 0.000 0.861 0.861
StdImpl 0.125 0.089 1.402 0.161 0.085 0.085
MarketPerf 0.269 0.055 4.913 0.000 0.708 0.708
FinancialPerf 0.484 0.069 7.066 0.000 0.720 0.720
EffPerf 0.400 0.061 6.548 0.000 0.562 0.562
QualComp 0.344 0.050 6.820 0.000 0.683 0.683
GovProgram 0.435 0.074 5.843 0.000 0.491 0.491
Cost ~~
Innovation 0.730 0.096 7.630 0.000 0.931 0.931
StdImpl 0.267 0.105 2.537 0.011 0.153 0.153
MarketPerf 0.287 0.057 5.041 0.000 0.636 0.636
FinancialPerf 0.650 0.092 7.053 0.000 0.814 0.814
EffPerf 0.569 0.077 7.354 0.000 0.674 0.674
QualComp 0.445 0.054 8.183 0.000 0.746 0.746
GovProgram 0.542 0.085 6.378 0.000 0.514 0.514
Innovation ~~
StdImpl 0.141 0.088 1.591 0.112 0.090 0.090
MarketPerf 0.245 0.053 4.627 0.000 0.612 0.612
FinancialPerf 0.539 0.085 6.347 0.000 0.758 0.758
EffPerf 0.505 0.073 6.875 0.000 0.673 0.673
QualComp 0.371 0.052 7.184 0.000 0.697 0.697
GovProgram 0.446 0.079 5.640 0.000 0.476 0.476
StdImpl ~~
MarketPerf 0.150 0.070 2.150 0.032 0.168 0.168
FinancialPerf 0.087 0.100 0.873 0.383 0.055 0.055
EffPerf 0.286 0.102 2.818 0.005 0.171 0.171
QualComp 0.121 0.080 1.516 0.130 0.102 0.102
GovProgram 0.595 0.119 4.988 0.000 0.284 0.284
MarketPerf ~~
FinancialPerf 0.358 0.068 5.291 0.000 0.876 0.876
EffPerf 0.369 0.087 4.250 0.000 0.856 0.856
QualComp 0.281 0.056 5.023 0.000 0.922 0.922
GovProgram 0.316 0.066 4.817 0.000 0.588 0.588
FinancialPerf ~~
EffPerf 0.629 0.085 7.411 0.000 0.822 0.822
QualComp 0.506 0.073 6.934 0.000 0.934 0.934
GovProgram 0.587 0.093 6.317 0.000 0.615 0.615
EffPerf ~~
QualComp 0.484 0.090 5.373 0.000 0.847 0.847
GovProgram 0.564 0.078 7.220 0.000 0.560 0.560
QualComp ~~
GovProgram 0.507 0.093 5.434 0.000 0.710 0.710
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.I2A13 0.379 0.058 6.564 0.000 0.379 0.346
.I2A8 0.413 0.064 6.468 0.000 0.413 0.399
.I2A12 0.654 0.080 8.137 0.000 0.654 0.744
.I3A2 0.406 0.078 5.221 0.000 0.406 0.422
.I3A3 0.357 0.055 6.440 0.000 0.357 0.423
.I3A4 0.377 0.067 5.652 0.000 0.377 0.452
.I4A1 0.509 0.079 6.410 0.000 0.509 0.452
.I4A4 0.461 0.101 4.543 0.000 0.461 0.494
.I4A2 0.372 0.056 6.590 0.000 0.372 0.414
.I5A2 0.312 0.046 6.725 0.000 0.312 0.412
.I5A4 0.283 0.044 6.385 0.000 0.283 0.376
.I5A6 0.511 0.062 8.177 0.000 0.511 0.807
.I6A4 0.296 0.040 7.399 0.000 0.296 0.343
.I6A3 0.290 0.035 8.267 0.000 0.290 0.338
.I6A2 0.309 0.035 8.850 0.000 0.309 0.436
.I7A4 0.373 0.043 8.784 0.000 0.373 0.416
.I7A3 0.289 0.041 7.056 0.000 0.289 0.342
.I7A7 0.311 0.040 7.829 0.000 0.311 0.378
.I8A5 0.392 0.049 8.034 0.000 0.392 0.471
.I8A4 0.347 0.088 3.920 0.000 0.347 0.421
.I8A1 0.381 0.049 7.798 0.000 0.381 0.492
.I9A1 0.324 0.047 6.920 0.000 0.324 0.341
.I9A4 0.226 0.028 8.027 0.000 0.226 0.301
.I9A2 0.278 0.034 8.105 0.000 0.278 0.380
.I10A1 0.382 0.048 7.973 0.000 0.382 0.302
.I10A4 0.327 0.040 8.154 0.000 0.327 0.290
.I10A2 0.333 0.061 5.493 0.000 0.333 0.320
.I11A8 0.389 0.126 3.091 0.002 0.389 0.358
.I11A5 0.420 0.080 5.238 0.000 0.420 0.405
.I11A7 0.365 0.052 6.959 0.000 0.365 0.381
.I12A7 0.711 0.175 4.065 0.000 0.711 0.170
.I12A6 0.469 0.097 4.848 0.000 0.469 0.117
.I12A9 0.385 0.070 5.531 0.000 0.385 0.098
.I13A10 0.949 0.196 4.840 0.000 0.949 0.805
.I13A9 0.875 0.170 5.142 0.000 0.875 0.684
.I13A3 0.517 0.082 6.303 0.000 0.517 0.448
.I14A7 0.530 0.064 8.260 0.000 0.530 0.423
.I14A1 0.393 0.062 6.352 0.000 0.393 0.384
.I14A4 0.378 0.061 6.205 0.000 0.378 0.382
.I15A5 0.517 0.097 5.343 0.000 0.517 0.390
.I15A6 0.305 0.055 5.589 0.000 0.305 0.236
.I15A4 0.497 0.082 6.087 0.000 0.497 0.404
.I16A10 0.753 0.146 5.168 0.000 0.753 0.650
.I16A3 0.488 0.087 5.626 0.000 0.488 0.463
.I16A5 0.525 0.097 5.402 0.000 0.525 0.511
.I17A4 0.339 0.056 6.065 0.000 0.339 0.212
.I17A1 0.654 0.125 5.216 0.000 0.654 0.435
.I17A3 0.432 0.061 7.056 0.000 0.432 0.281
Importance 0.717 0.135 5.292 0.000 1.000 1.000
Quality 0.557 0.079 7.079 0.000 1.000 1.000
Efficiency 0.617 0.112 5.518 0.000 1.000 1.000
Compliance 0.444 0.067 6.603 0.000 1.000 1.000
CustSat 0.566 0.073 7.766 0.000 1.000 1.000
RiskMgt 0.525 0.074 7.067 0.000 1.000 1.000
Supplier 0.441 0.069 6.361 0.000 1.000 1.000
Employee 0.625 0.074 8.498 0.000 1.000 1.000
Cost 0.882 0.107 8.203 0.000 1.000 1.000
Innovation 0.698 0.104 6.705 0.000 1.000 1.000
StdImpl 3.481 0.289 12.030 0.000 1.000 1.000
MarketPerf 0.230 0.076 3.040 0.002 1.000 1.000
FinancialPerf 0.724 0.108 6.701 0.000 1.000 1.000
EffPerf 0.809 0.141 5.736 0.000 1.000 1.000
QualComp 0.405 0.090 4.483 0.000 1.000 1.000
GovProgram 1.258 0.171 7.367 0.000 1.000 1.000
R-Square:
Estimate
I2A13 0.654
I2A8 0.601
I2A12 0.256
I3A2 0.578
I3A3 0.577
I3A4 0.548
I4A1 0.548
I4A4 0.506
I4A2 0.586
I5A2 0.588
I5A4 0.624
I5A6 0.193
I6A4 0.657
I6A3 0.662
I6A2 0.564
I7A4 0.584
I7A3 0.658
I7A7 0.622
I8A5 0.529
I8A4 0.579
I8A1 0.508
I9A1 0.659
I9A4 0.699
I9A2 0.620
I10A1 0.698
I10A4 0.710
I10A2 0.680
I11A8 0.642
I11A5 0.595
I11A7 0.619
I12A7 0.830
I12A6 0.883
I12A9 0.902
I13A10 0.195
I13A9 0.316
I13A3 0.552
I14A7 0.577
I14A1 0.616
I14A4 0.618
I15A5 0.610
I15A6 0.764
I15A4 0.596
I16A10 0.350
I16A3 0.537
I16A5 0.489
I17A4 0.788
I17A1 0.565
I17A3 0.7198.1 Fit Indices CFA
fi <- fitMeasures(fit_cfa, c("chisq","df","pvalue","cfi","tli",
"rmsea","rmsea.ci.lower","rmsea.ci.upper","srmr"))
fi_df <- data.frame(
Indeks = names(fi),
Nilai = round(fi, 4),
Cutoff = c("","",">.05",">0.90",">0.90","<0.08","","","<0.08"),
Status = c("","",
ifelse(fi["pvalue"] > 0.05,"✓","✗"),
ifelse(fi["cfi"] > 0.90,"✓","✗"),
ifelse(fi["tli"] > 0.90,"✓","✗"),
ifelse(fi["rmsea"] < 0.08,"✓","✗"),
"","",
ifelse(fi["srmr"] < 0.08,"✓","✗"))
)
kable(fi_df, row.names=FALSE, caption="Fit Indices CFA") %>%
kable_styling(bootstrap_options=c("striped","hover"), full_width=FALSE)| Indeks | Nilai | Cutoff | Status |
|---|---|---|---|
| chisq | 1743.1437 | ||
| df | 960.0000 | ||
| pvalue | 0.0000 | >.05 | ✗ |
| cfi | 0.9143 | >0.90 | ✓ |
| tli | 0.8993 | >0.90 | ✗ |
| rmsea | 0.0544 | <0.08 | ✓ |
| rmsea.ci.lower | 0.0503 | ||
| rmsea.ci.upper | 0.0584 | ||
| srmr | 0.0473 | <0.08 | ✓ |
8.2 Factor Loadings
load_df <- standardizedsolution(fit_cfa) %>%
filter(op == "=~") %>%
transmute(
Konstruk = lhs,
Indikator = rhs,
Loading = round(est.std, 3),
SE = round(se, 3),
z = round(z, 3),
p = round(pvalue, 4),
Valid = ifelse(abs(est.std) >= 0.50, "✓", "✗ Drop")
)
kable(load_df, caption="Standardized Factor Loadings (threshold ≥ 0.50)") %>%
kable_styling(bootstrap_options=c("striped","hover","condensed"),
full_width=FALSE, font_size=11) %>%
row_spec(which(load_df$Valid == "✗ Drop"), background="#ffe0e0")| Konstruk | Indikator | Loading | SE | z | p | Valid |
|---|---|---|---|---|---|---|
| Importance | I2A13 | 0.809 | 0.038 | 21.361 | 0 | ✓ |
| Importance | I2A8 | 0.775 | 0.042 | 18.626 | 0 | ✓ |
| Importance | I2A12 | 0.506 | 0.055 | 9.196 | 0 | ✓ |
| Quality | I3A2 | 0.760 | 0.039 | 19.423 | 0 | ✓ |
| Quality | I3A3 | 0.759 | 0.040 | 19.209 | 0 | ✓ |
| Quality | I3A4 | 0.740 | 0.038 | 19.512 | 0 | ✓ |
| Efficiency | I4A1 | 0.740 | 0.040 | 18.720 | 0 | ✓ |
| Efficiency | I4A4 | 0.711 | 0.049 | 14.532 | 0 | ✓ |
| Efficiency | I4A2 | 0.765 | 0.040 | 18.934 | 0 | ✓ |
| Compliance | I5A2 | 0.766 | 0.038 | 20.288 | 0 | ✓ |
| Compliance | I5A4 | 0.790 | 0.036 | 21.787 | 0 | ✓ |
| Compliance | I5A6 | 0.440 | 0.056 | 7.870 | 0 | ✗ Drop |
| CustSat | I6A4 | 0.810 | 0.029 | 28.346 | 0 | ✓ |
| CustSat | I6A3 | 0.813 | 0.027 | 30.119 | 0 | ✓ |
| CustSat | I6A2 | 0.751 | 0.031 | 24.022 | 0 | ✓ |
| RiskMgt | I7A4 | 0.764 | 0.032 | 23.899 | 0 | ✓ |
| RiskMgt | I7A3 | 0.811 | 0.031 | 25.972 | 0 | ✓ |
| RiskMgt | I7A7 | 0.789 | 0.034 | 23.512 | 0 | ✓ |
| Supplier | I8A5 | 0.728 | 0.037 | 19.898 | 0 | ✓ |
| Supplier | I8A4 | 0.761 | 0.046 | 16.685 | 0 | ✓ |
| Supplier | I8A1 | 0.713 | 0.041 | 17.400 | 0 | ✓ |
| Employee | I9A1 | 0.812 | 0.028 | 28.793 | 0 | ✓ |
| Employee | I9A4 | 0.836 | 0.023 | 35.707 | 0 | ✓ |
| Employee | I9A2 | 0.787 | 0.030 | 26.155 | 0 | ✓ |
| Cost | I10A1 | 0.835 | 0.026 | 32.235 | 0 | ✓ |
| Cost | I10A4 | 0.843 | 0.026 | 32.746 | 0 | ✓ |
| Cost | I10A2 | 0.824 | 0.032 | 25.942 | 0 | ✓ |
| Innovation | I11A8 | 0.801 | 0.058 | 13.897 | 0 | ✓ |
| Innovation | I11A5 | 0.771 | 0.042 | 18.485 | 0 | ✓ |
| Innovation | I11A7 | 0.787 | 0.032 | 24.747 | 0 | ✓ |
| StdImpl | I12A7 | 0.911 | 0.023 | 39.100 | 0 | ✓ |
| StdImpl | I12A6 | 0.940 | 0.014 | 68.456 | 0 | ✓ |
| StdImpl | I12A9 | 0.950 | 0.010 | 93.663 | 0 | ✓ |
| MarketPerf | I13A10 | 0.442 | 0.076 | 5.814 | 0 | ✗ Drop |
| MarketPerf | I13A9 | 0.562 | 0.061 | 9.273 | 0 | ✓ |
| MarketPerf | I13A3 | 0.743 | 0.049 | 15.108 | 0 | ✓ |
| FinancialPerf | I14A7 | 0.760 | 0.035 | 22.012 | 0 | ✓ |
| FinancialPerf | I14A1 | 0.785 | 0.037 | 21.036 | 0 | ✓ |
| FinancialPerf | I14A4 | 0.786 | 0.038 | 20.529 | 0 | ✓ |
| EffPerf | I15A5 | 0.781 | 0.038 | 20.381 | 0 | ✓ |
| EffPerf | I15A6 | 0.874 | 0.029 | 30.165 | 0 | ✓ |
| EffPerf | I15A4 | 0.772 | 0.039 | 19.798 | 0 | ✓ |
| QualComp | I16A10 | 0.591 | 0.056 | 10.574 | 0 | ✓ |
| QualComp | I16A3 | 0.733 | 0.052 | 14.123 | 0 | ✓ |
| QualComp | I16A5 | 0.699 | 0.051 | 13.718 | 0 | ✓ |
| GovProgram | I17A4 | 0.888 | 0.022 | 40.284 | 0 | ✓ |
| GovProgram | I17A1 | 0.752 | 0.047 | 15.876 | 0 | ✓ |
| GovProgram | I17A3 | 0.848 | 0.027 | 31.566 | 0 | ✓ |
8.3 CR & AVE
cr_ave_fn <- function(fit, k) {
s <- standardizedsolution(fit) %>% filter(op=="=~", lhs==k)
if (!nrow(s)) return(NULL)
lam <- s$est.std; err <- 1 - lam^2
cr <- sum(lam)^2 / (sum(lam)^2 + sum(err))
ave <- mean(lam^2)
data.frame(
Konstruk=k, N=length(lam), AvgLoad=round(mean(lam),3),
CR=round(cr,3), AVE=round(ave,3),
CR_OK=ifelse(cr>=0.70,"✓","✗"), AVE_OK=ifelse(ave>=0.50,"✓","✗")
)
}
cr_ave_tbl <- bind_rows(lapply(names(cols_def),
function(k) tryCatch(cr_ave_fn(fit_cfa,k), error=function(e) NULL)))
kable(cr_ave_tbl, caption="Composite Reliability & AVE") %>%
kable_styling(bootstrap_options=c("striped","hover"), full_width=FALSE) %>%
row_spec(which(cr_ave_tbl$CR_OK=="✗"), background="#ffe0e0") %>%
row_spec(which(cr_ave_tbl$AVE_OK=="✗"), background="#fff3cd")| Konstruk | N | AvgLoad | CR | AVE | CR_OK | AVE_OK |
|---|---|---|---|---|---|---|
| Importance | 3 | 0.697 | 0.746 | 0.504 | ✓ | ✓ |
| Quality | 3 | 0.753 | 0.797 | 0.568 | ✓ | ✓ |
| Efficiency | 3 | 0.739 | 0.783 | 0.547 | ✓ | ✓ |
| Compliance | 3 | 0.665 | 0.714 | 0.468 | ✓ | ✗ |
| CustSat | 3 | 0.792 | 0.835 | 0.628 | ✓ | ✓ |
| RiskMgt | 3 | 0.788 | 0.831 | 0.621 | ✓ | ✓ |
| Supplier | 3 | 0.734 | 0.778 | 0.539 | ✓ | ✓ |
| Employee | 3 | 0.812 | 0.853 | 0.659 | ✓ | ✓ |
| Cost | 3 | 0.834 | 0.873 | 0.696 | ✓ | ✓ |
| Innovation | 3 | 0.786 | 0.829 | 0.618 | ✓ | ✓ |
| StdImpl | 3 | 0.934 | 0.953 | 0.872 | ✓ | ✓ |
| MarketPerf | 3 | 0.582 | 0.612 | 0.354 | ✗ | ✗ |
| FinancialPerf | 3 | 0.777 | 0.820 | 0.604 | ✓ | ✓ |
| EffPerf | 3 | 0.809 | 0.851 | 0.657 | ✓ | ✓ |
| QualComp | 3 | 0.674 | 0.716 | 0.458 | ✓ | ✗ |
| GovProgram | 3 | 0.829 | 0.870 | 0.691 | ✓ | ✓ |
CR ≥ 0.70 dan AVE ≥ 0.50 = validitas konvergen (Fornell & Larcker, 1981)8.4 Validitas Diskriminan
lv_cor <- lavInspect(fit_cfa, "cor.lv")
ave_vec <- setNames(cr_ave_tbl$AVE, cr_ave_tbl$Konstruk)
fl_mat <- abs(lv_cor)
for (nm in rownames(fl_mat)) {
if (!is.na(ave_vec[nm])) fl_mat[nm,nm] <- sqrt(ave_vec[nm])
}
kable(round(fl_mat,3),
caption="Fornell-Larcker (diagonal=√AVE, off-diagonal=korelasi LV)") %>%
kable_styling(bootstrap_options=c("striped","hover","condensed"),
full_width=FALSE, font_size=10) %>%
scroll_box(width="100%")| Importance | Quality | Efficiency | Compliance | CustSat | RiskMgt | Supplier | Employee | Cost | Innovation | StdImpl | MarketPerf | FinancialPerf | EffPerf | QualComp | GovProgram | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Importance | 0.710 | 0.822 | 0.869 | 0.743 | 0.790 | 0.788 | 0.754 | 0.683 | 0.714 | 0.785 | 0.021 | 0.646 | 0.653 | 0.529 | 0.685 | 0.517 |
| Quality | 0.822 | 0.754 | 0.863 | 0.779 | 0.832 | 0.819 | 0.762 | 0.773 | 0.742 | 0.701 | 0.019 | 0.743 | 0.705 | 0.551 | 0.753 | 0.476 |
| Efficiency | 0.869 | 0.863 | 0.740 | 0.838 | 0.827 | 0.841 | 0.821 | 0.814 | 0.874 | 0.857 | 0.040 | 0.578 | 0.685 | 0.614 | 0.700 | 0.478 |
| Compliance | 0.743 | 0.779 | 0.838 | 0.684 | 0.934 | 0.884 | 0.857 | 0.780 | 0.721 | 0.676 | 0.083 | 0.758 | 0.644 | 0.543 | 0.703 | 0.417 |
| CustSat | 0.790 | 0.832 | 0.827 | 0.934 | 0.792 | 0.949 | 0.867 | 0.861 | 0.818 | 0.810 | 0.112 | 0.787 | 0.709 | 0.613 | 0.757 | 0.504 |
| RiskMgt | 0.788 | 0.819 | 0.841 | 0.884 | 0.949 | 0.788 | 0.916 | 0.855 | 0.858 | 0.806 | 0.082 | 0.713 | 0.756 | 0.635 | 0.824 | 0.496 |
| Supplier | 0.754 | 0.762 | 0.821 | 0.857 | 0.867 | 0.916 | 0.734 | 0.890 | 0.771 | 0.751 | 0.133 | 0.777 | 0.709 | 0.622 | 0.764 | 0.486 |
| Employee | 0.683 | 0.773 | 0.814 | 0.780 | 0.861 | 0.855 | 0.890 | 0.812 | 0.839 | 0.861 | 0.085 | 0.708 | 0.720 | 0.562 | 0.683 | 0.491 |
| Cost | 0.714 | 0.742 | 0.874 | 0.721 | 0.818 | 0.858 | 0.771 | 0.839 | 0.834 | 0.931 | 0.153 | 0.636 | 0.814 | 0.674 | 0.746 | 0.514 |
| Innovation | 0.785 | 0.701 | 0.857 | 0.676 | 0.810 | 0.806 | 0.751 | 0.861 | 0.931 | 0.786 | 0.090 | 0.612 | 0.758 | 0.673 | 0.697 | 0.476 |
| StdImpl | 0.021 | 0.019 | 0.040 | 0.083 | 0.112 | 0.082 | 0.133 | 0.085 | 0.153 | 0.090 | 0.934 | 0.168 | 0.055 | 0.171 | 0.102 | 0.284 |
| MarketPerf | 0.646 | 0.743 | 0.578 | 0.758 | 0.787 | 0.713 | 0.777 | 0.708 | 0.636 | 0.612 | 0.168 | 0.595 | 0.876 | 0.856 | 0.922 | 0.588 |
| FinancialPerf | 0.653 | 0.705 | 0.685 | 0.644 | 0.709 | 0.756 | 0.709 | 0.720 | 0.814 | 0.758 | 0.055 | 0.876 | 0.777 | 0.822 | 0.934 | 0.615 |
| EffPerf | 0.529 | 0.551 | 0.614 | 0.543 | 0.613 | 0.635 | 0.622 | 0.562 | 0.674 | 0.673 | 0.171 | 0.856 | 0.822 | 0.811 | 0.847 | 0.560 |
| QualComp | 0.685 | 0.753 | 0.700 | 0.703 | 0.757 | 0.824 | 0.764 | 0.683 | 0.746 | 0.697 | 0.102 | 0.922 | 0.934 | 0.847 | 0.677 | 0.710 |
| GovProgram | 0.517 | 0.476 | 0.478 | 0.417 | 0.504 | 0.496 | 0.486 | 0.491 | 0.514 | 0.476 | 0.284 | 0.588 | 0.615 | 0.560 | 0.710 | 0.831 |
9. Model SEM Penuh
sem_syntax <- paste0(
"# MEASUREMENT\n", make_meas(cols_def), "
# STRUKTURAL
# H1 – Importance → Mediator
Quality ~ Importance
Efficiency ~ Importance
Compliance ~ Importance
CustSat ~ Importance
RiskMgt ~ Importance
Supplier ~ Importance
Employee ~ Importance
Cost ~ Importance
Innovation ~ Importance
StdImpl ~ Importance
# H2 – Mediator → Kinerja
MarketPerf ~ Quality+Efficiency+Compliance+CustSat+RiskMgt+Supplier+Employee+Cost+Innovation+StdImpl
FinancialPerf ~ Quality+Efficiency+Compliance+CustSat+RiskMgt+Supplier+Employee+Cost+Innovation+StdImpl
EffPerf ~ Quality+Efficiency+Compliance+CustSat+RiskMgt+Supplier+Employee+Cost+Innovation+StdImpl
QualComp ~ Quality+Efficiency+Compliance+CustSat+RiskMgt+Supplier+Employee+Cost+Innovation+StdImpl
# H3 – GovProgram → Mediator
Quality ~ GovProgram
Efficiency ~ GovProgram
Compliance ~ GovProgram
CustSat ~ GovProgram
RiskMgt ~ GovProgram
Supplier ~ GovProgram
Employee ~ GovProgram
Cost ~ GovProgram
Innovation ~ GovProgram
StdImpl ~ GovProgram
# H4 – GovProgram → Kinerja langsung
MarketPerf ~ GovProgram
FinancialPerf ~ GovProgram
EffPerf ~ GovProgram
QualComp ~ GovProgram
")
cat("Menjalankan SEM penuh (MLR)...\n")Menjalankan SEM penuh (MLR)...fit_sem <- sem(
model = sem_syntax,
data = df_imp,
estimator = "MLR",
std.lv = FALSE
)
summary(fit_sem, fit.measures=TRUE, standardized=TRUE, rsquare=TRUE)lavaan 0.6-21 ended normally after 122 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 167
Number of observations 276
Model Test User Model:
Standard Scaled
Test Statistic 1975.509 1611.005
Degrees of freedom 1009 1009
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.226
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 10262.124 7999.105
Degrees of freedom 1128 1128
P-value 0.000 0.000
Scaling correction factor 1.283
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.894 0.912
Tucker-Lewis Index (TLI) 0.882 0.902
Robust Comparative Fit Index (CFI) 0.916
Robust Tucker-Lewis Index (TLI) 0.906
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -15187.182 -15187.182
Scaling correction factor 1.689
for the MLR correction
Loglikelihood unrestricted model (H1) -14199.427 -14199.427
Scaling correction factor 1.292
for the MLR correction
Akaike (AIC) 30708.364 30708.364
Bayesian (BIC) 31312.971 31312.971
Sample-size adjusted Bayesian (SABIC) 30783.442 30783.442
Root Mean Square Error of Approximation:
RMSEA 0.059 0.046
90 Percent confidence interval - lower 0.055 0.043
90 Percent confidence interval - upper 0.063 0.050
P-value H_0: RMSEA <= 0.050 0.000 0.936
P-value H_0: RMSEA >= 0.080 0.000 0.000
Robust RMSEA 0.051
90 Percent confidence interval - lower 0.047
90 Percent confidence interval - upper 0.056
P-value H_0: Robust RMSEA <= 0.050 0.297
P-value H_0: Robust RMSEA >= 0.080 0.000
Standardized Root Mean Square Residual:
SRMR 0.052 0.052
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
Importance =~
I2A13 1.000 0.722 0.690
I2A8 0.945 0.075 12.630 0.000 0.682 0.671
I2A12 0.583 0.083 7.065 0.000 0.421 0.449
Quality =~
I3A2 1.000 0.728 0.742
I3A3 0.964 0.076 12.625 0.000 0.702 0.764
I3A4 0.946 0.080 11.773 0.000 0.689 0.754
Efficiency =~
I4A1 1.000 0.782 0.737
I4A4 0.884 0.086 10.333 0.000 0.691 0.716
I4A2 0.929 0.077 12.145 0.000 0.726 0.767
Compliance =~
I5A2 1.000 0.664 0.764
I5A4 1.036 0.080 13.010 0.000 0.688 0.794
I5A6 0.520 0.090 5.785 0.000 0.345 0.434
CustSat =~
I6A4 1.000 0.749 0.807
I6A3 1.016 0.058 17.487 0.000 0.761 0.822
I6A2 0.835 0.063 13.317 0.000 0.625 0.742
RiskMgt =~
I7A4 1.000 0.725 0.765
I7A3 1.033 0.072 14.430 0.000 0.749 0.815
I7A7 0.984 0.067 14.787 0.000 0.714 0.787
Supplier =~
I8A5 1.000 0.670 0.734
I8A4 1.042 0.085 12.201 0.000 0.698 0.768
I8A1 0.917 0.097 9.419 0.000 0.614 0.698
Employee =~
I9A1 1.000 0.805 0.826
I9A4 0.895 0.057 15.788 0.000 0.720 0.832
I9A2 0.826 0.060 13.690 0.000 0.664 0.776
Cost =~
I10A1 1.000 0.924 0.822
I10A4 0.962 0.051 18.734 0.000 0.889 0.837
I10A2 0.931 0.060 15.410 0.000 0.860 0.843
Innovation =~
I11A8 1.000 0.828 0.794
I11A5 0.941 0.063 14.859 0.000 0.779 0.766
I11A7 0.945 0.069 13.741 0.000 0.782 0.800
StdImpl =~
I12A7 1.000 1.865 0.911
I12A6 1.010 0.038 26.795 0.000 1.884 0.941
I12A9 1.008 0.036 27.742 0.000 1.881 0.949
MarketPerf =~
I13A10 1.000 0.483 0.444
I13A9 1.315 0.239 5.504 0.000 0.634 0.561
I13A3 1.652 0.337 4.901 0.000 0.797 0.742
FinancialPerf =~
I14A7 1.000 0.853 0.760
I14A1 0.935 0.077 12.167 0.000 0.798 0.787
I14A4 0.922 0.081 11.336 0.000 0.787 0.788
EffPerf =~
I15A5 1.000 0.903 0.784
I15A6 1.096 0.059 18.525 0.000 0.989 0.870
I15A4 0.953 0.090 10.584 0.000 0.860 0.775
QualComp =~
I16A10 1.000 0.639 0.593
I16A3 1.181 0.178 6.636 0.000 0.755 0.734
I16A5 1.113 0.135 8.265 0.000 0.711 0.700
GovProgram =~
I17A4 1.000 1.124 0.889
I17A1 0.819 0.064 12.852 0.000 0.920 0.750
I17A3 0.936 0.054 17.398 0.000 1.052 0.848
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Quality ~
Importance 0.918 0.104 8.831 0.000 0.911 0.911
Efficiency ~
Importance 1.075 0.140 7.690 0.000 0.994 0.994
Compliance ~
Importance 0.913 0.139 6.586 0.000 0.993 0.993
CustSat ~
Importance 1.049 0.133 7.883 0.000 1.011 1.011
RiskMgt ~
Importance 1.026 0.135 7.618 0.000 1.022 1.022
Supplier ~
Importance 0.897 0.138 6.494 0.000 0.967 0.967
Employee ~
Importance 1.062 0.168 6.323 0.000 0.953 0.953
Cost ~
Importance 1.166 0.186 6.281 0.000 0.911 0.911
Innovation ~
Importance 1.060 0.148 7.171 0.000 0.924 0.924
StdImpl ~
Importance -0.219 0.181 -1.208 0.227 -0.085 -0.085
MarketPerf ~
Quality 0.289 0.121 2.385 0.017 0.436 0.436
Efficiency -0.532 0.235 -2.264 0.024 -0.862 -0.862
Compliance 0.317 0.186 1.707 0.088 0.437 0.437
CustSat 0.315 0.229 1.380 0.168 0.490 0.490
RiskMgt -0.245 0.221 -1.111 0.266 -0.369 -0.369
Supplier 0.367 0.236 1.555 0.120 0.510 0.510
Employee 0.040 0.116 0.342 0.733 0.066 0.066
Cost 0.016 0.138 0.115 0.908 0.031 0.031
Innovation -0.015 0.105 -0.140 0.889 -0.025 -0.025
StdImpl 0.006 0.016 0.375 0.708 0.023 0.023
FinancialPerf ~
Quality 0.245 0.186 1.320 0.187 0.209 0.209
Efficiency -0.454 0.271 -1.672 0.094 -0.416 -0.416
Compliance 0.063 0.227 0.276 0.783 0.049 0.049
CustSat -0.248 0.300 -0.827 0.408 -0.218 -0.218
RiskMgt 0.217 0.305 0.711 0.477 0.184 0.184
Supplier 0.201 0.228 0.879 0.379 0.157 0.157
Employee -0.007 0.171 -0.039 0.969 -0.006 -0.006
Cost 0.546 0.156 3.492 0.000 0.591 0.591
Innovation 0.183 0.161 1.133 0.257 0.177 0.177
StdImpl -0.052 0.022 -2.363 0.018 -0.114 -0.114
EffPerf ~
Quality -0.015 0.201 -0.076 0.940 -0.012 -0.012
Efficiency -0.072 0.247 -0.290 0.772 -0.062 -0.062
Compliance -0.025 0.229 -0.108 0.914 -0.018 -0.018
CustSat -0.045 0.337 -0.133 0.894 -0.037 -0.037
RiskMgt 0.114 0.328 0.349 0.727 0.092 0.092
Supplier 0.362 0.330 1.096 0.273 0.269 0.269
Employee -0.360 0.243 -1.481 0.139 -0.321 -0.321
Cost 0.299 0.186 1.609 0.108 0.306 0.306
Innovation 0.393 0.190 2.067 0.039 0.361 0.361
StdImpl 0.012 0.024 0.512 0.609 0.025 0.025
QualComp ~
Quality 0.230 0.159 1.441 0.149 0.262 0.262
Efficiency -0.284 0.249 -1.141 0.254 -0.348 -0.348
Compliance 0.073 0.174 0.417 0.677 0.075 0.075
CustSat -0.138 0.298 -0.462 0.644 -0.162 -0.162
RiskMgt 0.552 0.312 1.765 0.078 0.626 0.626
Supplier 0.249 0.202 1.233 0.218 0.261 0.261
Employee -0.226 0.170 -1.328 0.184 -0.285 -0.285
Cost 0.114 0.147 0.775 0.439 0.164 0.164
Innovation 0.032 0.125 0.255 0.799 0.041 0.041
StdImpl -0.026 0.019 -1.426 0.154 -0.077 -0.077
Quality ~
GovProgram -0.048 0.052 -0.922 0.357 -0.074 -0.074
Efficiency ~
GovProgram -0.081 0.088 -0.921 0.357 -0.117 -0.117
Compliance ~
GovProgram -0.108 0.086 -1.250 0.211 -0.183 -0.183
CustSat ~
GovProgram -0.071 0.084 -0.838 0.402 -0.106 -0.106
RiskMgt ~
GovProgram -0.078 0.084 -0.928 0.354 -0.121 -0.121
Supplier ~
GovProgram -0.057 0.093 -0.619 0.536 -0.096 -0.096
Employee ~
GovProgram -0.062 0.112 -0.554 0.580 -0.087 -0.087
Cost ~
GovProgram -0.024 0.128 -0.190 0.849 -0.030 -0.030
Innovation ~
GovProgram -0.054 0.107 -0.506 0.613 -0.074 -0.074
StdImpl ~
GovProgram 0.554 0.130 4.270 0.000 0.333 0.333
MarketPerf ~
GovProgram 0.111 0.048 2.297 0.022 0.258 0.258
FinancialPerf ~
GovProgram 0.211 0.060 3.498 0.000 0.278 0.278
EffPerf ~
GovProgram 0.210 0.071 2.954 0.003 0.261 0.261
QualComp ~
GovProgram 0.238 0.072 3.317 0.001 0.418 0.418
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Importance ~~
GovProgram 0.488 0.098 4.969 0.000 0.601 0.601
.MarketPerf ~~
.FinancialPerf 0.079 0.027 2.899 0.004 1.203 1.203
.EffPerf 0.133 0.051 2.640 0.008 1.364 1.364
.QualComp 0.058 0.022 2.680 0.007 1.427 1.427
.FinancialPerf ~~
.EffPerf 0.150 0.039 3.864 0.000 0.640 0.640
.QualComp 0.090 0.027 3.294 0.001 0.923 0.923
.EffPerf ~~
.QualComp 0.116 0.041 2.853 0.004 0.807 0.807
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.I2A13 0.575 0.072 8.021 0.000 0.575 0.524
.I2A8 0.568 0.068 8.331 0.000 0.568 0.549
.I2A12 0.702 0.104 6.775 0.000 0.702 0.798
.I3A2 0.433 0.083 5.201 0.000 0.433 0.450
.I3A3 0.351 0.053 6.604 0.000 0.351 0.416
.I3A4 0.360 0.066 5.436 0.000 0.360 0.432
.I4A1 0.515 0.086 5.960 0.000 0.515 0.457
.I4A4 0.455 0.105 4.341 0.000 0.455 0.488
.I4A2 0.370 0.055 6.679 0.000 0.370 0.412
.I5A2 0.315 0.048 6.504 0.000 0.315 0.417
.I5A4 0.277 0.044 6.322 0.000 0.277 0.369
.I5A6 0.514 0.064 8.087 0.000 0.514 0.812
.I6A4 0.301 0.043 7.000 0.000 0.301 0.349
.I6A3 0.277 0.033 8.390 0.000 0.277 0.324
.I6A2 0.319 0.037 8.657 0.000 0.319 0.449
.I7A4 0.372 0.042 8.889 0.000 0.372 0.414
.I7A3 0.283 0.040 7.018 0.000 0.283 0.336
.I7A7 0.314 0.039 7.996 0.000 0.314 0.381
.I8A5 0.384 0.047 8.186 0.000 0.384 0.461
.I8A4 0.338 0.093 3.648 0.000 0.338 0.409
.I8A1 0.398 0.053 7.469 0.000 0.398 0.513
.I9A1 0.302 0.045 6.683 0.000 0.302 0.318
.I9A4 0.231 0.030 7.768 0.000 0.231 0.309
.I9A2 0.291 0.038 7.750 0.000 0.291 0.398
.I10A1 0.411 0.054 7.620 0.000 0.411 0.325
.I10A4 0.337 0.041 8.260 0.000 0.337 0.299
.I10A2 0.300 0.057 5.256 0.000 0.300 0.289
.I11A8 0.401 0.132 3.033 0.002 0.401 0.369
.I11A5 0.428 0.083 5.166 0.000 0.428 0.414
.I11A7 0.344 0.051 6.728 0.000 0.344 0.360
.I12A7 0.713 0.174 4.098 0.000 0.713 0.170
.I12A6 0.461 0.095 4.838 0.000 0.461 0.115
.I12A9 0.392 0.071 5.532 0.000 0.392 0.100
.I13A10 0.946 0.195 4.839 0.000 0.946 0.802
.I13A9 0.877 0.171 5.123 0.000 0.877 0.685
.I13A3 0.518 0.083 6.277 0.000 0.518 0.449
.I14A7 0.533 0.063 8.450 0.000 0.533 0.422
.I14A1 0.392 0.061 6.431 0.000 0.392 0.381
.I14A4 0.377 0.061 6.186 0.000 0.377 0.379
.I15A5 0.512 0.096 5.316 0.000 0.512 0.386
.I15A6 0.314 0.053 5.923 0.000 0.314 0.243
.I15A4 0.492 0.080 6.141 0.000 0.492 0.400
.I16A10 0.753 0.145 5.186 0.000 0.753 0.649
.I16A3 0.488 0.088 5.572 0.000 0.488 0.461
.I16A5 0.526 0.097 5.402 0.000 0.526 0.510
.I17A4 0.335 0.056 5.929 0.000 0.335 0.210
.I17A1 0.657 0.126 5.224 0.000 0.657 0.437
.I17A3 0.432 0.062 7.013 0.000 0.432 0.281
Importance 0.522 0.101 5.144 0.000 1.000 1.000
.Quality 0.130 0.036 3.592 0.000 0.245 0.245
.Efficiency 0.085 0.033 2.594 0.009 0.139 0.139
.Compliance 0.088 0.039 2.258 0.024 0.199 0.199
.CustSat 0.053 0.022 2.363 0.018 0.095 0.095
.RiskMgt 0.047 0.025 1.893 0.058 0.090 0.090
.Supplier 0.075 0.022 3.355 0.001 0.167 0.167
.Employee 0.118 0.033 3.610 0.000 0.183 0.183
.Cost 0.171 0.046 3.741 0.000 0.201 0.201
.Innovation 0.152 0.041 3.689 0.000 0.222 0.222
.StdImpl 3.185 0.315 10.098 0.000 0.916 0.916
.MarketPerf 0.027 0.028 0.966 0.334 0.118 0.118
.FinancialPerf 0.158 0.041 3.830 0.000 0.217 0.217
.EffPerf 0.348 0.112 3.116 0.002 0.428 0.428
.QualComp 0.060 0.034 1.736 0.083 0.146 0.146
GovProgram 1.262 0.171 7.402 0.000 1.000 1.000
R-Square:
Estimate
I2A13 0.476
I2A8 0.451
I2A12 0.202
I3A2 0.550
I3A3 0.584
I3A4 0.568
I4A1 0.543
I4A4 0.512
I4A2 0.588
I5A2 0.583
I5A4 0.631
I5A6 0.188
I6A4 0.651
I6A3 0.676
I6A2 0.551
I7A4 0.586
I7A3 0.664
I7A7 0.619
I8A5 0.539
I8A4 0.591
I8A1 0.487
I9A1 0.682
I9A4 0.691
I9A2 0.602
I10A1 0.675
I10A4 0.701
I10A2 0.711
I11A8 0.631
I11A5 0.586
I11A7 0.640
I12A7 0.830
I12A6 0.885
I12A9 0.900
I13A10 0.198
I13A9 0.315
I13A3 0.551
I14A7 0.578
I14A1 0.619
I14A4 0.621
I15A5 0.614
I15A6 0.757
I15A4 0.600
I16A10 0.351
I16A3 0.539
I16A5 0.490
I17A4 0.790
I17A1 0.563
I17A3 0.719
Quality 0.755
Efficiency 0.861
Compliance 0.801
CustSat 0.905
RiskMgt 0.910
Supplier 0.833
Employee 0.817
Cost 0.799
Innovation 0.778
StdImpl 0.084
MarketPerf 0.882
FinancialPerf 0.783
EffPerf 0.572
QualComp 0.8549.1 Fit Indices SEM
sf <- fitMeasures(fit_sem, c("chisq","df","pvalue","cfi","tli",
"rmsea","rmsea.ci.lower","rmsea.ci.upper","srmr"))
sf_df <- data.frame(
Indeks = names(sf), Nilai = round(sf,4),
Cutoff = c("","",">.05",">0.90",">0.90","<0.08","","","<0.08"),
Status = c("","",
ifelse(sf["pvalue"]>0.05,"✓","✗"),
ifelse(sf["cfi"] >0.90,"✓","✗"),
ifelse(sf["tli"] >0.90,"✓","✗"),
ifelse(sf["rmsea"]<0.08,"✓","✗"),
"","",
ifelse(sf["srmr"] <0.08,"✓","✗"))
)
kable(sf_df, row.names=FALSE, caption="Fit Indices SEM Penuh") %>%
kable_styling(bootstrap_options=c("striped","hover"), full_width=FALSE)| Indeks | Nilai | Cutoff | Status |
|---|---|---|---|
| chisq | 1975.5094 | ||
| df | 1009.0000 | ||
| pvalue | 0.0000 | >.05 | ✗ |
| cfi | 0.8942 | >0.90 | ✗ |
| tli | 0.8817 | >0.90 | ✗ |
| rmsea | 0.0589 | <0.08 | ✓ |
| rmsea.ci.lower | 0.0551 | ||
| rmsea.ci.upper | 0.0628 | ||
| srmr | 0.0516 | <0.08 | ✓ |
10. Path Coefficients
paths <- standardizedsolution(fit_sem) %>%
filter(op == "~") %>%
transmute(
Endogen = lhs, Eksogen = rhs,
Beta = round(est.std,3),
SE = round(se,3),
z = round(z,3),
p = round(pvalue,4),
Sig = case_when(
pvalue<0.001~"***", pvalue<0.01~"**",
pvalue<0.05~"*", pvalue<0.10~".",
TRUE~"ns"),
Hasil = case_when(
pvalue<0.05 & est.std>0 ~ "Didukung (+)",
pvalue<0.05 & est.std<0 ~ "Didukung (-)",
TRUE ~ "Tidak Didukung")
) %>% arrange(Endogen, p)
kable(paths, caption="Path Coefficients Terstandarisasi") %>%
kable_styling(bootstrap_options=c("striped","hover","condensed"),
full_width=FALSE, font_size=11) %>%
row_spec(which(paths$p < 0.05), background="#e8f5e9") %>%
scroll_box(height="500px")| Endogen | Eksogen | Beta | SE | z | p | Sig | Hasil |
|---|---|---|---|---|---|---|---|
| Compliance | Importance | 0.993 | 0.098 | 10.117 | 0.0000 | *** | Didukung (+) |
| Compliance | GovProgram | -0.183 | 0.145 | -1.264 | 0.2061 | ns | Tidak Didukung |
| Cost | Importance | 0.911 | 0.105 | 8.677 | 0.0000 | *** | Didukung (+) |
| Cost | GovProgram | -0.030 | 0.156 | -0.190 | 0.8491 | ns | Tidak Didukung |
| CustSat | Importance | 1.011 | 0.084 | 12.069 | 0.0000 | *** | Didukung (+) |
| CustSat | GovProgram | -0.106 | 0.127 | -0.834 | 0.4043 | ns | Tidak Didukung |
| EffPerf | GovProgram | 0.261 | 0.085 | 3.084 | 0.0020 | ** | Didukung (+) |
| EffPerf | Innovation | 0.361 | 0.174 | 2.075 | 0.0380 |
|
Didukung (+) |
| EffPerf | Cost | 0.306 | 0.190 | 1.616 | 0.1061 | ns | Tidak Didukung |
| EffPerf | Employee | -0.321 | 0.204 | -1.570 | 0.1163 | ns | Tidak Didukung |
| EffPerf | Supplier | 0.269 | 0.238 | 1.130 | 0.2584 | ns | Tidak Didukung |
| EffPerf | StdImpl | 0.025 | 0.050 | 0.505 | 0.6137 | ns | Tidak Didukung |
| EffPerf | RiskMgt | 0.092 | 0.264 | 0.349 | 0.7274 | ns | Tidak Didukung |
| EffPerf | Efficiency | -0.062 | 0.214 | -0.290 | 0.7720 | ns | Tidak Didukung |
| EffPerf | CustSat | -0.037 | 0.281 | -0.133 | 0.8943 | ns | Tidak Didukung |
| EffPerf | Compliance | -0.018 | 0.169 | -0.108 | 0.9143 | ns | Tidak Didukung |
| EffPerf | Quality | -0.012 | 0.162 | -0.076 | 0.9395 | ns | Tidak Didukung |
| Efficiency | Importance | 0.994 | 0.075 | 13.324 | 0.0000 | *** | Didukung (+) |
| Efficiency | GovProgram | -0.117 | 0.126 | -0.931 | 0.3517 | ns | Tidak Didukung |
| Employee | Importance | 0.953 | 0.101 | 9.475 | 0.0000 | *** | Didukung (+) |
| Employee | GovProgram | -0.087 | 0.155 | -0.558 | 0.5765 | ns | Tidak Didukung |
| FinancialPerf | Cost | 0.591 | 0.160 | 3.700 | 0.0002 | *** | Didukung (+) |
| FinancialPerf | GovProgram | 0.278 | 0.074 | 3.766 | 0.0002 | *** | Didukung (+) |
| FinancialPerf | StdImpl | -0.114 | 0.049 | -2.340 | 0.0193 |
|
Didukung (-) |
| FinancialPerf | Efficiency | -0.416 | 0.237 | -1.752 | 0.0798 | . | Tidak Didukung |
| FinancialPerf | Quality | 0.209 | 0.156 | 1.342 | 0.1795 | ns | Tidak Didukung |
| FinancialPerf | Innovation | 0.177 | 0.157 | 1.132 | 0.2575 | ns | Tidak Didukung |
| FinancialPerf | Supplier | 0.157 | 0.177 | 0.888 | 0.3744 | ns | Tidak Didukung |
| FinancialPerf | CustSat | -0.218 | 0.259 | -0.838 | 0.4018 | ns | Tidak Didukung |
| FinancialPerf | RiskMgt | 0.184 | 0.255 | 0.723 | 0.4695 | ns | Tidak Didukung |
| FinancialPerf | Compliance | 0.049 | 0.177 | 0.275 | 0.7836 | ns | Tidak Didukung |
| FinancialPerf | Employee | -0.006 | 0.161 | -0.039 | 0.9685 | ns | Tidak Didukung |
| Innovation | Importance | 0.924 | 0.094 | 9.814 | 0.0000 | *** | Didukung (+) |
| Innovation | GovProgram | -0.074 | 0.145 | -0.506 | 0.6126 | ns | Tidak Didukung |
| MarketPerf | Efficiency | -0.862 | 0.326 | -2.649 | 0.0081 | ** | Didukung (-) |
| MarketPerf | GovProgram | 0.258 | 0.101 | 2.550 | 0.0108 |
|
Didukung (+) |
| MarketPerf | Quality | 0.436 | 0.181 | 2.411 | 0.0159 |
|
Didukung (+) |
| MarketPerf | Compliance | 0.437 | 0.243 | 1.800 | 0.0718 | . | Tidak Didukung |
| MarketPerf | Supplier | 0.510 | 0.286 | 1.781 | 0.0749 | . | Tidak Didukung |
| MarketPerf | CustSat | 0.490 | 0.361 | 1.356 | 0.1751 | ns | Tidak Didukung |
| MarketPerf | RiskMgt | -0.369 | 0.324 | -1.138 | 0.2551 | ns | Tidak Didukung |
| MarketPerf | StdImpl | 0.023 | 0.062 | 0.378 | 0.7058 | ns | Tidak Didukung |
| MarketPerf | Employee | 0.066 | 0.193 | 0.343 | 0.7317 | ns | Tidak Didukung |
| MarketPerf | Innovation | -0.025 | 0.181 | -0.138 | 0.8899 | ns | Tidak Didukung |
| MarketPerf | Cost | 0.031 | 0.265 | 0.115 | 0.9081 | ns | Tidak Didukung |
| QualComp | GovProgram | 0.418 | 0.096 | 4.359 | 0.0000 | *** | Didukung (+) |
| QualComp | RiskMgt | 0.626 | 0.328 | 1.911 | 0.0560 | . | Tidak Didukung |
| QualComp | Quality | 0.262 | 0.176 | 1.490 | 0.1362 | ns | Tidak Didukung |
| QualComp | StdImpl | -0.077 | 0.052 | -1.486 | 0.1373 | ns | Tidak Didukung |
| QualComp | Employee | -0.285 | 0.206 | -1.381 | 0.1674 | ns | Tidak Didukung |
| QualComp | Supplier | 0.261 | 0.205 | 1.274 | 0.2027 | ns | Tidak Didukung |
| QualComp | Efficiency | -0.348 | 0.291 | -1.196 | 0.2318 | ns | Tidak Didukung |
| QualComp | Cost | 0.164 | 0.213 | 0.770 | 0.4411 | ns | Tidak Didukung |
| QualComp | CustSat | -0.162 | 0.344 | -0.470 | 0.6383 | ns | Tidak Didukung |
| QualComp | Compliance | 0.075 | 0.180 | 0.418 | 0.6758 | ns | Tidak Didukung |
| QualComp | Innovation | 0.041 | 0.161 | 0.257 | 0.7971 | ns | Tidak Didukung |
| Quality | Importance | 0.911 | 0.047 | 19.572 | 0.0000 | *** | Didukung (+) |
| Quality | GovProgram | -0.074 | 0.081 | -0.920 | 0.3575 | ns | Tidak Didukung |
| RiskMgt | Importance | 1.022 | 0.080 | 12.837 | 0.0000 | *** | Didukung (+) |
| RiskMgt | GovProgram | -0.121 | 0.129 | -0.937 | 0.3490 | ns | Tidak Didukung |
| StdImpl | GovProgram | 0.333 | 0.073 | 4.588 | 0.0000 | *** | Didukung (+) |
| StdImpl | Importance | -0.085 | 0.071 | -1.197 | 0.2314 | ns | Tidak Didukung |
| Supplier | Importance | 0.967 | 0.097 | 9.944 | 0.0000 | *** | Didukung (+) |
| Supplier | GovProgram | -0.096 | 0.154 | -0.625 | 0.5317 | ns | Tidak Didukung |
11. R-Squared
rsq <- lavInspect(fit_sem, "r2")
rsq_df <- data.frame(
Variabel = names(rsq),
R2 = round(rsq,3),
Persen = paste0(round(rsq*100,1),"%"),
Kategori = case_when(
rsq>=0.50~"Kuat", rsq>=0.30~"Moderate",
rsq>=0.10~"Lemah", TRUE~"Sangat Lemah")
) %>% arrange(desc(R2))
kable(rsq_df, caption="R-Squared") %>%
kable_styling(bootstrap_options=c("striped","hover"), full_width=FALSE)| Variabel | R2 | Persen | Kategori | |
|---|---|---|---|---|
| RiskMgt | RiskMgt | 0.910 | 91% | Kuat |
| CustSat | CustSat | 0.905 | 90.5% | Kuat |
| I12A9 | I12A9 | 0.900 | 90% | Kuat |
| I12A6 | I12A6 | 0.885 | 88.5% | Kuat |
| MarketPerf | MarketPerf | 0.882 | 88.2% | Kuat |
| Efficiency | Efficiency | 0.861 | 86.1% | Kuat |
| QualComp | QualComp | 0.854 | 85.4% | Kuat |
| Supplier | Supplier | 0.833 | 83.3% | Kuat |
| I12A7 | I12A7 | 0.830 | 83% | Kuat |
| Employee | Employee | 0.817 | 81.7% | Kuat |
| Compliance | Compliance | 0.801 | 80.1% | Kuat |
| Cost | Cost | 0.799 | 79.9% | Kuat |
| I17A4 | I17A4 | 0.790 | 79% | Kuat |
| FinancialPerf | FinancialPerf | 0.783 | 78.3% | Kuat |
| Innovation | Innovation | 0.778 | 77.8% | Kuat |
| I15A6 | I15A6 | 0.757 | 75.7% | Kuat |
| Quality | Quality | 0.755 | 75.5% | Kuat |
| I17A3 | I17A3 | 0.719 | 71.9% | Kuat |
| I10A2 | I10A2 | 0.711 | 71.1% | Kuat |
| I10A4 | I10A4 | 0.701 | 70.1% | Kuat |
| I9A4 | I9A4 | 0.691 | 69.1% | Kuat |
| I9A1 | I9A1 | 0.682 | 68.2% | Kuat |
| I6A3 | I6A3 | 0.676 | 67.6% | Kuat |
| I10A1 | I10A1 | 0.675 | 67.5% | Kuat |
| I7A3 | I7A3 | 0.664 | 66.4% | Kuat |
| I6A4 | I6A4 | 0.651 | 65.1% | Kuat |
| I11A7 | I11A7 | 0.640 | 64% | Kuat |
| I5A4 | I5A4 | 0.631 | 63.1% | Kuat |
| I11A8 | I11A8 | 0.631 | 63.1% | Kuat |
| I14A4 | I14A4 | 0.621 | 62.1% | Kuat |
| I7A7 | I7A7 | 0.619 | 61.9% | Kuat |
| I14A1 | I14A1 | 0.619 | 61.9% | Kuat |
| I15A5 | I15A5 | 0.614 | 61.4% | Kuat |
| I9A2 | I9A2 | 0.602 | 60.2% | Kuat |
| I15A4 | I15A4 | 0.600 | 60% | Kuat |
| I8A4 | I8A4 | 0.591 | 59.1% | Kuat |
| I4A2 | I4A2 | 0.588 | 58.8% | Kuat |
| I7A4 | I7A4 | 0.586 | 58.6% | Kuat |
| I11A5 | I11A5 | 0.586 | 58.6% | Kuat |
| I3A3 | I3A3 | 0.584 | 58.4% | Kuat |
| I5A2 | I5A2 | 0.583 | 58.3% | Kuat |
| I14A7 | I14A7 | 0.578 | 57.8% | Kuat |
| EffPerf | EffPerf | 0.572 | 57.2% | Kuat |
| I3A4 | I3A4 | 0.568 | 56.8% | Kuat |
| I17A1 | I17A1 | 0.563 | 56.3% | Kuat |
| I6A2 | I6A2 | 0.551 | 55.1% | Kuat |
| I13A3 | I13A3 | 0.551 | 55.1% | Kuat |
| I3A2 | I3A2 | 0.550 | 55% | Kuat |
| I4A1 | I4A1 | 0.543 | 54.3% | Kuat |
| I8A5 | I8A5 | 0.539 | 53.9% | Kuat |
| I16A3 | I16A3 | 0.539 | 53.9% | Kuat |
| I4A4 | I4A4 | 0.512 | 51.2% | Kuat |
| I16A5 | I16A5 | 0.490 | 49% | Moderate |
| I8A1 | I8A1 | 0.487 | 48.7% | Moderate |
| I2A13 | I2A13 | 0.476 | 47.6% | Moderate |
| I2A8 | I2A8 | 0.451 | 45.1% | Moderate |
| I16A10 | I16A10 | 0.351 | 35.1% | Moderate |
| I13A9 | I13A9 | 0.315 | 31.5% | Moderate |
| I2A12 | I2A12 | 0.202 | 20.2% | Lemah |
| I13A10 | I13A10 | 0.198 | 19.8% | Lemah |
| I5A6 | I5A6 | 0.188 | 18.8% | Lemah |
| StdImpl | StdImpl | 0.084 | 8.4% | Sangat Lemah |
12. Mediasi (Bootstrap)
sem_med_syntax <- paste0(
"# Measurement
Importance =~ ", paste(cols_def$Importance, collapse="+"), "
Quality =~ ", paste(cols_def$Quality, collapse="+"), "
Efficiency =~ ", paste(cols_def$Efficiency, collapse="+"), "
MarketPerf =~ ", paste(cols_def$MarketPerf, collapse="+"), "
FinancialPerf =~ ", paste(cols_def$FinancialPerf, collapse="+"), "
# Structural dengan label
Quality ~ a1 * Importance
Efficiency ~ a2 * Importance
MarketPerf ~ b1*Quality + b2*Efficiency + c1*Importance
FinancialPerf ~ b3*Quality + b4*Efficiency + c2*Importance
# Defined parameters
ind_Qual_Mkt := a1*b1
ind_Eff_Mkt := a2*b2
ind_Qual_Fin := a1*b3
ind_Eff_Fin := a2*b4
total_Mkt := a1*b1 + a2*b2 + c1
total_Fin := a1*b3 + a2*b4 + c2
")
cat("Bootstrap 200x...\n")Bootstrap 200x...set.seed(2025)
fit_med <- sem(
model = sem_med_syntax,
data = df_imp,
estimator = "ML", # ← ganti MLR ke ML untuk bootstrap
se = "bootstrap",
bootstrap = 200
)
indirect <- parameterEstimates(fit_med, boot.ci.type = "bca.simple") %>%
filter(op == ":=") %>%
transmute(
Label = label,
Est = round(est, 3),
SE = round(se, 3),
CI_low = round(ci.lower, 3),
CI_up = round(ci.upper, 3),
p = round(pvalue, 4),
Sig = ifelse(
(ci.lower > 0 & ci.upper > 0) | (ci.lower < 0 & ci.upper < 0),
"Signifikan ✓", "Tidak ✗")
)
kable(indirect, caption = "Indirect Effects – Bootstrap 200x (95% BCa CI)") %>%
kable_styling(bootstrap_options = c("striped","hover"), full_width = FALSE) %>%
row_spec(which(indirect$Sig == "Signifikan ✓"), background = "#e8f5e9")| Label | Est | SE | CI_low | CI_up | p | Sig |
|---|---|---|---|---|---|---|
| ind_Qual_Mkt | 0.343 | 0.172 | 0.079 | 0.827 | 0.0468 | Signifikan ✓ |
| ind_Eff_Mkt | -0.224 | 0.803 | -4.281 | 0.040 | 0.7806 | Tidak ✗ |
| ind_Qual_Fin | 0.420 | 0.336 | 0.053 | 1.075 | 0.2114 | Signifikan ✓ |
| ind_Eff_Fin | 0.275 | 0.925 | -1.867 | 0.957 | 0.7664 | Tidak ✗ |
| total_Mkt | 0.355 | 0.090 | 0.186 | 0.532 | 0.0001 | Signifikan ✓ |
| total_Fin | 0.656 | 0.113 | 0.468 | 0.901 | 0.0000 | Signifikan ✓ |
13. Path Diagram
semPaths(fit_sem,
what="std", layout="tree2", rotation=2,
edge.label.cex=0.50, node.label.cex=0.65,
residuals=FALSE, intercepts=FALSE, nCharNodes=0,
sizeLat=9, sizeMan=3, fade=FALSE,
edge.color="#2E86AB", mar=c(3,3,3,3))
title("SEM – Dampak Standar terhadap Kinerja Perusahaan", cex.main=1.1)
14. Ekspor & Ringkasan
write.csv(paths, "hasil_jalur_sem.csv", row.names=FALSE)
write.csv(cr_ave_tbl, "hasil_cr_ave.csv", row.names=FALSE)
write.csv(alpha_tbl, "hasil_reliabilitas.csv", row.names=FALSE)
write.csv(indirect, "hasil_mediasi.csv", row.names=FALSE)
write.csv(rsq_df, "hasil_rsquared.csv", row.names=FALSE)
cat(" RINGKASAN HASIL SEM \n") RINGKASAN HASIL SEM Sampel : 276 responden Konstruk : 16 Indikator : 48 (3 per konstruk) CFI = 0.894 | TLI = 0.882 RMSEA= 0.059 | SRMR = 0.052 Jalur signifikan (p < 0.05):sig_paths <- paths %>% filter(p < 0.05)
cat(sprintf(" %d dari %d jalur signifikan\n", nrow(sig_paths), nrow(paths))) 19 dari 64 jalur signifikanReferensi: Hair et al. (2014); Fornell & Larcker (1981); Rhemtulla et al. (2012); Little et al. (2002)