Segmentation of Partial Least Square Structural Equation Modeling Using Kernel K-Means Clustering (PLS SEM KKC) is the development of a new method in PLS SEM segmentation that uses non-linear clustering methods. Segmentation is carried out based on residual values of measurement and structural PLS SEM modeling which often exhibit non-linear separability and thus require a non-linear separable, thus requiring a non-linear PLS SEM segmentation. Main contribution of this study is the integration of kernel-based clustering into PLS SEM segmentation. Method effectively addresses unobserved heterogeneity by capturing non-linear residual patterns, leading to more accurate modelsSegmentation of Partial Least Square Structural Equation Modeling Using Kernel K-Means Clustering (PLS SEM KKC) is the development of a new method in PLS SEM segmentation that uses non-linear clustering methods. Segmentation is carried out based on residual values of measurement and structural PLS SEM modeling which often exhibit non-linear separability and thus require a non-linear separable, thus requiring a non-linear PLS SEM segmentation. Main contribution of this study is the integration of kernel-based clustering into PLS SEM segmentation. Method effectively addresses unobserved heterogeneity by capturing non-linear residual patterns, leading to more accurate models.
This package is an extension of the ResiPLS package, which develops SEMPLS analysis with segmentation using Kernel K-Means. If you are interested in this package, please contact me at (cindy.cahyaning@umsida.ac.id).
To run this package, you can do this:
library(SEMPLSKKC)Running SEMPLSKKC
inputdata=import_dari_excel(file_path = "DataSeminter.xlsx")
hasil=plsalgorithm(data = inputdata$Data,inner_weight_scheme = "Path",matrix_outer_model = inputdata$MOM, matrix_inner_model = inputdata$MIM, indicator = inputdata$Indikator, laten_variables = inputdata$Variabel)## Banyak Iterasi: 7
## Outer Weight
## Variabel Laten Perfomance Expectancy
## [,1]
## [1,] 0.4078256
## [2,] 0.3948863
## [3,] 0.3516330
##
## Variabel Laten Effort Expectancy
## [,1]
## [1,] 0.4682900
## [2,] 0.3154480
## [3,] 0.3689983
##
## Variabel Laten Social Influence
## [,1]
## [1,] 0.7372356
## [2,] 0.4142433
##
## Variabel Laten Facilitating Conditions
## [,1]
## [1,] 0.3906090
## [2,] 0.4973840
## [3,] 0.3232516
##
## Variabel Laten Behavioral Intention
## [,1]
## [1,] 0.3231985
## [2,] 0.3223177
## [3,] 0.2944332
## [4,] 0.1980679
##
## Skor Variabel Laten
## Variabel Laten Perfomance Expectancy
## [,1]
## [1,] -0.193634
## [2,] -0.193634
## [3,] -0.193634
## [4,] -0.621821
## [5,] -0.193634
## [6,] -0.193634
##
## Variabel Laten Effort Expectancy
## [,1]
## [1,] -0.63653562
## [2,] 1.29973203
## [3,] 0.61976369
## [4,] -0.63653562
## [5,] 0.04343272
## [6,] -0.63653562
##
## Variabel Laten Social Influence
## [,1]
## [1,] -2.0094775
## [2,] -1.6434825
## [3,] -0.3188389
## [4,] 0.1604853
## [5,] -0.6848340
## [6,] 0.1604853
##
## Variabel Laten Facilitating Conditions
## [,1]
## [1,] -1.9626047
## [2,] -0.2291284
## [3,] -0.2291284
## [4,] -0.2291284
## [5,] 0.3041491
## [6,] -0.2291284
##
## Variabel Laten Behavioral Intention
## [,1]
## [1,] -2.54378552
## [2,] -2.54378552
## [3,] -0.44374851
## [4,] -0.03466219
## [5,] -0.44838802
## [6,] -0.03466219
##
## Koefisien Jalur:
## Variabel Laten Perfomance Expectancy
## NULL
##
## Variabel Laten Effort Expectancy
## NULL
##
## Variabel Laten Social Influence
## NULL
##
## Variabel Laten Facilitating Conditions
## NULL
##
## Variabel Laten Behavioral Intention
## [,1]
## [1,] 0.17984759
## [2,] -0.05737437
## [3,] 0.21005121
## [4,] 0.50697975
##
## Outer Loading:
## Variabel Laten Perfomance Expectancy
## [,1]
## [1,] 0.8715220
## [2,] 0.8914727
## [3,] 0.8319485
##
## Variabel Laten Effort Expectancy
## [,1]
## [1,] 0.8868386
## [2,] 0.8781684
## [3,] 0.8338407
##
## Variabel Laten Social Influence
## [,1]
## [1,] 0.9304480
## [2,] 0.7581066
##
## Variabel Laten Facilitating Conditions
## [,1]
## [1,] 0.8037971
## [2,] 0.9068410
## [3,] 0.7269303
##
## Variabel Laten Behavioral Intention
## [,1]
## [1,] 0.9253825
## [2,] 0.8702525
## [3,] 0.9257141
## [4,] 0.7465068
##
## Matriks Residual Gabungan:
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] -0.08791535 -0.1296811 0.2475975 0.2664125 0.04963479 -0.3805315
## [2,] -0.08791535 -0.1296811 0.2475975 0.1304387 -0.01545051 -0.1523294
## [3,] -0.08791535 -0.1296811 0.2475975 0.7334609 0.58167623 -1.4280850
## [4,] 0.28525906 0.2520359 -0.6138829 0.2664125 0.04963479 -0.3805315
## [5,] -0.08791535 -0.1296811 0.2475975 -0.3366097 -0.54749195 0.8952241
## [6,] -0.08791535 -0.1296811 0.2475975 0.2664125 0.04963479 -0.3805315
## [,7] [,8] [,9] [,10] [,11] [,12]
## [1,] -0.29818677 0.53068795 -1.1022605 0.3221855 0.8362000 -0.1498453
## [2,] 0.50788054 -0.90388342 0.1732348 0.1394588 -0.4239163 -0.1498453
## [3,] 0.42197524 -0.75099634 0.1732348 0.1394588 -0.4239163 -0.8274396
## [4,] -0.02401106 0.04273288 0.1732348 0.1394588 -0.4239163 0.0597423
## [5,] -0.38409207 0.68357502 -0.2554121 -0.3441392 0.8381570 0.4425970
## [6,] -0.02401106 0.04273288 0.1732348 0.1394588 -0.4239163 0.0597423
## [,13] [,14] [,15] [,16]
## [1,] 0.5135670 -0.51187444 0.1696938 -1.12838771
## [2,] 0.5135670 -0.51187444 0.1696938 -1.97301066
## [3,] 0.1230986 0.30880705 0.6908102 -0.19022938
## [4,] -0.2329097 -0.06988993 0.3854245 0.12310332
## [5,] 0.1271362 0.31310191 -1.3945346 -0.42141872
## [6,] -0.2329097 -0.06988993 0.3854245 0.04609492
##
## CR:
## Variabel Laten Perfomance Expectancy
## [1] 0.8993515
##
## Variabel Laten Effort Expectancy
## [1] 0.9004078
##
## Variabel Laten Social Influence
## [1] 0.8359482
##
## Variabel Laten Facilitating Conditions
## [1] 0.8555591
##
## Variabel Laten Behavioral Intention
## [1] 0.9252116
##
## AVE:
## Variabel Laten Perfomance Expectancy
## [1] 0.7488042
##
## Variabel Laten Effort Expectancy
## [1] 0.7509843
##
## Variabel Laten Social Influence
## [1] 0.7202296
##
## Variabel Laten Facilitating Conditions
## [1] 0.665626
##
## Variabel Laten Behavioral Intention
## [1] 0.7569728
##
## HTMT Matrix:
## Perfomance Expectancy Effort Expectancy
## Perfomance Expectancy NA 0.3737800
## Effort Expectancy 0.3737800 NA
## Social Influence 0.6172193 0.6160426
## Facilitating Conditions 0.4824333 0.7979404
## Behavioral Intention 0.5167693 0.4471064
## Social Influence Facilitating Conditions
## Perfomance Expectancy 0.6172193 0.4824333
## Effort Expectancy 0.6160426 0.7979404
## Social Influence NA 0.8150128
## Facilitating Conditions 0.8150128 NA
## Behavioral Intention 0.6839490 0.7799284
## Behavioral Intention
## Perfomance Expectancy 0.5167693
## Effort Expectancy 0.4471064
## Social Influence 0.6839490
## Facilitating Conditions 0.7799284
## Behavioral Intention NA
##
## R Square:
## Variabel Laten Perfomance Expectancy
## [1] NA
##
## Variabel Laten Effort Expectancy
## [1] NA
##
## Variabel Laten Social Influence
## [1] NA
##
## Variabel Laten Facilitating Conditions
## [1] NA
##
## Variabel Laten Behavioral Intention
## [1] 0.5115431resultbootstrap <- plsalgorithm_bootstrap(data = inputdata$Data,inner_weight_scheme = "Path",matrix_outer_model = inputdata$MOM, matrix_inner_model = inputdata$MIM, indicator = inputdata$Indikator, laten_variables = inputdata$Variabel, n_bootstrap=50)
print(resultbootstrap$path_coefficient)## [[1]]
## data frame with 0 columns and 0 rows
##
## [[2]]
## data frame with 0 columns and 0 rows
##
## [[3]]
## data frame with 0 columns and 0 rows
##
## [[4]]
## data frame with 0 columns and 0 rows
##
## [[5]]
## path_coefficient_original average_path_coefficient standar_error t_hitung
## 1 0.17984759 0.17910816 0.06187145 2.906795
## 2 -0.05737437 -0.06524304 0.05622533 -1.020436
## 3 0.21005121 0.20984486 0.07238816 2.901734
## 4 0.50697975 0.51770036 0.06892711 7.355302
## p_value
## 1 3.651530e-03
## 2 3.075216e-01
## 3 3.711030e-03
## 4 1.905143e-13dfmatrikserror <- as.data.frame(hasil$`Matrix Residual`)
print(head(dfmatrikserror))## V1 V2 V3 V4 V5 V6
## 1 -0.08791535 -0.1296811 0.2475975 0.2664125 0.04963479 -0.3805315
## 2 -0.08791535 -0.1296811 0.2475975 0.1304387 -0.01545051 -0.1523294
## 3 -0.08791535 -0.1296811 0.2475975 0.7334609 0.58167623 -1.4280850
## 4 0.28525906 0.2520359 -0.6138829 0.2664125 0.04963479 -0.3805315
## 5 -0.08791535 -0.1296811 0.2475975 -0.3366097 -0.54749195 0.8952241
## 6 -0.08791535 -0.1296811 0.2475975 0.2664125 0.04963479 -0.3805315
## V7 V8 V9 V10 V11 V12
## 1 -0.29818677 0.53068795 -1.1022605 0.3221855 0.8362000 -0.1498453
## 2 0.50788054 -0.90388342 0.1732348 0.1394588 -0.4239163 -0.1498453
## 3 0.42197524 -0.75099634 0.1732348 0.1394588 -0.4239163 -0.8274396
## 4 -0.02401106 0.04273288 0.1732348 0.1394588 -0.4239163 0.0597423
## 5 -0.38409207 0.68357502 -0.2554121 -0.3441392 0.8381570 0.4425970
## 6 -0.02401106 0.04273288 0.1732348 0.1394588 -0.4239163 0.0597423
## V13 V14 V15 V16
## 1 0.5135670 -0.51187444 0.1696938 -1.12838771
## 2 0.5135670 -0.51187444 0.1696938 -1.97301066
## 3 0.1230986 0.30880705 0.6908102 -0.19022938
## 4 -0.2329097 -0.06988993 0.3854245 0.12310332
## 5 0.1271362 0.31310191 -1.3945346 -0.42141872
## 6 -0.2329097 -0.06988993 0.3854245 0.04609492# Jalankan algoritma Kernel K-Means
hasil_kkmeans <- kernel_kmeans(dfmatrikserror, k=2, max_iter = 100, kernel_fun = rbf_kernel, sigma = 1)## Kernel K-Means konvergen pada iterasi ke- 5# Tampilkan hasil cluster
print(hasil_kkmeans$clusters)## [1] 2 2 2 1 2 1 2 1 1 2 1 1 1 1 2 2 1 2 2 1 2 2 1 2 1 2 2 2 2 1 2 2 1 2 1 2 2
## [38] 2 2 2 1 1 1 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 2 2 2 2 2
## [75] 2 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 2 2 2 2 2 2 1 1 2
## [112] 1 2 1 2 1 1 2 1 2 2 2 1 2 1 2 2 2 2 1 2 2 2 2 1 2 2 2 2 2 1 2 2 2 2 1 2 2
## [149] 1 1 2 2 2 2 1 2 1 1 2 2 1 1 1 2 1 2 2 2 1 1 2 2 2 2 2 2 2 1 1 2 2 1 1 2 1
## [186] 2 1 1 2 1 1 1 1 2 2 1 2 2 1 2 2 1 2 1 2 2 2 2 2 1 1 1 2 1 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 2 1 1 2 1 2 2 2 1 1 2 2 2 2 2 2 2 1 1 2 2
## [260] 1 1 2 2 1 1 2 1 1 1 2 2 1 2 2 2 2 2 2 1 2 1 1 1 2 1 2 2 2 1 2 1 2 1 1 2 1
## [297] 1 1 1 2 2 1 2 2 1 2 1 2 1 1 1 1 2 2 1 2 2 1 2 2 1 2 1 2 2 2 2 2 1 1 1 2 1
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 2 2 2 2 2 2 1 1 2data_matrix=as.matrix(dfmatrikserror)
print(table(hasil_kkmeans$clusters))##
## 1 2
## 121 245data_terkelompok_kernel <- split(inputdata$Data,hasil_kkmeans$clusters)
hasil_k1=plsalgorithm(data = data_terkelompok_kernel$"1",inner_weight_scheme = "Path",matrix_outer_model = inputdata$MOM, matrix_inner_model = inputdata$MIM, indicator = inputdata$Indikator, laten_variables = inputdata$Variabel)## Banyak Iterasi: 5
## Outer Weight
## Variabel Laten Perfomance Expectancy
## [,1]
## [1,] 0.3837338
## [2,] 0.3243786
## [3,] 0.3408206
##
## Variabel Laten Effort Expectancy
## [,1]
## [1,] 0.3333333
## [2,] 0.3333333
## [3,] 0.3333333
##
## Variabel Laten Social Influence
## [,1]
## [1,] 0.5319346
## [2,] 0.5102544
##
## Variabel Laten Facilitating Conditions
## [,1]
## [1,] 0.3428930
## [2,] 0.3295562
## [3,] 0.3484066
##
## Variabel Laten Behavioral Intention
## [,1]
## [1,] 0.2761567
## [2,] 0.2761567
## [3,] 0.2761567
## [4,] 0.2112467
##
## Skor Variabel Laten
## Variabel Laten Perfomance Expectancy
## [,1]
## [1,] -1.0248007
## [2,] -0.4894076
## [3,] -1.6642881
## [4,] -0.4894076
## [5,] -0.4894076
## [6,] 1.4285703
##
## Variabel Laten Effort Expectancy
## [,1]
## [1,] -0.8075271
## [2,] -0.8075271
## [3,] -0.8075271
## [4,] -0.8075271
## [5,] -0.8075271
## [6,] 1.2281142
##
## Variabel Laten Social Influence
## [,1]
## [1,] -0.4319952
## [2,] -0.4319952
## [3,] -0.4319952
## [4,] -0.4319952
## [5,] -0.4319952
## [6,] 1.4348411
##
## Variabel Laten Facilitating Conditions
## [,1]
## [1,] -0.4990059
## [2,] -0.4990059
## [3,] -0.4990059
## [4,] -0.4990059
## [5,] -0.4990059
## [6,] 1.3538611
##
## Variabel Laten Behavioral Intention
## [,1]
## [1,] -0.4479663
## [2,] -0.4479663
## [3,] -0.4479663
## [4,] -0.4479663
## [5,] -0.4479663
## [6,] 1.1563593
##
## Koefisien Jalur:
## Variabel Laten Perfomance Expectancy
## NULL
##
## Variabel Laten Effort Expectancy
## NULL
##
## Variabel Laten Social Influence
## NULL
##
## Variabel Laten Facilitating Conditions
## NULL
##
## Variabel Laten Behavioral Intention
## [,1]
## [1,] 0.01067045
## [2,] 0.03637059
## [3,] 0.20403669
## [4,] 0.76202446
##
## Outer Loading:
## Variabel Laten Perfomance Expectancy
## [,1]
## [1,] 0.9584553
## [2,] 0.9528553
## [3,] 0.9480721
##
## Variabel Laten Effort Expectancy
## [,1]
## [1,] 1
## [2,] 1
## [3,] 1
##
## Variabel Laten Social Influence
## [,1]
## [1,] 0.9612035
## [2,] 0.9577626
##
## Variabel Laten Facilitating Conditions
## [,1]
## [1,] 0.9852916
## [2,] 0.9621544
## [3,] 0.9904132
##
## Variabel Laten Behavioral Intention
## [,1]
## [1,] 0.9908249
## [2,] 0.9908249
## [3,] 0.9908249
## [4,] 0.8479716
##
## Matriks Residual Gabungan:
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.45409150 0.4062404 -0.89790921 2.220446e-16 2.220446e-16
## [2,] -0.05905885 -0.1039118 0.16539388 2.220446e-16 2.220446e-16
## [3,] 1.06701165 -0.9558438 -0.29162899 2.220446e-16 2.220446e-16
## [4,] -0.05905885 -0.1039118 0.16539388 2.220446e-16 2.220446e-16
## [5,] -0.05905885 -0.1039118 0.16539388 2.220446e-16 2.220446e-16
## [6,] 0.03913683 0.0399560 -0.08209303 -2.220446e-16 -2.220446e-16
## [,6] [,7] [,8] [,9] [,10] [,11]
## [1,] 2.220446e-16 -0.01009154 0.01052032 -0.03279443 0.09586926 -0.05840686
## [2,] 2.220446e-16 -0.01009154 0.01052032 -0.03279443 0.09586926 -0.05840686
## [3,] 2.220446e-16 -0.01009154 0.01052032 -0.03279443 0.09586926 -0.05840686
## [4,] 2.220446e-16 -0.01009154 0.01052032 -0.03279443 0.09586926 -0.05840686
## [5,] 2.220446e-16 -0.01009154 0.01052032 -0.03279443 0.09586926 -0.05840686
## [6,] -2.220446e-16 0.03351833 -0.03494249 0.00805459 -0.02635957 0.01700627
## [,12] [,13] [,14] [,15] [,16]
## [1,] -0.08427797 -0.08427797 -0.08427797 0.3305225 0.06073662
## [2,] -0.08427797 -0.08427797 -0.08427797 0.3305225 0.05502373
## [3,] -0.08427797 -0.08427797 -0.08427797 0.3305225 0.06756024
## [4,] -0.08427797 -0.08427797 -0.08427797 0.3305225 0.05502373
## [5,] -0.08427797 -0.08427797 -0.08427797 0.3305225 0.05502373
## [6,] 0.26260809 0.26260809 0.26260809 -1.0299000 -0.22798694
##
## CR:
## Variabel Laten Perfomance Expectancy
## [1] 0.9675067
##
## Variabel Laten Effort Expectancy
## [1] 1
##
## Variabel Laten Social Influence
## [1] 0.9586644
##
## Variabel Laten Facilitating Conditions
## [1] 0.986001
##
## Variabel Laten Behavioral Intention
## [1] 0.9775146
##
## AVE:
## Variabel Laten Perfomance Expectancy
## [1] 0.9084701
##
## Variabel Laten Effort Expectancy
## [1] 1
##
## Variabel Laten Social Influence
## [1] 0.9206107
##
## Variabel Laten Facilitating Conditions
## [1] 0.959153
##
## Variabel Laten Behavioral Intention
## [1] 0.9160644
##
## HTMT Matrix:
## Perfomance Expectancy Effort Expectancy
## Perfomance Expectancy NA 0.6923501
## Effort Expectancy 0.6923501 NA
## Social Influence 0.6849658 0.6076648
## Facilitating Conditions 0.8357941 0.7992708
## Behavioral Intention 0.8034515 0.7705566
## Social Influence Facilitating Conditions
## Perfomance Expectancy 0.6849658 0.8357941
## Effort Expectancy 0.6076648 0.7992708
## Social Influence NA 0.8360970
## Facilitating Conditions 0.8360970 NA
## Behavioral Intention 0.8878069 0.9844447
## Behavioral Intention
## Perfomance Expectancy 0.8034515
## Effort Expectancy 0.7705566
## Social Influence 0.8878069
## Facilitating Conditions 0.9844447
## Behavioral Intention NA
##
## R Square:
## Variabel Laten Perfomance Expectancy
## [1] NA
##
## Variabel Laten Effort Expectancy
## [1] NA
##
## Variabel Laten Social Influence
## [1] NA
##
## Variabel Laten Facilitating Conditions
## [1] NA
##
## Variabel Laten Behavioral Intention
## [1] 0.9388645resultbootstrap_k1 <- plsalgorithm_bootstrap(data = data_terkelompok_kernel$"1",inner_weight_scheme = "Path",matrix_outer_model = inputdata$MOM, matrix_inner_model = inputdata$MIM, indicator = inputdata$Indikator, laten_variables = inputdata$Variabel, n_bootstrap=500)
hasil_k2=plsalgorithm(data = data_terkelompok_kernel$"2",inner_weight_scheme = "Path",matrix_outer_model = inputdata$MOM, matrix_inner_model = inputdata$MIM, indicator = inputdata$Indikator, laten_variables = inputdata$Variabel)## Banyak Iterasi: 9
## Outer Weight
## Variabel Laten Perfomance Expectancy
## [,1]
## [1,] 0.4240390
## [2,] 0.4527869
## [3,] 0.3042690
##
## Variabel Laten Effort Expectancy
## [,1]
## [1,] 0.5053360
## [2,] 0.2482742
## [3,] 0.4409510
##
## Variabel Laten Social Influence
## [,1]
## [1,] 0.8631433
## [2,] 0.2887810
##
## Variabel Laten Facilitating Conditions
## [,1]
## [1,] 0.3768780
## [2,] 0.5697645
## [3,] 0.3177982
##
## Variabel Laten Behavioral Intention
## [,1]
## [1,] 0.3509757
## [2,] 0.3486298
## [3,] 0.3012877
## [4,] 0.1576645
##
## Skor Variabel Laten
## Variabel Laten Perfomance Expectancy
## [,1]
## [1,] -0.10118
## [2,] -0.10118
## [3,] -0.10118
## [4,] -0.10118
## [5,] -0.10118
## [6,] -0.10118
##
## Variabel Laten Effort Expectancy
## [,1]
## [1,] -0.5927826
## [2,] 1.3248519
## [3,] 0.5388844
## [4,] 0.1931850
## [5,] -0.9690724
## [6,] -0.5927826
##
## Variabel Laten Social Influence
## [,1]
## [1,] -1.77242853
## [2,] -1.17860849
## [3,] 0.04094556
## [4,] -0.55287447
## [5,] 0.35381257
## [6,] 0.35381257
##
## Variabel Laten Facilitating Conditions
## [,1]
## [1,] -1.7966877
## [2,] -0.1039619
## [3,] -0.1039619
## [4,] 0.3915052
## [5,] -0.5736815
## [6,] -0.1039619
##
## Variabel Laten Behavioral Intention
## [,1]
## [1,] -2.24776698
## [2,] -2.24776698
## [3,] -0.31207207
## [4,] -0.21203489
## [5,] 0.09640565
## [6,] 1.49596720
##
## Koefisien Jalur:
## Variabel Laten Perfomance Expectancy
## NULL
##
## Variabel Laten Effort Expectancy
## NULL
##
## Variabel Laten Social Influence
## NULL
##
## Variabel Laten Facilitating Conditions
## NULL
##
## Variabel Laten Behavioral Intention
## [,1]
## [1,] 0.1775119
## [2,] -0.0751141
## [3,] 0.2174542
## [4,] 0.4574628
##
## Outer Loading:
## Variabel Laten Perfomance Expectancy
## [,1]
## [1,] 0.8522918
## [2,] 0.8923138
## [3,] 0.7709198
##
## Variabel Laten Effort Expectancy
## [,1]
## [1,] 0.8635967
## [2,] 0.8048812
## [3,] 0.8249495
##
## Variabel Laten Social Influence
## [,1]
## [1,] 0.9625411
## [2,] 0.5858731
##
## Variabel Laten Facilitating Conditions
## [,1]
## [1,] 0.7356745
## [2,] 0.9020437
## [3,] 0.6569828
##
## Variabel Laten Behavioral Intention
## [,1]
## [1,] 0.9147023
## [2,] 0.8515168
## [3,] 0.9112096
## [4,] 0.6822166
##
## Matriks Residual Gabungan:
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] -0.08131099 -0.1305332 0.307566 0.3837992 0.06882952 -0.4785932
## [2,] -0.08131099 -0.1305332 0.307566 0.2225381 0.04098366 -0.2781073
## [3,] -0.08131099 -0.1305332 0.307566 0.9012971 0.67359415 -1.4121613
## [4,] -0.08131099 -0.1305332 0.307566 -0.2949598 -0.56378098 0.6554607
## [5,] -0.08131099 -0.1305332 0.307566 0.7087619 -1.14392398 -0.1681731
## [6,] -0.08131099 -0.1305332 0.307566 0.3837992 0.06882952 -0.4785932
## [,7] [,8] [,9] [,10] [,11] [,12]
## [1,] -0.10330742 0.3087776 -1.0132096 0.3525524 0.5694953 -0.076867637
## [2,] 0.37556420 -1.1225313 0.2341828 0.1477415 -0.5425965 -0.076867637
## [3,] 0.25214108 -0.7536295 0.2341828 0.1477415 -0.5425965 -0.683617501
## [4,] -0.22673054 0.6776794 -0.1303198 -0.2991915 0.6909520 0.388712982
## [5,] -0.04900629 0.1464759 -0.6666003 0.5714491 -0.2339989 0.106581705
## [6,] -0.04900629 0.1464759 0.2341828 0.1477415 -0.5425965 -0.009765741
## [,13] [,14] [,15] [,16]
## [1,] 0.4279465131 -0.45597519 0.09617572 -1.0669928
## [2,] 0.4279465131 -0.45597519 0.09617572 -1.8264392
## [3,] 0.0846371074 0.31540639 0.73192149 -0.2149787
## [4,] -0.0005462354 0.22425155 -1.29263448 -0.2384375
## [5,] -0.2631885482 -0.05680242 0.45325120 0.2270752
## [6,] -0.1499713791 -0.06449365 0.47660157 1.4400222
##
## CR:
## Variabel Laten Perfomance Expectancy
## [1] 0.877539
##
## Variabel Laten Effort Expectancy
## [1] 0.8703872
##
## Variabel Laten Social Influence
## [1] 0.7665277
##
## Variabel Laten Facilitating Conditions
## [1] 0.8127103
##
## Variabel Laten Behavioral Intention
## [1] 0.908082
##
## AVE:
## Variabel Laten Perfomance Expectancy
## [1] 0.7056476
##
## Variabel Laten Effort Expectancy
## [1] 0.6913916
##
## Variabel Laten Social Influence
## [1] 0.6348664
##
## Variabel Laten Facilitating Conditions
## [1] 0.5955087
##
## Variabel Laten Behavioral Intention
## [1] 0.7143709
##
## HTMT Matrix:
## Perfomance Expectancy Effort Expectancy
## Perfomance Expectancy NA 0.2129749
## Effort Expectancy 0.2129749 NA
## Social Influence 0.5786560 0.6518371
## Facilitating Conditions 0.2853007 0.7734470
## Behavioral Intention 0.3922148 0.3211511
## Social Influence Facilitating Conditions
## Perfomance Expectancy 0.5786560 0.2853007
## Effort Expectancy 0.6518371 0.7734470
## Social Influence NA 0.8214224
## Facilitating Conditions 0.8214224 NA
## Behavioral Intention 0.5747911 0.6840497
## Behavioral Intention
## Perfomance Expectancy 0.3922148
## Effort Expectancy 0.3211511
## Social Influence 0.5747911
## Facilitating Conditions 0.6840497
## Behavioral Intention NA
##
## R Square:
## Variabel Laten Perfomance Expectancy
## [1] NA
##
## Variabel Laten Effort Expectancy
## [1] NA
##
## Variabel Laten Social Influence
## [1] NA
##
## Variabel Laten Facilitating Conditions
## [1] NA
##
## Variabel Laten Behavioral Intention
## [1] 0.4021558resultbootstrap_k2 <- plsalgorithm_bootstrap(data = data_terkelompok_kernel$"2",inner_weight_scheme = "Path",matrix_outer_model = inputdata$MOM, matrix_inner_model = inputdata$MIM, indicator = inputdata$Indikator, laten_variables = inputdata$Variabel, n_bootstrap=500)
resultbootstrap_k1$path_coefficient## [[1]]
## data frame with 0 columns and 0 rows
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## [[2]]
## data frame with 0 columns and 0 rows
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## [[3]]
## data frame with 0 columns and 0 rows
##
## [[4]]
## data frame with 0 columns and 0 rows
##
## [[5]]
## path_coefficient_original average_path_coefficient standar_error t_hitung
## 1 0.01067045 0.01413965 0.06739955 0.1583163
## 2 0.03637059 0.03199431 0.02004987 1.8140064
## 3 0.20403669 0.20368414 0.08712341 2.3419273
## 4 0.76202446 0.76199453 0.11381854 6.6950822
## p_value
## 1 8.742075e-01
## 2 6.967675e-02
## 3 1.918445e-02
## 4 2.155498e-11resultbootstrap_k2$path_coefficient## [[1]]
## data frame with 0 columns and 0 rows
##
## [[2]]
## data frame with 0 columns and 0 rows
##
## [[3]]
## data frame with 0 columns and 0 rows
##
## [[4]]
## data frame with 0 columns and 0 rows
##
## [[5]]
## path_coefficient_original average_path_coefficient standar_error t_hitung
## 1 0.1775119 0.18588040 0.07788788 2.279069
## 2 -0.0751141 -0.07794092 0.05960726 -1.260150
## 3 0.2174542 0.22380002 0.08626445 2.520786
## 4 0.4574628 0.45371741 0.07991712 5.724215
## p_value
## 1 2.266294e-02
## 2 2.076152e-01
## 3 1.170930e-02
## 4 1.039130e-08