Penelitian ini bertujuan untuk menganalisis pengaruh Membership Type dan Discount Applied terhadap Customer Satisfaction dengan Average Rating sebagai variabel mediasi pada platform e-commerce menggunakan metode SEM-PLS.
Metode yang digunakan adalah Structural Equation Modeling - Partial Least Squares (SEM-PLS).
library(seminr)
library(readxl)data <- read.csv("E-commerce Customer Behavior - Sheet1.csv")names(data)## [1] "Customer.ID" "Gender"
## [3] "Age" "City"
## [5] "Membership.Type" "Total.Spend"
## [7] "Items.Purchased" "Average.Rating"
## [9] "Discount.Applied" "Days.Since.Last.Purchase"
## [11] "Satisfaction.Level"str(data)## 'data.frame': 350 obs. of 11 variables:
## $ Customer.ID : int 101 102 103 104 105 106 107 108 109 110 ...
## $ Gender : chr "Female" "Male" "Female" "Male" ...
## $ Age : int 29 34 43 30 27 37 31 35 41 28 ...
## $ City : chr "New York" "Los Angeles" "Chicago" "San Francisco" ...
## $ Membership.Type : chr "Gold" "Silver" "Bronze" "Gold" ...
## $ Total.Spend : num 1120 780 511 1480 720 ...
## $ Items.Purchased : int 14 11 9 19 13 8 15 12 10 21 ...
## $ Average.Rating : num 4.6 4.1 3.4 4.7 4 3.1 4.5 4.2 3.6 4.8 ...
## $ Discount.Applied : logi TRUE FALSE TRUE FALSE TRUE FALSE ...
## $ Days.Since.Last.Purchase: int 25 18 42 12 55 22 28 14 40 9 ...
## $ Satisfaction.Level : chr "Satisfied" "Neutral" "Unsatisfied" "Satisfied" ...head(data)## Customer.ID Gender Age City Membership.Type Total.Spend
## 1 101 Female 29 New York Gold 1120.20
## 2 102 Male 34 Los Angeles Silver 780.50
## 3 103 Female 43 Chicago Bronze 510.75
## 4 104 Male 30 San Francisco Gold 1480.30
## 5 105 Male 27 Miami Silver 720.40
## 6 106 Female 37 Houston Bronze 440.80
## Items.Purchased Average.Rating Discount.Applied Days.Since.Last.Purchase
## 1 14 4.6 TRUE 25
## 2 11 4.1 FALSE 18
## 3 9 3.4 TRUE 42
## 4 19 4.7 FALSE 12
## 5 13 4.0 TRUE 55
## 6 8 3.1 FALSE 22
## Satisfaction.Level
## 1 Satisfied
## 2 Neutral
## 3 Unsatisfied
## 4 Satisfied
## 5 Unsatisfied
## 6 Neutraldata_sem <- data[, c(
"Membership.Type",
"Discount.Applied",
"Average.Rating",
"Satisfaction.Level"
)]unique(data_sem$Membership.Type)## [1] "Gold" "Silver" "Bronze"unique(data_sem$Satisfaction.Level)## [1] "Satisfied" "Neutral" "Unsatisfied" ""data_sem$Membership.Type <- as.numeric(factor(data_sem$Membership.Type,levels = c("Bronze", "Silver", "Gold")))data_sem$Satisfaction.Level <- as.numeric(factor(data_sem$Satisfaction.Level,levels = c("Unsatisfied", "Neutral", "Satisfied")))data_sem$Discount.Applied <- ifelse(data_sem$Discount.Applied == TRUE,1,0)data_sem <- na.omit(data_sem) str(data_sem)## 'data.frame': 348 obs. of 4 variables:
## $ Membership.Type : num 3 2 1 3 2 1 3 2 1 3 ...
## $ Discount.Applied : num 1 0 1 0 1 0 1 0 1 0 ...
## $ Average.Rating : num 4.6 4.1 3.4 4.7 4 3.1 4.5 4.2 3.6 4.8 ...
## $ Satisfaction.Level: num 3 2 1 3 1 2 3 2 1 3 ...
## - attr(*, "na.action")= 'omit' Named int [1:2] 72 144
## ..- attr(*, "names")= chr [1:2] "72" "144"measurement_model <- constructs(
composite(
"Membership",
single_item("Membership.Type")
),
composite(
"Discount",
single_item("Discount.Applied")
),
composite(
"Rating",
single_item("Average.Rating")
),
composite(
"Satisfaction",
single_item("Satisfaction.Level")
)
)structural_model <- relationships(
paths(
from = c("Membership", "Discount"),
to = c("Rating", "Satisfaction")
),
paths(
from = "Rating",
to = "Satisfaction"
)
)pls_model <- estimate_pls(
data = data_sem,
measurement_model = measurement_model,
structural_model = structural_model
)## Generating the seminr model## All 348 observations are valid.summary(pls_model)##
## Results from package seminr (2.5.0)
##
## Path Coefficients:
## Rating Satisfaction
## R^2 0.908 0.747
## AdjR^2 0.907 0.745
## Membership 0.950 1.160
## Discount -0.073 -0.455
## Rating . -0.442
##
## Reliability:
## alpha rhoA rhoC AVE
## Membership 1.000 1.000 1.000 1.000
## Discount 1.000 1.000 1.000 1.000
## Rating 1.000 1.000 1.000 1.000
## Satisfaction 1.000 1.000 1.000 1.000
##
## Alpha, rhoA, and rhoC should exceed 0.7 while AVE should exceed 0.5boot_model <- bootstrap_model(
seminr_model = pls_model,
nboot = 5000,
cores = 1
)## Bootstrapping model using seminr...## SEMinR Model successfully bootstrappedsummary(boot_model)##
## Results from Bootstrap resamples: 5000
##
## Bootstrapped Structural Paths:
## Original Est. Bootstrap Mean Bootstrap SD T Stat.
## Membership -> Rating 0.950 0.950 0.005 191.284
## Membership -> Satisfaction 1.160 1.160 0.094 12.356
## Discount -> Rating -0.073 -0.073 0.017 -4.308
## Discount -> Satisfaction -0.455 -0.456 0.027 -16.924
## Rating -> Satisfaction -0.442 -0.442 0.084 -5.234
## 2.5% CI 97.5% CI Bootstrap P Val
## Membership -> Rating 0.940 0.959 0.000
## Membership -> Satisfaction 0.970 1.341 0.000
## Discount -> Rating -0.107 -0.040 0.000
## Discount -> Satisfaction -0.508 -0.404 0.000
## Rating -> Satisfaction -0.605 -0.276 0.000
##
## Bootstrapped Weights:
## Original Est. Bootstrap Mean Bootstrap SD
## Membership.Type -> Membership 1.000 1.000 0.000
## Discount.Applied -> Discount 1.000 1.000 0.000
## Average.Rating -> Rating 1.000 1.000 0.000
## Satisfaction.Level -> Satisfaction 1.000 1.000 0.000
## T Stat. 2.5% CI 97.5% CI Bootstrap P Val
## Membership.Type -> Membership . 1.000 1.000 0.000
## Discount.Applied -> Discount . 1.000 1.000 0.000
## Average.Rating -> Rating . 1.000 1.000 0.000
## Satisfaction.Level -> Satisfaction . 1.000 1.000 0.000
##
## Bootstrapped Loadings:
## Original Est. Bootstrap Mean Bootstrap SD
## Membership.Type -> Membership 1.000 1.000 0.000
## Discount.Applied -> Discount 1.000 1.000 0.000
## Average.Rating -> Rating 1.000 1.000 0.000
## Satisfaction.Level -> Satisfaction 1.000 1.000 0.000
## T Stat. 2.5% CI 97.5% CI Bootstrap P Val
## Membership.Type -> Membership . 1.000 1.000 0.000
## Discount.Applied -> Discount . 1.000 1.000 0.000
## Average.Rating -> Rating . 1.000 1.000 0.000
## Satisfaction.Level -> Satisfaction . 1.000 1.000 0.000
##
## Bootstrapped HTMT:
## Original Est. Bootstrap Mean Bootstrap SD 2.5% CI
## Membership -> Discount 0.004 0.043 0.032 0.002
## Membership -> Rating 0.950 0.950 0.003 0.943
## Membership -> Satisfaction 0.742 0.741 0.022 0.697
## Discount -> Rating 0.077 0.080 0.048 0.005
## Discount -> Satisfaction 0.425 0.426 0.045 0.335
## Rating -> Satisfaction 0.695 0.694 0.024 0.647
## 97.5% CI Bootstrap P Val
## Membership -> Discount 0.120 0.000
## Membership -> Rating 0.956 0.000
## Membership -> Satisfaction 0.784 0.000
## Discount -> Rating 0.184 0.000
## Discount -> Satisfaction 0.513 0.000
## Rating -> Satisfaction 0.740 0.000
##
## Bootstrapped Total Paths:
## Original Est. Bootstrap Mean Bootstrap SD 2.5% CI
## Membership -> Rating 0.950 0.950 0.005 0.940
## Membership -> Satisfaction 0.740 0.740 0.024 0.692
## Discount -> Rating -0.073 -0.073 0.017 -0.107
## Discount -> Satisfaction -0.422 -0.424 0.028 -0.479
## Rating -> Satisfaction -0.442 -0.442 0.084 -0.605
## 97.5% CI
## Membership -> Rating 0.959
## Membership -> Satisfaction 0.785
## Discount -> Rating -0.040
## Discount -> Satisfaction -0.368
## Rating -> Satisfaction -0.276plot(pls_model)Berdasarkan hasil analisis SEM-PLS, penelitian ini dapat digunakan untuk mengetahui pengaruh Membership Type dan Discount Applied terhadap Customer Satisfaction melalui Average Rating pada platform e-commerce.