Analysis of students’ online shopping behaviour using a partial least squares approach: Case study of Indonesian students

Figures & data

Table 1. Summary of the indicators

Latent variable	Indicator	Operational definition	Latent variable	Indicator	Operational definition
Perceived Risk	POR1	Consumer is in doubt about online shopping due to the high risk of getting a defective product	Enjoyment	EJY1	Online shopping saves time to shop
	POR2	Consumer thinks that there will be difficulty in solving problems that arise through online shopping (such as exchanged product)		EJY2	Online shopping makes it easy to purchase goods
	POR3	Consumer might receive a defect product		EJY3	Information and facility make it easy to compare and purchase goods
	POR4	It s difficult to assess the product quality online		EJY4	Online shopping is easy to be carried out in general
	POR5	Consumer might not receive the product ordered	Social influence	SF1	People around the customer frequently do online shopping
Trust and Security	TAS1	Consumer feels convenience with online shopping		SF2	People around the customer recommend online shopping over traditional shopping
	TAS2	Consumer trusts the online shopping website		SF3	Technological development influences the customer to shop online
	TAS3	Customer feels that privacy is maintained when doing online shopping	Online advertisement	OAD1	Online advertising influences the consumer to buy a new product
	TAS4	Online shopping website has good safety features		OAD2	Online advertising influences the consumer to try another type of product
	TAS5	Online shopping website has a good reputation		OAD3	Online advertising influences the decision to change to another brand
Quality of Website	QOW1	Consumer buys from online shop if it is visually interesting and has a well-organized appearance	Online shopping behaviour	OSB1	Customer will repeat the online shopping
	QOW2	Consumer buys from online shop if the navigation flow is user friendly		OSB2	Consumer prefers online shopping over conventional shopping
	QOW3	Consumer buys from online shop if it is easy to understand the website and its provided information
	QOW4	Consumer buys from online shop if it has free and easy shipment order fees and transaction procedures
	QOW5	Consumer buys from online shop if it gives complete information about the products

Figure 1. Concept design of the research.

Table 2. Testing for measurement model (outer model)

Indicator to variable	Loading factor	t-Statistics
EJY1<-EJY	0.797	11.928
EJY2<-EJY	0.878	31.856
EJY3<-EJY	0.765	8.792
EJY4<-EJY	0.775	8.045
OAD1<-OAD	0.900	12.768
OAD2<-OAD	0.918	16.923
OAD3<-OAD	0.869	14.508
OSB1<-OSB	0.895	39.24
OSB2<-OSB	0.799	11.285
POR1<-POR	0.729	8.536
POR2<-POR	0.635	4.894
POR3<-POR	0.764	7.511
POR4<-POR	0.685	6.475
POR5<-POR	0.699	5.003
QOW1<-QOW	0.799	9.629
QOW2<-QOW	0.834	19.922
QOW3<-QOW	0.806	13.872
QOW4<-QOW	0.664	6.361
QOW5<-QOW	0.739	8.119
SIF1<-SIF	0.722	7.584
SIF2<-SIF	0.715	5.551
SIF3<-SIF	0.705	6.229
TAS1<-TAS	0.847	12.011
TAS2<-TAS	0.853	11.977
TAS3<-TAS	0.738	6.366
TAS4<-TAS	0.834	12.766
TAS5<-TAS	0.792	9.881

Table 3. Reliability test

Variable	Composite reliability
EJY	0.880
OAD	0.924
OSB	0.836
POR	0.830
QOW	0.879
SIF	0.757
TAS	0.907

Table 4. Structural test (inner model)

Variable	Loading factor	t-Statistic
EJY -> OSB	0.259	2.077
OAD -> OSB	0.159	1.818
POR -> OSB	−0.269	3.507
QOW -> OSB	0.123	0.949
SIF -> OSB	0.271	2.558
TAS -> OSB	0.023	0.226

Table 5. Average weighted R²

Number of groups (k)	Average weighted R-Squares
2	0,787
3	0,893

Table 6. Percentage of respondents in each segment

Segment	Percentage
1	59.036
2	19.277
3	21.687

Table 7. Respondent clusters based on segment

Characteristics		Segment 1	Segment 2	Segment 3
Gender	Male	19(41%)	6(40%)	9(41%)
	Female	27(59%)	9(60%)	13(59%)
Frequency of online shopping (in a month)	1	26(57%)	11(73%)	11(50%)
	2	14(30%)	4(27%)	7(32%)
	>2	6(13%)	0(0%)	4(18%)
Online shopping platform	Berrybenka	1(2%)	0(0%)	1(5%)
	Bukalapak	6(13%)	1(7%)	0(0%)
	Lazada	6(13%)	1(7%)	5(23%)
	OLX	0(0%)	1(7%)	0(0%)
	Shopee	14(30%)	4(27%)	12(55%)
	Tokopedia	8(17%)	5(33%)	2(9%)
	Zalora	2(4%)	0(0%)	1(5%)
	Others	9(20%)	3(20%)	1(5%)
Price of the product	<100.000	7(15%)	3(20%)	3(14%)
	100.000–200.000	17(37%)	3(20%)	10(45%)
	200.001–300.000	9(20%)	5(33%)	8(36%)
	300.001–500.000	7(15%)	3(20%)	0(0%)
	500.001–1.000.000	5(11%)	1(7%)	0(0%)
	>1.000.000	1(2%)	0(0%)	1(5%)
Type of purchased product	Accessories	4(9%)	2(13%)	3(14%)
	Electronics	8(17%)	0(0%)	8(36%)
	Fashion	22(48%)	9(60%)	8(36%)
	Hobbies	2(4%)	4(27%)	1(5%)
	Household needs	1(2%)	0(0%)	0(0%)
	Skincare	7(15%)	0(0%)	2(9%)
	Mobile and internet credit	2(4%)	0(0%)	0(0%)

Table 8. Path coefficient

Variable	Segment 1	Segment 2	Segment 3	Global
EJY->OSB	−0.011	0.657	0.283	0.259
OAD>OSB	0.261	−0.184	−0.139	0.159
POR>OSB	−0.358	0.320	−0.251	−0.269
QOW>OSB	0.101	−0.823	0.902	0.123
SIF>OSB	0.483	0.095	0.142	0.271
TAS>OSB	0.238	0.277	−0.385	0.023