Moderating effect of product type on online shopping behaviour and purchase intention: An Indian perspective

Figures & data

Figure 1. Proposed research model.

Table 1. Factor matrix

Pattern Matrix^a
	Factor
	1	2	3	4	5	6	7	8
Cronbach’s alpha	0.921	0.865	0.848	0.815	0.902	0.818	0.907	0.922
APB1					0.814
APB2					0.982
INT1								0.585
INT2								0.700
INT3								0.600
ATT1				0.773
ATT2				0.762
ATT3				0.765
ATT4				0.769
SN1							0.772
SN2							0.752
SN3							0.768
TR1			0.795
TR2			0.761
TR3			0.751
TR4			0.779
TR5			0.808
TR6			0.792
PSE1						0.775
PSE2						0.776
PSE3						0.771
PR1		0.741
PR2		0.772
PR3		0.769
PR4		0.793
PR5		0.790
PR6		0.809
PU1	0.810
PU2	0.816
PU3	0.756
PU4	0.827
PU5	0.818
PU6	0.828

Extraction Method: Maximum Likelihood.

Rotation Method: Promax with Kaiser normalization.

^aRotation converged in six iterations.

Table 2. Questionnaire items

Constructs	Measurement Items
Attitude	Online shopping is a good idea.
	Purchasing through the Web is enjoyable.
	My general opinion of e-commerce/m-commerce is positive.
	Using the Internet to purchase a product seems an intelligent idea to me.
Subjective	The members of my family (e.g., parents, spouse, children) think that I should make purchases through the Internet.
Norms	The media frequently suggest to us to make purchases through Internet.
	My friends think that I should make purchases through Internet.
Trust	Based on my experience with the online store in the past, I know it is honest.
	Based on my experience with the online store in the past, I know it cares about its customers.
	Based on my experience with the online store in the past, I know it is not opportunistic.
	Based on my experience with the online store in the past, I know it keeps its promises to its customers.
	Based on my experience with the online store in the past, I know that the transactions will be successful.
	Based on my experience with the online store in the past, I know it is trustworthy.
Perceived	I feel capable of buying a product on the Internet.
Self-Efficacy	I feel capable of finding shopping sites on the Internet.
	I feel comfortable looking for information about a product on the Internet.
Perceived Risk	I think online websites are risky for financial transactions.
	I worry about the safety in using the credit card.
	I worry that my personal information may be misused if I shop online.
	I worry if the products ordered through online websites would be delivered.
	I am not comfortable with the security aspects of online transactions.
	I worry about the quality of the product that may be delivered if I order through online websites
Perceived Usefulness	Online store offers more useful information about the choices available.
	Online store improves my ability in assessing products online.
	Online store enhances my effectiveness to purchase products/services online.
	Using the Internet for shopping saves me money.
	Online store eliminates time constraint, so I can purchase products at any time I like.
	Online store is more user-friendly than an existing physical store.
Purchase Intention	Given that I had access to Internet purchasing, I predict that I would use it.
	I intend to use Internet purchasing in the future.
	I intend to use Internet purchasing as much as possible.
Actual Purchase Behaviour	I prefer online shopping for buying fashion/electronics products.
	I frequently use online shopping.

Table 3. Demographic characteristics of the sample

Measure	Item	Frequency	%
Gender	Male	225	65.4%
	Female	119	34.6%
Age	Under-18	35	10%
	18–25	110	32%
	25–35	148	43%
	35–45	38	11%
	>45	13	4%
Education	Undergraduate	69	20%
	Graduate	161	47%
	Postgraduate	114	33%
Occupation	Student	69	20%
	Office worker	185	53.8%
	Self-employed	27	7.8%
	Homemakers	63	18.3%

Table 4. Normality test values of construct variables

Decision Variables	Skewness	Kurtosis	Standard Deviation
APB1	0.035	−0.130	0.842
APB2	0.063	−0.020	0.795
INT1	−0.499	−0.382	0.780
INT2	−0.493	−0.492	0.770
INT3	−0.520	−0.372	0.771
ATT1	−0.763	0.326	0.739
ATT2	−0.587	−0.363	0.745
ATT3	−0.605	−0.177	0.733
ATT4	−0.605	−0.262	0.742
SN1	−0.609	−0.227	0.764
SN2	−0.661	0.150	0.747
SN3	−0.643	−0.107	0.755
TR1	−0.656	−0.395	0.747
TR2	−0.648	−0.258	0.740
TR3	−0.611	−0.215	0.738
TR4	−0.714	−0.240	0.738
TR5	−0.606	−0.409	0.736
TR6	−0.744	0.240	0.733
PSE1	−0.959	0.536	0.692
PSE2	−0.652	−0.569	0.681
PSE3	−0.825	−0.002	0.676
PR1	−0.989	0.514	0.721
PR2	−0.866	−0.023	0.735
PR3	−0.927	0.198	0.728
PR4	−0.887	0.012	0.724
PR5	−0.812	0.000	0.723
PR6	−0.946	0.186	0.741
PU1	−0.965	0.265	0.717
PU2	−1.031	0.439	0.719
PU3	−0.892	−0.002	0.716
PU4	−1.015	0.453	0.724
PU5	−1.063	0.593	0.708
PU6	−1.026	0.575	0.694

Table 5. Model fit—Measurement model

Measure	Estimate	Threshold	Interpretation
CMIN	764.484	—	—
df	467	—	—
CMIN/df	1.637	Between 1 and 3	Excellent
CFI	0.978	>0.95	Excellent
SRMR	0.032	<0.08	Excellent
RMSEA	0.030	<0.06	Excellent
PClose	1	>0.05	Excellent

Notes: Li-Tze and Bentler (1999); Gaskin and Lim (2016)

Figure 2. Measurement model.

Table 6. CFA—Validity and reliability

	CR	AVE	PRisk	PUse	Trust	Attitude	PSE	SNorm	APB	PINT
PRisk	0.907	0.619	0.787
PUse	0.922	0.662	−0.312***	0.814
Trust	0.902	0.604	−0.268***	0.423***	0.777
Attitude	0.848	0.582	−0.174***	0.246***	0.319***	0.763
PSE	0.818	0.599	−0.253***	0.392***	0.285***	0.181***	0.774
SNorm	0.815	0.595	−0.245***	0.230***	0.157***	0.06	0.207***	0.771
APB	0.922	0.855	−0.360***	0.499***	0.354***	0.228***	0.363***	0.271***	0.925
PINT	0.865	0.681	−0.562***	0.675***	0.579***	0.394***	0.545***	0.366***	0.799***	0.825

Table 7. Configural invariance test—Model fit values

Measure	Estimate	Threshold	Interpretation
CMIN	1245.762	—	—
df	934	—	—
CMIN/df	1.334	Between 1 and 3	Excellent
CFI	0.977	>0.95	Excellent
SRMR	0.038	<0.08	Excellent
RMSEA	0.022	<0.06	Excellent
PClose	1	>0.05	Excellent

Table 8. Metric and Scalar invariance test

	χ^2	df	P-value
Unconstrained base model^a	1245.762	934
Fully constrained model (factor loadings constrained)^b	1267.486	967
Fully constrained model (intercepts constrained)^c	1326.646	1000
Number of groups	2
Difference (a-b)	21.724	33	0.934^ns
Difference (a-c)	80.884	66	0.103^ns

ns represents not significant.

Table 9. Structural model fit values

Measure	Estimate	Threshold	Interpretation
CMIN	804.318	—	—
df	473	—	—
CMIN/df	1.700	Between 1 and 3	Excellent
CFI	0.975	>0.95	Excellent
SRMR	0.034	<0.08	Excellent
RMSEA	0.032	<0.06	Excellent
PClose	1	>0.05	Excellent

Figure 3. Structural model.

Table 10. Standardized path coefficients

Hypothesis	Path Direction	Path Coefficient	Rejected/Accepted
H1	PINT → APB	0.769***	Accepted
H2	Attitude → PINT	0.126***	Accepted
H3	SNorm → PINT	0.130***	Accepted
H4	PUse → PINT	0.349***	Accepted
H5	PSE → PINT	0.216***	Accepted
H6	Trust → PINT	0.213***	Accepted
H7	PRisk → PINT	−0.270***	Accepted

Notes: ***p < 0.001.

Table 11. Multi-group analysis of paths for Fashion and Electronics

	χ^2	df	∆χ^2 from base model
Unconstrained base model^a	1306.675	946
Constrained paths^b
PUse → PINT (H10)	1310.704		4.029*
PRisk → PINT (H13)	1306.865		0.19^ns
Trust → PINT (H12)	1306.820		0.145^ns
ATT → PINT (H8)	1306.881		0.206^ns
SNorm → PINT (H9)	1306.831		0.156^ns
PSE → PINT (H11)	1306.841		0.166^ns

Notes: ^aPaths for the two product groups were allowed to be freely estimated.

^bThe path specified was constrained to be equal across the two product groups. *p < 0.05, ns: not significant.

Figure 4. Standardized path coefficients for Fashion and Electronics groups.

Li-Tze, H., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. doi:10.1080/10705519909540118

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Gaskin, J., & Lim, J. (2016). Model fit measures. AMOS Plugin. Gaskination’s StatWiki.

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