Determinants of interaction intention to purchase online in less developed countries: The moderating role of technology infrastructure

Figures & data

Figure 1. The study model.

Figure 2. PLS algorithm results.

Table 1. Definition of constructs

Construct and its abbreviation	Definitions in context to study	Reference
Perceived usefulness (PU)	PU refers to the extent to which a person thinks that the use of online purchasing channels will boost her or his job performance.	(Venkatesh & Bala, 2008)
Perceives ease of use (PEOU)	PEOU is the degree to which a person thinks that the use of online purchasing channels will be possible and easy.	(Venkatesh & Bala, 2008)
Technology infrastructure (TI)	TI refers to hardware components, software, network resources, and services required for the existence, operation, and management of online purchasing channels.	(Techopedia, 2022)
Technology anxiety (TA)	TA refers to the tendency of an individual to be uneasy and/or worry about current or future use of online purchasing channels.	(Igbaria & Iivari, 1995)
Social influence (SI)	SI refers to the change in consumer behavior toward online purchasing because of someone, intentionally or unintentionally.	(Mei & Aun, 2019)
Self-efficacy (SE)	SE refers to the degree to which an individual believes that he or she has the capability to use online purchasing channels.	(Venkatesh & Bala, 2008)
Interaction intention to purchase online (IIPO)	IIPO is a consumer’s willingness to purchase a certain product or service online.	(Driediger & Bhatiasevi, 2019)

Table 2. The measurement model’s conditions

Threshold		Achieved?	Remarks
Measurement model	Reliability - Cronbach’s alpha (α) ≥0.70 (Hair et al., 2021).	Yes	Table 3
	- Composite reliability (CR) ≥0.70 (Urbach & Ahlemann, 2010).	Yes	Table 3
	Validity (convergent) - Factor loadings ≥70 (Urbach & Ahlemann, 2010).	Yes	Table 3; Figure 2
	- Average variance extracted (AVE) ≥0.50 (Hair et al., 2021).	Yes	Table 3
	-Cross loadings (CL): The CL of each variable must be above CL of all other variables in the variable’s row and column (Urbach & Ahlemann, 2010).	Yes	Appendix 2
	Validity (discriminant) Fornell-Larcker standard: the correlation values of each variable have to be lower than √AVE (Hair et al., 2021).	Yes	Table 3

Table 3. Measurement model

		Reliability and convergent validity					Discriminant validity and VIF
Variables and items		Factor loading	α	CR	rho_A	AVE	PU	PEOU	TA	SI	SE	TI	IIPO	VIF
PU	PU1	0.777	0.861	0.906	0.866	0.708	0.841							1.000
	PU2	0.888
	PU3	0.864
	PU4	0.832
PEOU	PEOU1	0.847	0.817	0.891	0.819	0.732	0.719	0.856						2.329
	PEOU2	0.871
	PEOU3	0.849
TA	TA1	0.932	0.743	0.882	0.764	0.789	0.145	0.019	0.889					1.226
	TA2	0.843
SI	SI1	0.714	0.710	0.826	0.824	0.614	0.637	0.511	0.107	0.784				2.031
	SI2	0.788
	SI3	0.845
SE	SE1	0.802	0.823	0.895	0.784	0.739	0.592	0.524	0.160	0.584	0.860			1.923
	SE2	0.865
	SE3	0.909
TI	TI1	0.813	0.758	0.860	0.837	0.672	0.712	0.541	0.201	0.582	0.552	0.820		2.423
	TI2	0.822
	TI3	0.825
IIPO	IIPO1	0.881	0.830	0.898	0.830	0.746	0.670	0.527	0.100	0.715	0.635	0.629	0.864	-
	IIPO2	0.857
	IIPO3	0.853

Table 4. Structural model

Path	β	t-value	p-value	Result
Direct impact
H1: PU -> IIPO	0.071	1.256	0.209	Invalid
H2: PEOU -> PU	0.719	19.294	0.000	Valid
H3: PEOU -> IIPO	−0.003	0.058	0.953	Invalid
H4: TA -> IIPO	0.009	0.175	0.861	Invalid
H5: SI -> IIPO	0.369	6.736	0.000	Valid
H6: SE -> IIPO	0.169	2.758	0.006	Valid
H7: TI -> IIPO	0.174	3.202	0.001	Valid
Moderation impact
H8: PU*TI -> IIPO	−0.034	0.515	0.606	Invalid
H9: PEOU*TI -> IIPO	0.204	3.345	0.001	Valid
H10: TA*TI -> IIPO	−0.119	2.791	0.005	Valid
H11: SI*TI -> IIPO	−0.046	0.851	0.395	Invalid
H12: SE*TI -> IIPO	0.218	3.008	0.003	Valid

PU = Perceived usefulness, PEOU = Perceives ease of use, TI = Technology infrastructure, TA = Technology anxiety, and IIPO = Interaction intention to purchase online.

Besides R², the predictive relevance (Q²) is presented in Table 4 and was found to be satisfactory, i.e., Q² > zero (Cohen, 1988). Q² > zero displays the predictive relevance of the model. Finally, the model fit is denoted in two ways: the SRMR and the overall model fit standard. The SRMR was discovered to be 0.07, which was less than the 0.08 criterion (Hair et al., 2021). The overall model fit is measured by the goodness-of-fit (GoF) index, which can be computed as GoF = √ (AVE average * R² average) (Tenenhaus et al., 2005). A GoF > 0.25 denotes a high overall model fit. As apparent in Appendix 3, GoF was 0.660, denoting a high overall model fit.

Table

Variables and items		PU	PEOU	TA	SI	SE	TI	IIPO
PU	PU1	0.777	0.517	0.161	0.515	0.456	0.554	0.525
	PU2	0.888	0.581	0.087	0.552	0.478	0.553	0.572
	PU3	0.864	0.592	0.147	0.586	0.578	0.658	0.619
	PU4	0.832	0.713	0.098	0.490	0.474	0.622	0.536
PEOU	PEOU1	0.567	0.847	−0.075	0.370	0.389	0.371	0.436
	PEOU2	0.647	0.871	0.038	0.446	0.483	0.51	0.451
	PEOU3	0.628	0.849	0.077	0.490	0.468	0.499	0.465
TA	TA1	0.162	0.058	0.932	0.032	0.169	0.200	0.103
	TA2	0.083	−0.044	0.843	0.190	0.107	0.150	0.070
SI	SI1	0.433	0.409	0.026	0.714	0.481	0.461	0.391
	SI2	0.350	0.382	0.045	0.788	0.462	0.298	0.434
	SI3	0.638	0.420	0.139	0.845	0.458	0.562	0.743
SE	SE1	0.434	0.397	0.200	0.537	0.802	0.441	0.510
	SE2	0.527	0.534	0.125	0.404	0.865	0.471	0.501
	SE3	0.559	0.430	0.098	0.556	0.909	0.509	0.615
TI	TI1	0.499	0.446	0.015	0.464	0.400	0.813	0.513
	TI2	0.636	0.437	0.317	0.494	0.499	0.822	0.575
	TI3	0.612	0.448	0.139	0.470	0.453	0.825	0.442
IIPO	IIPO1	0.596	0.537	0.154	0.597	0.609	0.596	0.881
	IIPO2	0.494	0.411	−0.010	0.669	0.484	0.518	0.857
	IIPO3	0.644	0.414	0.110	0.591	0.548	0.515	0.853
Note: Factors loadings are marked in bold

Table

Variable	SSO	SSE	Q^2 ( = 1-SSE/SSO)
IIPO	819.000	487.013	0.405
PU	1092.000	710.643	0.349
GoF
GoF = √ (AVE average * R^2 average) GoF = √ (0.714 × 0.611) GoF = 0.660

Notes: PU = Perceived usefulness and IIPO = Interaction intention to purchase online.

Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x

 Web of Science ®Google Scholar

Techopedia. (2022). IT Infrastructure. https://www.techopedia.com/definition/29199/it-infrastructure (Retrieved June 25, 2022).

 Google Scholar

Igbaria, M., & Iivari, J. (1995). The effects of self-efficacy on computer usage. Omega, 23(6), 587–605. https://doi.org/10.1016/0305-0483(95)00035-6

 Web of Science ®Google Scholar

Mei, Y. C., & Aun, N. B. (2019). Factors influencing consumers’ perceived usefulness of M-Wallet in Klang valley, Malaysia. Review of Integrative Business and Economics Research, 8(4), 1–23.

 Google Scholar

Driediger, F., & Bhatiasevi, V. (2019). Online grocery shopping in Thailand: Consumer acceptance and usage behavior. Journal of Retailing & Consumer Services, 48, 224–237. https://doi.org/10.1016/j.jretconser.2019.02.005

 Web of Science ®Google Scholar

Hair, J. F., Jr., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.

 Google Scholar

Urbach, N., & Ahlemann, F. (2010). Structural equation modeling in information systems research using partial least squares. Journal of Information Technology Theory and Application (JITTA), 11(2), 5–40.

 Google Scholar

Cohen, J. (1988). Statistical power analysis for the behavioural sciences (Second ed.). Routledge.

 Google Scholar

Tenenhaus, M., Vinzi, V., Chatelin, Y. M., & Lauro, C. (2005). Partial least squares. Computational Statistics & Data Analysis, 48(1), 159–205. https://doi.org/10.1016/j.csda.2004.03.005

 Web of Science ®Google Scholar