Exploring a new service prospect: customer’ intention determinants in light of utaut theory

Figures & data

Figure 1. Proposed research model.

Figure 2. Confirmatory factor analysis model.

Table 1. Respondents’ demographics and the characteristics of last purchased products.

Characteristic		Percentage(%)	Characteristic		Percentage(%)
Internet shopping	< 3	35.7	Product type	Electronics	10.6
frequency (times per month)	3 - 6	56		Food/ health	10.6
	7 - 10	7.2		Jewelry	12.9
	> 10	1.1		Clothing	21.6
Delivery Place	At Post office	13.9		Cosmetics	17.9
	At Stores	12.6		Book & Toy	16.4
	At Workplace	27.8		Household appliances	9.3
	At Home	41.4		Other	0.7
	Smart Locker systems	3.8
	Other	0.5

Table 2. Cronbach’s Alpha analysis result.

Observations	Corrected Item - Total Correlation	Cronbach’s Alpha	Observations	Corrected Item - Total Correlation	Cronbach’s Alpha
Convenience (CVN), Cronbach Alpha = 0.8629			Service Diversity (SD), Cronbach Alpha = 0.9078
CVN1	0.6498	0.8423	SD1	0.6154	0.9102
CVN2	0.6839	0.834	SD2	0.7709	0.8879
CVN3	0.7226	0.8259	SD3	0.8008	0.8838
CVN4	0.6987	0.8302	SD4	0.7256	0.8947
CVN5	0.6664	0.8395	SD5	0.786	0.8853
Privacy Security (PV), Cronbach Alpha = 0.9014			SD6	0.7841	0.8855
PV1	0.6877	0.8903	Social Influence (SI), Cronbach Alpha = 0.8712
PV2	0.7359	0.8833	SI1	0.7652	0.8076
PV3	0.8053	0.8726	SI2	0.8007	0.7738
PV4	0.8101	0.8721	SI3	0.6975	0.8672
PV5	0.7995	0.8739	Facilitating Conditions (FC), Cronbach Alpha = 0.8743
PV6	0.5769	0.9102	FC1	0.6846	0.8567
Reliability (REL), Cronbach Alpha = 0.8861			FC2	0.7484	0.8319
REL1	0.708	0.8697	FC3	0.7391	0.8364
REL2	0.7848	0.8406	FC4	0.7524	0.8304
REL3	0.7485	0.8553	Perceived Value (VAL), Cronbach Alpha = 0.8956
REL4	0.7686	0.8479	VAL1	0.7299	0.8763
Functionality (FUN), Cronbach Alpha = 0.8866			VAL2	0.6984	0.8804
FUN1	0.7273	0.8617	VAL3	0.726	0.8764
FUN2	0.7648	0.8532	VAL4	0.6534	0.8894
FUN3	0.7663	0.854	VAL5	0.7664	0.8697
FUN4	0.7102	0.8664	VAL6	0.7559	0.8715
FUN5	0.6695	0.8755	Intention (INT), Cronbach Alpha = 0.9175
			INT1	0.7527	0.9126
			INT2	0.7894	0.9029
			INT3	0.8617	0.8749
			INT4	0.8512	0.879

Figure 3. Parameter estimation of the theoretical model.

Table 3. Total results.

Relationships	β	p	Significant	Conclusion
SD → VAL	0.161	0.157	No	Not supported
PV → VAL	0.226	0,034**	Yes	Supported
REL → VAL	−0.038	0,458	No	Not supported
SI → VAL	0.336	0,000***	Yes	Supported
PV → FC	0.188	0,028**	Yes	Supported
REL → FC	−0.075	0,064*	Yes	Supported
SD → FC	0.344	0,000***	Yes	Supported
SD → INT	0.175	0,021**	Yes	Supported
PV → INT	0.546	0,000***	Yes	Supported
FC → INT	0.253	0,022**	Yes	Supported
SI → INT	0.32	0,000***	Yes	Supported

Note: *, **, *** indicate significance level at 10%, 5%, 1% respectively.

Table A1. CFA Model Fit.

Fit index	Model Fit Output Values	Model estimation	Evaluation	References
CMIN/DF	2-5	2.462	Satisfactory	(Hu & Bentler, 1999)
GFI	0-1	0.898	Satisfactory	(Hu & Bentler, 1999)
TLI	> =0.9	0.924	Satisfactory	(Hu & Bentler, 1999)
CFI	< =0.95	0.937	Satisfactory	(Hu & Bentler, 1999)
RMSEA	< =0.08	0.073	Satisfactory	(Hu & Bentler, 1999)

Table A2. T-test results on the level of intention to use Smart Locker service by gender.

Gender	N	Mean	Std. Err	Std. Dev
Mea	79	4.0759	0.1136	1.0099
Women	198	4.2273	0.0579	0.8148
Pvalue = 0.1946

Hu, L., &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. https://doi.org/10.1080/10705519909540118

 Web of Science ®Google Scholar

Data availability statement

The primary dataset is available from the corresponding author on reasonable request.