Mini-programs in mobile payment to access eGovernment in China’s Greater Bay Area - exploring the determinants and mechanism from self-determination and motivation theory

Figures & data

Figure 1. Research model.

Figure 2. Flowchart of methodology.

Table 1. Measurement items of constructs.

Variables	Measurement items and operational definitions	Sources
Service Quality (SQ) 5-items	GSMPs provide dependable services. GSMPs provide services at the times it promises. GSMPs give prompt service to citizens. GSMPs are designed with citizen’s best interest. GSMPs are planned to satisfy the needs of citizens. Operational definition: The overall evaluations and judgments regarding the excellence of service provision delivered by and via GSMPs.	(Teo et al., 2009)
Trust in eGovernment (TR) 9-items	I believe that GSMPs would act in my best interest. GSMPs are genuinely interested in my well-being. If I required help, GSMPs would do their best to help me. GSMPs are truthful in their dealings with me. I would characterize GSMPs as honest. GSMPs would keep their commitments. GSMPs are competent and effective in providing government services. GSMPs perform their roles of providing government services very well. In general, GSMPs are very knowledgeable about government affairs. Operational definition: Citizens’ expectation of competence, benevolence, and integrity in GBA eGovernment which will provide e-services in a socially responsible manner through GSMPs.	(Venkatesh et al., 2011)
Ubiquity (UB) 4-items	I can access to GSMPs anytime for the necessary information or service. I can access to GSMPs anywhere for the necessary information or service. I can use GSMPs anywhere, anytime at the point of need. GSMPs enable me to get service anywhere at any time. Operational definition: Users’ perception of GSMP usage flexibility in terms of time and location, where users can access services at the point of need.	(Kim & Kim, 2002; Lee, 2005)
Social Influence (SI) 4-items	People who influence my behavior think that I should use GSMPs. People who are important to me think I should use GSMPs. People who are in my social circle think I should use GSMPs. People whose opinions that I value prefer that I use GSMPs. Operational definition: The extent to which an individual perceives that important others believe they should use mini-programs to access government services.	(Venkatesh et al., 2011, 2012)
Perceived Value (PV) 4-items	I believe that using (WeChat / Alipay) GSMPs is valuable. Compared to the time I need to spend, the use of GSMPs is worthwhile to me. Compared to the effort I need to put in, the use of GSMPs is beneficial to me. Overall, the use of GSMPs delivers me good value. Operational definition: Citizen’s overall perception of a GSMP based on the considerations of its benefits and sacrifices needed to acquire and/or use it.	(Kim et al., 2007)
Perceived Risk (PR) 5-items	Using GSMPs to pay my bills would be risky. GSMPs are dangerous to use. Using GSMPs would add great uncertainty to me. Using GSMPs exposes me to overall risk, including financial, time, performance, privacy, social and psychological risk. On the whole, considering all sorts of risks combined, it is risky to sign up for and use GSMPs. Operational definition: The likelihood of perceived adverse consequences and the extent to control and accept a series of risks arising in the adoption process of GSMPs.	(Featherman & Pavlou, 2003)
Government Support (GS) 4-items	The government gives support to GSMPs. The government endorses GSMPs in GBA cities. The government is active in setting up the facilities to enable GSMPs. The government promotes the use of GSMPs. Operational definition: Overall guidance, regulations, policy support, and suitable measures from GBA governments.	(Nasri & Charfeddine, 2012; Tan & Teo, 2000)
Intention (IN) 3-items	Given the chance, I intend to use GSMPs. Given the chance, I predict that I should use GSMPs in the future. It is likely that I will access government services with GSMPs in the near future. Operational definition: Subjective willingness of citizens to adopt GSMPs through WeChat and Alipay.	(Pavlou, 2003)

Table 2. Demographic information of the sample.

Trait	Frequency (%)	Trait		Frequency (%)
Gender		Occupation
M	293(48.11%)		Students	165(27.09%)
F	316(51.89%)		Business managers	71(11.66%)
Age
Under 20	96(15.76%)		Ordinary employees	259(42.53%)
20-29	354(58.13%)		Professionals	41(6.73%)
30-39	131(21.51%)		Business service	28(4.6%)
40-49	24(3.94%)		Government	7(1.15%)
50-59	3(0.49%)		Self-employed	15(2.46%)
Over 59	1(0.16%)		Others	23(3.78%)
Education		Experience
High school	108(17.73%)		Less than 1 year	108(17.73%)
Diploma	221(36.29%)		1-2 years	146(23.97%)
Bachelor	236(38.75%)		2-3 years	140(22.99%)
Master	40(6.57%)		3-4 years	66(10.84%)
Doctor	4(0.66%)		Above 4 years	149(24.47%)

Table 3. Composite reliability & discriminant validity results.

Constructs	Measurement Items	Factor Loadings	Cronbach’s α	Composite Reliability	SQ	TR	UB	SI	PV	PR	GS	IN
Service Quality (SQ)	SQ1	0.793	0.896	0.898	0.798
	SQ2	0.810
	SQ3	0.805
	SQ4	0.780
	SQ5	0.802
Trust in eGovernment (TR)	TR1	0.784	0.935	0.936	0.821	0.785
	TR2	0.789
	TR3	0.778
	TR4	0.804
	TR5	0.810
	TR6	0.813
	TR7	0.795
	TR8	0.794
	TR9	0.718
Ubiquity (UB)	UB1	0.822	0.861	0.909	0.664	0.747	0.780
	UB2	0.854
	UB3	0.848
	UB4	0.855
Social Influence (SI)	SI1	0.743	0.909	0.862	0.606	0.699	0.642	0.845
	SI2	0.782
	SI3	0.801
	SI4	0.794
Perceived Value (PV)	PV1	0.792	0.879	0.880	0.707	0.777	0.711	0.651	0.804
	PV2	0.816
	PV3	0.808
	PV4	0.8
Perceived Risk (PR)	PR1	0.872	0.969	0.969	0.026	0.087	0.045	0.236	0.022	0.928
	PR2	0.942
	PR3	0.944
	PR4	0.946
	PR5	0.935
Government Support (GS)	GS1	0.737	0.886	0.888	0.672	0.711	0.66	0.631	0.735	0.109	0.815
	GS2	0.816
	GS3	0.863
	GS4	0.839
Intention (IN)	IN1	0.877	0.876	0.876	0.625	0.649	0.631	0.508	0.699	−0.033	0.772	0.839
	IN2	0.868
	IN3	0.767

Note: The diagonals are the square roots of the AVEs of the individual constructs.

Table 4. Initial path evaluation.

Path	Estimate	S.E	C.R	P
Service_Quality → Perceived_Value	0.151*	0.16	2.006	0.045
Service_Quality → Perceived_Risk	−0.214	0.126	−1.811	0.07
Service_Quality → Intention	0.209*	0.154	2.356	0.018
Trust_in_E_government → Perceived_Value	0.38***	0.206	2.926	***
Trust_in_E_government → Perceived_Risk	0.09	0.157	0.609	0.543
Trust_in_E_government →I ntention	−0.074	0.199	−0.647	0.518
Ubiquity → Perceived_Value	0.307***	0.137	4.765	***
Ubiquity → Perceived_Risk	−0.233*	0.10	−2.425	0.015
Ubiquity → Intention	0.232**	0.135	2.989	0.003
Social_Influence → Perceived_Value	0.116*	0.098	2.506	0.012
Social_Influence → Perceived_Risk	0.492***	0.082	6.415	***
Social_Influence → Intention	−0.07	0.101	−1.214	0.225
Perceived_Value →Intention	0.558***	0.075	6.093	***
Perceived_Risk → Intention	−0.05	0.053	−1.57	0.116

Note: *p < 0.05, **p < 0.01, ***p < 0.00.

Table 5. Revised path evaluation.

Path	Estimate	S.E	C.R	P
Service_Quality → Perceived_Value	0.153*	0.158	2.062	0.039
Service_Quality → Perceived_Risk	−0.159*	0.079	−2.13	0.033
Service_Quality → Intention	0.161**	0.105	2.627	0.009
Trust_in_E_government → Perceived_Value	0.377***	0.201	2.979	***
Ubiquity → Perceived_Value	0.312***	0.136	4.863	***
Ubiquity → Perceived_Risk	−0.206*	0.091	−2.389	0.017
Ubiquity → Intention	0.186**	0.135	2.989	0.003
Social_Influence → Perceived_Value	0.109*	0.097	2.374	0.018
Social_Influence → Perceived_Risk	0.503***	0.078	6.823	***
Perceived_Value → Intention	0.517***	0.068	6.197	***
Perceived_Risk → Intention	−0.07*	0.048	−2.37	0.018

Note: *p < 0.05, **p < 0.01, ***p < 0.001.

Figure 3. Finalized structural model.

Note: *p < 0.05, **p < 0.01, ***p < 0.001

Table 6. Model fit indices.

	χ^2/df	RMSEA	SRMR	GFI	IFI	NFI	TLI	CFI	PGFI	PNFI
Recommended value	<3	<.08	<.08	>.9	>.9	>.9	>.9	>.9	>.5	>.5
Measurement model	2.433	.049	.034	.874	.955	.925	.950	.954	.751	.838
Structural model	2.687	.053	.037	.874	.952	.926	.947	.952	.749	.841

Table 7. Estimation of mediating effect.

Direct effect	Estimate	Indirect effect	Estimate	Lower	Upper	P	Mediation
SQ→IN	0.161	SQ→PV→IN	0.079	−0.015	0.024	0.101	None
		SQ→PR→IN	0.011*	−.015	0.002	0.012	Full
TR→IN	---	TR→PV→IN	0.195*	0.054	0.4	0.01	None
UB→IN	0.186*	UB→PV→IN	0.161*	0.076	0.3	0.002	Partial
		UB→PR→IN	0.014*	0.004	0.033	0.005	Partial
SI→IN	---	SI→PV→IN	0.056	−0.011	0.131	0.095	None
		SI→PR→IN	−0.035*	−0.066	−0.014	0.003	None

Note: *p < 0.05.

Figure 4. Simple slope of moderating effect on Service Quality→Intention.

Figure 5. Simple slope of moderating effect on TR→Intention.

Figure 6. Simple slope of moderating effect on Ubiquity→Intention.

Figure 7. Simple slope of moderating effect on Social Influence→Intention.

Table 8. Moderation effect estimation.

	Model 1	Model 2	Model 3	Model 4	Model 5
Control Variable:
Gender	−0.060	−0.012	−0.0123	−0.014	−0.025
Age	0.155***	0.0659**	0.079*	0.077**	0.078**
Independent Variable:
Service Quality		0.160***
Trust in eGovernment			0.182***
Ubiquity				0.182***
Social Influence					0.067*
Moderator:
Government Support		0.632***	0.605***	0.609***	0.70***
Interaction item:
SQ x GS		−0.093***
TR x GS			−0.098***
UB x GS				−0.125***
SI x GS					−0.09**
R^²	0.03	0.629	0.632	0.642	0.613
△R^²	0.03	0.008	0.009	0.014	0.008
△F	9.488***	12.448***	12.971***	22.456***	11.973**

Note: *p < 0.05, **p < 0.01, ***p < 0.001.

Table 9. Hypothesis summary.

Hypothesis	Decision	Hypothesis	Decision
H1	Supported	H14	Supported
H2	Supported	H15	Supported
H3	Supported	H16	Supported
H4	Supported	H17	Supported
H5	Not Supported	H18	Supported
H6	Not Supported	H19	Not Supported
H7	Supported	H20	Not Supported
H8	Supported	H21	Supported
H9	Supported	H22	Not Supported
H10	Supported	H23	Supported
H11	Supported	H24	Not Supported
H12	Not Supported	H25	Supported
H13	Supported	H26	Not Supported

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