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73 - 83 頁 pp. 73 - 83

The Determinants and Effects of Consumer Trust on

Mobile Payment Adoption

Chen-Tzu Ho

1

Hui-Mei Wang

2

Yi-Yuan Liu

3,*

Abstract

This study aims to examine the antecedents and the effects of consumer trust on consumers’ willingness to adopt mobile payment. Drawing inspiration from previous research, we suggest that vendor reputation and opportunism, structural assurance, and environmental risk are determinants of consumer trust, which in turn contributes to consumers’ willingness to adopt mobile payment. The hypotheses largely find empirical support in data collected from 191 valid questionnaires in Taiwan. Empirically we found structure assurance is of paramount importance to foster consumer trust. Environmental risk and opportunism of vendors have negative and significant effects on consumer trust as predicted. However, the positive effect of vendor reputation on consumer trust is not significant. Meanwhile, consumer trust has a mediating effect between the four antecedents and willingness to adopt mobile payment. The findings of this research will provide help to firms in formulating their growth strategy in Taiwan market.

Keywords: Mobile payment adoption, Consumer Trust, Mobile Technology

Introduction

The overall usage of mobile payment in Taiwan has increased rapidly since the launch of Apple Pay in 2017. Several mobile payment applications such as Apple Pay, Android Pay, Samsung Pay, JKOPAY and so on have been adopted by Taiwan consumers. However, given Taiwan consumers’ interest in mobile payment, the lack of consumer trust has been identified as the most significant barrier to the success of e-commerce and e-payment systems (Keen, 1997). Dahlberg (2003) also suggested including trust into the technology acceptance model to explain consumer adoption decision within the context of mobile payment solutions. Since consumer trust is one of the most important factors for consumers when considering using mobile payment, what contributes to consumer trust? This is the first research question we are going to address.

Previous studies have discussed factors that influence consumer trust within the context of mobile commerce and

1 Finance and International Business Department, Fu Jen Catholic University, Taiwan 2 Finance and International Business Department, Fu Jen Catholic University, Taiwan

3 Department of Marketing and Distribution Management, Oriental Institute of Technology, Taiwan * Correspondence author: Yi-Yuan Liu

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largely focused on the characteristics of mobile service providers1, mobile technology and mobile payment providers (Chandra et al., 2010; Siau and Shen,2003; Xin et al.,2015). The determinants of consumer trust and its effects on mobile payment adoption are yet to be examined (Gefen & Streaub, 2003, Pablou & Gefen, 2004). To fill the gap, this research not only seeks to theoretically develop and empirically examine the factors contributing to consumer trust but its mediating effects between the antecedents and mobile payment adoption. The objective of this research is to contribute to the body of knowledge of consumer trust and mobile payment adoption. In the following section, we discuss the concept of consumer trust, characteristics of vendors and mobile technology to provide theoretical hypotheses for the study, followed by methodology, statistical analysis of data, discussions, and conclusion.

Literature Review and Hypothesis Development

Consumer Trust and Willingness to Adopt Mobile Payment

Trust is defined as “firm belief in the reliability, truth, or ability of someone or something” in Oxford English Dictionary. Yousafzai et al. (2003) defined consumer trust as a function of the degree of risk involved in financial transactions, which could reduce perceived risk and enhance the intention of e-payment adoption. Previous research studies had identified several reasons affect the willingness to adopt mobile payment and most of the reasons are originated from lack of consumer trust. For instance, Thales e-Security, a leading supplier of data protection and cyber security solutions, showed a statistic that the consumer trust on mobile payment can be damaged if the security of mobile payment has problems with theft from linked account, unauthorized charges, stolen password and increased spam. Keen (1997) suggested that consumer trust be the most significant explanatory variable to the success of e-commerce and e-payment systems. Using qualitative data, Dahlberg (2003) also found that trust factors offer an additional explanation to the consumer willingness of mobile payment solutions. Therefore, we hypothesized

H1: Consumer trust positively affects the willingness to adopt mobile payment.

Reputation of Vendors and Consumer Trust

Vendors refer to the merchants that conduct a transaction using a mobile device to make payment. The vendor and consumer form a seller and buyer relationship. Doney and Cannon (1997) noted that the seller’s reputation is an antecedent of trust in a traditional buyer‐seller relationship. Chandra et al. (2010) and Gefen (2002) defined reputation of vendors as “the extent to which consumers believe in the mobile payment vendor's competency, honesty, and benevolence.” Previous research has shown a positive association between a seller’s reputation and the buyer’s trust in e-commerce (Gefen & Straub, 2003; Jarvenpaa et al., 1999). Therefore, we hypothesized that

H2: Reputation of vendors positively affects consumer trust.

1 Mobile Service Provider (MSP)

A company that offers transmission services to users of wireless devices (smartphones and tablet PCs) through radio frequency (RF) signals rather than through end-to-end wire communication. For example, Chunghwa Telecom and Far East Tone Telecommunications Company.

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Opportunism of Vendors and Consumer Trust

Opportunism of vendors is defined as the possible opportunistic behavior of the vendor to consumers that might inappropriately exploit the consumer’s vulnerabilities (Chandra et al., 2010). Opportunistic behaviors include distortion of information and failing to fulfill promises and obligations to consumers (John, 1984). San-Martín and Jimenez (2017) suggested opportunistic behavior such as the exaggerating advertisement, breach of contract and data leakage. Grazioli and Jarvenpaa (2000) found that perceived opportunistic behaviors of vendors weaken the trusting relationship in online shopping. As such, we can state that opportunistic behavior of vendors will negatively affect consumer trust in mobile payment. In other words, if consumers perceive any opportunistic behaviors conducted by vendors, their trust in mobile payment will be reduced. Therefore, we have hypothesized below.

H3: Opportunism of vendors negatively affects consumer trust.

Structural assurance and Consumer Trust

Structural assurance is defined as the degree to which consumers believe in institutional structure such as guarantees, regulations, promises, legal recourse, or other procedures are in place to promote success (Chandra et al., 2010; McKnight et al., 2002). Applying this concept to mobile payment, we define structural assurance as consumer’s perception about the institutional environment that all structures such as guarantees, regulations, and promises are safe, secure and reliable for mobile payment, such as credit card guarantees, seals of approval, and transaction protection. Previous studies found that structural assurance contributes positively to consumer trust in mobile banking and online transactions (Cress, 2006; Tan and Thoen, 2000). The perceived security and privacy control through structural assurance helps develop consumer trust, assure all the parts of an e-commerce system with which consumers have uncertainty. Therefore, we hypothesized:

H4: Structural assurance positively affects consumer trust.

Environmental Risk and Consumer Trust

The perceived environmental risk is defined as the risk associated with the underlying mobile technological infrastructure and transaction security faced by consumers while using a mobile payment service through a wireless network (Chandra et al., 2010). Siau and Shen (2003) also argued that mobile technology related risks, such as service breakdown of the wireless communication network and loss of transactions, may lead consumers to have doubt in its ability to deliver services. Zhou (2011) suggested that perceived security, ubiquity and perceived ease of use have significant effects on initial trust. That is, the perceived environmental risk in the mobile wireless network will lower consumer trust in mobile payment. Thus, we propose hypothesis 5 as follows:

H5: Environmental risk negatively affects consumer trust.

The Mediating Role of Consumer Trust

Several studies have confirmed that trust is an important factor in e-commerce (Gefen & Streaub, 2004, Pavlou & Gefen, 2004) and mobile banking as well. Meanwhile, previous research also shows that trust is a key factor influencing the willingness to M-Pay (Andreev et al., 2011). Given its effects on willingness to adopt mobile payment, we wonder if consumer trust could be a key mediator between the antecedents of consumer trust and willingness to adopt mobile

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payment. Therefore, we hypothesize that

H6: Consumer trust has mediating effects on the relationship between the four antecedents and willingness to adopt mobile payment.

H6a: Consumer trust has mediating effects on the relationship between reputation of vendors and willingness to adopt mobile payment.

H6b: Consumer trust has mediating effects on the relationship between opportunism of vendors and willingness to adopt mobile payment.

H6c: Consumer trust has mediating effects on the relationship between structural assurance and willingness to adopt mobile payment.

H6d: Consumer trust has mediating effects on the relationship between environmental risk and willingness to adopt mobile payment.

Figure 1 Conceptual Framework of this research

Method

Questionnaire Design

The online survey was applied to empirically test the research hypotheses. Measures for reputation of vendors and opportunism of vendors were adapted from Chandra et al. (2010) and Xin et al. (2015). Measures of structural assurance and environmental risk were modified from Chandra et al. (2010). The questionnaire adopted the 7-point Likert scale.

Participants were asked whether they have any mobile devices. If not, they will be asked to stop answering the questionnaire. The content of questionnaire includes three parts. Part one is about personal data, including gender, age, and experience of using mobile payment. Part two and three are the main questions to measure the constructs. We asked the participants to visualize one vendor in mind to answer the questions of characteristics of vendors. We also design two reverse measure items in the questionnaire to check if the participants pay enough attention when answering

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questionnaire. To make sure that all the questions are clear and correctly understood, we conducted a pilot test with 30 participants. The feedback from the pilot test was used to modify the wording and layout of the questionnaire to improve readability and clarity. We revised the questions of environmental risk and opportunism of vendors with more examples.

Data Collection

Online questionnaire was distributed through some social media such as Facebook and Instagram during the period of May to June 2018. While most of previous research usually targeted on teenagers to send questionnaire, this research did not focus on any specific group because using mobile devices is now common for most of people in Taiwan. Subsequently, we collected 218 responses. Incomplete questionnaires and inconsistent answers found in some questionnaires were deleted. Only 191 valid responses are used for data analysis.

Statistical Method

For the measurement model, Cronbach's α and composite reliability were used to test the internal consistency of each construct as well as test each item. (Nunnally, 1972). Confirmatory factor analysis (CFA) was used to test the validity of measures. (Fornell & Larcker, 1981). Amos 24 was adopted to analyze the measurement model. For the hypothesis testing, we used SPSS 22 to run regression analysis to test the antecedents and effects of consumer trust. Meanwhile, regression analysis was also used to test whether consumer trust has mediating effects between the four antecedents and willingness to adopt mobile payment. To test mediating effect of consumer trust, we also run regression and adopted the three-step method introduced by Baron & Kenny (1986). We will first run a regression analysis to ensure the four independent variables significantly affect the presumed mediating variable (path a). We then run another regression analysis to ensure the presumed mediating variable significantly affects the dependent variable (path b). A separate regression analysis is run to ensure the four independent variables significantly affect the dependent variable (path c). Finally we add the presumed mediating variable to the regression model to see the change in the impact of the four independent variables on the dependent variable.

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Results

Table 1 shows model fit of Confirmative Factor Analysis (CFA) model. Due to the comparative low factor loadings, we deleted ER1, ER3, and ER5 and the overall model fit is corresponding with the criterion. All model fit indices (χ2/DF, GFI, AGFI, CFI, RMSEA) meet criteria as shown in Table 1. Thus, the measurement model of this study is acceptable to continue analyzing the following hypotheses.

Table 1 Model Fit of CFA

Model Fit Indices Criteria Results

χ2/DF < 5 4.891

(Lomax and Schumacker,2004)

GFI >0.8 0.960

(Hu & Bentler, 1999)

AGFI > 0.8 0.939 (Bentler, 1983 ) CFI > 0.9 0.975 (Bentler,1990) RMSEA < 0.08 0.065 (Hair et al., 2010)

Further, Table 2 shows that the standardized factor loadings of four constructs are all above 0.7. The composite reliability of all variables has good index except the willingness to adopt mobile payment and perceive convenience, which might be due to the small number of items of both variables.

Table 2 Reliability and validity test results

Constructs Items Standardized Factor Loadings Composite Reliability AVE Reputation of Vendors RV1 0.844 0.938 0.836 RV2 0.993 RV3 0.900 Opportunism of Vendors OV1 0.831 0.894 0.737 OV2 0.827 OV3 0.915 Structural Assurance SA1 0.872 0.913 0.777 SA2 0.875 SA3 0.897 Environmental Risk ER2 0.838

0.794 0.597 ER4 0.753 Consumer Trust CT1 0.916 0.922 0.749 CT2 0.939 CT3 0.721 CT4 0.869

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Constructs Items Standardized Factor Loadings

Composite Reliability AVE Willingness to Adopt Mobile

Payment USE1 0.877 0.545 0.375 USE2 0.855 Perceived Convenience PC1 0.850 0.620 0.450 PC2 0.792

We then run multiple regression to test the first six hypotheses (H1~H6). We first tested the effects of consumer trust on willingness to adopt mobile payment. (H1, Table 3), then tested the relationship between four antecedents and consumer trust. (H2~H5, Table 4), and finally tested the mediating effect of consumer trust between the four antecedents and willingness to adopt mobile payment. (H6, Table 5-8)

Table 3 The Results of Multiple Regression (DV= willingness to adopt mobile payment)

Model Standardized Coefficient T P. Beta 1(Constant) 1.801 0.073 Consumer Trust (CT) 0.541 10.05 0.000 R square: 0.662

As shown in Table 3, regression analysis showed a significant relationship between consumer trust and willingness to adopt mobile payment (β = 0.541, p =0.000), hypothesis 1 is supported.

Table 4. Results of Multiple Regression (DV= Consumer Trust)

Model

Standardized

Coefficients T P.

Beta

Reputation of Vendors (RV) H2 0.019 0.411 0.682 Opportunism of Vendors (OV) H3 -0.099 -2.063 0.041 Structural Assurance (SA) H4 0.456 8.007 0.000 Environmental Risk (ER) H5 -0.259 -4.498 0.000 R square: 0.712

Table 4 shows a positive but insignificant relationship between reputation of vendors and consumer trust (β = 0.019, p =0.682), hypothesis 2 is not supported. In addition, the relationship between opportunism of vendors and consumer trust is negative and significant (β = -0.099, p =0.041). Hypothesis 3 is also supported. Structural assurance is significantly and positively associated with consumer trust (β = 0.456, p =0.000), hypothesis 4 is thus supported. Finally, regression analysis showed a negative and significant relationship between environmental risk and consumer trust (β = -0.259, p =0.000), hypothesis 5 is also supported.

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and willingness to adopt mobile payment. From Table 5, we found that path a, b, and c all are significant and positive. When adding consumer trust into regression analysis, the influence between reputation of vendors and willingness to adopt mobile payment was weakened (β changed from 0.416 to 0.125). Results of Model 4 indicated consumer trust has mediating effects on the relationship between reputation of vendors and willingness to adopt mobile payment. Hypothesis 6a is supported.

Table 5 Regression Analysis of Mediating Effects – H6a

Model 1 DV: consumer trust Path a Model 2 DV: willingness to adopt Path b Model 3 DV: willingness to adopt Path c Model 4 DV: willingness to adopt Add “consumer trust”

Reputation of Vendor β=0.409*** β=0.416*** β=0.125* Consumer Trust β=0.763*** R2 0.167 0.582 0.173 0.595 P. 0.000 0.000 0.000 0.015 *p < 0.05; **; P < 0.01 ***; P < 0.001

Hypothesis H6b indicated consumer trust (CT) has mediating effects on the relationship between From Table 6, we can ensure path a, b, and c all have significant effects. After adding consumer trust into regression analysis, the results of Model 4 showed an insignificant relationship between opportunism of vendors and willingness to adopt mobile payment. (β changed from -0.396 to 0.004). Hypothesis 6b is also supported.

Table 6 Regression Analysis of Mediating Effects - Opportunism of Vendors –H6b

Model 1 DV: consumer trust Path a Model 2 DV: willingness to adopt Path b Model 3 DV: willingness to adopt Path c Model 4 DV: willingness to adopt Add “consumer trust”

Opportunism of Vendor β=-0.524*** β=-0.396*** β=0.004 Consumer Trust β=0.763*** R2 0.274 0.582 0.157 0.582 P. 0.000 0.000 0.000 0.935 *p < 0.05; **; P < 0.01 ***; P < 0.001

From Table 7 we can ensure path a, b, and c all have significant effects. When we added consumer trust into regression analysis, the influence between structural assurance and willingness to adopt mobile payment was weakened. (β = 0.156, p =0.042) Hypothesis 6c is supported. Table 8 shows path a, b, and c are positive and significant. After

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adding consumer trust into regression analysis, the results of model 4 showed the relationship between environmental risk and willingness to adopt mobile payment is weakened (β changed from -0.533 to 0.026). Hypothesis 6d is supported

Table 7 Regression Analysis of Mediating - Structural Assurance – H6c

Model 1 DV: consumer trust Path a Model 2 DV: willingness to adopt Path b Model 3 DV: willingness to adopt Path c Model 4 DV: willingness to adopt Add “consumer trust”

Structural Assurance β=0.791*** β=0.662*** β=0.156*

Consumer Trust β=0.763***

R2 0.625 0.582 0.438 0.591

*p < 0.05; **; P < 0.01 ***; P < 0.001

Table 8 Regression Analysis of Mediating Effects - Environmental Risk –H6d

Model 1 Model 2 Model 3 Model 4

Dependent variable: CT Dependent variable: USE Dependent variable: USE Add CT

Path a Path b Path c Dependent variable: USE Environment Risk β=-0.716*** β=-0.533*** β=0.026 Consumer Trust β=0.763*** R2 0.512 0.582 0.284 0.582 P. 0.000 0.000 0.000 0.700 *p < 0.05; **; P < 0.01 ***; P < 0.001

Conclusion and Discussion

Because of the rapid growth and demand for mobile payment in Taiwan, this research aims to gain understanding regarding the determinants of consumer trust and its effects on the willingness to adopt mobile payment. We have also examined the mediating effect of consumer trust between the four antecedents and mobile payment adoption. Overall, our empirical results largely support our theoretical prediction.

We find that, first of all, consumer trust strongly contributes to consumers’ willingness to adopt mobile payment. Secondly, among the four antecedents of consumer trust, structural assurance is of paramount importance to explain the variance of consumer trust. Thus, to enhance consumers’ willingness of mobile payment adoption, firms need to pay more attention to institutional structures such as guarantees, regulations, promises and other procedures. Thirdly, environmental risk was found to be the second most significant factor influencing consumer trust. As predicted, both

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vendor opportunism and environmental risk are negatively and significantly associated with consumer trust. However, the negative effects of environment risk on consumer trust are stronger than that of vendor opportunism, which implies that consumers may concern more about the underlying mobile technological infrastructure and transaction security than vendor opportunism when considering adopting mobile payment service in Taiwan. Finally, reputation of vendors has a positive but insignificant effect on consumer trust. Nevertheless, previous study suggested that reputation of vendors is important in e-commerce or virtual stores. (Gefen & Straub, 2003; Jarvenpaa et al., 1999). It might be due to Taiwan consumers’ strong confidence in the legal and regulation system. In addition, Taiwanese customers mostly use mobile payment in physical stores rather than virtual stores (Yang, 2017). As such, reputation of vendors may not be a major concern for Taiwanese consumers. As to control variable, perceived convenience is positively and significantly associated with consumer trust.

As to the mediating role of consumer trust, our findings suggest that consumer trust has complete mediating effects between two antecedents (opportunism of vendors and environmental risk) and willingness to adopt mobile payment. In other words, opportunism of vendors and environmental risk can only affect consumers’ willingness to adopt mobile payment through consumer trust. The two antecedents cannot directly affect the willingness to adopt mobile payment without consumer trust. Meanwhile, there are also partial mediating effects between the two antecedents (reputation of vendors and structural assurance) and willingness to adopt mobile payment.

There are a few limitations in this research. First, convenience sampling was used in this study, which might cause skewed data collection and biased results. Second, when conducting the online survey, we found that some respondents have not had hands-on experience with mobile payment. This implies that our study may be addressing the early stage of trust formation in mobile payment. Nevertheless, we designed the survey instrument to ensure that respondents are aware of what a mobile payment transaction involves. Future studies may want to examine post-adoption behaviors of mobile payment.

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數據

Figure 1    Conceptual Framework of this research
Figure 2    Mediating effect test model (Baron &amp; Kenny, 1986)
Table 1 shows model fit of Confirmative Factor Analysis (CFA) model. Due to the comparative low factor loadings,  we deleted ER1, ER3, and ER5 and the overall model fit is corresponding with the criterion
Table 4 shows a positive but insignificant relationship between reputation of vendors and consumer trust (β =  0.019, p =0.682), hypothesis 2 is not supported
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