Carefully choose your (payment) partner: How payment provider reputation influences m-commerce transactions

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Contents lists available at ScienceDirect

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Carefully choose your (payment) partner: How payment provider reputation influences m-commerce transactions

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Antonia Köster, Christian Matt ⇑, Thomas Hess

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Institute for Information Systems and New Media Ludwig-Maximilians-Universität München, Ludwigstr. 28 VG, 80539 Munich, Germany

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a r t i c l e

i n f o

Article history: Received 28 March 2015 Received in revised form 7 November 2015 Accepted 8 November 2015 Available online xxxx Keywords: Mobile payment provider Online vendor M-commerce Reputation Transaction intention

a b s t r a c t Due to the fragmentation of the mobile payment market, vendors have a plurality of mobile payment providers they can choose to execute payment processes in the mobile versions of their shops. Besides differences in transaction fees, mobile payment providers can also differ in respect of their reputation, and thus add to the reputation of the online vendor. It remains unclear how the reputation of mobile payment providers and online vendors interact and affect consumers’ risk perception and transaction intention. Therefore, our study analyses different combinations of mobile payment provider and online vendor reputations and finds that consumers attribute distinct trusting beliefs towards these two types of market subject, and that these substantially affect consumers’ intentions to transact. While online vendors with low reputation can profit from embedding reputable mobile payment providers, reputable online vendors do not increase transaction likelihood by integrating reputable mobile payment providers compared with less reputable payment providers. For research, the results provide a novel understanding of the interaction of two market players in the m-commerce value chain subject to varying degrees of reputation. For online vendors, our results give direct guidance in the process of selecting external payment entities to establish consumer trust and facilitate transactions. Ó 2015 Elsevier B.V. All rights reserved.

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1. Introduction

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Given the increasing use of mobile devices and consumers’ needs for convenient payment methods, mobile payment is becoming an important channel for payments on the Internet (Au and Kauffman 2008, Slade et al. 2013). Although most online shops nowadays offer mobile versions of their websites, abandonment rates at the checkout are about 85 percent for mobile consumers and 70 percent for ‘‘desktop” users (eMarketer 2015). Although both rates are undesirably high for online vendors, the higher abandonment rate for mobile transactions might be caused by the circumstance that a part of an m-commerce transaction is handled by a third-party when online vendors use an external mobile payment provider to fulfil the transaction. Consequently, consumers have to rely on two different parties before they obtain the purchased good: the online vendor and the mobile payment provider. The impact of third-parties has received considerable attention in the IS literature and in e-commerce, but usually focusing on trusted third-parties, such as BuySafe, that evaluate online vendors and serve as a signal for their trustworthiness (Clemons 2007, Kim

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⇑ Corresponding author. E-mail addresses: [email protected] (A. Köster), [email protected] (C. Matt), [email protected] (T. Hess).

and Kim 2011). In addition, prior research has examined the influence of affiliated trusted entities, such as the well-known Yahoo portal, on the trustworthiness of online vendors (Lim et al. 2006, Stewart 2006). Trusted third-parties are used as a signal to increase trust in an online vendor and can be particularly helpful for online vendors without a strong reputation. The difference in mcommerce is, however, that many mobile payment providers are relatively small startups with a non-established reputation and thus their reputation might be less established than that of the online vendor. As a result, instead of an external positive signal, the less reputable mobile payment provider might even have a negative effect on consumers’ perceived risk and their transaction intentions. On the other hand, less reputable mobile payment providers are able to offer lower transaction fees to online vendors than players with a higher reputation. However, it has yet to be understood how different combinations of online vendor and mobile payment providers with varying degrees of reputation interact and what the effects on consumers are. Our review of the extant literature indicates that no former studies in IS research have quantitatively assessed these types of interactions between different market players that jointly process a transaction. Therefore, empirical evidence is essential, since online vendors with different levels of reputation might be able to pursue different strategies when embedding payment options

http://dx.doi.org/10.1016/j.elerap.2015.11.002 1567-4223/Ó 2015 Elsevier B.V. All rights reserved.

Please cite this article in press as: Köster, A., et al. Carefully choose your (payment) partner: How payment provider reputation influences m-commerce transactions. Electron. Comm. Res. Appl. (2015), http://dx.doi.org/10.1016/j.elerap.2015.11.002

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at the checkout. For example, less reputable online vendors could embed reputable mobile payment providers on their websites to provide additional confidence for consumers at the checkout (Chandra et al. 2010, Mallat 2007), whereas reputable online vendors might not profit from reputable (and potentially more expensive) mobile payment providers and could therefore integrate less established mobile payment providers to save on transaction fees. These situations illustrate how important it is to understand the interactions between the reputations of online vendors and payment providers from the consumer’s point of view. It is therefore not sufficient to focus solely on the online vendor’s reputation in m-commerce. Instead, we expect that the reputation of mobile payment providers and online vendors jointly affect consumers’ behavior. Hence, we pose the following research question: - What effect do the varying reputations of online vendors and payment providers have on consumers’ perceived risk and transaction likelihood?

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Given that relatively new and less reputable online vendors encounter considerable difficulties in persuading consumers to engage in online transactions, we account for individual differences and incorporate – in line with previous research – the role of consumers’ disposition to trust (Grabner-Kräuter and Kaluscha 2003). Disposition to trust is especially relevant for initial trust building. Accordingly we analyze whether relatively unknown vendors compared with reputable vendors may be able to overcome these difficulties in order to attract consumers by persuading especially trusting consumers through reputable mobile payment providers to perceive less risk in the transaction and to engage in a transaction. An understanding of how to increase m-commerce transactions by decreasing risk perceptions both fills an important research gap, and provides practitioners with valuable insights into the selection of external partners. The remainder of this paper is structured as follows: The next section outlines the theoretical background of this study. Following this, we present our hypotheses. We then describe the research method, after which we report and discuss our results. Finally, we outline theoretical and practical implications, the limitations of this research, and directions for future research activities.

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2. Conceptual foundations

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2.1. Mobile payment and perceived risk

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Mobile payment refers to payments for products, services, and bills via mobile devices using wireless or other communication technologies (Au and Kauffman 2008, Dahlberg et al. 2008). Mobile payment applications can be classified into two types: remote payment and proximity payment (Chandra et al. 2010). Proximity payment means that consumers conduct payment transactions while the mobile device communicates through technologies such as near field communication with the vendors’ contactless payment-capable point-of-sale terminals. Remote payment means that consumers can conduct transactions independent of their location. Examples include mobile banking and mobile internet payment services. We focus on remote payment and view perceived risk in the transaction as consumers’ fear that the transaction partner behave opportunistically (transaction-specific uncertainty). In the case of m-commerce transactions, this can be the online vendor and mobile payment provider. Following Grabner-Kräuter and Kaluscha (2003), this study distinguishes transaction-specific risk from the risk associated with the underlying technological infrastructure (system-dependent uncertainty), which refers to security or privacy

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concerns associated with the mobile Internet connection (Li and Yeh 2010, Lu et al. 2011). Specifically, Zhou (2013) states that, compared to offline and online payment, mobile payment may also involve greater uncertainty and risk because of vulnerable mobile networks. In addition, users’ experiences may be negatively affected due to the constraints of mobile devices such as small screens and inconvenient input options. Therefore, risk assessment and trust building are important factors, when consumers conduct mobile transactions (Chandra et al. 2010, Zhou 2013).

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2.2. Triadic relationship in m-commerce

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Previous research has predominantly focused on trust in a dyadic relationship between consumers and specific online vendors. Trust in a dyadic sense has been described as the belief that the online vendor behaves in accordance with the consumer’s expectations (Jarvenpaa et al. 2000, Mayer et al. 1995). Pavlou and Gefen (2004) expanded this view by examining trust in online vendors as a group. In m-commerce, however, the consumer has to deal not only with the online vendor, but also with the payment provider. As shown in Fig. 1, consumers are the trustors and both the online vendors and mobile payment providers are the trustees (Mayer et al. 1995). Consumers order products and services at the online vendor and provide personal information such as financial data to the mobile payment provider. Accordingly, the payment provider as additional transaction partner might also influence consumer perceptions and behavior. We do not intend to discount the importance of dyadic trust, when a consumer visits a website, but we believe that the nature of m-commerce makes this triadic relationship deserving of attention, especially when the consumer enters the checkout process. Therefore, this paper deals with online vendor reputation and payment provider reputation, as opposed to taking into account only the online vendor’s reputation.

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2.3. Reputation

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The previous literature has identified several factors that consumers use to assess the relative trustworthiness of different online vendors, including reputation (Jarvenpaa et al. 2000), online vendor guarantees and promises (Clemons et al. 2013), a trusted thirdparty’s evaluation (Clemons 2007, Utz et al. 2012), an associated physical store (Lee et al. 2007), online ratings and online testimonials (Pavlou and Gefen 2004) and website design (Li and Yeh 2010). Pavlou and Gefen (2004) note that reputation as a key antecedent of a company’s trustworthiness may be more effective than perceptions of legally binding structures, such as guarantees, to boost the trust of consumers and facilitate transactions. Reputation is a valuable asset and companies need to invest resources and make sustained, long-term efforts to build reputation successfully (Jarvenpaa et al. 2000). These companies are expected to be reluctant to put their reputational assets at risk by exploiting the consumer’s vulnerabilities for short-term gains (Chiles and McMackin 1996, De Ruyter et al. 2001, Smith and Barclay 1997). In general, online vendors might strive to increase their own reputation by themselves, but they might also cooperate with third-parties. We propose that the positive impact of reputation should also apply to mobile payment providers. To our knowledge, no previous study has directly measured trusting beliefs in the mobile payment provider based on different reputation levels. Exploratory results suggest that consumers are more willing to conduct payments with trustworthy transaction parties and perceive established financial institutions and telecom operators as reliable mobile payment providers (Mallat 2007). Specifically, reliable and well-established mobile payment providers are better appreciated and trusted than unestablished and smaller mobile payment providers. In the present study, we consider the reputation of the mobile payment pro-

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Online vendor

Consumer

2. Forwards consumer

5. Redirects consumer

Mobile payment provider

6. Cash flow (payment - transaction fee)

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Fig. 1. A simplified view of the triadic relationship between consumer, online vendor and mobile payment provider. 210 211 212 213 214

vider, in addition to the online vendor’s reputation, and compare reputable mobile payment providers with unknown mobile payment providers. We expect that the payment provider’s reputation may also influence consumers’ perceived risks in the transaction and transaction likelihood.

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3. Hypotheses development

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3.1. Perceived risk in the transaction

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Perceived risk is defined as a consumers’ perception about the uncertain negative consequences of a transaction (Kim et al. 2008, Koufaris and Hampton-Sosa 2004). Research supports that perceived risk has a negative influence on transaction intentions (Jarvenpaa et al. 2000, Pavlou 2003). Transaction-specific risk is triggered by the behavior of actors who are involved in the transaction. The difficulty in identifying and authenticating online vendors or payment providers on the Internet makes it easy to conduct fraudulent activities. In fact, research shows that consumers are more concerned about privacy and security issues associated with disclosing their personal and payment information, than about costs and convenience (Jiang et al. 2008). Furthermore, the purchased product or service could be of low quality and the online vendor might refuse to replace it. Therefore, consumers’ perceived risks involved in transacting with the payment provider embedded at the online vendor negatively affect consumers’ decisions to transact (Kim et al. 2008). Prior studies demonstrate that online vendors with a good reputation are perceived to be reluctant to jeopardize their reputation by acting opportunistically (Chandra et al. 2010, Jarvenpaa et al. 2000). Accordingly, Mallat (2007) indicates that mobile payment providers with an established reputation might reduce the perceived risks of transactions. Therefore, we expect that a reputable online vendor or a reputable mobile payment provider can decrease consumers’ perceived risk.

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Hypothesis 1a. Consumers will perceive less risk in transactions with reputable online vendors. Hypothesis 1b. Consumers will perceive less risk in transactions with reputable mobile payment providers.

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3.2. Transaction intention

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We focus on the differential effects of online vendor’s reputation and their mobile payment provider’s reputation on transaction intention in m-commerce. Perceived reputation is expected to indirectly affect online transactions, or the intention to use a service, through trusting beliefs (Jarvenpaa et al. 2000, Pavlou 2003).

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McKnight et al. (1998) differentiate between trusting beliefs and trusting intention, defined as the extent to which an individual is willing to depend on the other person in a given situation. The theory of reasoned action (Fishbein and Ajzen 1975) supports the proposition that beliefs correspond to intentions (Davis et al. 1989). Research findings suggest that trusting beliefs affect both the likelihood that consumers will continue a relationship with an online vendor (Fang et al. 2014), and their intention to purchase from this online vendor (Doney and Cannon 1997, Jarvenpaa et al. 2000). By engaging in such transactions consumers make themselves vulnerable to an actor’s behavior. Moreover, studies have pointed out that consumers’ trusting beliefs in an online vendor affect both their online intentions (Fang et al. 2014, Gefen and Heart 2006, Schlosser et al. 2006), and their willingness to provide personal information during online transactions (McKnight et al. 2002a) because such beliefs reduce consumers’ perceived risks (Pavlou 2003). Both trust and perceived risk can be seen as beliefs that lead to behavioral intentions (Gefen and Pavlou 2012). Moreover, intention to use a technology has been found to be a strong predictor of actual behavior in the IS field (Chau and Hu 2001, Venkatesh et al. 2003). Therefore, we propose that transaction intention will vary according to the perceived reputation of each trustee.

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Hypothesis 2a. Consumers are more likely to engage in transactions with reputable online vendors.

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Hypothesis 2b. Consumers are more likely to engage in transactions with reputable mobile payment providers.

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3.3. Disposition to trust

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Disposition to trust is the tendency to believe in the positive attributes of others (McKnight et al. 1998). Several authors have identified a disposition to trust as a significant indicator of overall trust in e-commerce (McKnight et al. 2004, Ribbink et al. 2004). Disposition to trust is a relatively stable personality characteristic that online vendors cannot control. By nature, some consumers will be more reluctant to transact than others. McKnight et al. (2002a) note that trust-building strategies may be different for consumers with low versus high disposition to trust. Consumers who have a higher disposition to trust are more likely to place higher initial trust in an unfamiliar online vendor than those with lower levels of dispositional trust (Kim and Kim 2011). The affiliation with a well-known and reputable payment provider might have a stronger effect on transaction intention and perceived risk, because trusting consumers will perceive higher relatedness of the online vendor and payment provider based on their perceived similarity to one another (Campbell 1958, Stewart 2006). Since consumers with lower levels of disposition to trust have the tendency to show lower initial trust in an unfamiliar online vendor, they might be less easily persuaded (McKnight et al. 2002a). Thus, trust transference induced by reputable mobile payment providers might be greater for high trusting consumers, compared with less trusting consumers. Yamagishi (2001) argues that very trusting consumers engage in trusting behavior if they encounter trust cues. Dispositional trust is especially important in initial trust formation, when only few trust building cues or direct experiences are available (McKnight et al. 2004). This describes the situation when a consumer visits an unfamiliar website. Therefore, we propose that consumers with a higher disposition to trust might be more easily persuaded by reputable payment providers at less reputable online stores, and consider one trusted transaction partner as sufficient assurance. We posit the following hypotheses:

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Hypothesis 3a. Consumers with a high disposition to trust will perceive less risk in transactions with less reputable online vendors if a reputable mobile payment provider is used. Hypothesis 3b. Consumers with a high disposition to trust are more likely to transact with less reputable online vendors if a reputable mobile payment provider is used.

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4. Research method

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The research model was tested using a 2  2 between-subjects design experiment. We applied different scenarios to manipulate level of online vendor reputation (high, low) and level of mobile payment provider reputation (high, low). This research design enabled us to examine the main effects of online vendors’ and mobile payment providers’ reputation and their influences on the dependent variables. Table 1 depicts the experimental design and sample sizes in each treatment group.

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4.1. Experimental setup

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Amazon.de was chosen for the high reputation condition, because it is a leading e-commerce retailer. We chose Hitmeister. de, a relatively unestablished online store, for the low reputation condition. We used existing online stores to provide a consistent degree of situational normality (Pennington et al. 2003). Moreover, both companies have only an online store and no offline stores. Thus, we eliminated the possible effects that the presence of physical stores might have on trust (Lee et al. 2007). Furthermore, we manipulated the reputation of the mobile payment provider by presenting participants with either a reputable company in the financial service industry (MasterCard) or a rather unestablished payment provider (Mopay). Each participant was randomly assigned to one of four treatment groups. We reduced a possible social desirability bias by not allowing subjects to choose their preferred online vendors. The experiment was conducted in two stages. In stage 1, a brief introduction explained to participants that the study was designed to evaluate their experience during a shopping exercise. In particular, they were asked to imagine that they wanted to purchase earphones as a gift for a friend and that they had come across an online store through an internet search. We chose this product category for two main reasons: (1) earphones are nonessential and appeal to both female and male consumers and (2) trust is expected to be important because the quality is difficult to ascertain before consumption. Moreover, we used an unbranded product to exclude the effect of brand equity on trust formation and kept the product list and price the same among all participants (Lowry et al. 2008). Participants were then directed to either the reputable or less reputable online vendor. After seeing the product in the shopping cart, they were guided to the checkout point where either the reputable or less reputable mobile payment provider was displayed. In stage 2, participants were asked to answer questions about their shopping experience, in order to capture their levels of trust in each party, their perceived risk, and transaction intention. Moreover, we included manipulation checks and demographic measures. The questionnaire required participants to answer all questions, eliminating any potential missing values. Furthermore, no time limit was imposed for task completion. A flowchart of the experimental procedure and experiment materials are presented in Appendix A and B.

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4.2. Sample

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Online surveys have emerged as an effective means of collecting data for academic research (Corbitt et al. 2003, Hsu and Lin 2015). The empirical data for the study was collected through an online

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Table 1 Research design. Mobile payment provider

High reputation (H) Low reputation (L)

Online vendor High reputation (H)

Low reputation (L)

H  H (n = 111) H  L (n = 115)

L  H (n = 109) L  L (n = 97)

survey with four experimental conditions. During the time when the study was conducted, participants could respond to the online questionnaire by clicking on the survey URL provided in the invitation. Duplicate responses were eliminated by filtering out multiple uses of a single IP address. We distributed our survey in social networking site groups and over a student mailing list to obtain respondents from several universities. Participation was voluntary and anonymous. Voluntary participation might introduce a selfselection bias whereby some results may be affected because certain characteristics that relate to the respondents’ willingness to participate are over-represented in the data. To further prevent biased answers and to minimize non-response prior to the survey we applied several techniques suggested by Lynn (2008). We provided a short introduction and instructions to respondents and ensured a respondent-friendly questionnaire design and wording by conducting a pretest. In addition, we asked as few questions as were necessary to answer our research questions in order to avoid fatigue effects (Tarran 2010). Moreover, respondents were entered into a random drawing for four $25 gift cards to reduce non-response (Kim et al. 2012). Interviewer training and a short letter sent in advance to inform the sampled respondents about the upcoming survey, could not be applied as techniques to reduce non-response due to our web-based survey design (Lynn 2008).

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4.3. Measurement development

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The survey instrument was based on validated constructs, to which we applied minor wording changes to tailor them to the specific experimental context, and two self-developed constructs. All the questionnaire items were measured on a 7-point Likert scale, ranging from strongly disagree (1) to strongly agree (7), with (4) as a neutral response. Measures for reputation were adapted from Kim et al. (2009) and measures for transaction intention were adapted from Lim et al. (2006). Perceived risk with the online transaction was adapted from Pavlou and Gefen (2004). Consistent with prior empirical studies, we assessed trusting beliefs as an unidimensional construct, because the differential effects of trust’s underlying dimensions are beyond the scope of our study (Benlian et al. 2012, Doney and Cannon 1997). Moreover, McKnight et al. (2002b) note very high correlations between these dimensions, ranging from 0.77 to 0.90, which indicates that different types of trusting beliefs might have comparable effects. Moreover, we included items to assess online consumers’ product purchase intention, mobile internet usage and online shopping frequency, adapted from Benlian et al. (2012). Two binary variables were constructed (i.e., mobile payment provider reputation = 1 for high reputation, = 0 for low reputation; online vendor reputation = 1 for high reputation, = 0 for low reputation) to capture the four experimental group conditions. Pretests were conducted to refine scale items and to assess the appropriateness of the treatment conditions (Podsakoff et al. 2003). The pretests enabled us to determine highly reputable and less reputable payment providers and online vendors with sufficient face validity. The measurement scales are presented in Table 2.

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5. Results

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A consistency check showed that not all participants correctly recalled the combination of online vendors and payment providers

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in the post experimental questionnaire. We chose the conservative approach to exclude these participants from our analysis, although their inclusion might have added unexplained variance to the results (Straub et al. 2004). In total, 477 responses were collected for the study and 432 valid responses remained in the dataset. The final sample consisted of 157 male and 275 female respondents, with an average age of 24.7 years. Almost two-thirds of our participants reported having a household income of less than €10,000 and 62% of having a high school diploma. Almost 90% used mobile devices to access the internet and had, on average, shopped online 13.95 times in the 6 months prior to the survey. We used procedural remedies to reduce ex-ante the potential of common method bias. We provided a cover story for the purpose of the experiment, guaranteed the anonymity of participants’ answers, assured that there were no right or wrong answers, and stressed the importance of answering the questions as honestly as possible to reduce evaluation apprehension (Paulhus 2001, Podsakoff et al. 2003). Additionally, because all our constructs were measured using the same measurement method, we tested ex-post for common method variances using Harman’s single-factor test. We ran an unrotated principle component factor analysis for all constructs. The test indicated that it is unlikely that common method bias affected our results, because the first factor only accounted for 30.49% of the covariance among the measures (Podsakoff et al. 2003). Furthermore, non-response bias was assessed by verifying that the profiles of early and late respondents were not significantly different (Armstrong and Overton 1977, Pavlou and Gefen 2004). In this method, survey respondents are grouped according to the time period in which their answers returned. The late respondents are considered to be similar to the non-respondents and a comparison analysis of both groups provides an approximation of actual nonresponse bias (Armstrong and Overton 1977). Chi-square, one-way ANOVA and Kruskal–Wallis tests were calculated to verify that the

early (first 50) and late (last 50) participants did not differ significantly with regard to sociodemographics (p > 0.05), indicating that it is unlikely that non-response affected our results (Benlian et al. 2012, Valenzuela et al. 2009).

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5.1. Measurement characteristics

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The measurement model was tested using confirmatory factor analysis of the measurement scales to assess their convergent and discriminant validity. Convergent validity was confirmed both at the individual and construct level by verifying that the standardized factor loadings were significant (p < 0.01). Construct validity was calculated by analyzing the average variance extracted (AVE) and inter-construct correlations. All reflective constructs met the threshold value for the AVE (>0.50) and all constructs had acceptable composite reliability with values above the threshold of 0.70 (Bagozzi and Yi 1988). As shown in Table 3, discriminant validity was confirmed by showing that the square root of each construct’s AVE is greater than the inter-construct correlations. We assessed item reliability by examining the loading of each item on the construct. The results showed that all the items exceeded the criterion of 0.707 (Chin 1998). We used Cronbach’s alpha to assess the constructs’ reliability, and found that all values for Cronbach’s alpha were higher than 0.7. Table 2 outlines all constructs AVE values, standardized factor loadings, Cronbach’s alphas, and composite reliabilities.

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5.2. Manipulation checks

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Multiple controls were included to check for treatment manipulation (Perdue and Summers 1986). We performed unpaired t-tests to check the manipulation of the reputable and less reputable online vendors and payment providers across groups. Amazon.de was per-

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Table 2 Operationalization of constructs and measurement characteristics. Constructs

Items

Loading

Measures

Reputation

This online vendor has a good reputation This online vendor is widely recognized This online vendor offers good services

0.859 0.867 0.925

CA = 0.860 CR = 0.915 AVE = 0.782

Trusting belief

The online vendor is trustworthy The online vendor keeps customers’ best interests in mind I believe that the online vendor keeps its promises and commitments The online vendor seems to be reliable

0.911 0.890 0.932 0.956

CA = 0.941 CR = 0.958 AVE = 0.851

Reputation

This payment provider has a good reputation This payment provider is widely recognized This payment provider offers good services

0.930 0.878 0.895

CA = 0.885 CR = 0.929 AVE = 0.813

Trusting belief

The payment provider is trustworthy I trust the payment provider keeps customers’ best interests in mind I believe that the payment provider keeps its promises and commitments The payment provider seems to be reliable

0.926 0.827 0.927 0.946

CA = 0.887 CR = 0.930 AVE = 0.816

Transaction intention

I am considering purchasing from the online vendor using the mobile payment app from the payment provider I would seriously contemplate buying from the online vendor using the mobile payment app from the payment provider It is likely that I will buy from the online vendor using the mobile payment app of the payment provider I am likely to make future purchases from the online vendor using the mobile payment app from the payment provider

0.935 0.958 0.958 0.934

CA = 0.961 CR = 0.972 AVE = 0.896

Perceived risk

There is a considerable risk involved in transacting with the online vendor using the mobile payment app from the payment 0.921 0.840 provider 0.941 There is a high potential for loss involved in transacting with the online store using the mobile payment app from the payment provider My decision to transact with the online vendor using the mobile payment app from the payment provide is risky

CA = 0.885 CR = 0.929 AVE = 0.814

Disposition to trust

I I I I

0.890 0.872 0.848 0.969

CA = 0.900 CR = 0.930 AVE = 0.769

generally trust other people tend to count on other people generally have faith in humanity generally trust other people unless they give me reason not to

Product relevance If you actually had the money, how likely are you to buy the selected product (headphones) recommended on the previous N/A web site? Notes: CR = composite reliability; CA = Cronbach’s alpha; AVE = average variance extracted; N/A = not applicable.

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ceived as having a higher reputation (M = 6.11, SD = 0.68) than Hitmeister.de (M = 3.75, SD = 0.98), (t(430) = 28.78, p < 0.01). As expected, participants perceived the payment provider MasterCard to be more reputable (M = 5.88, SD = 0.75) than the less reputable payment provider Mopay (M = 3.51, SD = 0.90), (t(430) = 29.72, p < 0.01). Participants exposed to the reputable online vendor (M = 5.61, SD = 0.93) showed significantly more trust in the online vendor than those exposed to the less reputable online vendor (M = 4.26, SD = 0.91), (t(430) = 15.26, p < 0.01). Similarly, participants who were exposed to the reputable payment provider (M = 5.44, SD = 0.97) asserted higher trusting beliefs than those who were exposed to the less reputable payment provider (M = 3.98, SD = 0.94), (t(430) = 15.95, p < 0.01). The results are consistent with previous findings that reputation increases trusting beliefs. We performed several tests to confirm the randomness of participants’ assignment to the different treatment conditions. Kruskal– Wallis tests revealed that household income (v2 (3) = 0.71, p > 0.05) and education (v2 (3) = 4.84, p > 0.05) did not differ significantly across treatment groups. Chi-square test results confirmed that the groups were homogenous in terms of gender (v2 (3) = 2.53, p > 0.05) and mobile internet usage (v2 (3) = 5.87, p > 0.05). Accordingly, a one-way ANOVA confirmed that participants’ age (F(3,428) = 0.87, p > 0.05), personal relevance of the product type (F(3,428) = 2.44, p > 0.05), internet experience (F(3,428) = 0.25, p > 0.05), and online shopping behavior (F(3,428) = 2.25, p > 0.05) were equally distributed among the four groups.

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First, we estimated the means and standard deviations of perceived risk and transaction intentions for each of the four online vendor and payment provider combinations, as shown in Table 4. Second, we tested the main effects of reputation on the dependent variables to explain how different levels of reputation affect these constructs. Once these hypotheses were validated, we tested the interaction effects of disposition to trust on perceived risk and transaction intention, to explain how different levels of dispositional trust affect the relationships between these constructs. MANOVA is well-suited to test the effect of different group means on several dependent variables (Field 2013). Thus, we used MANOVA to measure the interplay of high and low reputation levels on the dependent variables. MANOVA has several assumptions such as homogeneity of variance–covariance matrices and multivariate normality. Boxplots showed that there were no extreme outliers in the data. According to the central limit theorem, we know that the assumption of normality matters less in large samples. Since normality is assumed with sample sizes of about 30, we can anticipate that our data follows a normal distribution. To assess the normality of our data, we tested the univariate normality by using frequency distributions to spot normality. We examined skewness and kurtosis statistics visually and numerically (Field 2013). The further the value is from zero, the more

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likely the data in non-normal. The results showed that absolute skew and kurtosis values for all dependent variables are less than 1, which indicated that the data in our study were close to the univariate normal distribution. However, prior work has shown that ANOVA is robust even if the data deviates from normality (Box and Andersen 1995, Hull 1993) and also MANOVA is robust to violations of multivariate normality (Tabachnick and Fidell 2001). Heteroscedastic data has a minimal effect on the result of the MANOVA test when the groups are of approximately equal size. In our study, the largest group size was 115 and the smallest group size was 97, giving a ratio of 1.18, which is less than the critical value of 1.5 (Hair et al. 1998). As sample sizes are similar, Box’s M test can be disregarded (Field 2013). Effect sizes were calculated using partial eta-squared (g2). Cohen (1988) suggests norms for partial g2, according to which 0.01 constitutes a small, 0.058 a medium, and 0.138 a large effect. A one-way MANOVA revealed a significant multivariate main effect for different treatment groups (Wilks lambda k = 0.957, F(6,854) = 3.18, p < 0.01, partial g2 = 0.022). Given the significance of the overall test, we examined the univariate main effects. Significant univariate main effects for the different groups were obtained for perceived risk associated with the transaction (F(3,428) = 5.01, p < 0.05, partial g2 = 0.034), and transaction intention (F(3,428) = 3.26, p < 0.05, partial g2 = 0.022). Significant pairwise differences were obtained for each of the four online vendor and payment provider combinations with post hoc tests (Field 2013). The differences in means for the dependent variables and significances obtained from the least significant difference (LSD) pairwise comparison procedure are shown in Table 5. Hypothesis 1a states that more reputable online vendors will be more likely to decrease consumers’ perceived risk in the transaction than less reputable online vendors. In line with Hypothesis 1a, there was a significant reduction in perceived risk associated with reputable online vendors compared to less reputable entities when less reputable mobile payment providers were involved (H  L vs. L  L, D = 0.43, p < 0.05). Interestingly, no significant mean difference was found when reputable mobile payment providers were involved, indicating that a less reputable online vendor, when combined with a reputable payment provider, provides comparable assurance (H  H vs. L  H, D = 0.07, p > 0.05). Thus, H1a is partially supported. Hypothesis 1b postulates that more reputable mobile payment providers will be more likely to decrease perceived risk in the transaction than less reputable mobile payment providers. Pairwise comparisons of the means showed that the effect of mobile payment provider reputation on perceived risk is only apparent in the context of less reputable online vendors (L  H vs. L  L, D = 0.64, p < 0.01). However, when reputable online vendors are involved the results indicate no significant difference in perceived risk (H  H vs. H  L, D = 0.14, p > 0.05). In other words, reputable mobile payment providers do not significantly reduce consumers’ perceived risk in the transaction, and less reputable mobile payment providers do not significantly increase perceived

Table 3 Discriminant validity. Construct Reputation online vendor Trusting belief online vendor Reputation mobile payment provider Trusting belief mobile payment provider Intention to transact Perceived risk Disposition to trust

1

2 0.884 0.818 0.056 0.109 0.170 0.123 0.122

3 0.992 0.115 0.233 0.255 0.262 0.154

4

0.902 0.831 0.317 0.268 0.080

5

0.903 0.392 0.344 0.129

6

0.946 0.459 0.112

7

0.902 0.181

0.877

Note: Diagonal elements in boldface are the square root of average variance extracted (AVE). These values should exceed inter-construct correlations (off-diagonal elements) for adequate discriminant validity.

Please cite this article in press as: Köster, A., et al. Carefully choose your (payment) partner: How payment provider reputation influences m-commerce transactions. Electron. Comm. Res. Appl. (2015), http://dx.doi.org/10.1016/j.elerap.2015.11.002

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A. Köster et al. / Electronic Commerce Research and Applications xxx (2015) xxx–xxx Table 4 Means and standard deviations of dependent variables. Reputation Online vendor Mobile payment provider Perceived risk Transaction intention

Table 5 Pairwise comparisons for testing differential effects of reputation.

High (H)

Low (L)

Means differences (I-J)

Perceived risk

Transaction intention

High (H) Low (L)

High (H) Low (L)

H  H (I)

3.79 (1.23) 3.19 (1.56)

3.72 (1.37) 3.32 (1.66)

0.14 0.07 0.57⁄⁄ 0.21 0.43⁄ 0.64⁄⁄

0.33 0.14 0.44⁄ 0.47⁄ 0.11 0.58⁄⁄

3.93 (1.28) 2.86 (1.48)

4.36 (1.25) 2.75 (1.46)

H  L (I) L  H (I) Notes: ⁄⁄p < 0.01, provider.

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risk, when the online vendor is highly reputable. Hence, H1b is partially supported, but only for less reputable online vendors. Concerning transaction intentions a pairwise comparison of the means between participants exposed to a reputable online vendor and mobile payment provider compared to participants with a reputable online vendor and a less reputable mobile payment provider revealed no significant differences in the means (H  H vs. L  H, D = 0.14, p > 0.05). Moreover, the results indicate that transaction intention is not significantly higher for reputable online vendors compared with less reputable online vendors that integrate a less reputable mobile payment provider (H  L vs. L  L, D = 0.11, p > 0.05). Thus, H2a could not be supported. Participants who were faced with a less reputable online vendor and a highly reputable mobile payment provider had significantly higher intentions to transact, compared to participants in groups in which both actors were less reputable (L  H vs. L  L, D = 0.58, p < 0.01). However, when comparing the influence of reputable and less reputable payment providers embedded at well-known and reputable online vendors, we find no evidence supporting a difference in transaction intention (H  H vs. H  L, D = 0.33, p > 0.05). Therefore, H2b is partially supported. The results of a two-way MANOVA show that different dispositional trust levels have significant multivariate interaction effects (Wilks lambda k = 0.984, F(2,427) = 3.48, p < 0.05, g2 = 0.016).1 Given the significance of the overall test, the significant univariate effects for dispositional trust were examined. Significant univariate interaction effects for dispositional trust were obtained for perceived risk (F(3,428) = 3.30, p < 0.1), and transaction intention (F(3,428) = 6.36, p < 0.05). The effect of disposition to trust on the dependent variables is contingent on the level of online vendor reputation. We dichotomized disposition to trust into more and less trusting consumers (MacCallum et al. 2002) and plotted the estimated means of perceived risk and intention to transact for each of the two levels of dispositional trust to visualize the differential effects (Fig. 2). More and less trusting consumers did not show significant difference in perceived risk and transaction intention when the online vendor reputation was already high. However, for consumers shopping at less reputable online stores, differences in disposition to trust seem to have an effect on perceived risk and transaction intention. Using an independent sample t-test, we tested how more or less reputable mobile payment providers embedded in unknown online stores affected participants with high and low disposition to trust with regard to their perceived risk and transaction intention. The results show, that high trusting consumers compared to less trusting consumers perceived significantly lower risk (L  H, D = 0.79, p < 0.01) and had significantly higher transaction intention (L  H, D = 0.97, p < 0.01) with a less reputable vendor and a reputable mobile payment provider. Thus, very trusting consumers are more willing than less trusting consumers to make transactions, and perceive unknown online vendors with reputable payment providers as less risky than low trusting consumers. Furthermore, comparisons of the means between the less reputable 1

Because of the decreased sample size in each group, we set the level of significance at 0.1 Gupta and Bostrom (2013), Rubin (2012).

H  L (J) L  H (J) L  L (J) L  H (J) L  L (J) L  L (J) ⁄

p < 0.05; X  Y, X = online vendor and Y = mobile payment

online vendor groups showed that only very trusting consumers (L  H vs. L  L, D = 0.70, p < 0.01), compared with less trusting consumers (L  H vs. L  L, D = 0.39, p > 0.05), perceived transactions with less reputable online vendors and reputable mobile payment providers as less risky. Accordingly, very trusting consumers (L  H vs. L  L, D = 0.65, p < 0.05) are more likely to transact with unknown online vendors using a reputable payment provider than less trusting consumers (L  H vs. L  L, D = 0.28, p > 0.05). A reputable mobile payment provider compared with a less reputable mobile payment provider decreases perceived risk and increases transaction intention at unknown online stores, only for trusting consumers. Hypotheses 3a and 3b are therefore supported. As can be seen in Fig. 2, the intention to transact and the perceived risk of the transaction depend on the online vendor’s reputation, the mobile payment provider’s reputation, and consumer’s disposition to trust.

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6. Discussion and implications

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In order to transact with online vendors, consumers must provide personal and financial information about themselves to the online vendor or a trusted third-party, such as a mobile payment provider. In addition, online vendors might act with an opportunistic profit motive. Under these circumstances, customers may have concerns over the possible negative behaviors of both transaction partners that risk a financial or privacy loss. Therefore, consumers have two major players that need to be trusted in m-commerce: the online vendor and the payment provider. One of the key contributions of this study is that it clearly distinguishes between two transaction partners involved in mcommerce and investigates the interaction effects between different levels of mobile payment providers’ reputation and online vendors’ reputation. The importance of trust in online vendors is well documented (Jarvenpaa et al. 2000, Koufaris and Hampton-Sosa 2004). However, only very few studies have explored the positive externalities of an online vendors’ embedded mobile payment provider, such as whether or not such third-parties create additional value and thus diminish perceived risk and increase consumers’ intentions to transact. We propose a triadic view and account for specific inter-organizational relations in m-commerce. Thereby, our findings extend existing research on trust within e-commerce that commonly examines trust only in online vendors. The findings show that trust can be engendered in both the online vendor and its integrated mobile payment provider if they have a good reputation. Participants in groups with both a highly reputable online vendor and payment provider perceived the transaction to be much less risky and had, as expected, a higher intention to make a transaction than participants in groups with less reputable transaction partners. The results show that highly reputable online vendors do not benefit from embedding a reputable mobile payment provider compared to a less reputable payment provider. In such a case, consumers’ perceived risk does not decrease and transaction intention does not increase. Therefore, reputable pay-

658

Please cite this article in press as: Köster, A., et al. Carefully choose your (payment) partner: How payment provider reputation influences m-commerce transactions. Electron. Comm. Res. Appl. (2015), http://dx.doi.org/10.1016/j.elerap.2015.11.002

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High Disposition to Trust

Low Disposition to Trust

4.0

High Disposition to Trust

Low Disposition to Trust

5

(a)

(b)

3.5

4.5

3.0

4

2.5

3.5

2.0

3 HxH

HxL

LxH

LxL

HxH

Transaction Intention

HxL

LxH

LxL

Perceived Risk

Fig. 2. Perceived risk and intention to transact as a function of dispositional trust.

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ment providers do not act as a trust-building factor for reputable online vendors. Consequently, reputable online vendors may decide to integrate a relatively new and less reputable mobile payment provider in order to save, for example, transaction fees. Moreover, participants shopping at less reputable online vendors with a reputable payment provider perceived significantly less risk and showed a higher intention to make a transaction than participants in groups with a less reputable payment provider. The results show that, compared with less reputable mobile payment providers, an affiliation with highly reputable payment providers significantly reduces consumers’ skepticism in the transaction with unknown online vendors. Thus, when consumers are dealing with lesserknown online vendors, mobile payment providers are an effective and favored means of mitigating the risk of new online vendors, who may not have sufficient reputation to convince potential consumers. The results suggest that the importance of implementing a reputable payment provider as a trust building mechanism is contingent on the reputation of the online vendor relative to that of the payment provider. We conclude that selecting the right payment provider is an efficient trust-building mechanism and can help to mitigate the risk associated with new and less reputable online vendors. We found evidence that also in the specific triadic context of m-commerce transactions consumers’ disposition to trust influences their risk perceptions and transaction intention with unfamiliar online vendors (Mayer et al. 1995). Only very trusting consumers were more likely to purchase from unknown online vendors with reputable mobile payment providers than from reputable online vendors with less reputable payment providers. That is, consumers who generally trust other people are more willing than untrusting consumers to engage in transactions with mobile payment providers at less reputable online vendors. Our results illustrate that it is especially difficult to gain trust from and reduce the perceived risk of untrusting online consumers. This contradicts earlier findings on trust cues, which show that, for instance, assurance seals primarily affect consumers who have a low disposition to trust (Kim and Kim 2011). However, studies have also found that assurance seals are more effective at persuading more trusting consumers (Utz et al. 2012). Online vendors could provide incentives to encourage skeptical consumers to transact (Bélanger and Carter 2008). Our results have important implications for practice. We recommend unestablished online vendors to select and cooperate with reputable mobile payment providers to alleviate drop-off rates at the checkout point. Established and highly reputable online vendors do not increase transaction intention by embedding a reputable mobile payment provider because consumers trust the online vendor. Overall, our results point out that reputable payment providers can reduce risk in transactions. Therefore, mobile payment providers should concentrate their abilities on improving their reputation to invoke perceptions of trustworthiness. It is thus essential for

unknown mobile payment providers as well as payment providers in the start-up stage to build up their reputation quickly to provide an added value for consumers and online vendors. Otherwise online vendors might avoid engaging with these mobile payment providers as the missing reputation could endanger online vendors’ business transactions. Mobile payment providers that fail to achieve sufficient reputation in the market could pursue the strategy of selling their technology as white label solutions to other more reputable payment providers or even online vendors.

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7. Conclusion

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This study examined the extent to which the reputation of online vendors and mobile payment providers influences consumers’ transaction intention beyond the extent to which simply an online vendor facilitates trusting intentions. This study shows how different actors involved in m-commerce can maximize transaction activities through cooperation. The findings of this study are especially useful for new online vendors who have not yet established a reputation in the market. Our findings suggest that they can improve their conversion rates relatively inexpensively by embedding a trustworthy mobile payment provider. In contrast, reputable online vendors do not benefit from integrating mobile payment providers, because consumers already trust the reputable online vendor. We hope that the present study’s results will inspire further research aimed at understanding how mobile payment providers are capable of influencing m-commerce transactions.

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8. Limitations and further research

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We acknowledge potential limitations inherent in the research design and provide avenues for future research. First, we used a scenario-based approach for the experimental task. Even though this has been frequently applied in research (Benlian et al. 2012, McKnight et al. 2002a, Utz et al. 2012), it represents a simplification of the real m-commerce context, which limits the generalizability of research results. To control for variation, we examined only a single product category. Further research should replicate our study in different online domains and with several product categories to increase the generalizability of the results. Second, it should be noted that the data in this study was collected through voluntary participation, which may have led to self-selection biases. However, we believe that self-selection would not cause substantial bias in the results, because a tendency to participate in the survey might not necessarily influence perceived trust or perceived risk (Kim et al. 2012). Third, our findings are mainly based on the perceptions of students, which might have caused the results to be biased. However, our sample participants were highly proficient and experienced in conducting m-commerce,

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Please cite this article in press as: Köster, A., et al. Carefully choose your (payment) partner: How payment provider reputation influences m-commerce transactions. Electron. Comm. Res. Appl. (2015), http://dx.doi.org/10.1016/j.elerap.2015.11.002

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which was beneficial for the understanding of the experimental task. Fourth, we provided respondents with a fictional purchasing situation because of the difficulty involved in examining actual behavior in an experimental setting. The use of role-playing is well-established in consumer behavior research, but a lack of involvement could have affected the validity of the outcomes (Arnold and Feldman 1981). Consequently, future research should be based on actual behavior as a dependent variable in order to improve the generalizability of the study’s results. An interesting avenue for future research would be the examination of payment providers’ effects on risk and trusting intentions compared to other cues, such as assurance seals or consumer reviews.

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Appendix A

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Flowchart of experimental procedure

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Appendix B

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Experimental materials

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Introduction mobile payment Please read the following description carefully: Mobile payment systems (mobile payment) such as Mpass, Yapital or Zooz give you the opportunity to pay mobile (i.e. on the go) over the Internet. To pay by this method, you only need to deposit your financial account data once at the mobile payment provider. The mobile payment provider stores your financial information and processes your payments with the preferred payment method (e.g. credit card or direct debit). Your financial information is not transmitted to the online vendor. Mobile Payment can be used in stationary retail, online shops and also when purchases are made via mobile devices (e.g. smartphones, tablets).

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Functioning of mobile payment The following exemplary graph presents the basic functioning of mobile payment for the purchase of products/services via the smartphone in three steps. Please cite this article in press as: Köster, A., et al. Carefully choose your (payment) partner: How payment provider reputation influences m-commerce transactions. Electron. Comm. Res. Appl. (2015), http://dx.doi.org/10.1016/j.elerap.2015.11.002

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Scenario description Imagine you have been looking to buy some headphones for quite some time. You want to give these headphones as a birthday present to a good friend. While searching on the internet you come across the online shop of ‘‘name”. The ‘‘name” online shop offers the headphones at a reasonable price. ‘‘Name” Online Shop: In the ‘‘name” online shop, it is possible that you purchase the headphones with a mobile payment app from ‘‘name”.  The next three pages describe the scenario.  Afterwards you will be asked some questions relating to this simulated online purchase and the related payment process. Page 12

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You insert the headphones into your cart. Then you come to the payment options at the checkout. Please note that the sale and delivery of the headphones is made by the ‘‘name” online shop Page 2

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You are being redirected to the mobile payment app from ‘‘name”. Page 3 In order to pay with the mobile payment app, you must have installed the app. Registration for the ‘‘name” mobile payment app  You need to register to use the app  You must deposit your login data, e-mail address and financial account data with the mobile payment provider ‘‘name”

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2 Please note that the screenshots are exemplary for the Amazon (high reputation online vendor) and Mopay (low reputation mobile payment provider) scenario. The content has been translated into English.

Please cite this article in press as: Köster, A., et al. Carefully choose your (payment) partner: How payment provider reputation influences m-commerce transactions. Electron. Comm. Res. Appl. (2015), http://dx.doi.org/10.1016/j.elerap.2015.11.002

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Appendix C. Supplementary data

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Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.elerap.2015.11.002.

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