To buy or not to buy: Influence of seller photos and reputation on buyer trust and purchase behavior

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Int. J. Human-Computer Studies 70 (2012) 1–13 www.elsevier.com/locate/ijhcs

To buy or not to buy: Influence of seller photos and reputation on buyer trust and purchase behavior Gary Bentea,n, Odile Baptistb, Haug Leuschnera a

Department of Psychology, University of Cologne, Germany b Diginet GmbH & Co. KG, Cologne, Germany

Received 2 February 2010; received in revised form 20 August 2011; accepted 22 August 2011 Communicated by F. F.-H. Nah Available online 27 August 2011

Abstract Reputation scores and seller photos are regarded as two types of signals promoting trust in e-commerce. Little is known about their differential impact when co-occurring in online transactions. Using a computer-mediated trust game, the current study combined three photo conditions (trustworthy, untrustworthy and no seller photo) with three reputation conditions (positive, negative and no seller reputation) in a 3  3 within-subject design. Buyers’ ratings of trust and number of purchases served as dependent variables. Significant main effects were found for reputation scores and photos on both dependent variables and there was no interaction effect. Trustworthy photos and positive reputation contributed towards buyers’ trust and higher purchase rates. Surprisingly, neither untrustworthy photos nor negative reputation performed worse than missing information. On the contrary, completely missing information (no reputation, no photo) led to distrust and differed significantly from completely negative information (low reputation, untrustworthy photo), which resulted in a neutral trust level. Overall, the data suggest that not only does positive information increase trust, but mere uncertainty reduction regarding a seller can also contribute towards trust in online transactions. & 2011 Elsevier Ltd. All rights reserved. Keywords: e-Commerce; Trust; Reputation; Seller photos; Self-disclosure; Uncertainty reduction; Trust game

1. Introduction In the last decade, the Internet has changed market transactions fundamentally. Consumer-to-consumer (C2C) platforms, such as eBay, constitute a global marketplace, accessible for everyone, anywhere, at any time. The evident degrees of freedom in e-commerce, however, come with a downside. In contrast to face-to-face (f2f) encounters, online transactions have been characterized as fraught with uncertainty and risk (Egger, 2001; Grabner-Krauter and Kaluscha, ¨ 2003; Metzger, 2006; Riegelsberger et al., 2005, 2007; Tan and Thoen, 2000). Trust is considered a crucial factor in dealing with this uncertainty and consequently an important, if not the most important, factor in the online exchange n Correspondence to: University of Cologne, Department of Psychology, Richard-Strauss-Str. 2, 50931 Cologne, Germany. Tel.: þ49 221 470 6502; fax: þ49 221 470 5299. E-mail addresses: [email protected], [email protected] (G. Bente).

1071-5819/$ - see front matter & 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.ijhcs.2011.08.005

of resources (Corritore et al., 2001; Fogg et al., 2001; Gefen, 2000; Gefen et al., 2003; Jarvenpaa et al., 2000; Ofuonye et al., 2008; Riegelsberger et al., 2007; Wang and Emurian, 2005). Flanagin (2007) posits: ‘‘In essence, C2C commercial transactions entail recurrent initial encounters among strangers who are at significant risk, given the financial and psychological costs of failed transactions and the relative lack of relevant, available information. Thus, engaging in C2C e-commerce requires that at least one party takes a substantial risk and invests trust in someone about whom little is known’’ (p. 403). In general terms, trust has been conceptualized as social capital enabling cooperation and coordination in human society (Corritore et al., 2003; Glaeser et al., 2000; Hardin, 2001; Riegelsberger et al., 2005; Uslaner, 2000). Trust is expected to reduce the complexity of the social environment when multiple outcomes of interactions are possible and difficult to predict (Luhmann, 1979). Without trust, we would freeze in uncertainty and indecision, finally

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paralyzing social interaction (Lewis and Weigert, 1985). Referring to the ‘‘Faith-Trust-Confidence Continuum’’ (Arion et al., 1994), Egger (2001) concludes: ‘‘ytrust acts as a mental mechanism, based on incomplete information, that helps reduce complexity to allow for decision making under uncertainty’’ (p. 317). Consequently, trust can be conceived as a signaling problem (Bacharach and Gambetta, 2001), which becomes particularly virulent when interaction partners are strangers and social cues or signaling options are limited, as particularly the case in computer-mediated communication (see Walther and Parks, 2002, for an overview of relevant CMC theories). Beyond frictions in temporal and spatial contingencies that characterize online transactions (see Brynjolfsson and Smith, 2000; Riegelsberger and Sasse, 2001), signaling constraints have been identified as a major threat to trust and cooperative behavior in e-commerce (Riegelsberger, 2005). Bolton et al. (2004b) posit: ‘‘It is not just the scattering of trade in space and time that pose[s] a challenge to Internet exchange, it is also the medium of communication per se. Computer-mediated communication makes it more difficult to signal trustworthiness and to promote cooperation than ‘richer’ communication media such as face-to-face communication’’ (p. 185). Given the considerable signaling limitations in mediated interactions, however, the few cues available might become extremely influential for judgment and decision making (see Flanagin, 2007), and hence, the following question should be answered: Which signals bear the potential to reduce uncertainty and to foster trust in online transactions? Haley and Fessler (2005) point out that ‘‘decisionmaking processes often employ both explicit propositional knowledge and intuitive or affective judgments elicited by tacit cues’’ (p. 245). With regard to trust, both types of signals have recently attracted special attention from e-commerce practitioners as well as researchers in the field: On the one hand, reputation systems, as introduced by eBay in 2004, provide information about the past behavior of a transaction partner and are expected to build a rational basis for trust (Corritore et al., 2001, 2003; Riegelsberger, 2003; Paine Schofield and Joinson, 2008). On the other hand, photos of trading partners (available on eBay since 2007) are supposed to ‘‘re-embed’’ otherwise depersonalized and decontextualized online interactions and thus to build a more affective basis for trust (Fogg et al., 2001; Giddens, 1990; Riegelsberger, 2003, 2005; Wang and Emurian, 2005). Although aiming at the same goal – namely, to foster trust and decisions to purchase – both types of signals (i.e., reputation and seller photos) can be considered to feed into different trust-building mechanisms. Lewis and Weigert (1985) observe: ‘‘Trusting behavior may be motivated primarily by strong positive affect for the object of trust (emotional trust) or by ‘good rational reasons’ why the object of trust merits trust (cognitive trust), or, more usually, some combination of both’’ (p. 972; see also Bacharach and Gambetta, 2001;

Bente et al., 2004, 2008; Kanawattanachai and Yoo, 2002; Lahno, 2004; McAllister, 1995). While reputation scores constitute propositional knowledge and deliver good reasons to trust or distrust (cognitive trust), the effects of tacit social cues conveyed in a photo are more likely to impact our feelings and intuitive judgments of a seller’s trustworthiness (emotional trust). Both types of signals are of interest in this research and are therefore studied as independent variables in our experiment, which examines the potential effects of these signals. Hardin (2001) proposed to differentiate trust as an attitude (trusting beliefs) from trusting behavior (see also McKnight and Chervany, 2001; Riegelsberger et al., 2005; Yang et al., 2006). It might well be that a trustworthy seller photo positively influences our trusting beliefs (attribution of trustworthiness to the seller) but fails to pass the threshold of trusting behavior (purchase decision and money transfer) while a good reputation would lead directly to a purchase. To account for such differential effects, we examine trust ratings (trusting attitude) and purchase decisions (trusting behavior) as dependent variables. In fact, little has been known about the specific effects of photos when compared to reputation (see Riegelsberger, 2005) and no systematic data are available regarding the relative impact of photos and reputation on trust when co-occurring in e-commerce transactions. Using a standard trust game (Bolton et al., 2004a), the current study aims to close this empirical gap under controlled experimental conditions. More specifically, we investigate how positive, negative and missing reputation information may interact with trustworthy, untrustworthy and missing photos to influence trust in online transactions under monetary risk. To differentiate between attitudinal and behavioral effects, dependent measures in our study include subjective judgments of trust (trusting beliefs) as well as purchases (trust related behavior). Results are intended to contribute to a deeper understanding of trust building mechanisms in economic transactions and to inform practical decision making regarding online impression management and e-commerce interface design. 2. Background and hypotheses 2.1. The influence of seller reputation on online trust Reputation systems can be considered as the central feedback and trust-building mechanism in e-commerce (Resnick et al., 2000). In fact, numerous field studies, mostly referring to online auctions, have demonstrated strong reputation effects, particularly when the stakes are high (Bajari and Hortacsu, 2003; Dewan and Hsu, 2001; Houser and Wooders, 2000; Lucking-Reiley, 1999). Controlled experiments have also demonstrated the relevance of reputation for trust and cooperative behavior in the context of economic games and social dilemmas (Charness et al., 2010; Baurmann and Leist, 2004; Bohnet and Huck, 2004; Ely et al., 2008; Keser, 2003).

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Overall, previous findings have shown that reputation and trust are closely related phenomena and that reputation scores constitute a very influential factor in online transactions (see Corritore et al., 2003; Dellarocas, 2003; Jøsang et al., 2007; Mui et al., 2002; Ofuonye et al., 2008; Paine Schofield and Joinson, 2008; Resnick et al., 2000; Resnick and Zeckhauser, 2002). In line with this empirical evidence, we hypothesize that: H1. Reputation scores affect trust and purchase behavior in online transactions. Compared with missing reputation, positive reputation leads to higher trust and purchase rates (H1a) while negative reputation leads to distrust and lower purchase rates (H1b). 2.2. The influence of seller photos on online trust The effects of photos on trust in CMC and C2C transactions are more equivocal and less straightforward than those for reputation. Partially contradictory results have been reported from field studies (Fogg, 2002; Riegelsberger and Sasse, 2002; Steinbru¨ck et al., 2002) as well as from experiments employing social dilemmas and economic games (Bohnet and Frey, 1999a,b; Brosig et al., 2003; Zheng et al., 2002). A reason for this unclear picture might lie in the multi-dimensionality of the signal: Exposing our face to strangers in an e-commerce setting by submitting a portrait photo can affect trust or the attribution of trustworthiness in at least two ways: (1) by conveying visual cues relevant to impression formation and social judgment (see Bacharach and Gambetta, 2001), and (2) by signaling the readiness to expose oneself to others and to disclose one’s identity (see Tanis and Postmes, 2003, 2007; Zheng et al., 2002). With regard to impression formation, it has been shown that the human face, even if only presented through a photo, can serve as evidence for disposition to honesty (Dion et al., 1972; Felser, 2007) and trustworthiness (Kim and Rosenberg, 1980; Rosenberg and Gordon, 1968; Todorov, 2008; Todorov et al., 2008; Wyer and Srull, 1989). Further, it has been shown that impressions based on faces are largely automatic and uncontrollable as well as extremely fast and robust (Todorov et al., 2009; Todorov and Duchaine, 2008; Willis and Todorov, 2006). Todorov (2008) concludes: ‘‘In fact, it is very difficult to find a trait judgment that is not correlated with trustworthiness. These high correlations suggest that trustworthiness judgments from faces may reflect the general evaluation of the face or, at least, approximate this evaluation rather well’’ (p. 210). Hence, trustworthiness is a crucial dimension when evaluating human faces. Therefore, we hypothesize: H2. Seller photos influence buyer trust in online trading. More specifically, trustworthy seller photos lead to higher trust and purchase rates than untrustworthy or missing photos (H2a).

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Taking into account the potential effects of mere selfdisclosure, H2a cannot be simply reversed for untrustworthy photos: Even an untrustworthy appearing seller might still receive credit for her/his readiness to expose her/ himself to the buyer. In fact, there is ample evidence that self-disclosure plays a central role in the development and maintenance of relationships (Archer, 1980; Collins and Miller, 1994; Greene et al., 2006). A photo provided in an otherwise impersonal or even anonymous interaction, such as text-based CMC, can be seen as an act of self-disclosure (see Tanis and Postmes, 2003, 2007; Tidwell and Walther, 2002), which holds potential to foster trust and cooperative behavior in online transactions by reducing interpersonal uncertainty and potentially making the discloser recognizable and ‘‘appear vulnerable’’ (Zheng et al., 2002, p. 142). With regard to the effects of untrustworthy photos, we thus formulate the hypothesis: H2b. The mere presence of a photo positively influences trust: photos with a moderately untrustworthy appearing seller produce higher trust levels and purchase rates than missing photos. 2.3. Combining reputation and seller photos Studies combining reputation and seller photos are surprisingly rare. In fact, only one study by Riegelsberger et al. (2003) came to our attention, where the effects of trustworthy and untrustworthy photos placed on existing e-commerce sites with either good or bad reputation were analyzed. Riegelsberger et al. (2003) analyzed buying decisions on 12 existing e-commerce sites. Seller photos were selected in a pretest resulting in sets of positively and negatively evaluated portraits, which were systematically varied across the websites. Reputation, however, was not manipulated but instead was based on a priori reputation judgments of these known websites. No main effects were found. A weak interaction effect, however, indicated that in the presence of a seller photo, participants were less able to distinguish between vendors with good and bad reputations. It is to be noted, however, that reputation was not experimentally varied and any explicit indicators (e.g., seals) had been removed from the websites. In addition, the reputation of a company (i.e., in a B2C, or business to consumer, context) rather than the reputation of a single person as the seller (i.e., in a C2C, or consumer to consumer, context) was analyzed. Thus, it remains an open empirical question how seller photos and reputation scores interact when co-present in online trading with an unknown seller. From a theoretical perspective, dual-process theories of persuasion, such as the elaboration likelihood model (ELM; Petty and Cacioppo, 1986) or the heuristic-systematic model (HSM; Chaiken et al., 1989), can help to formulate the hypothesis regarding the combined effects of reputation and photos in online trust building (see Kim and Benbasat, 2003; Yang et al., 2006). Despite some

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fundamental differences between ELM and HSM (see Eagly and Chaiken, 1993; Petty, 1994), both of them share the perspective that attitudes and behavior can rely on two different pathways or modes of information processing: (1) a peripheral or heuristic mode, implying a quick but shallow processing of tacit cues, and (2) a central or systematic mode, implying deeper cognitive processing of message content. The central route should prevail when involvement is high and cognitive capacity is sufficient to process the available information. Both criteria are met by our experimental trust game, which involves monetary risk and provides seller information on previous shipping behavior. Furthermore, dual process models hold that peripheral/heuristic processing accounts for transient attitude changes, while the central/systematic mode leads to more robust influences on actual behavior. Consequently, we expect seller reputation information, when it is available, to be the primary source for trust in the trust game and to show a stronger effect than seller photos with regard to formulation of trusting beliefs and trusting behavior (purchases). Hence, we expect the impact of seller photos to depend on the presence and valence of reputation scores, leading to the hypothesis: H3. Seller photos and reputation scores show an interaction effect with regard to trust and purchase decisions. 3. Method 3.1. Study design and experimental setup To analyze the influence of reputation scores and photos on trust and purchase behavior, we chose a 3  3 withinsubject design, combining three reputation conditions (negative, positive, and no seller reputation) with three photo conditions (untrustworthy, trustworthy, and no seller photo). To avoid ceiling and floor effects, only moderately positive and negative reputation scores as well as moderately trustworthy and untrustworthy photos were used in the study. Appropriate reputation scores and photos were selected in two pretests (see Section 3.2 for details). Seller photos and reputation scores were embedded in a standard trust game. This trust game by Bolton et al. (2004a) clearly differentiates between a trustor and a trustee and avoids confounds of trust with other motives such as fairness, reciprocity, and efficiency, which are present in other games (see Riegelsberger, 2003). It models a trust situation framed as a sales transaction between a buyer (trustor) and a seller (trustee). No products are exchanged in this game but only monetary equivalents. Fig. 1 depicts the payoff matrix in the trust game. Both the buyer and the seller are endowed with 35 units (35 euro cents in our case), which is the payoff when no trade occurs. The seller offers an item for sale at a price of 35 units, which for the buyer has a value of 50 units, if the seller ships. The seller’s cost of providing the buyer with

Fig. 1. Payoff matrix for the basic trust game (see Bolton et al., 2004b, p. 188).

the item (e.g., handling, shipping) is fixed at 20 units. If the buyer decides to buy the item and the seller ships the item, the buyer receives 50 units (item value) at a cost of 35 units (item price), and the seller receives 35 units (item price) at a cost of 20 units. Thus, each successfully completed trade creates a buyer surplus of 15 units and a net profit of 15 units for the seller, so each of them then owns 50 units (i.e., 35 units of initial endowment plus 15 units of surplus or profit) instead of 35 units when no trade occurs. However, if the seller decides not to ship, he receives the price plus his endowment (70 units), while the buyer loses his initial endowment and ends up with nothing (0 unit). A special computer program was developed to run the trust game as a Web application. The program allows for full experimental control over the pay-off matrix and simulation of the fictitious sellers by presenting randomized photos and reputation scores. Moreover, the software automatically prompts for buying decisions and trust ratings and stores the buyer responses for later analysis and the calculation of individual payoffs. Fig. 2 depicts an example of a screenshot from our trust game’s user interface, which shows the prompt for the trust rating. 3.2. Independent variables To select the appropriate reputation scores and seller photos for the main study, two independent pretests were conducted. Both were guided by the following principles: (1) To avoid ceiling effects due to pronounced negativity or positivity items which can produce extreme trust or distrust in the final stimulus set, we aimed at identifying stimuli which deviated about one scale point from the scale mean in the positive and negative directions, thus representing moderate levels of trust and distrust respectively.

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Fig. 2. Screenshot from the trust game user interface with photo and reputation score, prompting for the trust rating. Note. Purchase decision was prompted before trust rating, offering two buttons: ‘‘Buy’’ and ‘‘Don’t buy’’. Reference values for the five-star reputation score were permanently displayed on the right side of the screen.

(2) Average trustworthiness ratings for the positive versus negative photos and the positive versus negative reputation scores should differ significantly. (3) To select stimuli for the filler trials in the main study, we attempted to identify those receiving neutral trust ratings (see Section 3.5). Thirty students (16 men, 14 women) from the University of Cologne, with an average age of 24.67 years (SD ¼ 5.08), took part in the first pretest to identify appropriate reputation scores for the main study. Reputation was presented in the form of a commonly used five-star-index, which indicated the percentage of previous trades in which a seller decided to ship the product after payment (one star: 0–20%; two stars: 21–40%; three stars: 41–60%; four stars: 61–80%; and five stars: 81–100%). All possible cases (one to five stars) were shown to participants in the pre-test to ask for their trust ratings on a 7-point scale (1: high distrust to 7: high trust, with 4 as the neutral scale mean). Two of the star-indexes were identified as adequate representations of moderately negative and positive seller reputations: three stars (the seller shipped 41–60% of past trades) led to an average trust rating of 2.90 (SD ¼ 1.30), thus representing moderate distrust, whereas four stars (the seller shipped 61–80% of past trades) led to an average trust rating of 5.03 (SD¼ 1.22), indicating moderate trust. Both means deviated about one point from the mean of the trust scale. The t-test comparison of trust ratings for three- and four-star reputation revealed a significant difference, t(29) ¼ 8.95, po .001, d¼ 1.7. As no neutral star-index could be found between the three- and four-star scores, the remaining scores (one-, two- and fivestar) were equally distributed across the filler trials. In the second pretest, another group of 30 students (14 men, 16 women) from the University of Cologne, with an average age of 24.43 years (SD¼ 2.53), rated their trust in 60 stimulus persons based on their portrait photos (26 men

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and 34 women between 18 and 35 years old), using the same 7-point trust scale as in pretest one. The photos were taken from student volunteers of other universities who were assured that their pictures would only be used in the current experiment and not published in any way. Accordingly, the stimuli are not displayed here. As the aim of the current study is not on exploring the visual details responsible for perceived trustworthiness, the characteristics of the photos are not presented and are outside the scope of this study. In addition to the general criteria named above, the selection of photos aimed to meet the following criteria: (1) All photos in the same category (trustworthy, untrustworthy, and neutral photos) should result in similar trustworthiness ratings (no significant differences). (2) Trustworthiness ratings for each photo should show low standard deviations. (3) At least one representative of the opposite sex should appear among the three negative and three positive photos and sex should be balanced among the six neutral photos. Average trust ratings for the three selected untrustworthy photos were M ¼ 2.60 (SD ¼ .93), M ¼ 2.83 (SD ¼ .91) and M ¼ 2.83 (SD ¼ .95). No significant difference was found among the three photos, F(2, 58) ¼ .89, p¼ .42. Trust ratings for the three selected trustworthy photos were M ¼ 5.17 (SD ¼ 1.21), M ¼ 5.17 (SD¼ 1.15) and M ¼ 5.23 (SD¼ 1.21) and there was no significant difference among them, F(2, 58) ¼ .07, p ¼ .93. The neutral photos for the filler trials achieved the values of M ¼ 3.83 (SD ¼ 1.26), M ¼ 3.83 (SD¼ 1.21), M¼ 3.93 (SD ¼ 1.17), M ¼ 3.97 (SD ¼ 1.30), M ¼ 4.10 (SD ¼ 1.27) and M ¼ 4.17 (SD ¼ 1.37). No significant difference was found among the neutral photos, F(5, 145) ¼ .54, p ¼ .75. ANOVA (with Huynh–Feldt correction) for the trust ratings across the three categories (averaged for all photos in each category) revealed a significant difference, F(1.7, 50.3) ¼ 137.40, po .000, Z2 ¼ .826. Pairwise t-tests confirmed that not only the positive and negative photos differed significantly in the trust ratings, t(29) ¼ 13.68, p o .001, d ¼ 2.97, but significant differences were also observed between the positive and neutral photos, t(29) ¼ 8.41, p o.001, d¼ 1.26, as well as the negative and neutral photos, t(29) ¼ 10.43, p o .001, d ¼ 1.9. Two of the selected trustworthy photos were women, while two of the untrustworthy photos were men. Of the six neutral photos for the filler trades, three were women and three were men. Thus, overall, the photo selection nearly perfectly matched the criteria. 3.3. Dependent measures Although Bolton et al. (2004a) claim that purchase decisions in the trust game rely solely on trust in the seller, both variables – trust and purchase decisions – were measured separately as dependent variables to determine the differential impact of the independent variables – seller reputation and photos – on attitudes and behavior (see Yang et al., 2006) as well as to assess their correlations in the specific context of our enriched trust game. Buying decisions were indicated by a button click (Buy/Not Buy)

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and stored as a value of either ‘‘0’’ or ‘‘1’’. In addition, buyers had to indicate after every purchase decision how much they trusted the particular seller, using the same 7-point rating scale as in the pretests, ranging from 1 (high distrust) to 7 (high trust). Buyers were prompted for their responses by our program as shown in Fig. 1. 3.4. Participants Assuming at least medium-size effects, a minimal sample size of 34 was calculated to guarantee sufficient test power for all statistical procedures (r¼ .5, a ¼ .05, 1–b ¼ .80). Overall, 36 students and graduates of the University of Cologne (16 men, 20 women) participated in the study. Their average age was 26.56 (SD ¼ 2.09). All participants rated their Internet use as ‘‘5–6 times per week,’’ while their online shopping experience varied between ‘‘4–6 times’’ and ‘‘7–9 times’’ per year (in 2009). 3.5. Procedures Participants were invited via email to take part in a computer-mediated interaction study in which they could earn up to 11 euros. Upon arrival, they received a printed instruction sheet containing the rules of the trust game and its payoff matrix (see Fig. 1). All participants were assigned to the buyer’s role and were informed that they would play via computer against 18 other participants (assigned to the seller role) sitting in other rooms, whom they would not meet personally after the game. These sellers in fact did not exist and were simulated by the computer. To ensure anonymity, the participants picked a personal identification code out of a bowl, which was used to record the earnings in each trade and to assign payments later. The participants were further informed that the sellers had the choice to disclose or hide their photo, which had been taken before the study. If they chose to hide their photo, a standard silhouette would appear instead. The five-star reputation score was then explained, telling the participants that it would indicate the percentage of previous trades in which the seller had decided to ship. The possible lack of a reputation score was framed as a system blockade, occurring when another trade with the seller was still in progress. The buyers were told that there would be no immediate information about the shipping decision of the seller but that the money they earned would be added up depending on the seller’s decision and paid out at the end of the experiment. In the negative reputation condition, a three-star score was displayed, whereas in the positive condition, four stars were shown. Three untrustworthy appearing photos and three trustworthy photos that were identified in the pretest were used for the negative and positive conditions, respectively. Each participant was presented with all nine conditions (3  3 within-subject design: trustworthy, untrustworthy, and no seller photo in combination with positive, negative,

and no seller reputation) in a random order automatically assigned by the computer program. In addition to the nine trades representing the experimental conditions, nine filler trades were included (four initial trades, three trades within the experimental block and two end trades). In these filler trades, reputation scores that were other than three or four stars were presented, together with either a photo rated as neutral in the pretest (six trades) or a silhouette (three trades). Filler trades were included to mask a repeated and conspicuous reputation with only three or four stars. The four initial filler trades further served to familiarize the participants with the platform. End trades were included to discard drops in cooperativeness, which can be expected when participants do not anticipate further transactions (see Bolton et al., 2005). The eighteen seller pages were successively displayed, and the buyers had to decide whether they would buy from this particular person, indicated by clicking the respective button. Following each purchase decision, participants were prompted for their trust rating. After completion of the trades, the subjects answered demographic questions and indicated their Internet use and online shopping experience. At the end of the experiment, they received their payment, which ranged between 9.20 and 11 euros (M ¼ 9.84, SD ¼ .44). Purchase decisions in fact were always rewarded with 50 cents and never with 0 cents, since sellers did not exist, and therefore sellers’ shipping decisions did not really take place. 4. Results 4.1. Correlation of trust and purchase behavior As posited by Bolton et al. (2004a), trust is the only factor influencing purchase behavior in the trust game. To test this proposition and to determine whether trusting attitudes and trusting behavior systematically covary in the context of our ‘‘enriched’’ trust game, we conducted a point-biserial correlation for the two dependent variables – trust ratings and purchase decisions. Correlation analysis across all subjects and all trades revealed a significant result (rpb ¼ .61, p o .01), confirming a strong correlation between the attitudinal and behavioral measures in our study, but still leaving some variance to be explained. Consequently, both dependent measures were further analyzed. 4.2. Effects of reputation and seller photos Tables 1 and 2 summarize the effects of reputation and photos on trust ratings and purchase behavior. A two-way (factorial) ANOVA with repeated measures was applied to analyze the effects of both independent variables on the trust ratings. Significant main effects were found for reputation, F(2, 70)=28.70, po .001, Z2=.10, as well as for photo, F(2, 70)=19.62, po .001, Z2=.15. No interaction was observed, F(4, 140)=1.44, p=.11.

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Cochran Q-test (Bortz et al., 2008) was conducted for the binary variable, purchase decision. A significant overall effect was observed, indicating that the treatment conditions systematically influenced the buying decisions, w2(8, N ¼ 36) ¼ 81.03, p o .05, w¼ .63. To further specify the influence of both independent variables, two separate one-way ANOVAs were applied to the marginal distributions of purchases (aggregated either across the three reputation levels or the three photo levels). ANOVAs revealed significant and strong main effects for reputation, F(2, 70) ¼ 24.07, p o .001, Z2 ¼ .27, as well as for photo, Table 1 Mean trust ratings and standard deviations (in parentheses) for trust ratings. Trust ratings

Reputation (1st factor) None

Photo (2nd factor) None 3.03 (1.40) Negative 3.64 (1.22) Positive 4.86 (1.31) Total

3.84c (.88)

Total

Negative

Positive

3.42 (1.18) 3.81 (1.28) 4.67 (1.31)

4.50 (1.56) 4.67 (1.45) 5.58 (1.11)

3.96d (.77)

3.65a (1.10) 4.04b (1.04) 5.04ab (1.03)

4.92cd (.99)

Table 2 Number of purchases and expected frequencies (in parentheses). Reputation (1st factor) None Photo (2nd factor) None 9 (12.69) Negative 14 (13.14) Positive 23 (20.17) Total

46c

Total

Negative

Positive

17 (17.66) 17 (18.29) 30 (28.06)

30 (25.66) 27 (26.57) 36 (40.77)

64c

93c

56a 58b 89ab 203

Note: Numbers with identical alphabetic characters in a row or a column differ significantly (po.05).

no

40

F(2, 70) ¼ 19.78, p o .001, Z2 ¼ .20. As the Cochran Q-test does not allow for the calculation of interaction effects, configuration frequency analysis (CFA) for single cells was applied. No significant deviation of empirical values from expected values was indicated, suggesting that there is no interaction effect of reputation and photo on purchases. Overall these findings support the general hypotheses of H1 and H2, which state that reputation scores as well as photos both exert significant influence on trust and purchase behavior in the trust game. However, no interaction effect of reputation and photos was observed. Thus, no support could be found for H3, which states that the effect of photos depends on the presence and valence of reputation. In fact, as is clearly visible from Fig. 3, the effects of reputation and photos appear as largely independent from each other and strictly additive. 4.3. Effects of information valence

Note: Means with identical alphabetic characters in a row or a column differ significantly (po.05).

Purchases

7

Reputation negative

To test the specific hypotheses regarding the effects of information valence within both factors (H1a,b and H2a,b), pairwise post-hoc comparisons for the three levels of each factor were conducted separately. The results of post-hoc tests are shown in Table 3 (refer to Tables 1 and 2 for means and frequencies). Post-hoc tests indicated that positive reputation led to significantly higher trust ratings and purchase rates than negative as well as missing reputation, thus supporting H1a. Also, trustworthy photos differed significantly from untrustworthy photos and missing photo conditions in the predicted direction which supported H2a. No support, however, was found for H1b, which hypothesized a negative effect of bad reputation when compared to missing reputation. Surprisingly, trust ratings for negative and missing reputation did not differ significantly and purchase rates were even significantly lower for missing reputation than for negative reputation. Hence, H1b is not supported. There were no significant differences between untrustworthy photos and missing photos for trust ratings and

positive

7

35

no

Reputation negative

positive

6 Trust Rating

Purchases

30 25 20 15

5 4 3

10 2

5 0

no

negative Photo

positive

1

no

negative Photo

positive

Fig. 3. Effects of reputation and photos on purchase decisions (0–36) and trust ratings (1–7).

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Table 3 Results of post-hoc comparisons for trust and purchases across the levels of each factor. Dependent variable

Factor

Factor levels

Reputation

Positive/negative Positive/missing Negative/missing Trustworthy/untrustworthy Trustworthy/missing Untrustworthy/missing Positive/negative Positive/missing Negative/missing Trustworthy/untrustworthy Trustworthy/missing Untrustworthy/missing

Trust

Photo

Purchase Reputation

Photo

Significance

Effect size

t(35) 6.39 5.90 .92 5.23 5.58 1.61

p .000 .000 .999 .000 .000 .350

g 1.07 .98 .15 .87 .93 .27

w2(1, 36) 20.71 44.72 7.36 21.36 23.99 .08

p .000 .000 .007 .000 .000 .777

w .31 .45 .18 .31 .33 .02

Note: Significant results are marked in bold. Following Rosenthal et al. (2000), effect sizes for t-values were calculated as g (.2¼small, .5 ¼medium, .8¼large effect) and for w2 values as w (.1¼ small, .3¼medium, .5¼ large effect). Criterion values for effect sizes are based on Cohen (1988).

purchase rates. Hence, H2b, which assumed a trust-building effect of mere visual self-disclosure, is not supported and the theoretical explanation for arriving at H2b is questionable. Interestingly, the same pattern (no significant difference) was observed when comparing the influence of negative versus missing reputation on trust. However, in contrast to photos, the availability of reputation was not framed in the study as dependent on the sellers’ deliberate decision to self disclose. Thus, overall, this data pattern might be better understood and explained in terms of uncertainty reduction than visual self-disclosure.

4.4. Secondary analysis: comparison of extreme cases To further validate this alternative explanation, we conducted a secondary analysis comparing the two most extreme conditions: no information at all (no reputation/no photo) versus completely negative information (negative reputation/ untrustworthy photo). The t-test result for trust ratings revealed a significant difference in favor of negative information, t(35)¼  2.497, p¼ .017, g¼ .42. A Cochran Q-test for the binary variable, purchase decision, led to a consistent significant result, w2(1, N¼ 36)¼ 4.00, po.05, w¼ .23, indicating that missing information led to lower purchase rates than negative information. Trust ratings in the missing information condition (M¼ 3.03, SD¼ 1.40) deviated significantly from the neutral scale mean (‘‘4’’), t(35)¼  4.155, p¼ .001, g¼  1.385, indicating distrust, while trust ratings for completely negative information (M¼ 3.81, SD¼ 1.28) did not differ significantly from the scale mean, t(35)¼  .909, p¼ .37, g¼  .303, indicating neutral attitudes were formed. Binomial tests against the chance level of .5 produced similar results for purchase rates. While the purchase rates for missing information (9/36) deviated significantly from the chance level (p¼ .004, g¼  .25), the purchase rates for

negative information (17/36) are approximately equal to the result of flipping a coin (p¼ .868, g¼  .03).

5. Summing up: an integrated model of online trust Our results suggest that reputation scores and photos generate comparable effects on trust in online transactions (see Table 3) and that these effects are additive. Trustworthy photos as well as positive reputation scores significantly increase trust and purchase rates. Negative reputation, however, does not perform worse than missing reputation, and untrustworthy photos do not perform worse than missing photos. The latter, most surprising finding raises the question of how far trust might be dependent on mere availability of information as opposed to its valence. As our data show, missing information, i.e., complete uncertainty for decision making, leads to distrust while moderately negative information leads to neutral trust levels. This finding is aligned with the central claim of uncertainty reduction theory (URT; Berger, 1979; Berger and Bradac, 1982; Berger and Calabrese, 1975), which emphasizes the value of uncertainty reduction in early stages of contact. This position, however, has been challenged by Sunnafrank (1986, 1989, 1990) in his theory of predicted outcome value (POV), in which he highlights the importance of information valence and posits that uncertainty reduction follows the pathway of positive outcome expectations. It is questionable whether both theories are really contradictory. In fact, a combination of both URT and POV appears to hold higher explanatory value for our results (see also Yoo, 2009a, 2009b), suggesting that information availability and information valence constitute independent factors that add up to a final trust level: complete information with positive valence resulted in maximal trust ratings and 100% purchase rates, while complete information with negative valence led to neutral trust values. Keeping the above discussion in mind and given the moderate level of

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Fig. 4. Integrated model for the combined influence of seller photo and reputation.

Fig. 5. Aggregated values for trust and purchase decisions (z-transformed), rank ordered according to model predictions.

negativity in our study, we formulated a multi-level model of trust building, which includes three factors: (1) information type (reputation score/seller photo), (2) information availability (uncertainty reduction: yes/no) and (3) information valence (positive/negative). The model is depicted in Fig. 4. Unitary weights were used at this stage to equally quantify the contribution of each variable and to account for the observed neutralizing effects of availability and valence in the negative information conditions. The model implies that trust sums up across the validation levels resulting in the nine possible outcomes displayed at the bottom of Fig. 4. As a first test of the model, we correlated empirical values (see Tables 2 and 3) for the nine treatment conditions with the values predicted by our model. Pearson correlations revealed significant results for trust (r=.968, po.001) and for purchases (r=.954, po.001) explaining over 90% of the variance for both dependent variables, thus indicating a

very good model fit. Fig. 5 depicts the standardized values for both dependent measures, rank ordered according to model prediction. The graphical inspection further confirms that our theoretical model serves as a good approximation of the empirical data.

6. Discussion Using a standard trust game, the current study aimed to identify differential contributions of seller reputation and seller photos on trust and purchase decisions in online trading. Overall, we found significant and, in terms of effect sizes, comparable main effects of both factors (seller reputation and seller photos) on both dependent measures (trust and purchases). No interaction effect occurred. This data pattern suggests that both information types are

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processed independently, leading to equally strong and largely additive effects. Separate post-hoc comparisons for the three factor levels of each independent variable (reputation and photos) revealed that the main effects were mainly due to the influence of positive information. Positive reputation as well as trustworthy photos led to significantly higher trust ratings and purchases than negative or missing information. Surprisingly, no differences in the expected direction were found between negative and missing reputation, or between untrustworthy and missing photos (silhouettes). Missing reputation even led to significantly lower purchase rates than negative reputation. Comparing the two extreme treatment combinations (no photo/no reputation and untrustworthy photo/negative reputation), we further found that completely negative information resulted in significantly better trust ratings and purchase rates than completely missing information. These results are not only counter-intuitive but also contradict earlier findings on risk aversion (Kahneman and Tversky, 1979) and the negativity effect (Kellerman, 1984), which suggest stronger negative effects of negative than positive information. Dimoka (2010) also showed that distrust and trust are based on distinct neural mechanisms and that distrust had a stronger impact on buyer decisions (price premiums) in an IT-enabled exchange. In light of these previous findings, the effects found for negative information in our study require further explanation. One possible explanation is that we used moderately negative information, rather than extreme negativity information, for both factors (seller reputation and photos). According to the disclosure-liking hypothesis (Collins and Miller, 1994), we expected willful presentation of a photo to produce positive effects, potentially counteracting any negative impression of the portrayed person. However, if this explanation is valid, we should not find such a pattern for reputation scores, which were not dependent on the sellers’ willful self-disclosure. Since we found a similar pattern for reputation as well as for photos, it is likely that this effect is due to availability of information rather than to willful self-disclosure (which is not applicable in the case of reputation) and the observed effect is not so much dependent on inferences about the trustworthiness of the trustees than on trustors’ feelings of uncertainty and risk. In fact, evidence from basic research reveals that people in some cases show stronger emotional responses to uncertainty than to negative outcomes as reflected in the mundane statement ‘‘the devil you know is better than the devil you don’t know’’. Hirsh and Inzlicht (2008) state: ‘‘Whereas exposure to familiar negative stimuli produces a well defined threat, exposure to the unknown can be even more threatening because the potential danger is not clearly specified. Consequently, uncontrollable and unexpected threats produce greater anticipatory anxiety and physiological responses than do controllable and predictable threats’’ (p. 962; see also Dickerson et al., 2004; Feldstein and Witryol, 1971). If uncertainty reduction

explains the neutral effects of moderately untrustworthy photos as well as moderately negative reputation on trust, the effect is independent of willful action and instead relies solely on the fact that we know more about the transaction partner. Referring to dual process theories of attitude change (Petty and Cacioppo, 1986; Chaiken et al., 1989), we expected differences in the effect sizes of reputation and seller photos as well as an interaction effect. More specifically, we expected in our high involvement game (with monetary risk) that a central/systematic processing mode based on factual information (reputation) should prevail and thus the effects of photos should depend on the availability and valence of reputation. No support was found for the interaction hypothesis. Moreover, effect sizes for reputation and photos were very much alike across all comparisons and both dependent measures. There was only one exception: Negative reputation as compared to missing reputation led to higher purchase rates, while untrustworthy photos compared to omitted photos did not. Given the small effect size in the comparisons between negative and missing information (see Table 3), however, one should be cautious to interpret the findings in light of the dual process perspective. In fact, stronger evidence was found for the independent and equally sized effects of photos and reputation, casting some doubt on the explanatory value of dual-process theories regarding the effects of photos and reputation in our trust game. To account for these findings, we formulated a theoretical prediction model which implies strictly additive effects of information type (reputation score/seller photo), information availability (yes/no), and information valence (positive/negative). The model showed a nearly perfect match with the empirical data, which supports our view that both trust signals are processed on two levels, independently, based on the mere availability of the information (uncertainty reduction), as well as the valence of the signal (predicted outcome value). This finding partially aligns with those by Yoo (2009b), who suggests that ‘‘uncertainty reduction up to a certain level is a necessary, but not a sufficient, condition for achieving positive relational outcome’’ (p. 5). In contrast to Yoo (2009b), however, we did not find a larger impact of information valence on trust. Uncertainty reduction through completely negative information (bad reputation and untrustworthy photo) led to a neutral attitude toward the seller and purchase rates close to the chance level, while missing information (no reputation/no photo) led to slight distrust and purchase rates below the chance level. This indicates that the moderate level of negativity in the signal could be leveled out by uncertainty reduction whereas missing information about a seller resulted in a negative effect on trust. This effect could be due to an expectancy violation (see Burgoon, 1993; Burgoon et al., 1984). Hence, future studies should examine the relative frequencies of trust signals that are systematically varied in a trading series to create different expectation levels.

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Overall, the integration of URT and POV theory seems to be worth future examinations to more deeply understand the specific trust-building mechanisms underlying uncertainty reduction and predicted outcome value in online transactions. Along these lines, research designs that explicitly incorporate measures of perceived uncertainty as a potential predictor for trust and purchase decisions are necessary (see Yoo, 2009a). Structural equation modeling approaches (see Kline, 2010; Mulaik, 2009; Pearl, 2000) could then be useful to model the causal relations between different aspects of information (availability, type, valence) and different aspects of trust (cognitive vs. emotional trust, trusting attitudes vs. trusting behavior). There are some limitations in the current study. It is important to note that our study used only moderately negative information. Therefore, one might argue that negativity, as manipulated in our study, was too weak to produce specific effects. It is most likely that extreme negativity in one or the other information channel will cause calamitous effects, which cannot be compensated for by any other information. This discussion is possibly related to and of relevance to signal strength and decision thresholds. These factors could explain, why, for instance, negativity effects could be found in some studies (Ba and Pavlou, 2002; Standifird, 2001), while other studies failed to identify a negativity effect in online trust (Resnick et al., 2006; Yoo, 2009b). This aspect should be systematically addressed in future research efforts, varying the information valence across a larger range and in smaller steps, as well as including neutral material and more extreme negativity/positivity. Another limitation concerns the type of reputation used in our study. Reputation scores were explained to the participants as automatically calculated by the system, indicating the percentage of previous cooperative actions of the seller (in shipping the products). Thus, reputation was defined as a hard fact or objective information in contrast to reputation systems where other users provide a subjective feedback. Our approach thus allowed us to address ‘‘primary trust’’ – that is, how much we trust a seller with a good or bad reputation – but it excluded aspects of ‘‘secondary trust’’ – that is, how much we trust the information source (see Bacharach and Gambetta, 2001, p. 149) – which might be relevant in online trading. The problem of secondary trust has been elaborated in the context of CMC within the framework of warranting theory (see Walther and Parks, 2002; Walther et al., 2009), which could serve as a theoretical basis for future research, systematically comparing different types of reputation and source characteristics. Beyond the theoretical and methodological implications, our findings also hold some practical value. The results, in general, underpin the notion that ‘‘face work’’ matters in online transactions. This might be self-evident regarding reputation. Consistently, positive seller behavior as reflected in reputation scores should increase the chance

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of a seller staying in business, and a bad reputation is likely to cause a decrease in sales or even a breakdown of the seller’s market. In contrast, the decision to provide a photo might be fraught with higher levels of uncertainty because we are unable to reliably predict the inferences other people draw from our image. Professional photos and impression checks before placing a photo on a website might help to fully exploit the impact of visual selfdisclosure. Further research, however, is needed to identify the specific cues inherent in the seller photos, which influence trust, including attire and nonverbal cues as well as technical aspects of the portraits. At this stage, however, our findings support the notion that, in case of doubt, placing a photo on a website is a better option than omitting it. Even if the content of a photo causes a slightly negative impression, the worst that can result is a neutral attitude, because the positive valence of uncertainty reduction is likely to counteract the negative valence of content.

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