Table of Content Background of research at University of Groningen .. 2 Introduction ..................................................................................................................... 2 Methodology ................................................................................................................... 7 Empirical survey ......................................................................................................... 7 Experiments ................................................................................................................ 7 Testing the reputation system ..................................................................................... 8 Trust: Definitions, Antecedents and Consequences ... 9 Trust from multiple perspectives .................................................................................... 9 Literature review on trust ................................................................................................ 9 Trust constructs ............................................................................................................. 12 Importance of trusting beliefs ....................................................................................... 14 Summary ....................................................................................................................... 17 Risk-reduction strategies (RRS) .............................. 19 Description and types of Auction markets: .............. 22 Consumer motivations and concerns in online auctions ............................................... 23 Prior research on auction studies .................................................................................. 24 Research gaps: .............................................................................................................. 34 Product catageories sold in online auctions .................................................................. 35 B2B versus B2C ........................................................................................................ 35 Fee and commissions ................................................................................................ 36 Useful input for simulations..................................... 36 Glossary ................................................................... 38 Links: ....................................................................... 43 1 Background of research at University of Groningen Introduction In recent years the Internet contributed to a growth of consumer-to-consumer (C2C) business (Ba and Pavlou 2002). Several auction sites are available, facilitating the exchange of goods between individual consumers. Auction sites may be very generic concerning the products being offered and operate on a global scale (e.g., E-Bay), or may focus on specific products on a national scale (e.g., national car auction sites). At the moment, virtually all consumer products are being auctioned on the Internet, ranging from used toys and CDs to cars and houses. The online environment is more uncertain due to the difficulty of assessing the seller’s reputation and product quality; lack of trust is one of the most frequently cited reasons that prevent consumers from making online purchases (Lee and Turban 2001). This study examines how customers use reputation mechanisms1 to assess the seller reputation and product quality with the aim to reduce their perceptions of risk and facilitate satisfying transactions. Next, it is investigated which reputation mechanisms are most effective in promoting trust in consumer-to-consumer (C2C) transactions under varying circumstances (availability of similar products, product type, and customer experience/expertise) and across different stages within the bidding process. Reputation is particularly important when engaging in online purchasing given the anonymous nature of the Internet (Grabner-Kräuter et al., 2003). On auction sites most transactions occur between parties that have never met (Ba and Pavlou, 2002). As such, this may give rise to opportunistic behavior (Akerlof, 1970; Grabner-Kräuter et al., 2003), as it is relatively easy for a dishonest seller to lure an unsuspected buyer into a fraudulent transaction (Neumann, 1997); the detrimental effects on the seller’s reputational equity are much less strong than, for instance, for retailers who have invested heavily in physical buildings and facilities (Doney and Cannon, 1997). Apart from the difficulty in assessing the seller’s expected behavior, consumers may also face difficulties in assessing the quality of physical products due to the limited ability to (physically) examine the product prior to purchase (Alba et al., 1997; Standifird, 2002). Finally, online transactions often involve asynchronous exchange of goods and money –i.e. spatial and also temporal separation between exchange partners is common (Grabner and Kräuter et al., 2003). Consumers are frequently required to share sensitive personal information (e.g. mailing address, telephone number) and financial information (e.g. credit card numbers), although they are located in different parts of the country or even in In this study, we use the term ‘reputation mechanisms’ to refer to all cues that bidders can use to infer the reputation or trustworthiness of a seller. 1 2 other countries and have limited history of prior online transactions (Bhattacherjee, 2002). In online auctions, consumers are confronted with a myriad of offerings and (contradicting) marketing messages. One effective heuristic or means to reduce the complexity of the decision is by means of trust (Luhmann, 1989). Trust is often viewed as a catalyst in many buyer-seller transactions (Doney and Cannon, 1997; Shapiro 1987) but seems to be particularly important in the case of ECommerce due to the less verifiable and less controllable business environment (Ba and Pavlou 2002; Gefen 2000; Reichheld and Schefter 2000). Trust generally is a crucial enabling factor when there is uncertainty, interdependence, incomplete information, and fears of opportunism (Bhattacherjee 2002; Hoffman, Novak and Peralta 1999; Mayer, Davis and Schoorman 1995, McKnight and Chervany 2002); it plays a key role in markets that involve high uncertainty and lack of legal protection, of which C2C markets are a typical example (Luo 2002). Reputation is often used synonymously with trust. However, reputation differs from trust2; reputation refers to the public evaluation of the credibility and accountability of sellers, whereas trust refers to an individual’s willingness to rely on the other party. Reputation emerges as a result of social network effects when information on an object or individual in on one relation spreads (i.e., shared voice) to others via an information network (Granovetter, 1985; Sabater, Paolucci and Conte 2006). Trust is more personal than reputation, and is, for example, also influenced by direct experiences and the individual’s trust propensity. Some authors conceptualized trust in conative or behavioral terms (i.e., relying on another party) (Einwiller, 2003; Ganesan 1994; Mayer, Davis and Schoorman 1995); others use more cognitive or evaluative definitions of trust, arguing the link between trust evaluations and behavioral response should be open to empirical investigation and is likely subject to the influence of other contextual factors (Doney and Cannon 1997; Morgan and Hunt 1994). Following Morgan and Hunt (1994, p. 23), we define trust as existing when “one party has confidence in the exchange partner’s reliability and integrity.” Sellers on the Internet may actively try to communicate their trustworthiness to potential buyers. Trust can facilitate transactions by mitigating the perceptions of risk, and may generate price premiums for reputable sellers. Although empirical studies have shown conflicting results, on balance, it seems that trustworthy sellers can ask for higher price premiums (Ba and Pavlou 2002; Melnik and Alm 2002). Sellers –but also auction 2 Reputation is also sometimes used synonymously with image (Gotsi and Wilson, 2001), but authors (e.g., Einwiller 2003; Sabater, Paolucci and Conte 2006) mention the difference between reputation and image. In contrast to the collective nature of reputation (i.e., meta-evaluation), an image is the mental picture a person has stored in his or her memory, or –in psychological terms – it can be defined as the cognitive schema a person has of an object. Image does not necessarily have to relate to the predictive and benevolent behavior of a person, it can also contain other aspects, such as the novelty of the merchandise from this seller, or the friendliness during interactions. 3 markets such as eBay– can facilitate transactions by making use of reputation mechanisms that inspire trust, such as feedback mechanisms, third-party escrow services (e.g., Paypal.com and Escrow.com), and credit card guarantees (Pavlou and Gefen 2004). An increase in the perceived effectiveness of these mechanisms engenders trust, reduces risk and, consequently increases intentions to transact online. Many online services have emerged that provide feedback on sellers’ reputation, such as Bizrate.com, eBay’s Feedback Forum, Epinions.com, and Rapleaf.com (cf. Ba and Pavlou, 2002). To reduce the information asymmetry for potential buyers, the eBay Feedback Forum site3, for instance, shows potential buyers the performance ratings or trustworthiness of sellers (e.g., sellers can become Platinum Power Sellers). Reputation mechanisms reduce the chances of opportunistic (i.e. fraudulent) behavior, as traders are motivated to maintain good reputation records in order to maximize their profits (Conte and Paolucci 2003; Lin et al. 2006). However, such reputation feedback systems apply to sellers that sell on a regular basis, and such indicators of trust do not apply to first time or very irregular sellers. Here potential buyers need other indicators and/or additional information to estimate the trustworthiness of a seller, such as the information provided by the seller about the goods for sale (e.g., quality of the pictures, detail of description), information on the seller (e.g., address, email address, level of experience), arrangement for payment (upfront or afterwards), and interactions with the seller (by email or telephone). Consumers may differ in their use (type and degree) of mechanisms to assess the transaction risks involved and trustworthiness of sellers, depending on product factors (e.g., homogenous vs. heterogeneous goods, level of product involvement), consumer characteristics (e.g., prior experience/expertise, trust propensity), market conditions (e.g., number of similar items offered at auction site) and seller characteristics and interventions (e.g., reputation feedback score, minimum opening bid, information provided about product, interactions with bidder), and these are likely to differ according to the bidding phases (i.e., entering the bid, during the bid, end of bid). This study focuses on understanding how and to what extent consumers use (a variety of) reputation mechanisms in C2C online auction exchanges. We expect product involvement and perceived risk to be key factors determining consumers’ use of indicators for sellers’ reputation. Risk is often viewed as antecedent of involvement, as the consequences of making a bad decision influence the importance of the decision and subsequent search behavior (Chaudhuri 2000; Choffee and McLeod, 1973; Zaichkowsky, 1986). When a decision is less important (in terms of consequences) decision-makers are more likely to use a simpler heuristic instead of using all information available (Tversky 1969; 1972). 3 In contrast to the eBay Feedback Forum where bidders and sellers can only provide feedback about the other party in terms of a positive or negative evaluation, open forum sites display posted messages from individuals about sellers and bidders without a predetermined format. 4 For expensive products, risk perceptions will be higher, and buyers will spend more time and effort to reduce their transaction-specific risk (Bettman 1973). The more expensive a product is, the less incentive the seller will have to cooperate since the benefits of cheating are greater. Given the greater risk involved with expensive products, buyers need to develop more trust before they engage in an exchange. Hence, we hypothesize that buyers spend more cognitive effort in estimating the trusting beliefs (e.g., benevolence, integrity, ability, predictability) of a seller when they are confronted with more complex and expensive products, and that the trustworthiness of a seller becomes more critical in facilitating these transactions. Apart from the monetary expenses of a product, its complexity plays a role in influencing buyer’s use of reputation mechanisms. In contrast to search goods (e.g., CDs, books, etc.), experience goods are less amenable to be sold through the Internet, as the product quality cannot easily be assessed prior to purchase (Alba et al. 1997; Girard et al. 2003), and hence increases buyer’s perceptions of risk. In circumstances where intrinsic product attributes are absent, consumers tend to rely stronger on extrinsic attributes, such as the seller’s reputation (Teas and Agarwal, 2000; Zeithaml, 1988), and hence engage in more extensive search. In sum, we hypothesize that the type of product category influences both the type of and the degree to which reputation mechanisms are used. Additional factors that are expected to relate to evaluation and use of reputation information are (1) the experience/expertise of buyers, (2) the relative opening price of the good, (3) level of product differentiation, and (4) the number of competing items being sold. First, the level of experience or expertise buyers have is an important factor in explaining online auction behaviors (Wilcox 2000); the more experience an auction buyers is, the better they will be able to judge when and how much to bid for an item. Consumers’ expertise on the product domain and on the bidding process is likely to influence the way they evaluate sellers. Compared to experienced buyers, inexperienced buyers are more likely to depend on extrinsic cues (e.g., seller’s reputation) when intrinsic cues are absent (Ariely and Simonson, 2003; Zeithaml, 1988). In line with this reasoning, Einwiller (2003) demonstrated that experienced online buyers rely less strongly on third-party evaluations (i.e., reputation) in determining their trust. As such, we expect that buyers having more experience/expertise with auctions and the product category are less likely to rely on impersonal reputation feedback mechanisms, and rely more strongly on their own prior experiences in developing their trust. Second, we expect that the lower the minimum price of a product is (i.e., initial price to similar items on auction site), the lower the initial trust of the buyer, as price discounts are viewed as compensation to buyers for bearing higher than average risks (cf. Ba and Pavlou, 2002)4. 4 In the study of Ariely and Simonson (2003) lower opening bids drew more bidders (lower initial risk levels), but in the end led to lower final prices, which they explained as the result of anchoring effects of the opening bid. 5 We therefore hypothesize that the lower the relative price, the more distrust consumers develop and the higher their risk perceptions, which stimulates them to make more use of reputation mechanisms to infer the seller’s trustworthiness. However, this effect of relative opening price is expected to be attenuated in highly competitive markets (see below), as consumers focus more strongly on evaluating the product rather than the supplier. Thirdly, for differentiated products (e.g., second-hand products, products that vary in product quality) individuals generally rely stronger on reputation mechanisms. Lee et al. (2001) found that for new products (i.e. sealed products), the influence of negative feedback scores was negligible. Yet, when bids were placed for second-hand or refurbished goods, feedback scores had a great influence in the purchase decision and size of the bid. Fourthly, we hypothesize that in a highly competitive market5 buyers make less use of reputational indicators. The reputation mechanisms may function as a filter to select a subgroup of interesting sellers; however, as the market is more transparent, buyers can more easily compare offerings and rely on the offerings itself. In a scarce market (very few items are offered), they are less capable of addressing whether they get good value for their money, and hence rely more strongly on the reputation mechanisms to reduce their risk. The study of Ariely and Simonson (2003) shows the interaction effect between the market’s competitiveness and the opening price on final prices; they found that when participants can compare the prices of similar items (i.e., competitive market), high or low initial starting prices do not seem to have an effect on final price; in contrast, when there are few comparative alternatives (i.e., scarce market) high opening price leads to higher final prices. 5 The number of competitive offerings (and competitive bidders) is highly dependent on the product category (Ariely and Simonson 2003). 6 Methodology The proposed research will combine an empirical survey of actual C2C business on auction sites, experimental study of the use of reputation indicators, and the development and experimental testing of reputational systems. Empirical survey In an empirical survey, we plan to study the buyer’s use and perceived effectiveness of reputation indicators. We will select four types of markets differing on the average price of products offered (relative cheap versus relative expensive) and the degree to which information on the relevant/dominant attributes can be distributed through the Internet (e.g., tactile versus non-tactile/differentiated versus standardized products). In a pilot study we will conduct in-depth interviews with a sample of consumers that have considerable experience in C2C auctions to (1) select archetypical markets that significantly differ on involvement/perceived risk, and (2) get an indication which reputation mechanisms are used and how frequently. After selecting four archetypical markets, we will study the search behavior of consumers in a natural (nonmanipulated) setting (e.g., eBay). Respondents will be assigned to a shopping task. The search behavior of these respondents will be followed using a click-track following methodology. These data will reveal the actual use of reputation indicators in the selection process. Additionally, through a questionnaire we will measure background variables (sociodemographics, auction experience/expertise), attitudinal variables (product involvement, trust propensity), and transaction-specific variables (perception of risk, effectiveness of reputation mechanism). Next, we will select a sample of sellers in the four markets, and (1) rate them on their explicit use of reputation mechanisms, and (2) question them on their motives to use certain of these indicators. This sample of sellers will be controlled for seller experience. The results of this empirical survey will provide information on how different reputation indicators are being used –by both consumers and sellers– in different markets. Experiments While the empirical survey produces an overview of the relevant reputation indicators, the experiments will focus on the specific trade-offs buyers make in a well-controlled auction environment. For different product categories varying on involvement and perceived risk (as selected on the basis of the survey), we will offer respondents an experimentally designed auction site. Respondents will be confronted with this auction site and will be assigned to select one seller to conduct business with. Different variables 7 will be tested experimentally. First we will vary the type and number of reputation indicators as used by the sellers (e.g. all positive, mixed positive and negative, all negative). Sellers of similar goods will systematically differ on the reputation indicators as provided. These experiments will reveal which reputation indicators are the most important in what type of market. Next we will vary the available product information, as to experimentally test the trade-off function between risk and reputation indicators. Here the sellers will systematically differ on critical reputation indicators as presented (as identified in the previous experiment) and the level of detail in the product description. Following that we will introduce variations in relative product prices in the experimental design in order to identify the relation between the use of reputation information and the relative prices of goods offered. The final experiment will vary the number of sellers in the market to test if reputation indicators play a less role in highly competitive markets. The experiments will use a click-tracking methodology along with measuring background, attitudinal and transaction-specific variables. The proposed experiments will reveal what reputation indicators are being used and appear most effective under differing market conditions. Testing the reputation system The previous experiments provide useful information about the effectiveness of reputation indicators under varying conditions. Based on this, we will develop a system allowing sellers to indicate their reputation through a number of mechanisms (e.g., escrow, feedback). Respondents will be challenged to sell their goods in an experimental context, where buyers are represented by computer-simulated agents as developed by the simulation project. Sellers are prompted to obtain a maximum of profit from a series of selling actions. Artificial buyers can choose varying reputation systems to select the sellers they want to buy from. The performance of the reputation system will be measured in terms of effectiveness by measuring the outcome quality of the transactions. 8 Trust: Definitions, Antecedents and Consequences Trust from multiple perspectives Prior research has sought to identify valid and relevant dimensions (Ganesan 1994; Smith and Barclay 1997). Trust is a complex and multidimensional (i.e., it comprises multiple components) concept (Lewis and Weigert 1985). Many theorists and researchers of trust focus on interpersonal relationships; however, the analysis of trust in the context of online auctions should also consider impersonal forms (e.g., systems/technologies) of trust (Grabner-Kräuter et al. 2003). Multiple disciplines/perspectives have defined trust: Personality psychologists: a belief, or expectancy, or feeling that is deeply rooted in the personality and has its origins in the individual’s early psychological development (e.g., Rotter, 1967;1971): personal trait or belief Social psychologists: an expectation about the behavior of others in transactions, focusing on the contextual factors that serve either to enhance or inhibit the development and maintenance of trust (Lewicki and Bunker, 1995): social structure. Economists/socialists: how institutions and incentives are created to reduce the anxiety and uncertainty associated with transactions (e.g., Granovetter, 1985; Zucker, 1986; Williamson, 1993): economic-choice mechanism. Literature review on trust Table 1 shows the definitions, antecedents or components and consequences of trust derived from several studies. Table 1: Prior studies on trust Author(s) McKnight and Chervany (2001, p. 42) Definition of trust To willingly become vulnerable to the trustee, whether another person, an institution or people generally, having taken into consideration the characteristics of the trustee. Antecedents/components of trust 1. Competence: belief that other party has the ability or power to do for one what one needs done. 2. Benevolence: belief that other party cares about one and is motivated to act in one’s interest. 3. Integrity: belief that the other party makes good faith agreements, tells the truth, acts ethically, and fulfills promises. 4. Predictability: belief that the other party’s actions (good or bad) are 9 Consequences of trust Trusting behaviors: Purchasing Cooperating Information sharing consistent enough that one can forecast them in a given situation. Urban et al. (2000) Mayer et al. (1995, p. 712) Moorman, Zaltman and Deshpandé (1992, p. 315) (1993) Morgan and Hunt (1994, p. 23) Sirdeshmukh et al. (2002) GrabnerKräuter et al. 2003 Trust is not being afraid even if you are vulnerable. The willingness of a party to be vulnerable to the actions of another party based on the expectation that the other will perform particular action important to the trustor, irrespective of the ability to monitor or control that other party A willingness to rely on an exchange partner in whom one has confidence. Ability Benevolence Integrity Trust exists when one party has confidence in the exchange partner’s reliability and integrity. the expectations held by the consumer that the [service provider] is dependable and can be relied on to deliver its promises. Risk taking in relationship. Outcomes of trusting behaviors are updated of prior perceptions of the ability, benevolence and integrity (dynamic nature of trust) Trust creates value by (1) providing relational benefits derived from interacting with a service provider that is operationally competent, benevolent toward the consumer, and committed to solving exchange problem, and (2) reducing exchange uncertainty and helping the consumer form consistent and reliable expectations of the service provider in ongoing relationships. In case of little trust, there will be a need for more information. The more trust in Trust is a mechanism to reduce the complexity of 10 human conduct in situations where people have to cope with uncertainty (based on Luhmann 1989). Trust is a very effective complexity reduction method; although it does not really enable people to control or even anticipate without error the behavior of others. a specific transaction, the less the need for more complete information. 1. Reduce uncertainty 2. Comprehensive organization of the activities performed Source: McKnight and Chervany (2001, p.42) 11 Trust Interpersonal trust: Institutional trust: Dispositional trust: The trustor trusts the trustee The bidder trusts the seller The bidder trusts the auction site/The bidder trusts the feedback system The bidder trusts others generally Source: McKnight and Chervany (1996) Trust constructs Some authors have conceptualized trust in conative/behavioral terms (Coleman, 1990; Deutsch, 1962; Ganesan 1994; Giffin, 1967; Mayer, Davis and Schoorman 1995; Schlenker, Helm and Tedeschi, 1973): trusting behaviors (or behavioral intentions). Other researchers use cognitive/evaluative definitions of trust (trusting beliefs), arguing the link between trust evaluations and behavioral response should be open to empirical investigation and likely subject to the influence of other contextual factors (Doney and Cannon 1997; Morgan and Hunt 1994). Here we distinguish between the cognitive evaluations and behavioral outcomes of trust. 12 Trusting Behavior: the extent to which one person voluntarily (Lewis & Weigert 1985) depends on another person in a specific situation with a feeling of relative security, even though negative consequences are possible. Depends is a behavioral term, which distinguishes Trusting Behavior from the intentional term Trusting Intention (willingness to depend). Trusting Behavior construct implies acceptance of risk (i.e., placing a bid, making a payment). Trusting Intention: the extent to which one party is willing to depend on the other party in a given situation with a feeling of relative security, even though negative consequences are possible. Trusting Intention is situation-specific, intentional, personal (originating in a person) and (one-way) directional: one person is willing to depend on the other. Trusting Beliefs: the extent to which one believes (and feels confident in believing) that the other person is trustworthy in the situation. The most prevalent trusting beliefs in the literature involve benevolence, honesty/integrity, competence, and predictability. Benevolence means one cares about the welfare of the other person and is therefore motivated to act in the other person's interest. A benevolent person does not act opportunistically toward the other person. Honesty/integrity means one makes good faith agreements (cf. Bromiley & Cummings, 1995), tells the truth, and fulfills any promises made. Competence means one has the ability to do for the other person what the other person needs to have done. So the essence of competence is efficacy. Predictability means one's actions are consistent enough that another can forecast what one will do in a given situation; it embodies an element of temporal continuity that can be related to the other trusting beliefs. Examples in auction market: trusting beliefs can be developed by evaluating the seller based on personal (communication with the seller) and impersonal (reputation feedback mechanism, quality of picture, information on product/seller) cues; these beliefs are influenced by individual customer characteristics (trust propensity; level of experience/expertise). System Trust (=institution-based trust): the extent to which one believes that proper impersonal structures are in place to enable one to anticipate a successful future endeavor (Luhmann 1991; Lewis & Weigert 1985; Shapiro 1987). Personal attributes of the other are not at issue with System Trust. Hence, it does not support Trusting Beliefs about the other; but it does support Trusting Intention. Two types of impersonal structures can be differentiated: (a) structural assurances, and (b) situational normality. Structural assurances include such safeguards as regulations, guarantees, or contracts (Shapiro, 1987; Zucker, 1986) (i.e. reduction of the impact of negative consequences). Situational normality may include one's own role and others' roles in the situation (Baier, 1986); it is based on the appearance that things are normal (Baier, 1986; Garfinkel, 1967) or in 13 "proper order" (Lewis & Weigert, 1985a: 974). System Trust supports Trusting Intention in that it makes it feel safe to depend on that person because of type (a) System Trust's safeguards, which act like a 'safety net,' or type (b) System Trust's reduction of uncertainty, which enables one to feel more secure in taking risks with other people. Examples in auction market: System trust can refer to eBay as a whole, but also at the lower levels of the reputation systems, including (1) buyer-driven feedback systems, (2) escrow services (auction house authorizes payment only after the buyer is satisfied), (3) credit card guarantees, and (4) trust in intermediary (Pavlou and Gefen 2002). Dispositional Trust (trust propensity): the extent that an individual has a consistent tendency to trust across a broad spectrum of situations and persons (noncontext-specific). Dispositional Trust is directed toward people (i.e., that others generally should be trusted). Situational Decision to Trust: the extent to which one intends to depend on a nonspecific other party in a given situation. It means that one has formed an intention to trust every time a particular situation arises, irrespective of one's beliefs about the attributes of the other party in the situation (Riker 1971). For instance, when intending to buy an auction item below €30, it is better to just trust the seller automatically (without searching for additional information, or contacting the person). Like Dispositional Trust, it means that one has decided to trust without regard to the specific persons involved -because the benefits of trusting in this situation outweigh the possible negative outcomes of trusting. Kee and Knox (1970:p. 360) suggested that this may occur “when there is much to gain from trusting..., but little attendant risk.” Situational Decision to Trust differs from Dispositional Trust in that it is an intentional construct and relates only to specific situations, not across situations generally. It differs from System Trust in that it does not imply structural safeguards. It is simply an individual, situational strategy. Because it does not concern specific individuals, Situational Decision to Trust does not support Trusting Beliefs about specific individuals. But because it encourages a willingness to depend on others in the situation, Situational Decision to Trust supports Trusting Intention directly. Importance of trusting beliefs All four trusting beliefs (benevolence, integrity, ability, and predictability) are important, as they complement and are likely to reinforce each other; a bad score on a belief may also the negatively impact the scores on other beliefs. If a person fears that a seller is incompetent to protect his credit card number, then this person is more likely to think that the seller will also sell personal information to other companies (i.e., integrity) relative to 14 competent sellers. Next, the asymmetric impact of negative and positive attribute-level performance on intentions (Mittal, Ross and Baldasare 1998) is also likely to apply to the trusting beliefs. This asymmetric impact implies that a negative performance (below a certain threshold) on a trusting belief (e.g., competence) has a greater impact on purchase intentions than a positive performance (above a certain threshold) on that same belief. Mayer et al. (1995) argued that the importance of the trusting beliefs is also dependent on the stage within the relationship. In the initial stages, the effect of honesty/integrity on trustworthiness is very strong. Next, the effect of benevolence on trust will increase in time, as the relationship between the parties develops. 15 Cognitive processes 16 A similar trust model (based on Mayer et al. 1995) Mayer et al. (1995) argue that trustworthiness is based on ability, benevolence and integrity (omitting predictability). They also take into account the trust propensity (disposition to trust). The level of risk is dependent on the situation (e.g., buying a second-hand car vs. new CD) and affects the reliance on trust in risk taking. The level of trust will affect the amount of risk the trustor (i.e., bidder or seller) is willing to take; the more risk involved the stronger the relationship between trust and risk-taking behavior. Outcomes of trusting behaviors (positive or negative) will lead to updating of prior perceptions of the ability and benevolence, and integrity of the trustee (Note: this is also likely to update institution-based beliefs). Summary The trust literature shows that consumers develop trusting beliefs, based on the trust propensity (personal, general disposition towards people), situational-based trust (context), institution-based trust (impersonal, related to structures) and seller characteristics (impersonal feedback mechanisms, information on seller and product) and interventions (personal communications). Consumers can actively engage in search activities (using Google, asking other buyers that bought from the seller), or ‘passively’ in order to develop the trustworthiness of a seller. In developing these trusting beliefs, consumers update prior beliefs with new information. Figure 2 (below) shows the trust constructs and their hypothesized relationships. 17 Figure 2: Modeling trust-related behaviors in an online auction context Institutionbased trust Consumer characteristics Prior experience/ expertise Trust propensity Structural assurance (i.e., eBay) Situational normality Trusting beliefs about seller Competence Benevolence Integrity Predictability Trusting intentions Willingness to depend Subjective probability of depending Perceived risk Situational trust Trust based on specific situations (e.g., below €30) Trust-related (risk taking) behaviors Seller characteristics and interventions Information about product Minimum/relative opening bid Reputation (feedback score) Payment options & guarantees Communication with bidder (email, phone) Third party seals Escrow services Purchasing Paying ‘premium’ price Cooperating Information sharing Market conditions 18 Number of competitive bids at auction Product factors Price Homogeneity/ heterogeneity Risk-reduction strategies (RRS) Consumers usually rely on risk relievers to allay perceived risk to a tolerable level by (1) reducing the amount at stake, and (2) increasing the degree of certainty that consequences would be favorable (Akaah and Korgaonkar 1988; Mitchell and McGoldrick 1996). Consumers may seek information from formal and informal sources, rely on information acquired in the past (Cox and Rich, 1964), use brand image or price as quality guide, or shop only in stores with high quality image (Akaah and Korgaonkar 1988). Among various risk-reduction strategies, information seeking has been identified as the most desirable strategy for reducing uncertainty in a purchase situation (Lutz and Reilly, 1973; Jasper and Oullette, 1994). Information seeking has been found to be a direct function of perceived risk (Hisrich, Dornoff and Kernan 1972). In a ‘risky’ situation, the consumer may have a variety of information needs to satisfy (Cox, 1967); the type of perceived risk being faced by the consumer may dictate his or her information needs. Past research has shown a rather weak positive relationship between perceived risk and tendency to seek word-of-mouth information regarding a product (Cunningham, 1967; Sheth and Venkatesan, 1968). When faced with increasing perceived performance risk, consumers tend to use more sources of information. Over a wide range of products low or moderate in risk, the most frequently used method of information acquisition is simply to buy the product, presumably on a trial basis. However, when product performance risk is high, trial purchase is the least likely form of information acquisition (Lutz and Reilly, 1974). In these circumstances, persons are likely to acquire information from observation and own experience (Lutz and Reilly, 1974; Locander and Hermann, 1979). Mitchell and McGoldrick (1996) reviewed the literature concerning risk-reduction strategies (RRS). They distinguish between personal (direct contact with person) and impersonal risk-reduction strategies, which can simplify, clarify or do both. Clarifying strategies involve looking for additional information and asking experts, whereas simplifying strategies refer to buying from a well-known seller, buying from a reputable seller, buying through the same seller. Sometimes they include both (e.g. asking family and friends, past experience, use price information as indication of quality). For example, past experience may provide the buyer an insight into the buying problem: what and how to evaluative (clarify), or may lead to copying past behavior (simplify). 19 Rely on seller without thinking Reputation of seller Ask other buyers to evaluate seller Interaction with seller Price as an indicator of product quality and trustworthiness Source: Mitchell and McGoldrick (1996). Risk-reduction strategies in auction markets will predominantly involve impersonal strategies, as personal sources (friends, families and other buyers) that can assess the seller in question are difficult to find. Varying risk levels during the bidding stages Ariely and Simonson (2003) argue that in English auctions (ascending prices), there is little risk involved in the initial entry decision, because the initial bidding prices is typically low relative to the value of the item. However, after the initial decision consumers are likely to experience escalation of commitment to the action (e.g., Staw 1976). Auctions might be particularly susceptible to escalation of commitment because participation in an online auction may often trigger an intense emotional response (“I 20 don’t want to lose this bidding”). Many bidders see other bidders as “competitors” and referred to outcomes as “winning” or “losing”. In addition, to escalation of commitment early bids, by a consumer might be later interpreted as a signal that they value the particular item (Bem 1972; Ariely et al. 2001; Drolet, Simonson and Tversky 2001). For example, an early decision to bid $10 on a watch might be interpreted a few days later as an indication that the watch was attractive (and not just for $10), thus providing a justification to submit a higher bid. A related way of thinking about this process is through the endowment effect (e.g., Kahneman, Knetsch & Thaler 1990). In the standard demonstration of the endowment effect, a person’s value for item X increases once it is owned. One explanation of this effect is that the ownership causes increased attachment to the item, which, in turn, increases its subjective value (e.g., Carmon and Ariely 2000). Of course, as long as auctions are in progress, no bidder owns the product. However, during the process of an auction, psychological ownership could take place (Strahilevitz and Loewenstein 2001). For instance, a consumer who was the highest bidder for an item on the first day of an auction and does not visit the auction site for the next four days might get more attached to the item during this time, as it becomes a part of his or her psychological endowment. When this consumer returns to the auction site on the fifth day of the auction they might be sorely disappointed to see that their items has been “lost”. This reaction might increase the consumer’s willingness to raise the previous bid to reclaim the endowment. 21 Description and types of Auction markets: Market settings for auctions (Anandalingam, Day and Raghavan, 2005) Number of Type of items Number of items Differentiation participants One seller, many Divisible Single-item Identical items buyers Discrete Multiple items Distinct items One buyer, many sellers Many buyers and sellers Some auction sites use an ascending bid protocol with a fixed end time (e.g., eBay and Yahoo.com). At these sites the practice of last minute bidding is prevalent (Matsubara 2001; Teich, Wallenius, Wallenius and Zaitsev 1999). On other sites, which more closely resemble traditional auctions (going, going, gone), the auction closing time is received within 10 minutes of the current closing time. In general, auctions range from a few days to three weeks. Bidding stages and assessments (Ariely and Simonson 2003): 1. Auction entry decisions: value assessments (low opening biddings can be positive and negative on end price: attactiveness of entering the bid vs. low anchoring may signal low value) 2. Bidding during the auction: value assessments (more biddings can have both positive and negative effects: getting a bargain vs. perceived quality (everybody sees it is of high quality) Decision dynamics (behavior of one stage influences behavior at later stage). There is likely a disproportional attention and weight attributed to price relative to other product attributes (color, quality,etc.). 3. Bidding at the end of an auction: decisions are final and often irreversible. Decision dynamics. At the end the winner may experience the winner’s curse (regretting having paid too much), and the loser may experience the loser’s curve (regretting having bid sufficiently high). It seems that the loser’s curse plays a more significant role. 22 Consumer motivations and concerns in online auctions Benefits/motivations for buyers Costs/concerns for buyers Purchase related costs and benefits Access to products otherwise unavailable Potential fraudulent transactions Lower prices than through retail Comparison price points Information on competition Information on product availability Entertainment (thrill of the hunt, bargaining, ( Anticipated) Frustration of lost bid getting a bargain) Nonpurchase related costs and benefits Friendship and community feelings Lower self-esteem Source: Bosnjak et al. (2006); Cameron and Galloway (2005) 23 Prior research on auction studies Ariely and (2003a) Research aim/content Simonson Performed three studies of consumers value assessments 1st study: search and price comparison with online retailers 2nd study: determinants of final prices 3rd study: comparative search and starting price on magnitude of bid Type of research 1st Study: Empirical field study, 500 auctions for music CDs, books and movies (VHS &DVDs): comparison prices with online retailers 2nd study: Empirical field study for RoseBowl game on eBay: 275 participants 3rd study: Empirical study, experiment, DVDs, VHS, webcams, keyboards, trackballs: 48 auctions: 2X2 (starting price high-low)X (comparability highlow) 24 Results 1st study: auction site was cheapest in 1.2% of the cases, and more expensive than online retailers in 98.8% of the cases. Consumers paid on average 15.3% more in auctions compared with the lowest regular online retail prices. Consumers fail to look and search for cheaper (and more reliable) alternatives. Consumers believe they are getting cheaper products through auction sites, but they do not search. 2nd study: Starting prices, total number of bids and total number of bidders are positively related to final prices, but seller’s reputation is not significantly related to final price. Auction duration and date started (later bidders are less interest in getting the ticket) had a negative impact on final price. 3rd study: Higher starting price caused participants to bid higher for the goods, but only when there were no immediate comparisons. In case of high comparability (other items) there was no effect for the starting price. Low opening bids led to more bidders (and more bids per bidder) compared with high opening price. Ariely and (2003b) Simonson Bapna et al. 2001b) Experience of buyers Black (2005) Determine the impact of socio-economic variables on the degree of online participation (auction purchasing behavior) Empirical, 753 transactions of two eBay clothing sellers in the USA: (1) a pure online auction seller (Platinum Power Seller), (2) 25 However, while lower starting prices draw more bidders, these bidders bid relatively low (presumably because of the anchoring effect of the staring bid) and hence this strategy is not always successful. On the other hand, to the extent that there are bidding wars (as reflected by the multiple bids of individuals), the magnitude of bids increase. Starting prices, lower comparability are positively related to final prices. Significant two-way interaction: higher starting prices lead to higher prices, but only when there were no immediate comparisons. When participants could compare the prices of the two items, there was no effect of starting price. Uninformed consumers are poor auction decision makers and have been found to pay an average of 18./5 percent more than experienced bidders Regions play a role People from rural areas are more likely to make a purchase than those of urban areas. However, urban people are more willing to pay higher prices per eBay purchases. Females are more likely to make an eBay multichannel retailer who complements sales through eBay Empirical, 294 individuals completed the first questionnaire prior to bidding (INTENTION), 188 individuals also completed second questionnaire (USE) Bosnjak, Obermeier and Predicting and Tuten (2006) explaining the propensity to bid in online auctions: a comparison of two action-theoretical models (Theory of Planned Behavior (TPB) and Technology Acceptance Model (TAM) Cameron and Galloway Consumer motivations Empirical, exploratory (2005) and concerns in online in-depth interviews auctions 26 purchase than are males. However, males are more willing to pay higher prices per eBay purchases. TAM: PEOU and PU significant predictors of intentions to bid (R2= .87), and intention is significant predictor of # of bids (R2=.10) TPB: Attitude and PBC significant predictors of intention to bid (R2=.84), and # of bids (R2=.10) TAM was somewhat better in explaining intentions but equal in ³ of bids. Auction users strongly favor one auction site. Then they know their way around, and how the system works, and it is easy to find what they are looking for. Auction users reveal obsessive behavior traits. Obsessive behavior like gambling; becomes a daily routine. Online auctions complement traditional retail stores. Buying limited editions and discontinued items. No clear preference between new and used items. Last-minute bidding as a key strategy. “Winning the game”. “Sniping: bidding at the very last 27 second” User experience influences bidding success rate. More winnings, and being more able to distinguish between genuine and fraudulent offerings. Feedback system impacting on purchase decision. Feedback systems were vital input for bidding. Especially for more expensive products. Also the other way around: not selling to not trustworthy buyers. There were some doubts based on the quality of the feedback system. It can be manipulated. Mixed views on the importance of user interaction. Several users found interacting with other auction users important (community message boards), but only during the first months because they learned how to operate on eBay; after that it became less vital. The main motivations for interacting with other users was for practical reasons (additional information and receiving suggestions on how specified problems could be solved. Auction benefits outweigh threat of fraud. There is always a risk of becoming caught up in a fraudulent transaction, whether as a buyer or a seller. However, almost without exception respondents felt that the benefits of using the online auction system far outweigh the potential threat of fraud. Diekmann and Wyder (2002) Ding, Eliashberg, Huber Effects of anticipated and Saini (2005) regret and anticipated excitement in multiple biddings at a reverse auction Experiment, empirical setting, computer, 87 students Gilkeson and Reynolds How certain online (2003) features affect the success and final price of auctions. Pieces of sterling silver flatware 28 Starting prices are not related to final prices Reputation and minimum increase required to participate are related to final price A bidder changed her bids after each round (dynamic behavior of bidding behavior!). The direction (increase or decrease) of such change is conditional on the outcome of the previous bid. Emotions are an integral component of a bidder’s decision state (mood) and bidding strategy. A bidder in a relatively favorable environment (higher chances of winning) emphasizes more the amount of gain if she wins (thus make lower bids), but gradually put more emphasis on the probability of winning as she becomes more interested in the bidding. In a hostile environment (lower chances of winning), she emphasizes more the probability of a win (and thus overbid), although this effect is not as strong as in the favorable environment. Relative opening price, number of bids, and whether or not the auction format chosen included a reserve price, were positively associated with final price, but were negatively Gregg (2006) and Walczak Developing an online auction advisor Ha (2002) Heyman, Orhun Ariely (2005) Simulation The way information affects the risk assessment prior to bidding in online auctions and Quasi-endowment 2 experimental designs: (feeling of owning the 2x2 (high/low product during bedding) competition)x(low and opponent (winning vs. high duration of the competition) effect relationship) 2x2x2x4: same+ (order of auctioned goods)x(2 gift certificates, t-shirt, chocolates) 29 associated with auction success. Shipping and handling charges, duration of auction, daytime of ending, and sellers’ feedback rating… (not related to auction outcomes) Both buyers and sellers achieve tangible benefit from the information acquired by and recommendations made by the Auction Advisor Agents Both quasi-endowment and the opponent effect are apparent Houser (2006) and Wooders Reputation from bidders Empirical, use additional and sellers in auctions at data about list item eBay (quality, second-hand, including warranties, etc. to account for heterogeneity) and actual feedback profile scores (at moment of purchase). Kshetri (2002) Factors influencing Experiment, actual consumers’ reaction to a auction users (student price and intention to bid sample, N=80), in a C2C Internet competitive bidding for auction a low-risk calculator with varying buyer and seller reputation ratings Kwon et al. (2002) To what extent do design factors and customer characteristics influence evaluations of web-based auctions Consumer’s reaction to feedback scores and comments has a significant positive influence on the (final) auction price. Overall evaluations has positive effect on intentions to bid Design has strongest impact on overall evaluation, followed by usability and information comprehensiveness. Higher risk-seeking respondents are more willing to bid. Financial status and previous bidding experience does not influence bidding intentions 30 Bidders pay a statistically and economically significant premium to sellers with better reputations (seller reputation). Bidder reputation also has impact on price. Lin, Li, Janamanchi and Role of reputation at Huang (2006) macro level: how the distributions of online traders’ reputation scores reflect the structure of the C2C auction market, and how the distribution changes reflect the transition of the market status Empirical data of buyers (N1=293 & N2=1061) and sellers (N1=352 & N2=1745) Lin, Ramanathan and Agent-based simulation Kandula (2001) of C2C Internet auctions with online escrow services Lucking-Reiley (1999) (2000) ABM, 2 tier system (buyer-seller) and 3 tier system (buyer, escrow, seller) Use spider (computer program) to analyse data on eBay coin auctions Lucking-Reiley, Bryan, Prasad and Reeves (2001) Melnik and Alm (2002) Does a seller’s reputation matter on final price Significant effect of seller’s reputation on final price. A seller’s e-commerce reputation is an influencer in bidding prices; however, it is only one of a number of factors taken into account, hence only has a small impact on the price of the winning bid. 31 Seller reputation rather than buyer reputation is lognormally distributed. (Gibrat’s law). It seems that both seller and buyer reputation are rightskewed. There is a tendency that reputation level rises; i.e. overall the reputation of the market as a whole goes up. A higher percentage of sellers leave the market than buyers, while a higher rate of new buyers entered the market than sellers. No results yet. Working paper Negative feedback has a significant effect on price, but positive feedback does not. Ockenfelds and Roth The effect of end rules (2001) (hard vs. soft end rules) Rafaeli and Noy (2002) The influence of social interaction (“virtual social presence”) offered by auction sites on auction behavior Experiment with 65 students, who took part in a number of simulation auctions on specifically designed website with increasing levels of social interaction: (1) only number of bidders, (2) number and names and pics of bidders, (3) direct communication access to bidders, (4) full access/full communication 32 With regard to whether or not consumers want to rate sellers: Not every transaction results in a feedback comment, there is lttle economi motivation for buyers to provide feedback after a transaction has been completed, sellers can change their Internet identities…(and) the measures do not provide a complete indicator of seller quality. The end rule has large effects on bidding behavior. Social facilitation also occurs online: virtual presence. In experimental examination, participants improve their results and stay longer in the auction under conditions of higher virtual presence. Participants indicate preference for auction arrangements with higher degrees of virtual presence. Resnick et al. 2000 Resnick and Zeckhauser Stafford (2002) and Standifird (2001) Wilcox (2000) Data from eBay, two types of homogeneous products Reputation systems help to foster better behavior in both buyers and sellers, because they seek to enhance their online-auction reputation. Positive and negative feedback do not have significant effect on final price. PEOU (ease of use), PU (usefulness), Intention to use, affinity with computers and involvement are significantly related to propensity to bid online Demonstrating the Systematic and asymmetrical effect of unobtrusive observations positive versus negative reputation Influence of level of experience on auction behavior Positive reputation has mildly positive effect on final price, whereas negative reputation has strong negative effect on final price and auction success More experienced bidders are better able to judge when and how much to bid for an item. Experienced bidders tend to wait until the moments before an auction closes to submit a bid and tend to go for items with few or no bidders. Stern Explaining propensity to bid online, using TAM 33 Research gaps: The literature on the willingness to bid and the actual bidding behavior of consumers in online auctions is currently dominated by approaches based on the economic decision making and information processing paradigm and are primarily focused on what influences auction outcomes (e.g., opening bids (Gilkeson and Reynolds, 2003) or reserve prices (Häubl and Popkowski Lesczcyc 2001), bid increments (Diekmann and Wyder, 2002), and seller reputation (Standifird, 2001) or herding behavior (Dholokia and Soltysinski 2001). No serious attempts have been made to stringently test and compare existing models derived from the action-theoretical perspective to predict and explain consumers’ propensity to use online auctions (Bosnjak et al. 2006). Research on the subject to date has largely concentrated on monitoring and collecting descriptive information on the processes involved in Internet auction, and although this is an important area, very little concern has been given to consumers’ motivations and concerns over using the online auction system. With millions of people using auction sites every day, it is essential for traditional retailers to know why consumers are turning to online auctions in increasingly large numbers (Cameron and Galloway 2005, p. 181) Research in the field of auction sites has investigated the effect of seller reputation on end prices and on the propensity to bid. However, little is known about which reputation mechanisms consumers use and to what degree they rely on them -during each of the stages of the bidding. process. Additionally, relatively little is known about the effect of experience/expertise on the use of reputation mechanisms. 34 Product catageories sold in online auctions Category Collectibles Antiques Celebrity memorabilia Stamps Coins Toys Trading cards Electronics and computers Jewellery Computer software Used equipment Sporting goods Travel services Real estate Wine Source: Lucking-Reilly (2000, p. 233) # sites featuring that category 90 40 16 11 17 17 14 48 17 16 15 13 7 4 3 # sites specializing in that category 56 10 7 5 2 0 0 9 1 0 7 4 5 2 2 Lucking-Reily (2000) showed that electronics and computers, as well as antiques, were the major product groups involved in C2C transactions. Additionally, even for these product categories, the number of specialist sites would appear to be low, showing that the general auction sites are the preferred option for consumers, regardless of the product type they wish to buy. B2B versus B2C While a large proportion of online auction participants are individuals, there is evidence that businesses themselves are both selling and marketing by utilizing auctions such as eBay (Cameron and Galloway 2005). 35 Fee and commissions From the three major auction sites (Yahoo!,eBay and Amazon), only Yahoo! did not charge a fee and a commission (Lucking-Reilly 2000). Yahoo! tended to have very high minimum bids (or reserve prices) compared to eBay and Amazon; the reason could be that eBay and Amazon sellers know that they have to pay the fee, even if they do not sell, and so they lower the minimum price levels to increase the chances of a sale. As of May 2002, Yahoo! alleged with eBay in Europe. Useful input for simulations Houser and Wooders (2006) have modeled the following in a market with one seller and n buyers: Bidder’s reputation: Seller’s reputation: Bidder i’s value: rB ε (0,1] (this reputation is publicly known; bidder pays once he has won the item) rS ε (0,1] (this is publicly known; seller delivers the item once he has received payment) vi (with vi > 0) (this is only known to the bidder) If Bidder i wins the auction and is to pay the price b then with the probability rBi he delivers payment and his expected payoff is (rSvi –b) with probability (1-rBi) he defaults and his payoff is zero. His expected utility therefore is: rBi (rSivi - b) Jaiswal, Kim and Gini (2004) develop a multi-agent marketplace (MAGNET) (Multi-AGent Negotiation Testbed). They develop a software agent that can help bidders. The trust model of MAGNET is somewhat different from other online auction systems since the marketplace, which mediates all communications between agents, acts as a partially trusted third party. 36 Lin, Ramanathan and Kandula (2001) designed an agent-based model for online marketplaces with online escrow services (OES). There are three architectures (in the three tier architecture) for the experimental system if different configurations are defined: (1) buyer (2) marketplace (3) seller. The market module has six processes: (1) the market module sends current information about the item for bidding to the buyer module; (2) the buyer module bids on the item until auction closes (3) the winner decides if online escrow to be used: (4) all these messages: agreed price, buyer parameters, and OES decisions are sent from the buyer modulate to the market module (5) the market module finalizes the auction by disseminating all information to both buyer and seller in the trade (6) the market module let a seller initialize a new auction (cheat or no cheat). Input variables: OES fee rate, cheating rate 37 Glossary Auction: an auction is a mechanism of information submission, together with rules for assigning items and payments to participants based on this submitted information (Anandalingam, Day and Raghavan, 2005). A market with an explicit set of rules to determine resource allocation, based on the bids of participants (McAfee and McMillan, 1987 cited in Brosnjak et al, 2006) Auction market: Auction markets: market where buyers and sellers and buyers come together to bid on products/services. They have characteristics that distinguish them form general forms of consumer decision making: (1) multi-stage process (no fixed price purchase, auctions take place of time, forming sequence with bidding activities leading to a final bid), (2) value signals (the online environment provides different types of value cues (i.e. intangible) that bidders can rely on), (3) decision dynamics – the fact that multiple viding decisions are made during the auction process suggests that earlier decisions can dynamically impact subsequent decisions. (Ariely and Simonson 2003). Benevolence: A trusting belief that the other party cares about the welfare of the other person and is therefore motivated to act in the other person's interest. A benevolent person does not act opportunistically toward the other person. (McKnight and Chervany 2001). Bidder: someone who bids on an auction market. This is different from a buyer: someone who wins the bid (Lin et al. 2006) Bid increment: the minimum amount of money that can be bid in top of the currently bid price. Combinatorial auctions: multiple item auctions for which bids may be placed on packages of items, and are often referred to as auctions with package bidding. Competence: trusting belief that the other party has the ability or power to do for one what needs to be done. Here, the essence of competence is efficacy. (McKnight and Chervany 2001). Disposition to trust: (see also trust propensity) Double auctions: auction with many sellers and many buyers. Open market in which transactions have the opportunity to occur at any time and require only the agreement of the transactos, as in the stock market (Anandalingam, Day and Raghavan, 2005) 38 Dutch auction: auction where bids are publicly called out in descending order and the bidder who calls first wins English auction: auction where bids are publicly called out in ascending order and the bidder offering the highest price wins (also on eBay). eBay’s feedback forum: A trader’s reputation is measured by an overall rating, which is counted and saved a number (e.g., 6442, or 46). After an auction, both the buyer and the seller can rate each other using the services provided by Feedback forum. A positive comment from a unique trading partner adds one point to the net reputation score, and a negative comment deducts one point from the net reputation score; a neutral comment has no effect on the net reputation score. (Lin et al. 2006). For each transaction, only the buyer and seller can rate each other by leaving feedback. Each feedback left consists of a positive, negative, or neutral rating, and a short comment. Leaving honest comments about a particular eBay member gives other Community members a good idea of what to expect when dealing with that member. Once it is left, the feedback becomes a permanent part of the member's profile (eBay.com). The percentage of traders that give a rating are 52.1% from buyers to sellers, and 60.6% from sellers to buyers (Resnick and Zeckhauser 2002). Endowment effect: This effect is a hypothesis that people value a good or service more once their property right to it has been established. In other words, people place a higher value on objects they ‘own’ relative to objects they do not (e.g., Thaler 1980). The effect is related to loss aversion and status quo bias in prospect theory. Escrow service: A trusted third party (examples: Paypal) acts as an intermediary between seller and buyer. Faith in humanity: one assumes that others are usually competent, benevolent, honest/etical, and predictable. (McKnight and Chervany 2001; Mayer et al. 1995). This is an underlying construct of disposition to trust. Fee: Sellers (for example from eBay and Amazon) need to pay an auction fee, even if they do not sell the product. Fishing: illegal activity to withdraw private information of sellers and buyers (on auction sites). Forward auction: auction with a single seller (Anandalingam, Day and Raghavan (2005) 39 Hard stopping rule: an auction that has been set to end at a fixed time (e.g., 12 p.m. November 5). eBay uses this approach. (the opposite is soft stopping rule) Honesty: see integrity Institution-based trust: one believes that favorable conditions are in place that are conducive to situational success. Behaviors are situationally constructed (socialist perspective): action or behaviors are not determined by factors within the person but by the environment or situation. E.g. one trusts the auction market eBay (or at a higher level, the Internet), or credit card company. Integrity: a trusting belief that the other party makes good faith agreements (cf. Bromiley & Cummings, 1995), tells the truth, acts ethically, and fulfills any promises made. (McKnight and Chervany 2001). Also referred to as humanity honesty. Interpersonal trust: the trust one has in a specific other (seller, bidder, etc.). See also trust. Platinum Power Seller: Power Sellers are eBay top sellers who sustain a consistent high volume of monthly sales and a high level of total Feedback --with 98% or better positive rating by other eBay users (eBay.com). Predictability: trusting belief that the other party’s actions (good or bad) are consistent enough that one can forecast them in a given situation; it embodies an element of temporal continuity that can be related to the other trusting beliefs. (McKnight and Chervany 2001). Procurement auction: auction with a single buyer and several sellers (also reverse auction) Propensity to trust (see trust propensity) Proxy bidding: a bidder enters the maximum amount that he is willing to bid, and then ‘the system will bid for you as the auction proceeds, bidding only enough to outbid other bidders. If someone outbids you, the system immediately ups your bid. This continues until someone exceeds your maximum bid, or the auction ends, or you win the auction.’(eBay) Reverse auction: auction with single buyer and several sellers (also procurement auction) (Anandalingam, Day and Raghavan, 2005) 40 Reputation: the public evaluation of the credibility and accountability of individuals or sellers (Einwiller 2003). The second-hand rumor that one has positive general traits (McKnight and Chervany 2001). The public information on the hitherto trustworthiness of an individual (Picot et al 2001). Reputation system: a system that collects, distributes, and aggregates feedback about participants’ past behaviors. (Resnick et al. 2000). Online reputation systems record and report an online trader’s reputation accorfding to other traders’ feedback (Lin et al 2006). They help to build trust and elicit cooperation among loosely connected and geographically dispersed economic agents (Dellarocas 2003) Reserve prices: minimum (opening) bids (Cameron and Galloway 2005) Reverse auction: Rather than requiring consumers to find the supplier with the lowest offering, a company (e.g. Priceline.com) takes a bid from a consumer and then searches to find suppliers who match that bid. (Ding et al. 2005). Suppliers are actually bidding for consumer’s business. Self-efficacy: belief of individuals that they have the capabilities and resources to perform the behavior (e.g., making an online auction purchase) (Bandura, 1977). The level of experience of sellers and bidders is positively associated with their level of expertise (Alba and Hutchinson, 1985), and hence increases their efficacy levels (the belief becomes stronger that they are able to perform the behavior in case of successful attempts). Shilling: illegal practice of driving up prices by using bids with fake email addresses Sniping: the legal practice of bidding at the very last second (i.e., this frequently occurs with hard stopping rules). Situational normality: the belief that the situation in a purchase is normal or favorable or conducive to situational success (Chervany and McKnight 2001). The perception that things are normal, proper, customary, fitting, or in proper order (Garfinkel 1963). Soft stopping rule: an auction that has not been set to stop at a specific time. Amazon.com, for example, has a system whereby an auction is scheduled to stop at a certain time, only if no bids were accepted within the last 10 min. of the auction. 41 Structural assurance: the belief that protective structures –guarantees, contracts, regulations, promises, legal recourse, process, or procedures- are in place that are conducive to situational success (McKnight and Chervany, 2001; Shapiro 1987). This is part of institution-based trust. For example, belief of protection of encryption from credit card theft. Trust: The willingness of a party to be vulnerable to the actions of another party based on the expectation that the other will perform particular action important to the trustor, irrespective of the ability to monitor or control that other party (Mayer et al. (1995). A willingness to rely (or depend) on an exchange partner in whom one has confidence (Moorman et al. 1993). Trust propensity: a general disposition to trust people in life (this is a consumer trait that is relatively stable: “faith in humanity” & “trusting stance”). People differ in this trait; some are always distrusting others, whereas others believe that people can be trusted. Disposition to trust is a general, i.e. not situation specific, inclination to display faith in humanity and to adopt a trusting stance toward others (Mayer et al. 1995; McKnight et al. 1998), and is perceived as a personality trait (Grabner-Kräuter et al., 2003). This tendency is not based upon experience with or knowledge of a specific trusted party (ref), but is the result of an ongoing lifelong experience and socialization. As an antecedent of trust, disposition to trust is most effective in initial phases of a relationship when the parties are still mostly unfamiliar with each other (Mayer et al. (1995) and before extensive ongoing relationships provide a necessary background for the formation of other trust-building beliefs, such as integrity, benevolence and ability. Trusting stance: regardless of what one assumes about other people generally, one assumes that one will achieve better outcomes by dealing with people as though they were well-meaning and reliable. (McKnight and Chervany 2001). This is an underlying construct of disposition to trust. 42 Links: http://www.internetauctionlist.com/Default.asp A list of online auction sites (contains more than 2,500 auction sites) 43