conducting international business at ebay: the...

A b s t r a c tMany small businesses have started to use

eBay as their electronic commerce platform.

Such a strategy increases their reach to

global customers without high financial and

technical costs in building their own e-

commerce infrastructure or in intense

advertising. How can these e-stores conduct

successful international business on eBay?

This paper addresses this complex issue by

discussing how online customers’ percep-

tions of an e-store’s culture, size, and

history affect the e-store’s feedback rating

and successful sales. We analyze data of the

completed transactions of 400+ e-stores on

eBay across twenty-two countries. The

results suggest that feedback rating is a

significant determinant of the success of an

e-store, and that an e-store’s culture, size,

and history significantly affect the e-store’s

feedback rating.

Keywords: electronic commerce, international

business, culture, feedback rating, trust

A u t h o r s

Paul Komiak([email protected]) is a PhDStudent in International Business andStrategy at Texas A&M InternationalUniversity. His research focuses on thestrategy and policy implications of IB.Sherrie Y. X. Komiak([email protected]) is an AssistantProfessor of Information Systems atMemorial University of Newfoundland,Canada. Her research focuses onelectronic commerce, human computerinteraction and trust.Michael Imhof([email protected]) is aPhD.Student in International Businessand Finance at Texas A&M InternationalUniversity.

Conducting International Business at eBay:

The Determinants of Success of e-Stores

PAUL KOMIAK, SHERRIE Y. X. KOMIAK AND MICHAEL IMHOF

INTRODUCTION

The growing use of the Internet as amedium for conducting business isone the most significant factorsshaping the world economy (Gibbset al. 2003). However, cost signifi-cantly deters electronic commerceadoption in small to medium-SizedEnterprises (SMEs) (Al-Qirim2005). To overcome this constraint,many SMEs have started to use awell-known electronic market likeeBay as their electronic commerceplatform. Such a strategy increasestheir reach to global customerswithout high financial and technicalcosts in building their own e-com-merce infrastructure or in intenseadvertising. Since its inception in1995, eBay has grown to be a hugeand best-known electronic market.Online retailers can use eBay tolaunch e-stores, listing their itemsunder specific product headings for asmall monthly fee. For a larger fee,sellers can list their e-store as ananchor store, with premium place-ment opportunities. Sellers some-times use eBay as their only onlinesales effort because they do not havethe cost and expertise in-house tohandle online payment, websitedevelopment and maintenance, andother necessities that come withbeing an independent online mer-chant. Other companies use eBay as

another online channel in additionto their own websites or promotethe items they are selling on eBay ontheir own websites. Though usingeBay as a platform for operatingtheir own e-stores may providebusinesses with a potentially vastnumber of international and domes-tic customers without broad andintense advertising, the benefits canonly occur when international anddomestic customers actually patron-ize their eBay-based websites.

How can e-stores on eBay con-duct successful international busi-ness? While a large quantity ofresearch addresses the determinantsfor success of traditional multina-tional corporations (MNCs)(Ahlstrom et al. 2005,D’Annunzio-Green 2002, Darlingand Box 1999, Dow 2006, Elenkovand Fileva 2006, Fey and Bjorkman2001, Galan et al.1999, Goodbody2005, Gorg and Strobl 2003,Holtbrugge 2004, Jennex et al.2004, McLarney and Dastrala2001, Reid 1995, Robson et al.2002, Tixier 1995, Tudor andTrumble 1996, Yang and Lee2002, Yang 1998), few scholarlyworks address the determinants ofsuccess for companies engaging ininternational electronic commerce.Among the studies on electronicmarkets, most emphasize a directInternet-based exchange. For

Cop

yrig

ht�

2008

Elec

tron

icM

arke

tsVol

um

e18

(2):

187-2

04.w

ww

.ele

ctro

nic

mar

kets

.org

DO

I:10.1

080/1

0196780802045120

RESEARCH

Downloaded By: [Schmelich, Volker] At: 14:35 24 March 2010

example, the transaction cost perspective focuses on theeffect of the Internet on the efficiency of the transactionprocess (Rotondaro 2002). Another perspective exam-ines the nature of the product, called the product fitnesstheory (Liang et al. 2004). Only recently has researchfocused on the electronic intermediary and on externalvariables such as consumer demographics (Black 2005,Kshetri 2002). Even if the use of the Internet can lowercosts and speed up activities in the value chain,differences that drive purchasing behaviour, i.e., con-sumer perceptions, beliefs, preferences, and attitudes,may remain (Lynch et al. 2001). Many academics haveincluded consumer trust and intended behaviour, e.g.,intention to purchase, in their research (Gefen et al.2003a). Other academics have introduced culture intotheir research on e-commerce behaviour (Johnston andJohal 1999, Kacen and Lee 2002, Lynch and Beck2001). However, culture has usually been left out ofresearch on trust and trust beliefs in e-commerce (Gefenand Heart 2006).

This paper addresses the complex issue of e-commercesuccess by examining the roles that culture, store size,store temporal history and feedback score (i.e. custo-mers’ perceptions and beliefs about an e-store) play inthe success of international online retailers in a real-lifeenvironment, in which actual buyers selected e-storesand completed their purchase transactions. This paperextends the research on beliefs, attitudes and intentionsto actual behaviour. We propose and test the central roleof feedback score in the success of e-stores on eBay.

Since customer’s perceptions and beliefs can beinfluenced by e-store design, the results of this studywill generate some useful suggestions for electronicmarket researchers, practitioners and policymakers. First,researchers may find such knowledge helpful in addres-sing the growing international presence of Internetstores and the central role of feedback scores in e-storesuccess. Second, for web design and online sellerpractitioners, knowing what attracts buyers, and whatdoes not, may enable the development of better e-commerce strategies. Finally, knowing the determinantsof e-commerce performance may help policymakersenhance the effectiveness of consumer protection lawsby helping them to understand better the purchasingbehaviour and legal demands of online consumers.

The rest of the paper is structured as follows: the nextsection establishes the theoretical grounds underlyingthe feedback rating of e-stores on eBay, and therelationship between the feedback-rating and success ofe-stores. This is followed by a section that exploresseveral factors (e-store’s culture, size, and history)hypothesized to have an impact on the e-store’sFeedback Rating. Then the research methodology ispresented. We analyze data of the completed transac-tions of 84 e-stores on eBay across 14 countries. Wecontrolled the factor of product types by selectingthese 84 e-stores who sold the same products. We

cross-validate our findings with a second sample of 346e-stores from a different product type and a more recentsample timeframe. The results are then presented and arefollowed by a discussion on the theoretical, managerial,and policy implications of this study. The final sectionconcludes the paper.

FEEDBACK SCORES OF E-STORES AT EBAY

The concept of feedback scores

eBay’s feedback rating system is a very well knownreputation system in electronic markets. On every e-store’s main webpage, on every item selling, and onevery bid, a Feedback Rating score will show up besidethe name of that eBay member (e-store, buyer or seller).As explained by eBay at: http://pages.ebay.com/services/forum/feedback.html, for each completedtransaction, only the buyer and seller can rate eachother by leaving feedback. Each feedback consists of apositive, negative, or neutral rating and a shortcomment. Once it is entered, the feedback becomes apermanent part of the member’s profile. Feedbackratings are used to determine each member’s feedbackscore. The feedback score represents the number of eBaymembers that are satisfied doing business with aparticular member. It is usually the difference betweenthe number of members who left a positive rating andthe number of members who left a negative rating. Amember can increase or decrease another member’sscore by only 1 no matter how many transactions theyshare. Feedback score can be conceptualized as a proxyof trust (Ba and Pavlou 2002). Therefore, it is likely thatfeedback scores can serve as the proxy of customer trustin an eBay e-store.

Although the importance of trust as a foundation forbuyer-seller relationships in traditional commerce(Morgan and Hunt 1994, Swan et al. 1999) and inelectronic commerce (Gefen et al. 2003a, Komiak andBenbasat 2006, McKnight et al. 2002a) is widelyaccepted, trust has been conceptualized in various waysand in different disciplines. McKnight et al.’s trustmodel (2002a) consists of disposition to trust (frompsychology), institution-based trust (from sociology),trusting beliefs, and trusting intentions (from socialpsychology). Blomqvist (1997) analyzes the variouslevels and dimensions of trust in the fields of socialpsychology, philosophy, economics and marketing.Blomqvist proposes that trust is an actor’s expectationof the other party’s competence and goodwill. Dwyerand Lagace (1986) proposes that trust can be con-ceptualized in one of three ways: (1) trust as apersonality trait or generalized expectancy about thetrustee’s competence and reliability (Anderson andNarus 1990, Anderson and Weitz 1989, Magrath andHardy 1989), (2) trust as a predisposition toward

188 Paul Komiak, Sherrie Y. X. Komiak and Michael Imhof & International Business at eBay


another or belief that another will behave in amatter beneficial to the other party (Morgan andHunt 1994), and (3) trust from the standpoint ofrisking behaviours, that reflects a willingness on thepart of the buyer to accept the possibility of vulnerabilityon her part in the transaction (Hawes et al. 1993,Mayer et al. 1995, Moorman et al. 1993, Swan et al.1985).

Many researchers conceptualize trust as a truster’strusting beliefs, in other words, a truster’s beliefs in atrustee’s multi-dimensions (Mayer et al. 1995). Trustingbeliefs means the confident truster perception thatthe trustee has attributes that are beneficial to thetruster (McKnight et al. 2002a). Many types oftrusting beliefs exist in the trust literature, while threeare utilized most often: competence (ability of thetrustee to do what the truster needs), benevolence(trustee caring and motivation to act in the truster’sinterests), and integrity (trustee honesty and promisekeeping) (Mayer et al. 1995, McKnight et al. 2002a). Itis worthy noting that predictability is also used in atleast nine prior studies on trust (Gabarro 1978,Rempel et al. 1985), as summarized by McKnight etal. (2002a) and by Chopra and Wallace. (2003).Predictability is defined as the degree to which thetrustee’s behaviour conforms to expectations (Chopraand Wallace 2003). Expectations can be based on thetrustee’s history or role. It is suggested to be thefourth trusting belief (Chopra and Wallace 2003,McKnight et al. 1998).

Based on the information about the eBay feedbackscore and prior research on trust and reputation, weconceptualize the feedback score of an e-store on eBay asa proxy of online customers’ trusting belief in the e-store’s trustworthiness. While online customers’ shortcomments on an eBay member (a part of feedbackratings) contain their assessments on the eBay member’svarious dimensions of trustworthiness (competence,benevolence and integrity), feedback score, as a singlenumber calculated from the accumulated feedbackratings, represents online customers’ overall trustingbelief in that eBay member’s trustworthiness. It is asummary of the total number of satisfied tradingpartners minus dissatisfied trading partners for a memberon eBay.

Particularly, feedback score is related to the predict-ability trusting belief. Feedback score represents an eBaymember’s trading history, i.e. how well this eBaymember behaved in prior transactions. Such a historyis revealed to the new customers through the presenta-tion of feedback score. New customers can developan understanding of the eBay member’s (an e-store inthis paper) behaviours in the past, based on which thenew customers can develop expectations or predictionson this eBay member’s future behaviours (Luhmann1979).

Feedback score affects the success of e-stores

Our research model is shown in Figure 1. Based on priorliterature on trust and culture in electronic commerce,the current study examines the central role of feedbackscore in the success of e-stores on eBay, defined as thenumber of successful sales conducted by an e-storewithin a certain period of time. It also predicts thatcustomers’ perceptions of e-stores (i.e. culture, historyand size) will significantly affect feedback score. Ourresearch model does not examine the impact of producttypes. To control the impact of product types, weexamine the sales within a single product category (a firstsample for consumer electronics, and a second samplefor health and beauty products) across different e-stores.

Trust theories provide the conceptual foundations tounderstand the effect of feedback score on the success ofe-stores. When a new customer is considering whetherto buy from an e-store at eBay, the feedback score isclearly shown besides the name of this eBay member.The higher this feedback score, the higher priorcustomers’ trust in this e-store is. The new customerwill transfer trust from prior customers through thefeedback score, based on the theory of transferred trust.Transferred trust means ‘‘trust in trust’’, which assertsthat the level of trust that others place in a trustee (an e-store in this case) can serve as a rational basis for trust(Chopra and Wallace 2003, Doney and Cannon 1997,Luhmann 1979). Similarly, a new customer can developa high level of trust in an e-store based through thereputation categorization process (McKnight et al.1998). A higher feedback score indicates a higherreputation for an e-store. According to the reputationcategorization process, those with good reputations arecategorized as trustworthy individuals; thus new custo-mers will quickly develop a higher level of initial trustingbeliefs about that individual (the e-store in this case),even without first-hand knowledge.

A customer can also develop a higher level of trust inan e-store with higher feedback scores through calcula-tive-based trust (Doney and Cannon 1997, Gefen et al.2003a). According to the calculative-based trust para-digm, trust can be shaped by rational assessments of thecosts and benefits of another party cheating or cooperat-ing in the relationship; Trust in this view is derived froman economic analysis occurring in ongoing relationships,namely that it is not worthwhile for the trustee to engagein opportunistic behaviour if the costs of being caughtoutweigh the benefits of cheating, then trust iswarranted since cheating is not in the best interest ofthe trustee (Coleman 1990, Gefen et al. 2003a).According to this theory, an e-store with a higherfeedback score has a higher cost for cheating than an e-store with a lower feedback score, thus the e-store with ahigher feedback score is more trustworthy than the e-store with a lower feedback score.

Electronic Markets Vol. 18 No 2 189


Particularly, feedback score is related to the predict-ability trusting belief, as we discussed above. A newcustomer will develop trust in an e-store with highfeedback score through prediction process (Doney andCannon 1997), also called knowledge-based trust(Gefen et al. 2003a). Trust is created in this processwhen the truster’s knowledge about the other partyallows it to predict the behaviour of the otherparty (Doney and Cannon 1997). A new customercan learn from an e-store’s high feedback scorethat the store behaved well in prior trading; thus thenew customer can develop trust in this store based onthe prediction that that the e-store will continue tooperate in the same fashion as it has previously.In addition, prediction is related to consistency(McKnight et al. 2002a). eBay also show the percentageof positive feedback ratings in all the feedbackratings. When this percentage is higher, and whenthe feedback score is higher, the new customer canperceive a higher consistency of the e-store’s pastbehaviour, thus the new customer’s confidence in hisor her expectation in the e-store will be higher, leadingto higher level of trusting belief.

When a higher feedback score elicits higher trustingbeliefs from a new customer, higher trusting beliefs willlead to higher success of the e-store, because thecustomer’s buying from the e-store is a trustingbehaviour. Online customers’ trusting beliefs are sig-nificantly and positively associated with trusting beha-viours or trusting behaviour intentions (e.g. buying froman e-store), according to the ‘Theory of reasoned action’(Fishbein and Ajzen 1975) and many empirical studies(Ba and Pavlou 2002, Gefen et al. 2003b, McKnight etal. 2002b). Therefore, we expect:

Hypothesis 1: Higher feedback score will generate higher sales for

an e-store.

CULTURE, HISTORY AND SIZE AFFECT FEEDBACKSCORES

Each eBay customer can easily learn about an eBay e-store through the e-store’s website and member profile.From the e-store’s website and its owner’s member

Figure 1. Research model - conceptual



profile, the customer can learn the e-store’s culture, sizeand history. On each e-store’s website, a customer canperceive the size of the e-store (e.g. 2367 items found inall categories), in addition to a list of products for sale.Each e-store’s member profile displays the e-store’slocation (e.g. United States) and history (e.g. Membersince: Dec-26-98), and detailed feedback ratings. Thecustomer can perceive the e-store’s culture based on thelocation of the e-store.

Culture (individualism and uncertainty avoidance)affects feedback scores

Most definitions of culture currently used are modifica-tions of Tylor’s (1871) definition of the concept as thatincluding knowledge, belief, morals, law, customs andany other habits imbedded as a social member. Theterm, however, has been criticized as being conceptuallyweak (Child 1981). More recently, culture has beenconceptualized as the shared attitudes and values of themembers of a country (O’Grady and Lane 1996) andthese attitudes and values may be more helpful inunderstanding behaviours than sociodemographic vari-ables such as age, education and income (Deng et al.2006). In continuing research, the definition andusability of national culture, is under debate and openfor redefinition (House 2004: 266, Srite and Karahanna2006: 265). Culture is an important factor in buyer-seller relationships in international markets becausecultural values and norms can significantly impactconsumer preferences (Johnson-Page and Thatcher2001). Culture is an important variable adding to thecomplexity of international business but can also be asource of conflict rather than synergy (Hofstede et al.1990). Understanding the impact of culture can give aseller or a buyer an advantage which may translate intomore successful results.

Hofstede’s seminal works (1980, 1983) develop awell-defined, empirically based, and widely cited typol-ogy to describe cultures. Hofstede’s model of nationalcultural differences includes five cultural dimensions(see: http://www.geert-hofstede.com/). (1) PowerDistance Index (PDI) is the extent to which the lesspowerful members of organizations and institutions (likethe family) accept and expect that power is distributedunequally. (2) Individualism (IDV) on the one side,versus its opposite, collectivism, is the degree to whichindividuals are integrated into groups. Individualism-Collectivism is the extent to which the self-concept ofindividuals in a country revolve around that of anindividual or that of a member of a group (Hofstede1980). (3) Masculinity (MAS) versus its opposite;Femininity, refers to the distribution of roles betweenthe genders. (4) Uncertainty Avoidance Index (UAI)deals with a society’s tolerance for uncertainty andambiguity; it ultimately refers to man’s search for truth.

(5) Long-Term Orientation (LTO) versus Short-TermOrientation: values associated with Long TermOrientation are thrift and perseverance; values associatedwith Short Term Orientation are respect for tradition,fulfilling social obligations and protecting one’s ‘face’.

The current study uses Hofstede’s index scores for theIndividualism-Collectivism and Uncertainty Avoidancedimensions to represent an eBay e-store’s culture.Individualism-Collectivism and Uncertainty Avoidanceare considered to be important predictors of Internetshopping rates (Lim et al. 2004). There also appears tobe a strong positive correlation between Individualismand Internet usage, and a strong negative correlationbetween high Uncertainty Avoidance and Internet usage(Ess and Sudweeks 2005). These correlations have beennoted in numerous earlier studies conducted on a globalscale (cf. Barnett and Sung 2005; Maitland and Bauer2001). We choose the Individualism-Collectivismdimension because it is the most significant culturaldimension explaining differences historically and cross-culturally (Triandis 2001). Individualism-Collectivism asa cultural typology has been extensively replicated andsupported in prior research focused on cultural differ-ences (Triandis and Suh 2002). In addition, we choosethe Uncertainty Avoidance dimension because webelieve that it is the most trust-relevant dimensionamong Hofstede’s five dimensions of culture.Uncertainty avoidance is directly related to people’sbeliefs and attitude on risk and trust.

Individualism affects feedback scores. eBay customersand eBay e-stores are viewed as out-group members,because eBay customers are anonymous and new to an e-store, and an e-store is generally unknown andanonymous to the customers. These entities potentiallycome from different cultures worldwide. The customerwho gives feedback to the e-store has one and only onetransaction with the store, because an eBay store’sfeedback score only includes a customer’s first feedback,even though this customer may be a repeat customer.Thus when a customer gives an e-store feedback, there isa very short transaction history between them.

In dealing with out-members, collectivists differenti-ate between in-group and out-group members morethan individualists (Iyengar et al. 1999, Triandis 1972).Individualists are more cooperative with out-groupmembers than collectivists (Yamagishi 1988, Yamagishiet al. 1998). Thus individualist cultures exhibit higherInternet shopping rates than collectivist cultures (Lim etal. 2004). Individualists emphasize efficiency anddirectness (Grimm et al. 1999, Triandis 2001), andtend to stress consistency and stability of attitudes(Iyengar et al. 1999). In contrast, collectivists behavemore favourably towards in-group members rather thanout-group members (Elahee et al. 2002). It is some-times considered acceptable in collectivistic societies toexploit and deceive out-group members, whereas it



would not be socially appropriate to do so to in-groupmembers (Triandis and Suh 2002). Therefore, we expectthat e-stores in individualist cultures will behave morefair and efficiently towards the anonymous eBaycustomers (out-group members), which will lead tohigher feedback scores, than e-stores in collectivismculture.

In addition, customers give a feedback score based ontheir first transaction with an e-store. With only onetransaction, it is easier for customers in individualismcultures to develop trust (thus higher feedback scores)than customers in collectivism cultures. Relative tocounterparts in collective cultures, people in individualistcultures are more likely to form trust via a calculativeprocess (trusting based on cost/reward calculation) or acapability process (assessing a target’s ability to fulfil hisor her promises); however, relative to counterparts inindividualist cultures, people in collectivism cultures aremore likely to form trust via a prediction process(predicting a target’s behaviours based on a history),an intentionality process (evaluating a target’s motiva-tions) (Doney et al. 1998). One transaction may beenough to enable a capability process and a calculativeprocess for individualist people, but multiple transac-tions are generally needed to enable a prediction processand an intentionality process for collectivism people.Therefore:

Hypothesis 2: The feedback score of an e-store in anindividualism culture will be higher than the feedback score incollectivism culture.

Uncertainty avoidance affects feedback scores. Un-certainty Avoidance Index (UAI) deals with a society’stolerance for uncertainty and ambiguity (Hofstede 2001).It indicates to what extent a culture programmes itsmembers to feel either uncomfortable or comfortable inunstructured situations. Uncertainty avoiding cultures (i.e.high uncertainty avoidance) try to minimize the possibilityof unstructured situations by strict laws and rules, etc. Incontrast, uncertainty accepting cultures (i.e. low uncer-tainty avoidance) are more tolerant for unstructured,unclear or unpredictable situations. Uncertainty is anecessary condition of trust (Doney and Cannon 1997,Luhmann 1979), and uncertainty avoidance reflectspeople’s attitude to uncertainty and risk, thus uncertaintyavoidance will affect the perceived trustworthiness of e-stores on eBay in different cultures.

An e-store in uncertainty accepting cultures may gainhigher feedback scores than an e-store in uncertaintyavoiding cultures, because compared to people inuncertainty avoiding culture, the people in uncertaintyaccepting cultures are more likely to hold an overallhigher regard of other people, and they are more willingto accept not only familiar but also unfamiliar risks(Hofstede 2001). Thus they are more likely to have

higher trust (giving higher feedback scores) in these e-stores in eBay, which are new, unknown and unstruc-tured, compared to traditionally branded and physicallyexisting stores.

In addition, eBay customers give an e-store feedbackscores based on only one transaction. It is easier forcustomers in uncertainty accepting cultures to developtrust (giving higher feedback scores) than customers inuncertainty avoiding cultures. Relative to counterparts inuncertainty avoiding cultures, customers in uncertaintyaccepting cultures are more likely to form trust viacalculative process, because the level of uncertainty (orrisk) a culture considers tolerable influences economicrationality and consequently, relative to counterparts inuncertainty accepting cultures, people in uncertaintyavoiding cultures are more likely to form trust via aprediction process and an intentionality process (Doneyet al. 1998). However, it usually takes multipletransactions before people in uncertainty avoidingcultures are able to develop trust through predictionprocesses or intentionality processes. Therefore, thefeedback score of an e-store in uncertainty acceptingcultures can be higher because the people in uncertaintyavoiding cultures do not have enough time andopportunities to fully develop their trust in an e-store.Therefore:

Hypothesis 3: The feedback score of an e-store in uncertaintyavoiding culture will be lower than the feedback score inuncertainty accepting culture.

Size affects feedback scores

Prior research finds that non-price cues are important inthe formation of store images (Kshetri 2002).Prospective buyers are willing to pay higher prices whennon-price cues such as extra services, late hours,expensive interiors, etc. are present in a store (Brown1969). Literature suggests that consumers evaluate e-store alternatives on a number of attributes which aredefined differently from those used to evaluate brick-and-mortar stores (Lim and Dubinsky 2004).

One cue about an eBay store is its size. On every eBaye-store’s website, customers can easily see a line at thetop of product lists and pictures: ‘‘[number of] itemsfound in All Categories.’’ The number can be as large asthousands or as small as a single digit. This numberrefers to the numbers of items available from an e-store,thus representing the size of the e-store.

As discussed above, the underlying concept of feed-back score is online customer’s trusting belief in an e-store. The trust literature suggests that buyers use size asa signal that a seller can be trusted. In a traditionalmarket, a store’s size assists consumers in forming theirimpressions regarding the store’s trustworthiness as well



as their willingness to patronize the store (Andersen2001, Chow and Holden 1997). In electronic markets,consumers recognize differences in size among Internetstores; perceived size significantly influences consumers’assessments of an e-store’s trustworthiness and theirperceptions of risk regarding doing business with that e-store (Jarvenpaa et al. 2000).

Our research model predicts that an e-store’s size willinfluence the e-store’s feedback score (customer trust inthe e-store) for several reasons. First, the perception oflarge organizational size implies that other buyers trustthe organization and conduct business successfully withit. Second, large size also signals that the firm shouldhave the necessary expertise and resources for supportsystems such as customer and technical services; theexistence of these systems encourages trust (Doney andCannon 1997). Third, large sellers have more resourcesinvested in their businesses and hence are perceived by abuyer to have more to lose than smaller firms by actingin an untrustworthy way. According to the calculative-based trust prospective (Coleman 1990, Gefen et al.2003a), more to lose means higher possibility of not-cheating. Therefore, a bigger size e-store is moretrustworthy than a smaller-size e-store.

Hypothesis 4: An e-store’s perceived size will increase its

feedback score.

HISTORY AFFECTS FEEDBACK SCORES

In every eBay member’s profile, customers can see a linesuch as ‘‘Member since: Mar-11-03,’’ thus customersknow how long this member has operated on eBay.Customer can also see the ID history of the member.

It is possible that the temporal history will affect an e-store’s feedback score since temporal history may affectcustomer trust in that e-store. It takes time for an e-storeto accumulate a trading history. Such a history containstrust-related knowledge based on which a customer canunderstand an e-store and its owner, thus judging itscompetence, integrity, benevolence and predictability(i.e. trustworthiness). With a long history, customers canjudge whether an e-store’s particular behaviour isattributed to the store’s internal characteristics or is atemporary trick. Second, a long history of an e-storesignals competence in e-commerce. It is relatively easy tostart a new Internet retail web space but it takes effort tobecome an e-store with a history. Thus a store’s longhistory is more valuable than a short history. From theperspective of calculative-based trust (Coleman 1990,Gefen et al. 2003a), the more valuable the history is, theless likely that an e-store will engage in a distrustbehaviour. In addition, store reputation, includingcompany history, contributes to a customer’s comfort

level in dealing with a firm through the Internet (Lohseand Spiller 1998). Therefore,

Hypothesis 5: An e-store’s temporal history will increase itsfeedback score.

METHODOLOGY

Data collection and analysis

Our study uses field data to examine whether a goodfeedback profile leads to success, as measured by salesvolume, in real auction settings. As antecedents, theeffects of culture, store size and store temporal historyare evaluated. Table 1 displays the measures used tooperationalize our constructs.

As there is no direct measure of trust or success in theeBay data, the research model had to be modified.Instead of testing the relationship between trust andsuccess, we tested a direct relationship between feedbackprofiles and sales volume and the antecedent effects oflocation of seller, store size and store history on thatrelationship.

Several strategies exist to increase Internet sales foronline stores: such strategies include displaying custo-mer endorsements (e.g., eBay.com’s Feedback Forum),website endorsements (e.g., the Yahoo portal), andincorporating seals of approval (e.g., WebTrust). eBay’sFeedback Forum is one implementation of the strategyinviting existing customers to provide comments abouttheir buying and selling experiences to other potentialcustomers. The forum is a way an e-store can provideinformation, or signals, to differentiate it from othersellers. The incentive to the e-store is that by collectingand disseminating information about the store’s transac-tion history, the feedback profile provides proof of the e-store’s reputation. Within eBay, buyers can assess thepast behaviour of all available sellers, and they are able tochoose. Buyers will trust sellers who have good feedbackprofiles and this not only provides a signal of trust-worthiness to buyers, but also indicates that sellers haveincentives to maintain good feedback profiles (Ba andPavlou 2002).

The relationship between trust, i.e., the analysis of theseller’s feedback profile, and intention, i.e., a buyer’sconfidence in the seller, has been characterized as one ofdiminishing marginal utility (Thaler 1985). In otherwords, the incremental impact of a given increase inreputation on intention to buy decreases as thereputation goes up. The relationship is not linear.Hence our empirical dependent variable is the logarithmof the net number of positive and negative ratings,similar to that used by Ba and Pavlou (2002).

Determining the number of successful auctions withina 30-day period developed the measure of success of the



e-stores. Given the insufficiency of empirical assessmentof e-store performance measures and because mostInternet retailers were not profitable from an accountingpoint of view (Mahajan and Srinivasan 2002), we wereprecluded from using traditional performance metrics,such as return on assets, return on sales, and so on,which are used to examine relative business performance(Palepu et al. 1996). We find further support in that thedifferences among industries suggest that the character-istics of the industry in which the firm operates be takeninto consideration when measuring performance(Chrisman et al. 1998). Moreover, it is purported thatwithin e-commerce industries, such as on-line auctions,there are particular goals that stem from the conditionsand characteristics unique to the industry (Mahajan andSrinivasan 2002). For example, using revenues or thenumber of employees as performance measures isespecially relevant to small and new businesses thatfrequently do not have profit histories and are notexpected to show profitability during the first years ofexistence. Moreover, revenues are considered a validmeasure for presenting overall performance, especially inrelatively homogenous samples, when firms from aparticular industry operate under very similar cost

structures (Haber and Reichel 2005). Thus we modifiedthe hypotheses and use the numbers of e-storecompleted sales as a proxy for success.

In addition, it is appropriate to stress that feedbackscore is different and independent from success (i.e., thenumber of successful transactions in a given timeperiod). Feedback score is determined from the timethe e-store became an eBay member, while success refersto the number of transactions within a one monthperiod – the first sample in 2005, the second in 2007.Feedback scores exist before the success metric isdetermined. Additionally, feedback score is calculatedas the number of satisfied eBay members minus thenumber of dissatisfied eBay members; it does not includerepeated transactions from the same member – eachmember can only contribute to a feedback score once.However, success (i.e., the number of transactions) cancontain repeated transactions from the same eBaymembers and not every one who participated intransactions will give feedback ratings. In reality, lessthan 50% buyers and sellers gives feedback as evidenceby seller forums such as: http://www.amazonseller-community.com/forums/thread.jspa?messageID51143431and#1143431.

Table 1. Measures and metrics were developed for our constructs based on prior research and publications

Construct Measure Metric References

Culture Location of seller Located in an individualistic versus

collectivistic culture per Hofstede’s

cultural dimensions

Hofstede (1980, 2001)

Hofstede et al. (1990)

Lim et al. (2004)

Triandis (1995)

Triandis and Suh (2002)

Located in an low uncertainty avoidance

culture versus a high uncertainty

avoidance culture per Hofstede’s

cultural dimensions

Korgaonkar and Wolin (1999)

Donthu and Garcia (1999)

De Mooij and Hofstede (2002)

Straughan and Albers-Miler (2001)

Doney et al. (1998)

e-store History How long has the Internet

merchant been an eBay e-store

Number of days as an eBay e-store –

at the end of the sample period

Kshetri (2002)

Lim and Dubinsky (2004)

Lohse and Spillar (1998)

e-store Size What is the number of items for

sale in the product category

Number of items for sale in the product

category at the end of the sample period

Jarvenpaa et al. (2000)

Andersen (2001)

Chow and Holden (1997)

Doney and Cannon (1997)

Trust eBay Feedback Rating Overall number of positive ratings minus

the number of negative ratings

Ba and Pavlou (2002)

McKnight et al. (2002)

Gefen et al. (2003)

Jarvenpaa et al. (2000)

Macintosh and Lockshin (1997)

Thaler (1985)

Success Sales transactions Number of successful transactions during

the 30-day sample period

Mahajan and Srinivasan (2002)

Haber and Reichel (2005)

Westhead et al. (2001)

Chrisman et al. (1998)



Data were collected from eBay, which encompassestransactions on local, national, and international bases.Its feedback forum allows buyers to leave commentsabout sellers with whom they have transacted, ratingthem as positive, negative, or neutral and is readilyavailable to any prospective buyer. For the period from 3November 2005 to 2 December 2005 we collected datafrom 84 eBay e-store affiliates consisting of 65,067completed auctions for the category of consumerelectronics. Data included the location of the seller,seller history in days, the number of items for sale, thefeedback profile of each seller, and the number ofsuccessfully completed transactions during the sampleperiod. Sellers were located in fourteen countries. Thecountry location of the Internet portal was alsocollected. Figures 2 to 5 contain Internet print screensrelevant to the collection of the data. Data collection wasfacilitated by the uniformity of eBay’s webpages acrossall portals. We accessed eBay’s international websitesfrom hyperlinks at the bottom of most eBay main webpages, e.g., www.ebay.com. From these main pages, weselected Consumer Electronics as the product categoryand collected the data from the ‘‘eBay Stores’’ presentedin this category. eBay e-stores were deemed to beinternational if 1) worldwide shipping was available fromthe e-store, and 2) at least one transaction during thesample time frame was conducted by a buyer whose eBayidentification located him(her) outside of the eBayseller’s country.

In marketing research as in the behavioural sciences,there is growing concern of the degree to which the

research can be validated and its findings generalized(Permut 1976). Validation, in this sense, is concernedwith the ability of a study to bear replication. Theconcern for replicability also involves consideration ofreliability, and reliability is defended most rigorously bycross-validation (Smith 1970). Pertinent to this study,we collect a second sample for the product category ofhealth and beauty products for the period 1 – 31 March2007. This sample represented 148,273 completedauctions from 346 eBay e-stores across 22 countries(including the 14 countries covered in the first sample).The second sample allows for cross-validation in thisstudy to determine the reliability of data for the model –by examining a different product type, to determinewhether socio-cultural changes over time have occurredin the dynamic electronic marketplace – by obtaining amore recent sample, and to aid in the determination ofthe generality of obtained findings.

Tables 2 and 3 show the descriptive statistics of thelocation, history and feedback ratings of the twosamples.

Data analysis

We use OLS regression to analyze the data. Prior toperforming the analyses, we ensured that the basicassumptions of regression analysis were satisfied. To testthe relationships between size of the store, storehistory and culture, as independent variables, and trustin the e-store, as the dependent variable, multivariate

Figure 2. eBay e-stores listed as a selection in the left margin of eBay’s Ireland portal for the category of Consumer Electronics. eBay’suniformity across its portals facilitated ease of data collection



regression analysis was performed. To test the relation-ship between feedback ratings and e-store success,multivariate regression analysis was again used, withthe independent variable being the logarithm of the netnumber of positive and negative ratings. The dependentvariable was the success of the e-store as measured by thevolume of store sales. All regressions have high toleranceand low values for VIF.

RESULTS

Table 4 presents the regression results between trust(feedback profile) and culture (individualism versuscollectivism and uncertainty avoidance), size (numberof items for sale), and history (number of days as an eBaye-store). Regression analysis found significant correla-tion between culture, size, and history and trust. This

Figure 4. eBay Brazilian portal. Note consistency in Web site presentation with eBay US

Figure 3. eBay seller feedback profile for an e-store located in Hong Kong and featured in the eBay Ireland portal. eBay member profiles arealso uniform throughout all eBay portals



provides evidence that a positive buyer feedback profilewill exist and have a greater positive impact in cultureswith higher individualism versus in cultures with highercollectivism and that buyers do take into accountreputation indicators such as a seller’s size and history.Therefore field data provide support for our theoreticalarguments that culture may account for buyer trust in anInternet store and that positive indicators of size andhistory may induce higher trust, confirming Hypothesis1, Hypothesis 2, Hypothesis 3 and Hypothesis 4 in theresearch model. It was expected that higher relativefeedback ratings would lead to higher sales volume. Theregression results indicate that store sales are significantlyinfluenced by trust in that store, confirming Hypothesis5 in our research model. Table 5 summarizes the keyfindings of the study. The data, cross-validating thefeedback score and its antecedents in both productcategories, generally support the proposition that trustand trust beliefs differ across cultures, and thus may be avariable aspect of e-commerce.

DISCUSSION

Theoretical implications

This study was carried out in a real-life environment, inwhich actual buyers selected e-stores and completedpurchase transactions. Comments provided by previousbuyers, contained in the e-stores’ feedback profiles, andthe locations of sellers were readily available for allprospective buyers to examine. This study examined thenumber of completed purchase transactions as compared

to previous studies (Ba and Pavlou 2002; Gefen et al.,2003b; Jarvenpaa et al. 2000), where buyer intention orbelief was studied. Moreover, our study attempts tomake theoretical inferences about the role of culture inthe global competitiveness of Internet businesses.

Our results confirm and extend previous researchfindings. The factors of individualism-collectivism anduncertainty avoidance were shown to be significant inexplaining differences across cultures. Previous literaturesuggests that consumers evaluate e-store alternatives ona number of attributes that are defined differently fromthose used to evaluate brick-and-mortar stores. Thisstudy shows that store reputation, including companyhistory, contributes to a customer’s comfort level indealing with a firm through the Internet. This study alsoconfirms the importance of store size and its ability toevoke consumer trust.

Practical and policy implications

Our results clearly point to a number of practicalimplications for successful Internet marketing strategiesusing eBay as the electronic market platform. First, ourstudy has implications for the structure and operation ofeBay.com’s Feedback Forum. It seems better to separatea member’s feedback score as a buyer versus his or herfeedback score when this eBay member participates as aseller. The two feedback scores are different in theirnature. For a seller, the buyer’s feedback score as a buyeris not very important, because the risk for the seller issmall. For example, the seller can wait to get paymentbefore the seller sends the goods to the buyer. However,

Figure 5. eBay seller feedback profile from Brazilian portal



for a buyer, the seller’s feedback score as a seller is veryimportant. Although mediated, the buyer still has to paybefore he or she receives the goods. In addition, if aneBay member wants to fabricate a more positive feed-back score, it is much easier for this member to create (inother words, purchase) a feedback score as a buyer bybuying multiple cheap items. However, it is much moredifficult to earn feedback score as a seller by successfullyselling many goods.

Overall, the Feedback Ratings are numerically highlypositive. Some members may be afraid to give negativefeedback for fear that the recipient member will givenegative feedback ratings as revenge – this negativeexchange of feedback was observed in both samples.Cultures differ in their relationship between conflict andsatisfaction (Griffith et al. 2000). As a result, the level ofconflict reported by a business partner may be reduced.This is supported by earlier research which finds thatmembers of a cultural group (i.e., collectivist, largepower distance, strong uncertainty avoidance) will oftenavoid conflict in order to preserve a relationship(Moghaddan et al. 1993). It may be as simple asadopting a policy to not post one transacting member’sfeedback until both transacting members have com-pleted their feedback ratings, or until a deadline ofgiving feedback rating has past for both transactingmembers. In sum, eBay may be able to improve itselectronic market efficiency (e.g., overcome informationasymmetry, lack of trust) by implementing culturallycognizant procedures.

Second, our results make evident several tactics foreBay e-stores. It appears that e-stores in individualisticcultures can earn higher feedback scores. It seems tobehove an e-store to open a branch (i.e., become amember and e-store) in a county with individualisticculture. Of course, high feedback scores and culturaldimensions can have alternative implications: collectivis-tic e-stores should learn from individualistic e-storesabout how to treat anonymous buyers (out-groupmembers). Equally as important as culture in assessingand predicting buyer behaviour in eBay e-stores are storeattributes. It has been shown here that store size and theamount of time an e-store has been in business mayaffect the level of trust a buyer will have in that store.Customers, it appears, equate size and store history withtrustworthiness. This has practical implications foronline retailers using or planning to use web portalssuch as eBay to launch their own e-stores. Following ourresults, maintaining an accessible store history andincreasing perceived size might be advantageous strate-gies for e-store success. In terms of size, e-stores mayconsider combining with other e-stores or listingindividual items separately, rather than in groups. Ashistory is also valuable, these stores should considercombining with long tenured e-stores.

Results of the study highlight the need for managersto systematically develop the appeal and trustworthinessof their websites. Because they engender trust in buyers,adding culturally specific features such as localizing thefeedback mechanism may be the most importantimprovements an e-store can make. The study’s findingsunderline the importance for managers to maintain acompetitive advantage by evoking positive feelingsduring the shopping experience. To compete globallyon the Internet, merchants designing for only onemarket are going to have to change their way of doing

Table 2. Descriptive statistics of the location of the 84 e-stores in

Sample 1 and the 346 e-stores in Sample 2. Sellers were located

in 22 countries

Location of Seller Frequency %

Sample 1

Australia 4 4.8

Brazil 5 6.0

Canada 4 4.8

China 8 9.5

France 5 6.0

Germany 3 3.6

Hong Kong 5 6.0

India 5 6.0

Italy 10 11.9

Mexico 4 4.8

Spain 5 6.0

Taiwan 2 2.4

UK 5 6.0

US 19 22.6

Total 84 100

Sample 2

Argentina 7 2.0

Australia 14 4.0

Austria 10 2.9

Belgium 13 3.8

Brazil 5 1.4

Canada 4 1.2

France 16 4.6

Germany 38 11.0

Hong Kong 44 12.7

India 1 0.3

Italy 23 6.6

Japan 2 0.6

Mexico 9 2.6

Netherlands 17 4.9

Poland 4 1.2

Singapore 4 1.2

Spain 16 4.6

Switzerland 2 0.6

Taiwan 1 0.3

Thailand 1 0.3

UK 46 13.3

US 69 19.9

Total 346 100



business; adopting strategies that capitalize on theunique cultural preferences for particular marketsappears to be a better approach.

Finally, the results of this study may give policymakersa better understanding of the role culture, store size andstore history play in shaping online consumer behaviour.Differences in pre- and post-purchase behaviour, secur-ity and privacy concerns, and methods of disputeresolution as well as values, norms, and traditions mayimpact the needs and demands of increasingly divergentonline consumer markets. For those responsible for theprotection of online businesses and consumers as well as

the facilitation of information technology, understand-ing the determinants of eBay e-store success may providea clearer picture of those factors affecting the success ofonline retailers in general.

Government initiatives, suited to the local culture, canbe used in promoting electronic commerce. Thegeographic landscape of e-commerce is changing.Foreign consumers are increasingly demanding localcontent (Gibbs et al. 2003), customized websites,information, and products particular to their individualinterests (Ha 2004). Once lacking the necessarytechnological infrastructure needed to connect their

Table 3. Data included the location of the seller in terms of Hofstede I-C and UA indices, seller history in days, the number of items for

sale, the feedback profile of each seller, and the number of successfully completed transactions during the sample period

A. Descriptive statistics of the individualism-collectivism, uncertainty avoidance, size, history, feedback ratings, and items sold of the 84 e-

stores in Sample 1.

N Minimum Maximum Mean Std. deviation

I-C cultural index 84 17 91 63.02 26.881

UAI cultural index 84 29 86 55.73 19.844

Size 84 43 6889 1018.49 1339.339

History 84 160 2932 1069.92 568.371

Feedback 84 67 197085 13679.11 32284.047

Items Sold 84 14 8593 774.61 1511.078

Correlations

Pearson Correlation Size History I-C cultural index UA cultural index

Size 1.0

History .069 1.0

I-C cultural index .043 .361 1.0

UA cultural index 2.324 2.176 .022 1.0

B. Descriptive statistics of the individualism-collectivism, uncertainty avoidance, size, history, feedback ratings, and items sold of the 346 e-

stores in Sample 2.

N Minimum Maximum Mean Std. deviation

I-C cultural index 346 17 91 67.82 24.055

UAI cultural index 346 8 94 55.82 20.970

Size 346 2 32409 1005.65 2550.444

History 346 7 3371 1032.02 651.828

Feedback 346 3 113098 4802.26 11071.640

Items sold 346 0 17847 428.53 1139.488

Correlations

Pearson Correlation Size History I-C cultural index UA cultural index

Size 1.0

History .087 1.0

I-C cultural index .157 .289 1.0

UA cultural index 2.205 2.176 2.025 1.0



economies to the World Wide Web, developing coun-tries are making structural improvements, often bypass-ing obsolete technologies still heavily used in countrieslike the United States and Canada (Johnson-Page andThatcher 2001). The policy key for increasing thediffusion and acceptance of electronic markets as a wayof doing business is to include culture in a significantway. For example, in high uncertainty avoidancecultures, perceived uncertainty may be the impedimentto e-commerce. Policies aimed at protecting the interestsof transacting parties or the creation of secure transac-tion infrastructures would be effective. Similarly, forcollectivist cultures, creating an aura of in-groupacceptance of e-commerce, may increase its adoptionin both developed and developing countries.

Limitations and future research

First, this study examines the effect of culture asdetermined by the location of the seller, i.e., the e-store.A relationship between the seller’s culture and trust inthat seller was hypothesized and supported in this study.Alternative approaches could examine the relationbetween buyer’s culture and trust in the seller or theeffects of cultural alignment between both buyer and

seller. Replicating the study using different and largergroups of subjects could only assess whether the resultsare applicable to other Internet users.

Second, as in Ba and Pavlou’s (2002) study, alimitation was the use of secondary data, which did notallow for the measurement of trust perceptions.Although elements of the written comments thataccompanied sellers’ ratings were collected, e.g., loca-tion of buyer, dispute resolution, etc., the writtencomments were not evaluated and used in assessingthe degree of buyer trust. Buyers’ comments offernotable information that cannot be captured bysimple ratings (Ba and Pavlou 2002). Analysis ofwritten comments may reveal new information aboutthe role of feedback mechanisms. Future research couldanalyze the role of written comments in determiningtrust and the relative importance of buyer and sellerlocations.

Future studies can extend this research in additionalways. For example, research that compares the relativeeffectiveness of different web strategies across cultures isimportant because each trust-building mechanism, suchas a web strategy, can come with a cost to the userorganizations. Results of such research could thusinform organizations operating in different cultures ofhow their websites could be customized for maximum

Table 4. Regression results (1) between trust (feedback profile) and cultural dimensions (IC, UA), size (number of items for sale), and

history (number of days as a registered eBay e-store) and regression results (2) between trust (feedback profile) and sales

Sample 1 Sample 2

Regression 1 R2 (adjusted) F-value Bi t R2 (adjusted) F-value Bi t

DV IV

Log(P-N) .403 15.325a .488 83.236a

LS-IC .259 2.847a .290 7.454a

LS-UA 2.191 22.113b 2.338 28.519a

HistDay .381 4.122a .335 8.282a

SizeNoAll .193 2.169b .256 5.816a

Regression 2

ItmSld1M

Log(P-N) .488 81.975a .363 172.318a

Collinearity Statistics

Sample 1 Sample 2

Model 2 Tolerance VIF Tolerance VIF

LS-IC .849 1.178 .969 1.032

LS-UA .859 1.165 .906 1.103

HistDay .822 1.216 .886 1.128

SizeNoAll .888 1.126 .878 1.139

P-N5 positive ratings – negative ratings; ap,0.01, bp,0.05



effectiveness, while due consideration could be given tominimize the associated costs.

While our research examined the success of e-stores,measured by the number of successful transactionscompleted on eBay, many relevant refinements andextensions beckon. Success can be measured by a highpercentage of international transactions or high pricepremiums. Furthermore, what antecedents influence thesuccess of these e-stores? The antecedents examined inthis research include a store’s feedback profile, size andlocation. Extensions include the examination of producttype, product price, payment method and delivery speed.An alternative approach would study the effects of thenew or modified trust-building methods available oneBay: the PayPal Buyer Protection Policy, the BuyerCompliant Policy, and eBay’s Standard PurchaseProtection Program, together with the FeedbackForum mechanism.

CONCLUSION

This research has empirically investigated the impact ofculture on the propensity for shopping on the Internet.It demonstrates the need to consider specific cultural

dimensions, such as individualism versus collectivism anduncertainty avoidance, as well as to maintain anddisseminate positive store attributes, in the design ofwebsites and web strategies to build trust. This may beparticularly important for new or relatively new Internetstores, but in such a competitive environment as the e-business market, well-established stores should alsorecognize the importance of being culturally sensitive.

This research shows that the seller feedback profilestrategy is particularly effective for collectivistic con-sumers. This research highlights the importance of beingculturally sensitive. Only by addressing the culturalneeds of consumers can Internet merchants achieve theiraims of participating in an increasingly global market-place.

ReferencesAhlstrom, D., Foley, S., Young, M. N. and Chan, E. S. (2005)

‘Human Resource Strategies in Post-WTO China’,

Thunderbird International Business Review 47(3): 263–85.

Al-Qirim, N. (2005) ‘An Empirical Investigation of an e-

Commerce Adoption–Capability Model in Small Businesses

in New Zealand’, Electronic Markets 15(4): 418–37.

Table 5. The key findings of the study vis-a-vis hypotheses

Construct Measure Hypothesis

Expected

Relationship Findings Discussion

Culture Location of seller:

individualism versus

collectivism

Positive customer endorsements

will have a greater impact on

trust in cultures higher on

collectivism versus in cultures

higher on individualism.

Positive Supported

Location of seller: Low

versus high uncertainty

avoidance

Positive customer endorsements

will have a greater impact on

trust in cultures lower on

uncertainty avoidance versus in

cultures higher on uncertainty

avoidance.

Negative Supported

e-store Size Number of items for sale An e-store’s perceived size will

significantly and positively

affect consumer’s trust in

an Internet store.

Positive Supported

e-store History Number of days as an

eBay e-store

An e-store’s temporal history

will significantly and positively

affect consumer’s trust.

Positive Supported

Trust Net feedback rating:

Number of positive

ratings – number of

negative ratings

Higher consumer trust toward

an Internet store will generate

higher sales from the store.

Positive Supported Trust not explicitly measured

– eBay feedback profile

used as a proxy for trust.

Success Sales: Number of

successful transactions

Relative success measured

by the number of

successful transactions



Andersen, R. H. (2001) ‘Relationship Development and

Marketing Communication: An Integrative Model’, Journal

of Business and Industrial Marketing 16(3): 167–82.

Anderson, E. and Weitz, B. (1989) ‘Determinants of

Continuity in Conventional Industrial Channel Dyads’,

Marketing Science 8: 310–23.

Anderson, J. C. and Narus, J. A. (1990) ‘A Model of

Distributor Firm and Manufacturer Firm Working

Partnerships’, Journal of Marketing 54: 42–58.

Ba, S. and Pavlou, P. (2002) ‘Evidence of the Effect of Trust

Building Technology in Electronic Markets: Price

Premiums and Buyer Behavior’, MIS Quarterly 26(3):

243–68.

Barnett, G. A. and Sung, E. (2005) ‘Culture and the Structure

of the International Hyperlink Network’, Journal of

Computer-Mediated Communication 11(1): 217–38.

Black, G. S. (2005) ‘Is eBay for Everyone? An Assessment of

Consumer Demogaphics’, S.A.M. Advanced Management

Journal 70(1): 50.

Blomqvist, K. (1997) ‘The Many Faces of Trust’,

Scandinavian Journal of Management 13(3): 271–86.

Brown, E. (1969) ‘Price Image versus Price Reality’, Journal of

Marketing Research 6(2): 185–91.

Child, J. (1981) ‘Culture, Contingency and Capitalism in the

Cross National Study of Organizations’, in: L. L.

Cummings and B. M. Staw (eds) Research in

Organizational Behavior, (Vol. 3), Greenwich, CT: JAI

Press.

Chopra, K. and Wallace, W. A. (2003) ‘In Trust in Electronic

Environments’, paper presented at the Proceedings of the

36th Hawaii International Conference on System Sciences

(HICSS 2003), Hawaii: IEEE.

Chow, S. and Holden, R. (1997) ‘Toward an Understanding

of Loyalty: The Moderating Role of Trust’, Journal of

Managerial Issues 9(3): 275–98.

Chrisman, J. J., Bauerschmidt, A. and Hofer, C. W. (1998)

‘The Determinants of New Venture Performance: An

Extended Model’, Entrepreneurship Theory and Practice

23(1): 5–29.

Coleman, J. C. (1990) Foundations of Social Theory,

Cambridge, MA: Harvard University Press.

D’Annunzio-Green, N. (2002) ‘An Examination of the

Organizational and Cross-cultural Challenges Facing

International Hotel Managers in Russia’, International

Journal of Contemporary Hospitality Management 14(6):

266–73.

Darling, J. R. and Box, T. M. (1999) ‘Keys for Success in the

Leadership of Multinational Corporations, 1990 through

1997’, S.A.M. Advanced Management Journal 64(4):

16–21.

Deng, J., Walker, G., Swinnerton, G., Walker, G. J. and

Vilet, V. (2006) ‘A Comparison of Environmental Values

and Attitudes between Chinese in Canada and Anglo-

Canadians’, Environment and Behavior 38(1): 22–47.

Doney, P.-M. and Cannon, J.-P. (1997) ‘An Examination of

the Nature of Trust in Buyer-seller Relationships’, Journal

of Marketing 61(2): 35–51.

Doney, P. M., Cannon, J. P. and Mullen, M. R. (1998)

‘Understanding the Influence of National Culture on the

Development of Trust’, Academy of Management Review

23(3): 601–20.

Dow, D. (2006) ‘Adaptation and Performance in Foreign

Markets: Evidence of Systematic Under-adaptation’,

Journal of International Business Studies 37(2): 212–26.

Dwyer, F. R. and Lagace, R. R. (1986) ‘On the Nature and

Role of Buyer-Seller Trust’, AMA Summer Educators

Conference Proceedings, Chicago.

Elahee, M. N., Kirby, S. L. and Nasif, E. (2002) ‘National

Culture, Trust and Perceptions about Ethical Behavior in

Intra- and Cross–cultural Negotiations: An Analysis of

NAFTA Countries’, Thunderbird International Business

Review 44(6): 799–818.

Elenkov, D. and Fileva, T. (2006) ‘Anatomy of a Business

Failure: Accepting the ‘‘Bad Luck’’ Explanation vs

Proactively Learning in International Business’, Cross

Cultural Management 13(2): 132–41.

Ess, C. and Sudweeks, F. (2005) ‘Culture and Computer-

mediated Communication: Toward New Understandings’,

Journal of Computer-Mediated Communication 11(1):

179–91.

Fey, C. F. and Bjorkman, I. (2001) ‘The Effect of Human

Resource Management Practices on MNC Subsidiary

Performance in Russia’, Journal of International Business

Studies 32(1): 59–75.

Fishbein, M. and Ajzen, I. (1975) ‘Belief, Attitude, Intention

and Behavior: An Introduction to Theory and Research’

Reading, MA: Addison-Wesley.

Gabarro, J. J. (1978) ‘The Development of Trust, Influence

and Expectations’, in: A. G. Athos and J. J. Gabarro (eds)

Interpersonal Behavior: Communication and Understanding

in Relationships, Englewood Cliffs, NJ: Prentice

Hall. pp. 290–303.

Galan, J. I., Galende, J. and Gonzalez-Benito, J. (1999)

‘Determinant Factors of International Development: Some

Empirical Evidence’, Management Decision 37(10):

778–85.

Gefen, D. and Heart, T. (2006) ‘On the Need to Include

National Culture as a Central Issue in e-Commerce Trust

Beliefs’, Journal of Global Information Management 14(4):

1–30.

Gefen, D., Karahanna, E. and Straub, D. (2003a) ‘Trust and

TAM in Online Shopping: An Integrated Model’, MIS

Quarterly 27(1): 51–90.

Gibbs, J., Kraemer, K. L. and Dedrick, J. (2003) ‘Environment

and Policy Factors Shaping Global e–Commerce Diffusion:

A Cross-country Comparison’, Information Society 19(1):

5–18.

Goodbody, J. (2005) ‘Critical Success Factors for Global

Virtual Teams’, Strategic Communication Management

9(2): 18–22.

Gorg, H. and Strobl, E. (2003) ‘Multinational Companies,

Technology Spillovers and Plant Survival’, The

Scandinavian Journal of Economics 105(4): 581–95.



Griffith, D. A., Hu, M. Y. and Ryans, J. K. J. (2000) ‘Process

Standardization across Intra- and Inter-cultural

Relationships’, Journal of International Business Studies

31(2): 303–24.

Grimm, S. D., Church, A. T., Katigbak, M. S. and Reyes, J. A.

S. (1999) ‘Self-Described Traits, Values and Moods

Associated With Individualism and Collectivism: Testing I-

C Theory in an Individualistic (U.S.) and Collectivistic

(Philippine) Culture’, Journal of Cross Cultural Psychology

30(4): 466–500.

Ha, H.-Y. (2004) ‘Factors Influencing Consumer Perceptions

of Brand Trust Online’, The Journal of Product and Brand

Management 14(4/5): 329–42.

Haber, S. and Reichel, A. (2005) ‘Identifying Performance

Measures of Small Ventures – The Case of the Tourism

Industry,’ Journal of Small Business Management 43(3):

257–86.

Hawes, J. M., Rao, C. P. and Baker, T. L. (1993) ‘Retail

Salesperson Attributes and the Role of Dependability in the

Selection of Durable Goods’, Journal of Personal Selling

and Sales Management 13(Fall): 61–71.

Hofstede, G. (1980) Culture’s Consequences, Beverly Hills:

Sage.

Hofstede, G. (1983) ‘The Cultural Relativity of Organizational

Practices and Theories’, Journal of International Business

Studies 14: 75–89.

Hofstede, G. (2001) Culture’s Consequences: Comparing

Values, Behaviors, Institutions and Organizations across

Nations, (2nd ed.), Thousand Oaks, CA: Sage Publications.

Hofstede, G., Neujen, B., Ohayv, D. D. and Sanders, G.

(1990) ‘Measuring Organizational Cultures: A Qualitative

and Quantitative Study Across Twenty Cases’,

Administrative Science Quarterly 35: 286–316.

Holtbrugge, D. (2004) ‘Management of International

Strategic Business Cooperation: Situational Conditions,

Performance Criteria and Success Factors’, Thunderbird

International Business Review 46(3): 255–74.

Iyengar, S. S., Lepper, M. R. and Ross, L. (1999)

‘Independence from Whom? Interdependence with Whom?

Cultural Perspectives on Ingroups versus Outgroups’, in:

D. Miller and D. Prentice (eds) Cultural Divides:

Understanding and Overcoming Group Conflict, New York:

Sage, pp. 273–301.

Jarvenpaa, S. L., Tractinsky, N. and Vitale, M. (2000)

‘Consumer Trust in an Internet Store’, Information

Technology and Management 1: 45–71.

Jennex, M. E., Amoroso, D. and Adelakun, O. (2004) ‘e-

Commerce Infrastructure Success Factors for Small

Companies in Developing Economies’, Electronic

Commerce Research 4(3): 263–86.

Johnson-Page, G. F. and Thatcher, R. S. (2001) ‘B2C Data

Privacy Policies: Current Trends’, Management Decision

39(4): 262–71.

Johnston, K. and Johal, P. (1999) ‘The Internet as a ‘‘Virtual

Cultural Region’’: Are Extant Cultural Classification

Schemes Appropriate?’, Internet Research 9(3): 178–86.

Kacen, J. J. and Lee, J. A. (2002) ‘The Influence of Culture on

Consumer Impulsive Buying Behavior’, Journal of

Consumer Psychology 12(2): 163–76.

Komiak, S. Y. X. and Benbasat, I. (2006) ‘The Impact of

Internalization and Familiarity on Trust and Adoption of

Recommendation Agents’, MIS Quarterly 30(4): 941–60.

Kshetri, N. (2002) ‘In Factors Influencing Consumers’

Reaction to a Price and Intention to Bid in a C-to-C

Internet Auction’, (Vol. 13), paper presented at the

American Marketing Association, Austin, TX, pp. 44–50.

Liang, T. P., Lin, C. Y. and Chen, D. N. (2004) ‘Effects of

Electronic Commerce Models and Industrial Characteristics

on Firm Performance,’ Industrial Management and Data

Systems 104(7): 538–45.

Lim, H. and Dubinsky, A. J. (2004) ‘Consumers’ Perceptions

of e-shopping Characteristics: An Expectancy-value

Approach’, The Journal of Services Marketing 18(6/7): 500.

Lim, K. H., Leung, K., Sia, C. L. and Lee, M. K. O. (2004) ‘Is

e-Commerce Boundary-less? Effects of Individualism-

Collectivism and Uncertainty Avoidance on Internet

Shopping’, Journal of International Business Studies 35(6):

545–59.

Lohse, G. L. and Spiller, P. (1998) ‘Electronic shopping: How

Do Customer Interfaces Produce Sales on the

INTERNET?’ Communications of the ACM 41(7): 81–7.

Luhmann, N. (1979) Trust and Power, Chichester, UK: Wiley.

Lynch, P. D. and Beck, J. C. (2001) ‘Profiles of Internet

Buyers in 20 Countries: Evidence of Region Specific

Strategies’, Journal of International Business Studies 32(4):

725–48.

Lynch, P. D., Kent, R. J. and Srinivasan, S. S. (2001) ‘The

Global Internet Shopper: Evidence from Shopping Tasks

in Twelve Countries’, Journal of Advertising Research

(May–June): 15–23.

Magrath, A. J. and Hardy, K. G. (1989) ‘A Conceptual

Framework for Assessing the Level of Mutual Trust

Between Manufacturers and Their Resellers’, paper

presented at the Proceedings of the 5th IMP Conference,

State College/University Park, Pennsylvania.

Mahajan, V. and Srinivasan, R. (2002) ‘The Dot.com Retail

Failures of 2000: Were There Any Winners?’ Journal of the

Academy of Marketing Science 30(4): 474–86.

Maitland, C. and Bauer, J. (2001) ‘National Level Culture and

Global Diffusion: The Case of the Internet’, in: C. Ess (ed.)

Culture, Technology, Communication: Towards an

Intercultural Global Village, Albany, NY: State University

of New York Press, pp. 87–120.

Mayer, R. C., Davis, J. H. and Schoorman, F. D. (1995) ‘An

Integrative Model of Organizational Trust’, Academy of

Management Review 20(3): 709–34.

McKnight, D. H., Choudhury, V. and Kacmar, C. (2002a)

‘Developing and Validating Trust Measures for e-

Commerce: An Integrative Typology’, Information Systems

Research 13(3): 334–59.

McKnight, D. H., Choudhury, V. and Kacmar, C. (2002b)

‘The Impact of Initial Consumer Trust on Intentions to



Transact With a Web Site: A Trust Building Model’,

Journal of Strategic Information Systems 11(3–4): 297–323.

McKnight, D. H., Cummings, L. L. and Chervany, N. L. (1998)

‘Initial Trust Formation in New Organizational Relationships’,

Academy of Management Review 23(3): 473–90.

McLarney, C. and Dastrala, R. (2001) ‘Socio-political

Structures as Determinants of Global Success. The Case of

Enron Corporation’, International Journal of Social

Economics 28(4): 349–68.

Moghaddan, F. M., Taylor, D. M. and Wright, S. C. (1993)

Social Psychology in Cross-cultural Perspective, New York:

Freeman.

Moorman, C., Deshpande, R. and Zaltman, G. (1993) ‘Factors

Affecting Trust in Market-research Relationships’, Journal

of Marketing 57(1): 81–101.

Morgan, R.-M. and Hunt, S.-D. (1994) ‘The Commitment-

trust Theory of Relationship Marketing’, Journal of

Marketing 58(3): 20–38.

O’Grady, S. and Lane, H. (1996) ‘The Psychic Distance

Paradox’, Journal of International Business Studies 27(2):

309–33.

Palepu, K. G., Bernard, V. L. and Healy, P. M. (1996) Business

Analysis and Valuation: Using Financial Statements,

Cincinnati: South-Western.

Permut, S. E. (1976) ‘The Researcher’s Sample: A Review of

the Choice of Respondents in Marketing Research’, Journal

of Marketing 13: 278–83.

Reid, D. M. (1995) ‘Critical Success Factors in Japanese

Consumer Products Market: Guidance for Foreign MNCs’,

International Executive 37(6): 555–82.

Rempel, J. K., Holmes, J. G. and Zanna, M. P. (1985) ‘Trust

in Close Relationships’, Journal of Personality and Social

Psychology 49(1): 95–112.

Robson, M. J., Leonidou, L. C. and Katsikeas, C. S. (2002)

‘Factors Influencing International Joint Venture

Performance: Theoretical Perspectives, Assessment and

Future Directions’, Management International Review

42(4): 385–418.

Rotondaro, R. G. (2002) ‘Defining the Customer’s

Expectations in e-Business’, Industrial Management and

Data Systems 102(9): 476–82.

Smith, N. C. (1970) ‘Replication Studies: A Neglected Aspect of

Psychological Research’, American Psychologist 25: 970–97.

Srite, M. and Karahanna, E. (2006) ‘The Role of Espoused

National Cultural Values in Technology Acceptance’, MIS

Quarterly 30(3): 679–704.

Swan, J. E., Bowers, M. R. and Richardson, L. D. (1999)

‘Customer Trust In The Salesperson: An Integrative Review

And Meta-analysis of the Empirical Literature’, Journal of

Business Research 44(2): 93–107.

Swan, J. E., Trawick, F. I. and Silva, D. W. (1985) ‘How

Industrial Salespeople Gain Customer Trust’, Industrial

Marketing Management 14: 203–11.

Thaler, R. H. (1985) ‘Mental Accounting and Consumer

Choice’, Marketing Science 4(Summer): 199–214.

Tixier, M. (1995) ‘Trends in International Business Thought

and Literature: Mixed Management Teams: How West

European Businesses Approach Central and Eastern

Europe’, International Executive 37(6): 631–44.

Triandis, H. (2001) ‘Individualism-Collectivism and

Personality’, Journal of Personality 69(9): 907–24.

Triandis, H. and Suh, E. M. (2002) ‘Cultural Influences on

Personality’, Annual Review of Psychology 53: 133–60.

Triandis, H. C. (1972) The Analysis of Subjective Culture,

New York: John Wiley.

Tudor, T. R. and Trumble, R. R. (1996) ‘Cultural Integration:

An Examination of Success for United States and Japanese

Business Mergers’, International Journal of Management

13(1): 52–9.

Tylor, E. B. (1871) Primitive Culture: Researches into the

Development of Mythology, Philosopy, Religion, Language,

Art and Custom, New York: Holt, Reinhart and Winston.

Yamagishi, T. (1988) ‘Exit from the Group as an

Individualistic Solution to the Free Rider Problem in the

United States and Japan’, Journal of Experimental Social

Psychology 24: 530–42.

Yamagishi, T., Cook, K. S. and Watabe, M. (1998)

‘Uncertainty, Trust and Commitment Formation in the

United States and Japan’, American Journal of Sociology

104(1): 165–94.

Yang, J. and Lee, H. (2002) ‘Identifying Key Factors for

Successful Joint Venture in China’, Industrial Management

and Data Systems 102(1/2): 98–109.

Yang, J. Z. (1998) ‘Key Success Factors of Multinational Firms

in China’, Thunderbird International Business Review

40(6): 633–68.



conducting international business at ebay: the...

Documents