Information & Management 50 (2013) 439–445

Building trust online: Interactions among trust building mechanisms§

Man Kit Chang a,*, Waiman Cheung b, Mincong Tang c

a Department of Finance and Decision Sciences, Hong Kong Baptist University, Hong Kong, Chinab Department of Decision Sciences and Managerial Economic, The Chinese University of Hong Kong, Hong Kong, Chinac School of Economics and Management, Beijing Jiaotong University, China

A R T I C L E I N F O

Article history:

Received 8 July 2010

Received in revised form 6 May 2013

Accepted 17 June 2013

Available online 8 July 2013

Keywords:

Interaction effect

Online shopping

Social exchange theory

Online trust production

Scenario method

A B S T R A C T

Lack of trust has been shown to be a major obstacle to the adoption of online shopping. However, there

has been little investigation of the effectiveness of various trust building mechanisms and their

interactions. In our study, three trust building mechanisms (third-party certification, reputation, and

return policy), were examined. A scenario survey method was used for data collection. 463 usable

questionnaires were collected from respondents with diverse backgrounds. Regression results showed

that all three trust building mechanisms had significant positive effect on trust of the online vendor.

However, their effects were not simple; they interacted to produce a different overall effect on the level

of trust. These results have both theoretical and practical implications.

� 2013 Elsevier B.V. All rights reserved.

Contents lists available at SciVerse ScienceDirect

Information & Management

jo u rn al h om ep ag e: ww w.els evier .c o m/lo c ate / im

1. Introduction

B2C electronic commerce is here to stay, despite the recentdownturn in the dot-com arena. It provides companies anadditional channel through which to sell their products. However,because there is a lack of physical presence of the products andthere may be a large physical distance between buyers and sellers,e-commerce provides a unique environment in which trust is ofparamount importance. Thus, it is important to understand how topromote customers’ trust in online shopping, IS researchers haveproposed a number of online trust models that offer insight intothe antecedents of online trust. However, only a few of them havebeen empirically tested and the findings on the effect of severalimportant antecedents, such as third-party certification, andfamiliarity, are inconsistent with the predictions of the modelsor inconsistent among the studies [6].

Many researchers focused their work on trust that builds upgradually through ongoing interactions; in these, both actors gainmore knowledge of the integrity and the ability of their counter-parts. Because such interaction may be lacking in the initialencounter between a customer and an online vendor, it seemed

§ An early version of this article has appeared in the Proceedings of the Thirty-

Eighth Annual Hawaii International Conference on System Sciences, 2005.

* Corresponding author at: Department of Finance and Decision Sciences, School

of Business, Hong Kong Baptist University, Kowloon Tong, Hong Kong.

Tel.: +852 3411 7564; fax: +852 3411 5585.

E-mail address: mkchang@hkbu.edu.hk (M.K. Chang).

0378-7206/$ – see front matter � 2013 Elsevier B.V. All rights reserved.

http://dx.doi.org/10.1016/j.im.2013.06.003

fruitful to investigate other ways of building trust that do notrequire prior interaction. Another area neglected in prior studies ofonline trust was the effect of interaction among trust buildingmechanisms, whose effectiveness may depend on the presence orabsence of other mechanisms. We designed our study to providesome answers to such questions.

The objective of our study was therefore to investigate theeffectiveness of various trust building mechanisms by extendingour understanding of how they work in a context in which theparties involved in the transaction are not familiar with each other.

2. Theoretical background

2.1. Online shopping and social exchange theory

Exchanges in online shopping between customers and onlinevendors contain elements that are typical components of socialexchanges. Applying social exchange theory (SET) therefore can beused to enhance our understanding of the sources of trust.

Social exchanges are actions of individuals who hope to gainreturns such as money, goods, or social attention by interactingwith others. To continue receiving benefits, individuals need toreciprocate for the benefits receive. Participants then continue tointeract while they perceive that the exchange is the bestalternative. Otherwise, they find it more valuable to interact withothers who can provide what they need. These processes evolveover time as the actors mutually and sequentially demonstratetheir trustworthiness.

M.K. Chang et al. / Information & Management 50 (2013) 439–445440

According to SET, trust builds up slowly, starting withminor transactions in which little trust is required becauselittle risk is involved. Thus, small favors are exchanged at the startof an exchange relationship. As individuals discharge theirobligations, they demonstrate trustworthiness and show theircommitment. Thus mutual services gradually expand and mutualtrust grows. However, if a party fails to reciprocate, the exchangerelationship will cease. Demonstrating one’s trustworthiness andcredibility also enhances one’s reputation, which becomes anasset of the individual or firm; this is one of the main sources oftrust.

Therefore, online vendors should provide a transactionarrangement that involves little risk to their customers andencourage them to try it out. For instance, the firm may allowcustomers to pay on delivery and adopt an easy return policy. Aftersome time, the consumers may be willing to engage in anarrangement that is more risky, such as paying by credit card.

2.2. Modes of trust production

About 27 years ago Zucker1 described three ways that trust isproduced:1. Characteristic-based, which reflect personal characteristics,

such as family background, age, sex and ethnicity. Theseindicate membership in a common cultural system and definean area of low-risk interpersonal trust in which individualsconfer depersonalized trust to an in-group member. This mode oftrust production may not be very effective in the context ofonline shopping because the globally oriented electronicmarketplace is, by definition, an attempt to attract customersfrom diverse and potentially world-wide backgrounds.

2. Process-based, which is tied to past or anticipated exchanges.The former is obtained either indirectly (by reputation,branding, warranties of quality, etc.) or directly from positiveexperience in prior exchanges. Firms should invest in formalways of establishing process-based trust, through advertisingfor example or by making a commitment to potentialcustomers, such as making a generous returns policy thatchanges the incentive structure of providing sub-standardgoods.

3. Institutional-based, which is tied to broad societal institutionsand intermediary mechanisms. There are two types ofinstitutional-based trust. The first is specific to persons orfirms. It is engendered by acquiring such things as a professionalcredential, membership of an association, or third-partycertification. The second results from using intermediarymechanisms, such as insurance, escrow, or legal rules. It islikely to be required when there is an exchange across groupswith significant social or geographical distance. Pavlou andGefen [17] found that institutionally based mechanismsengendered trust in an auction marketplace in which neitherthe product characteristics nor seller identity could be fullyassessed. This kind of trust needs to be established for onlineshopping.

2.3. Definition of trust

Some researchers confuse trust with other concepts, such ashonesty, confidence, and faith. As pointed out by Shankar et al.[19], most of the studies on online trust do not make a cleardistinction between the underlying dimensions and the ante-cedents of online trust. Thus it is important to distinguish what istrust and what leads to trust [13].

1 L.G. Zucker, Prediction of Trust: Institutional Sources of Economic Structure,

1840–1920, Research in Organizational Behavior, 8, 1986, pp. 53–111.

We define trust as a psychological state that allows a person toaccept vulnerability based upon positive expectations of theintentions or behavior of others. This definition separates theconstruct of trust from its antecedents and outcomes, and thus isappropriate for the object of our study.

3. Hypotheses development

Our choice of specific trust building mechanisms was partlybased on the criterion that they could provide cues to engender thecustomer’s initial trust in an online vendor, about whom thecustomer has little or no credible information.

We chose one institutional-based mechanism, certification by atrusted third-party, and two process-based mechanisms, reputa-tion and return policy, as topics for investigation in our study.

3.1. Institutional-based trust building mechanism – third party

certification

Miyazaki and Krishnamurthy [16] reported on two studiesinvestigating how the presence of an Internet seal of approval logoaffected a consumer’s perceptions of a licensee web site’s privacypractices and the willingness of consumers to disclose theirpersonal information; they found that its presence resulted in anincreased feeling of trustworthiness of the site and willingness todisclose name, e-mail, and mailing address. Liu et al. [14] foundsimilar result when investigating the effect of procedural fairnesson information privacy.

Aiken and Boush [1] explored context-specific nature of trust ine-commerce and found that third-party certification (i.e. trust-mark) was an effective method for developing trust. It influencedsrespondents’ beliefs about security and privacy, general beliefsabout firm trustworthiness, and willingness to provide personalinformation. Similarly, Jiang, Jones and Javie [9] found thatdisplaying third-party logo increased the willingness of thecounsumers to accept vulnerability in online transaction with e-marketers.

However, Kimery and McCord [11] and Bahmanziari et al. [3]did not find any significant relationship between third-partyassurances and consumer trust.

We therefore hypothesized:

H1. Third-party certification will lead to higher levels of customertrust in the online vendor.

3.2. Process-based trust building mechanism I – return policy

A guaranteed return policy is a commitment often made byonline vendors. This may convince consumers that they can trust asite. Compensation commitment plays a most important role inengendering trust. A guaranteed return policy also assures a lessrisky environment that will increase trust.

A number of studies have examined return policy as a riskreliever for online shopping. Cases [5] found that a money backguarantee was among the top risk relievers, as assessed by onlinecustomers. As reported by Egger [7], a study conducted by theNielsen Norman Group tested 64 users of 20 electronic commercesites found that a clearly written return policy was positivelyappreciated by the participants. Bahmanziaria et al. [3] also foundthat internally provided e-Assurances (IPeA) which include returnpolicies, guarantees etc. affected participants’ initial trust signifi-cantly.

Thus, we hypothesized:

H2. A favorable return policy will lead to higher levels of customertrust in online vendors.

Table 1Items measuring trust.

T1: I can count on this online bookstore to be trustworthy.

T2: I feel that this online bookstore can be trusted.

T3: I believe sometimes I can NOT trust this online bookstore.

T4: I trust this online bookstore.

2 J.K. Butler, Toward Understanding and Measuring Conditions of Trust:

Evolution of a Conditions of Trust Inventory, Journal of Management, 17(3),

1991, pp. 643–663.

M.K. Chang et al. / Information & Management 50 (2013) 439–445 441

3.3. Process-based trust building mechanism II – reputation

Reputation is the extent to which a buyer believes that a supplieris honest and concerned about its customers. Reputation can bemeasured based on direct experience and, other forms ofcommunication and symbolism that provide information aboutthe firm’s actions or a comparison of the firm’s actions with those ofits leading rivals. In the area of online shopping, studies that haveinvestigated the factors affecting consumers’ willingness to buyonline found that reputation positively influenced trust of the onlinestore [10,12,18,20]. When purchasing legal advice from a web site,reputation has been shown to have a positive effect on trust [15].

We therefore hypothesized:

H3. Reputation will have a positive effect on the level of trust inonline vendors.

4. Research methodology

4.1. The scenario method and manipulations

Our study used a survey to provide inputs to statistical tools totest our hypotheses. One limitation of such a scenario method is itslimited generalizability when compared to field studies. Anothercriticism concerns its perceived realism as seen by the participantsof the survey.

Our treatments consisted of two levels of reputation (good orunknown), two levels of third-party certification (yes or no), andtwo levels of return policy (can be returned within 7 days or cannotbe returned). Unlike the U.S., only some shops in Hong Kong allowthe customer to return purchased goods even if they are unopened,unless the goods are defective. Eight scenarios were created byvarying the levels of these three independent variables (see theAppendix for an example scenario). This resulted in a 2 � 2 � 2between-subject factorial design. The scenarios asked the respon-dents to imagine that they were book lovers who had found a bookin an online bookstore described by the scenarios. Then they wereasked the extent that they trust the online vendors.

A well-known and well-established bookshop, the Commercial

Press, was chosen to represent a shop with a good reputation, and alesser-known bookshop, Eureka, was chosen to represent a shopwith an unknown reputation.

A policy of allowing a return within seven days of purchase waschosen as being an acceptable return policy, because mostreputable stores in Hong Kong will allow you to exchange productsif they are not satisfactory within seven days of purchase. Thus,setting this limit would be likely to match the perception of anacceptable return policy for Hong Kong citizens.

Two trusted third-parties were considered via a pilot test whilethe scenarios were being developed: the Office of the Privacy

Commissioner of Hong Kong and TRUSTe. The former was chosen asthe third-party who was to be the certifier of the company. Thepilot was completed in two rounds with 33 respondents. As aresult, TRUSTe was not chosen because some respondents did notknow of its operations.

4.2. The questionnaire

The questionnaire was divided into three sections. The firstpresented one of the eight scenarios to the respondents andinstructed them to read it carefully before answering the questions.

Manipulation checks in the second section measure the extentto which treatments had been perceived by the subjects and toensure that they had, indeed, been manipulated as intended. Onemanipulation check question was included for each treatment; it

asked whether, on a 7-point scale, the respondents believed thatthe online shop was certified by a third-party, had an acceptablereturn policy, and had a good reputation.

Trust in online vendors was operationalized as overall trustsbased on the definition of trust we adopted for the current study.The items of the trust scales, as shown in Table 1, are based onthose of Butler,2 with the wording adapted to our context of onlineshopping. The scale used four items to assess overall trust and hadan internal consistency reliability of 0.91 and a test–retestreliability of 0.91 in his study. The items were rated on a seven-point scale ranging from strongly agree (1) to strongly disagree (7).

The demographics section asked respondents to provideinformation on their gender, age, income, education level,occupation, and industry in which they were working. It alsoposed a number of questions related to the use of the Internet andonline shopping, including time spent on the Internet per week,years of experience in using the Internet, whether they hadexperience with online shopping websites, whether they hadpurchased anything online in the past 6 months, and finallywhether they had ever purchased online. The question about theirexperience with online shopping was used to improve the surveyby removing respondents who had never visited or used an onlineshopping website.

4.3. Subjects and data collection

The population of interest for our study was those Internetusers who had experienced online shopping, both as adopters andas potential adopters. This population was chosen because therespondents should at least have some knowledge of onlineshopping to provide their opinions on our questions.

Because there is no sampling frame of the Internet users ofHong Kong, it was impossible to perform random sampling. Thus, anon-probability sample was used in our study. To increase therepresentativeness of the sample, participation was solicited fromrespondents from a wide range of backgrounds. Respondentsincluded both students and the working public. For students,questionnaires were distributed in the classrooms and theirparticipation was voluntary; they included students from thebusiness, social sciences, and sciences faculties. Questionnaireswere distributed to the working public through the personalcontacts of the researchers. A central contact person was identifiedin each company; he or she was asked to randomly distribute 16 to40 questionnaires to any of his or her colleagues who were willingto participate in the survey. People from a wide range of industrialsectors were contacted, including banking and finance,manufacturing, education, IT, and the public service. All respon-dents were randomly assigned to the 8 different scenarios.

5. Results

5.1. Demographic profile of the respondents

640 questionnaires were distributed and 463 usable ques-tionnaires were returned: a 72 percent response rate. Fifty-six

Table 2Regression analysis for the effect of reputation, third-party certification, and return

policy on trust in online vendor.

Source Sum of squares df Mean square F Sig.

Reputation (Rep) 58.9 1 58.9 41.0 0.00

Third-party (3p) 246.1 1 246.1 171.3 0.00

Return policy (Ret) 2.6 1 2.6 1.8 0.18

Rep � 3p 7.2 1 7.2 5.0 0.03

Rep � Ret 8.6 1 8.6 6.0 0.02

Ret � 3p 0.4 1 0.4 0.3 0.60

Rep � 3p � Ret 0.5 1 0.45 0.3 0.56

Regression 325.4 7 46.5 32.3 0.00

Residual 653.6 455 1.4

M.K. Chang et al. / Information & Management 50 (2013) 439–445442

percent of the respondents held undergraduate degrees or higher,and 17.5 percent were receiving tertiary education. The mean ageof the respondents was 28.2 years with a standard deviation of 7years. They worked in a wide variety of industries; 10 percent fromthe manufacturing sector; 9.7 percent from the financial sector; 6.5percent from the wholesaling and retailing sector; 11.2 percentfrom the IT sector; 13.2 percent from public services; and the restfrom other industries.

Most of the respondents (over 80 percent) had four or moreyears of experience in using the Internet, with most of them fallingbetween five to seven years. Forty-four percent of the respondentshad bought goods from online shops and around 27 percent hadpurchased goods online in the past six months.

5.2. Manipulation checks and reliability

An ANOVA was used to assess the effectiveness of themanipulations. The main effect of reputation on the manipulationcheck question was F1,454 = 260, p < 0.001 and the means showedthat Commercial Press was perceived as having a better reputationthan Eureka (mean = 4.9 vs 3.4). The main effect of third-partycertification from its manipulation check question was alsosignificant (F1,454 = 395, p < 0.001). The respondents beingassigned the scenarios with third-party certification did perceivethis with a mean of 5.56 vs 2.90. A final ANOVA showed thatrespondents being assigned scenarios that allowed books to bereturned within seven days did perceived this return policy to bemore favorable (mean = 5.2 vs 3.3) and this difference wassignificant (F1,454 = 389, p < 0.001). The findings showed that themanipulations of the scenarios were effective, and the effect of thetrust building mechanisms could be analyzed.

Cronbach’s alphas were used to assess the internal consistencyreliability of the trust scales. The reliability coefficient was 0.89,which was higher than the normally accepted level of 0.7 for ourkind of study.

5.3. Effect of trust building mechanisms on trust in online vendors

Since the cell frequencies in the factorial design were unequal,multiple regression analysis was used including effect coding forthe three independent variables (reputation, third-party certifica-tion, and return policy). If any interaction effect was significant,then the analysis of simple effects, i.e., the differential effects oftreatments of one factor at each treatment level of the other factor,could be performed using multiple regression analysis.

The presence of multicollinearity for all regression equationswas examined by using the variance inflation factor (VIF). For allregression equations, VIF values for the independent variables

Table 3Tests of simple main effects of reputation, third-party certification and return policy o

Source Sum of squares df

Third-party at unknown reputation 163.8 1

Third-party at good reputation 86.4 1

Reputation at no 3rd party 53.8 1

Reputation at have 3rd party 13.3 1

Return policy at unknown reputation 0.3 1

Return policy at good reputation 10.5 1

Reputation at no return 13.2 1

Reputation at allow return 57.1 1

Return policy at no 3rd party 2.6 1

Return policy at have 3rd party 0.4 1

Third-party at no return 129.4 1

Third-party at allow return 116.3 1

Residual 653.6 455

* p < 0.025.

were all close to one. The VIF values obtained indicated that therewas no serious multicollinearity problem among the independentvariables. Moreover, the Durbin-Watson test, used to detect theexistence of autocorrelation among the residuals, indicated thatthere was no autocorrelation in any of the regression equations.

Trust in Online Vendor was regressed on Reputation, Third-party Certification, and Return Policy. Table 2 shows its ANOVAresults. The regression equation is significant at an alpha level of0.001 and the independent variables accounted for 33 percent ofthe variance of trust in the online vendor. The three-wayinteraction was not significant. Two two-way interactions,reputation by third-party certification and reputation by returnpolicy, were significant. The results also showed that the maineffect of reputation and third-party certification were significant.However, if any two-factor interaction is significant, neither of themain effects has meaning; thus an analysis of the simple maineffects at different levels of treatment was performed. First, thetrust in the online vendor was regressed on third-party certifica-tion at two levels of reputation. The results, as shown in the secondand third rows of Table 3 show the differential effects of third-party certification at different levels of reputation as the differencein the regression coefficients (b). At unknown reputation, the effectof third-party certification on trust in online vendors wassignificant and the regression coefficient was equal to 0.85. Atgood reputation, the effect of third-party certification on trust inonline vendors was also significant, but the regression coefficientwas 0.61, which is smaller. Thus, the effect of third-partycertification was higher when a company had an unknownreputation. These effects can also be seen in Fig. 1.

The interaction effect between reputation and third-partycertification was analyzed from another angle. This time trust inthe online vendor was regressed separately on reputation at twolevels of third-party certification. The results are shown in thefourth and fifth rows of Table 3. When there is no third-party

n trust in the online vendors.

Mean square F b R2

163.8 114.0* 0.85 0.34

86.4 60.2* 0.61 0.20

53.8 37.4* 0.48 0.13

13.3 9.3* 0.24 0.04

0.3 0.2 0.00 0.00

10.5 7.3* 0.21 0.02

13.2 9.2* 0.24 0.03

57.1 39.8* 0.49 0.11

2.6 1.8 0.11 0.01

0.4 0.3 0.04 0.00

129.4 90.1* 0.76 0.29

116.3 81.0* 0.70 0.22

1.4

5.0

4.5

4.0

3.5

3.0

2.5

No Yes

Tru

st i

n t

he

On

lin

e V

end

or

Third-party Certification

5.18

4.69

3.97

3.00

5.5

Reputation

Unknown

Good

b = 0.61*

b = 0.85*

Fig. 1. Effect of third-party certification on trust in the online vendor: by reputation.

4.0

3.5

3.0

Reputation

Unknown

Good

No Return Allow Return

Tru

st i

n t

he

Onli

ne

Ven

dor

Return Policy

4.79

3.813.88

4.36

4.5

5.0

b = 0.21*

b = -0.004

Fig. 3. Effect of return policy on trust in the online vendor: by reputation.

M.K. Chang et al. / Information & Management 50 (2013) 439–445 443

certification, the effect of reputation on trust in the online vendor issignificant and the regression coefficient is 0.48, while theregression coefficient is 0.24 when there is third-party certifica-tion. Thus, the effect of reputation is higher when a company hasno third-party certification. These differential effects can also beseen in Fig. 2.

We also looked at the interaction of reputation and returnpolicy. First trust in the online vendor was regressed separately onreturn policy at two levels of reputation, the results were alsoshown in the sixth and seventh rows of Table 3. When thereputation was unknown, the effect of the return policy on trust inthe online vendors was not significant. However, when thereputation was good, the effect was significant with a regressioncoefficient of 0.21. These differential effects can also be seen inFig. 3.

Trust in the online vendor was also regressed on reputation attwo levels of return policy, the results are shown in the eighth andninth rows of Table 3. The effect of reputation was significant atboth levels of return policy. However, the effect was higher whenreturn was allowed (b = 0.49) compared to 0.24 when return wasnot allowed. These differential effects can also be seen in Fig. 4.

5.0

4.5

4.0

3.5

3.0

2.5Unknown

Tru

st i

n t

he

On

lin

e V

end

or

Reputation

4.69

3.00

5.5

b = 0.24*

b = 0.48*

Fig. 2. Effect of reputation on trust in the on

Although the interaction effect of the return policy and third-party certification were not significant, there may still be a simplemain effect; the result only shows that the effects are the same atdifferent levels of the factors. The regression results are shown inthe tenth to the thirteenth row in Table 3. The effects of a returnpolicy on trust in the online vendor were not significant at the twolevels of third-party certification. However, there was a significantpositive effect of third-party certification on the level of trust in theonline vendor at both levels of return policy, and their effects aresimilar (b = 0.76 where return was not allowed and 0.70 whenreturn was allowed). These effects are also shown in Figs. 5 and 6.

These results supported hypotheses H1, H2, and H3.

6. Discussion

All three trust building mechanisms, third-party certification,reputation, and return policy, increase trust in the online vendor.However, their effect is not simple. They interact to producedifferent levels of influence on the trust. Overall, our resultsshowed that institutional-based trust building mechanisms aremost effective in enhancing customer trust.

Good

5.18

3.97

Third Party

Certification

No

Yes

line vendor: by third-party certification.

4.0

3.5

3.0

Return Policy

No Return

Allow Return

Unknown Good

Tru

st i

n t

he

Onli

ne

Ven

dor

Reputation

4.79

3.81

3.88

4.36

4.5

5.0

b = 0.49*

b = 0.24*

Fig. 4. Effect of reputation on trust in the online vendor: by return policy.

4.0

3.5

3.0

Return Policy

No Return

Allow Return

No Yes

Tru

st i

n t

he

On

lin

e V

end

or

Third-Party Certification

4.98

3.59

3.38

4.90

4.5

5.0

b = 0.70*

b = 0.76*

Fig. 6. Effect of third-party certification on trust in the online vendor: by return

policy.

M.K. Chang et al. / Information & Management 50 (2013) 439–445444

Third-party certification significantly increases customer trust inthe online vendor under all treatment levels of the other twomechanisms; the magnitudes of the effect under the conditions ofhaving an acceptable return policy and disallowing returns aresimilar. Reputation significantly increases customer trust in theonline vendor under all treatment levels of the other twomechanisms. It had most effect when there was no third-partycertification or when there was an acceptable return policy.However, the return policy only has effect when vendors have agood reputation. This is consistent with the fact that the returnpolicy is a kind of promise; if vendors do not have good reputations,their promises will not be deemed important.

Our findings have both theoretical and practical significances.Theoretically, recognizing the existence of the interactions amongtrust building mechanisms is crucial for interpreting results fromprior studies; past inconsistent findings may be due to thisinteraction effect. For instance, Bhattacherjee [4] found a signifi-cant effect of familiarity on trust. He chose Amazon while otherswere asked to name the vendor from which they had lastpurchased. Amazon has a good reputation, but it is difficult to

4.0

3.5

3.0

Third-party Certification

No

Yes

No Return Allow Return

Tru

st i

n t

he

Onli

ne

Ven

dor

Return Policy

4.98

3.59

3.38

4.90

4.5

5.0

b = 0.04

b = 0.11

Fig. 5. Effect of return policy on trust in the online vendor: by third-party

certification.

assess the reputation of other participant-chosen firms. Whileexamining one mechanism, the other potential trust buildingmechanisms must be carefully controlled, or their interactioneffect must be determined.

In practical terms, our results suggest that different trustbuilding mechanisms should be used by online vendors indifferent stages of development. As a return policy is onlyeffective when an online vendor has a good reputation, thismethod should not be used as the only means to engender trust bya new online vendor. However, if a new online shop is establishedby a reputable operator of a physical store, having a good returnpolicy will increase the trust of its customers and encourage themto shop online. New online vendors must create opportunities todemonstrate their trustworthiness. As reputation has a positiveeffect on customer trust, various means should be employed byonline vendors to improve their reputations. One way is to investin trust developing measures and signaling activities. Onlinevendors can also participate in an electronic marketplace that iscoordinated by a prestigious operator. Creating a virtualcommunity that is closely related to a company’s product canenhance reputation by word-of-mouth [8]. Feedback from otherconsumers was also found to affect the perceived trust in a sellerusing auction sites [2]. Because electronic commerce or onlineshopping is globally oriented, world-recognized, trusted third-parties are needed, in addition to certification organizations thatare local to a country.

Our study had a number of limitations; some were due in themethodology; others came from our choices and compromises.One was its limited generalizability when compared to fieldstudies. Another was that the scenarios may not have provided areal-world context for the respondents. The use of a non-probability based sample in our study may also have compromisedthe generalizability of the findings.

7. Conclusion

Since risk cannot be totally eliminated in online shopping, it isimportant to engender customers’ trust. Our study established theeffectiveness of institutional-based and process-based trustbuilding mechanisms. More importantly, their effects on trusthave been shown to interact with one another. These findings areimportant both theoretically and practically. The findings cangenerate appropriate suggestions to provide increased trust foronline vendors at different stages of their business development.

M.K. Chang et al. / Information & Management 50 (2013) 439–445 445

Acknowledgement

This research project is funded in part by the Asian Institute ofSupply Chains & Logistics, CUHK.

Appendix A. Scenario

Imagine that you are a book lover. You like to browse throughbooks in both physical bookstores and online bookstores.

Commercial Press set up its first outlets in Hong Kong in 1914and has over 10 retail outlets now.

Commercial Press’s online bookstore was certified to haveenough procedures to protect the personal data of its customerafter an audit by the Office of the Privacy Commissioner of HongKong.

After browsing through the online bookstore of CommercialPress, you found a book that you might like to buy. The book isavailable from both the physical store and the online bookstore.

When you buy a book from its online bookstore, CommercialPress allows you to return the book for a refund within 7 days.

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Man Kit Chang is an Assistant Professor in theDepartment of Finance and Decision Sciences at theSchool of Business, Hong Kong Baptist University. Hisrecent research activities have focused on addressingthe issue of the adoption of electronic commerce,knowledge sharing in virtual communities, impact ofsocial media, and information system ethics. Hisresearch has recently appeared in the ACM Transactionson Information Systems, Decision Support Systems,Information and Management, Journal of the OperationalResearch Society, Journal of Organizational Computing,Journal of Business Ethics, among others.

Waiman Cheung Director of Asian Institute of SupplyChains & Logistics and Director of Center of CyberLogistics, holds an MBA and a PhD in Decision Sciencesand Engineering Systems from Rensselaer PolytechnicInstitute. Currently he is also the Chairman of theDepartment of Decision Sciences and ManagerialEconomics in the Business School, The Chinese Univer-sity of Hong Kong. Dr. Cheung is very keen on workingclosely with local industries and has conducted studies.His research interests are mainly in applying IT onlogistics and supply chain management. He hascontributed articles to ACM Transactions on InformationSystems, Decision Sciences, IEEE Transactions on Systems,

Man and Cybernetics, Decision Support Systems, Information & Management, Annals ofOperations Research, International Journal of Electronic Commerce, etc.

Mincong Tang received his PhD degree in ManagementInformation Systems from the Chinese University ofHong Kong in 2011. He is currently a postdoctoralresearch fellow in Beijing Jiaotong University (BJTU). Heis working in the international center for informaticsresearch of BJTU. His recent research interests includeorganizational culture and its impacts on informationtechnologies, supply chain management practices andlogistics management, information systems and infor-mation securities.