a parsimonious model of the antecedents and consequence of online trust: an uncertainty perspective

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A Parsimonious Model of theAntecedents and Consequenceof Online Trust: An UncertaintyPerspectiveArifin Angriawan & Ramendra ThakurPublished online: 27 May 2009.

To cite this article: Arifin Angriawan & Ramendra Thakur (2008) A Parsimonious Modelof the Antecedents and Consequence of Online Trust: An Uncertainty Perspective,Journal of Internet Commerce, 7:1, 74-94, DOI: 10.1080/15332860802004337

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A Parsimonious Model ofthe Antecedents and Consequence of

Online Trust: An UncertaintyPerspective

Arifin AngriawanRamendra Thakur

ABSTRACT. Online trust is an important issue in e-commerce.Recent research has indicated that online trust, or the absence of onlinetrust, is a key inhibitor in an individual consumer’s acceptance ofe-commerce. In an uncertain and complex environment such ase-commerce, online trust is an important mechanism for consumers toreduce uncertainty. Uncertainty inhibits these individuals from makingonline purchases and becoming loyal customers of e-commerce organi-zations. We developed and tested a model of online trust that addressesthe major sources of e-commerce uncertainty and consumer loyalty. Wefound that website usability, expected product performance, security,and privacy collectively explained 70% of the variance in online trust.The strongest predictors were consecutively security, website usability,expected product performance, and privacy. We also found that onlinetrust and privacy explain 50% variance in consumer loyalty.

KEYWORDS. e-commerce, and uncertainty, e-CRM, loyalty,Online trust

Address correspondence to Ramendra Thakur, Assistant Professor ofMarketing, Department of Business Management, School of Business, UtahValley State College, 800 W. University Parkway, Orem, UT 84058. E-mail:[email protected]

Journal of Internet Commerce, Vol. 7(1) 2008Available online at http://jicom.haworthpress.com# 2008 by The Haworth Press. All rights reserved.

doi: 10.1080/15332860802004337 74

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INTRODUCTION

In recent years, we have seen the increased popularity and impor-tance of e-commerce. E-commerce has enabled consumers and pro-ducers around the world to meet in virtual marketplaces, and tocomplete transactions more efficiently and effectively. The internetand its related technologies erase the constraints of time and spaceand provide organizations with tremendous business opportunities.Producers and merchants can now serve global markets via the inter-net. Consumers have many more choices of products and merchantsthat serve them at their finger tips. However, the ease of the mediumhas increased the level of competition as well. In this dynamicenvironment of electronic commerce, many consumers are faced withinformation overload and uncertainty. The exposure to multiplemerchants and options from around the world, and the new exchangeinfrastructures of virtual commerce brings increased uncertainty.

Hoffman, Novak, and Peralta (1999) argued that online trust is animportant issue in e-commerce. These authors suggest that trust ischaracterized by lack of control, uncertainty, abnormity, and poten-tial opportunism. Trust, a mechanism to reduce uncertainty, isimportant if we want to take advantage of opportunities and benefitsthat e-commerce offer. Researchers have found that online trust isa major determinant of e-commerce success (Pavlou & Fygensen,2006; Suh & Han, 2003; Keeney, 1999). In this competitive environ-ment, trust is important not only in attracting new consumers, butalso in retaining them to make subsequent purchases. Resear-chers have found the positive impact of retention and relationshipbuilding on organizational performance (Schoder & Madeja, 2004;Reichheld & Teal, 1996; Morgan & Hunt, 1994).

Trust has been a topic of study for many researchers. Some havefocused on the antecedents of online trust and simply assumedits consequences such as purchase intention, loyalty, and referrals(Hoffman, Noval, & Peralta, 1999; Koufaris & Hampton-Sosa,2004). Few have empirically tested both the antecedents and conse-quences of trust. Lee and Turban (2001) focused on and categorizedantecedents of trust as the trustworthiness of internet merchant, thetrustworthiness of the internet shopping medium, contextual factors(e.g. privacy and security) and other factors (e.g. company size anddemographic variables). They found that merchant integrity was animportant determinant of online trust. Lee and Turban also found

Arifin Angriawan and Ramendra Thakur 75

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that the relationship was moderated by consumers trust propensity.Koufaris and Hampton-Sosa (2004) found that major determinantsof initial trust are perceived company reputation, willingness tocustomize products or services, perceived website usefulness, ease ofuse, and security. They found no support for the hypothesized effectof trust and consumer trust propensity. Suh and Han (2003) foundthat privacy and data integrity had a significant impact on online trust,and online trust had a significant impact of e-commerce acceptance.

These studies have shown that online trust is a complex construct.Online trust is a multidimensional construct (Kim, Song, Braynov, &Rao, 2005). This study attempts to develop a parsimonious modelof online trust and business to consumer (B2C) relationship build-ing from the perspective of uncertainty reduction. We investigatedboth the antecedents and consequence of online trust. Other research-ers have found that electronic customer relationship management(e-CRM) is a critical factor of e-commerce success (Schoder &Madeja, 2004). Thus, we include e-CRM in our study. Specifically,the research questions are: (1) what are the major determinantsof online trust? (2) what is the relationship between online trustand loyalty? (3) does e-CRM moderate the relationship betweenonline trust and loyalty? The contribution of the study is that it isone of the first studies to investigate the impact of consumer uncer-tainties on his or her online trust, and how online trust influenceshis or her online loyalty.

HYPOTHESES DEVELOPMENT

Following Morgan and Hunt (1994), we define online trust aswhen a consumer has confidence in an e-merchant’s reliability andintegrity to perform online transactions successfully. This requiresthe merchant to have the ability to conduct business through internetinfrastructure, and to deliver products or services as promised. Thus,according to this definition, online trust would result from amerchant’s privacy policy, security efforts, website effectiveness,expected product performance, and ‘‘after sale’’ support.

Wicks, Berman, and Jones (1999) argued that trust is a mecha-nism that can reduce complexity and uncertainty. According toGrabner-Krauter and Kaluscha (2003) two major sources of uncertain-ties in e-commerce include the internet as a medium of exchange, and

76 JOURNAL OF INTERNET COMMERCE

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the merchant’s ability and willingness to deliver the promised productsand services. Thus, we believe that an online trust model shouldaddress those sources of uncertainties.

Keeney (1999) and Torkzadeh and Dhillon (2002) have argued thate-commerce success must deliver both means and fundamental objec-tives. Keeney (1999) suggests that successful e-commerce depends oncustomers’ perception and belief of the net effects of costs and benefitsof both product and process. We believe that a customer’s perceptionand belief are influenced by their perceived uncertainties regardingboth product and process. According to Torkzadeh and Dhillon(2002), means objectives include product choice, online payment, ven-dor trust, shopping travel, and shipping error. Fundamental objectivesinclude product value, shopping convenience, customer relation, andinternet ecology. It seems that means objectives are related to uncer-tainty in the exchange medium while fundamental objectives arerelated to uncertainty with product or merchants. Thus, the argumentsof Keeney (1999) and Torkzadeh and Dhillon (2002) provide supportto our reasoning of the importance of uncertainty reduction.

From the discussion above, we believe that privacy, security, websiteusability, and expected product performance are the major determinantsof online trust. They serve as the independent variables in our model.The consequences of trust include intention to purchase and customerloyalty (Gefen, Karahanna, & Straub, 2003). Online trust is importantin attracting and maintaining customers. Both are important to ensurebusiness success, especially in e-commerce based businesses. Therefore,we included customer loyalty as a dependent variable.

We also included e-CRM as a moderating variable in our study.Olson and Olson (2000) reported that different types of interactionprovide different levels of trust. They found that face-to-face inter-action produce the highest level of trust. Since e-commerce is not aface-to-face interaction, generating trust in e-commerce setting ismore difficult. We believe that e-CRM can be an important mod-erator of trust and loyalty. Figure 1 shows the research model.

Website Usability

Wicks, Berman, and Jones (1999) argued that information is animportant mechanism to reduce uncertainty. In the context ofe-commerce, a company’ s website is the major method of interactionfor the consumer. Thus, an effective website can generate trust by


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reducing consumer uncertainties by providing necessary informationto consumers. Previous research has found support for the hypothe-sized relationship of website usability and trust (Everard & Galletta,2006; Flavi�aan, Guinalıu, & Gurrea, 2005; Koufaris & Hampton-Sosa,2004). Everard and Galletta (2006) found an inverse relationshipbetween poor design, errors, incompleteness and perceived quality;and this perceived quality directly related to customer trust on thewebsite and their intention to purchase. Flavi�aan, Guinalıu, andGurrea (2005) found a positive relationship between trust andperceived website usability. They defined website usability as theperceived ease of navigating the site or making purchases throughthe internet. Koufaris and Hampton-Sosa (2004) found thatperceived website usefulness, ease of use, and security are significantantecedents of initial trust. We believe that effective website usabil-ity provides consumers with a mechanism and information that canhelp them reduce uncertainty. We hypothesize:

H1: There is a positive relationship between website usability andonline trust.

Expected Product Performance

An important component of e-commerce success is reducingconsumer uncertainty through the integrity and reliability of

FIGURE 1. Research Model


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merchants and their products. We believe that consumer percep-tion of merchants will be reflected in their perception of productperformance. We term this as expected product performance. Thus,the ability to increase the perception of merchant integrity or theirproduct performance will increase consumer trust (Morgan & Hunt,1994). Recently, Kim, Xu, and Koh (2004) found that customer sat-isfaction has a stronger impact on trust for repeat customers thanpotential buyers. Since customer satisfaction depends on the differencebetween expected product performance and perceived product per-formance, Kim, Xu, and Koh’ s finding might suggest that expectedproduct performance has stronger impact on trust for potentialbuyers. We believe that there is a relationship between expectedproduct performance and online trust. We hypothesize:

H2: There is a positive relationship between expected productperformance and online trust.

Privacy

Online transactions often require the sharing of sensitive personaland financial information. Consumers will not make a transactionunless they are confident, or receive assurances, about the abilityand reliability of the merchant to protect the sensitive information.This makes privacy an important issue in trust development.

Privacy is about a merchant’ s policies on customer information man-agement. The merchant’ s information management can include usagetracking and data collection, choice, and the sharing of information withthird parties (Belanger, Hiller, & Smith, 2002). Belanger, Hiller, andSmith found that consumers do not trust merchants that are not ableto protect customer information or demonstrate poor management ofcustomer information. Liu, Marchewka, Lu, and Yu (2004) suggest thattrust mediates the relationship between privacy and behavioral intentionto make an online transaction. Suh and Han (2003) found that privacyand data integrity have a significant impact on trust. Eastlick, Lotz, andWarrington (2006) found strong negative impact of privacy on purchaseintent both directly and indirectly through trust. Consistent with the pre-vious research results, we hypothesize:

H3: There is a positive relationship between privacy and online trust.


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Security

Security issues in e-commerce are related to the abilities ofe-merchants to protect their online transaction systems. Securitythreats include destruction, disclosure, modification of data, denialof service, and=or fraud and abuse (Kalakota & Whinston, 1996).As mentioned earlier, online transactions often require the sharingof sensitive personal and financial data, thus without guarantees ofa secured infrastructure, customers will feel uncertainty and willnot transact with that merchant. Perception of system security is animportant component of online trust. Koufaris and Hampton-Sosa(2004) found that website security is a significant antecedent of initialtrust. Suh and Han (2003) found that privacy and data integrity havea significant impact on trust. We also hypothesize:

H4: There is a positive relationship between security and onlinetrust.

Trust and Loyalty

Loyalty is defined as a customer’ s preference towards ane-merchant that results in repeat buying behavior (Srinivasan,Anderson, & Ponnavolu, 2002). Trust is a significant predictor ofe-commerce acceptance (Suh & Han, 2003), is a determinant ofloyalty (Luarn & Lin, 2003), is positively related to consumersrepurchase, revisit, and positive comments and recommendationsof the site to others (Liu, Marchewka, Lu, & Yu,2004). We believethat trust from the perspective of uncertainty reduction can alsogenerate loyalty.

H5: There is a positive relationship between trust and loyalty.

Electronic Customer Relationship Management (e-CRM)

The objective of customer relationship management is to allow anorganization to treat each customer individually. It can be seen asservice customization. It is believed that such a service will producehighly satisfied and loyal customers (Pepper & Roger, 1999; Verhoef,2003). E-merchants serve a wide variety of consumers, thus customer


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relationship can be very important. Pepper and Rogers (1999)mentioned that relationship marketing is possible as a result of infor-mation technology development. Consistent with their statement,customer relationship is appropriate and important in e-commercesettings which are information technology driven. Verhoef (2003)found that e-CRM has positive effects on customer retention andcustomer share development. We suspect that e-CRM can strengthenthe relationship between online trust and loyalty. We hypothesize:

H6: e-CRM moderates the relationship between online trust andloyalty.

METHODOLOGY

Characteristics of Subjects

Data for this study was collected through a convenience sample.The final usable sample consisted of 759 respondents, which wascomposed of 306 females (40.3 percent) and 453 males (59.6 percent).The majority (96.5 percent) of the subjects were in the age group of19 to 30 years. Approximately 47.1 percent had 1 to 5 years of workexperience whereas 42 percent of the subjects had more than 5 yearsof work experience. About 71.5 percent were Caucasian whereas 17.4percent, 5.1 percent, 2.9 percent, 0.9 percent, and 2.1 percent respect-ively were African American, Asian, Hispanic, Latino, and others.

Constructs Used in this Study

Seven constructs were used in this study. These constructs were (1)Website usability, (2) Expected Product performance, (3) Privacy, (4)Security, (5) Trust, (6) Loyalty, and (7) e-CRM. Thirty three itemswere used to measure these constructs. All items were anchored ona five point scale (1 ¼ strongly disagree and 5 ¼ strongly agree).Appendix 1 provides the items and their sources.

ANALYSIS

EQS was used to test the proposed model. As suggested by Andersonand Gerbing (1988), a two step process was used for analysis. First,


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the measurement model was estimated, and then the structural modelwas tested.

Measurement Model

Confirmatory factor analysis (CFA) was used to model the 26items against the six proposed constructs (i.e. web usability, expectedproduct performance, privacy, security, trust, and loyalty).The resultsindicated that the normalized estimate of multivariate kurtosisexceeded the recommended cutoff point of 3. Hence, as recom-mended by Bentler (1990), the robust maximum likelihood estimationmethod was used. After establishing a good model fit, each constructwas assessed for undimensionality, reliability, and convergent anddiscriminant validity.

According to literature, a construct is said to achieve unidimen-tionality if all the items measuring the underlying constructs are0.50 or above (Bollen, 1990). In this study, the standardizedloading of all the items range from 0.584 to 0.823. This indicatesthat the unidimentianality threshold had been achieved. The analy-sis also indicated that the cronbach’ s alpha for the six constructswere 0.520 or above. As suggested by Hair et al. (1998), compositereliabilities were also calculated for all the constructs. The resultsindicated that the composite reliabilities were in the range from0.671 to 0.866. Refer to Table 1 for each construct’ s items, itemloadings, Cronbach’ s alpha reliability, and composite reliabilityvalues.

After testing for unidimentionality and reliability, constructs werealso tested for convergent and discriminant validity. Convergentvalidity is supported if the average variance extracted (AVE) esti-mates exceed 0.50; discriminant validity is shown when the sharedvariance between any two constructs is less than the square root ofthe AVE by the items measuring the construct (Fornel & Lacker,1981). The result of convergent and discriminant validity amongthe constructs is shown in Table 2.

Structural Model

After estimating the measurement model, the structural model wastested. The overall fit of the structural model was assessed usingSatorra-Bentler Scaled Chi-Square (S-Bv2) and other fit indices.


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The results indicated that the S-Bv2 was 474.14 at 288 degrees offreedom and was therefore significant. As suggested by Baumgartnerand Homburg (1996), other fit indices should also be taken intoconsideration to assess the overall model fit. The results indicted thatthe root mean square error of approximation (RMSEA) was 0.045which was less than the recommended cutoff value of 0.08. Otherfit indices were: CFI of 0.927, IFI of 0.928, and NNFI of 0.918.All these values were above the recommended value of 0.90, thusindicating that the structural model provided a relatively good fit

TABLE 1. Measurement Model, Reliability, and Average VarianceExtracted

Standardized

loadings

t-value� Reliablity Construct=

composite

reliability

Average

variance

extracted (AVE)

Website usablity USAB1 0.742 n=a 0.840 0.833 0.555

USAB2 0.767 19.263�

USAB3 0.753 16.213�

USAB4 0.715 8.541�

Expected EPP1 0.698 n=a 0.520 0.671 0.505

Product

Performance

EPP2 0.718 6.705�

Privacy PRIV 1 0.584 n=a 0.783 0.815 0.597

PRIV 2 0.662 11.790�

PRIV 3 0.820 9.337�

PRIV 4 0.823 8.785�

Security SEC 1 0.767 n=a 0.854 0.833 0.502

SEC 2 0.731 17.752�

SEC 3 0.710 13.988�

SEC 4 0.718 15.345�

Trust Trust 1 0.661 n=a 0.894 0.866 0.481

Trust 2 0.683 16.326�

Trust 3 0.713 13.876�

Trust 4 0.743 12.881�

Trust 5 0.717 11.877�

Trust 6 0.662 13.172�

Trust 7 0.680 13.752�

Loyality Loy 1 0.727 n=a 0.851 0.850 0.534

Loy 2 0.661 11.671�

Loy 3 0.767 10.563�

Loy 4 0.802 10.777�

Loy 5 0.676 9.712�

�All factor loadings are significant at p ¼ 0.005.


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to the data. Table 3 shows the result of the model fit indices for thebase model.

Hypotheses Testing

Five hypotheses were tested in the base model. From the results,it can be seen that website usability (USAB), expected product per-formance (EPP), privacy (PRIV), and security (SEC) are signifi-cant predictors of trust (TRUST) with path coefficients of 0.303(p < 0.01),0.191 (p < 0.05), 0.109 (p < 1.635), and 0.403 (p < 4.843).

TABLE 3. Model Fit Indices-for the Base Model

Fit Indices Acceptable

Fit thresholds

Fit indices of

proposed model

v2=df �3 1.646

RMSEA �0.05 0.045

CFI >0.90 0.927

NFI >0.90 0.836

IFI >0.90 0.928

NNFI >0.90 0.918

90% CI of RMSEA Between 0 and 1 (0.038, 0.053)

Hair et al. (1998) suggested that a value of RMSEA ranging from 0.05 to 0.08

are deemed acceptable whereas Browne and Cudeck (1989) suggested that a

value of RMSEA� .05 indicates a close fit of the model.

TABLE 2. Mean, Standard Deviation, Convergent and DiscriminantValidity Matrix

Construct Mean Standard

Deviation

USAB PPP PRIV SEC TRUST LOY

UASB 4.019 0.712 0.745 0.328�� 0.294�� 0.524�� 0.600�� 0.403��

EPP 3.374 0.893 0.711 0.390�� 0.385�� 0.441�� 0.413��

PRIV 3.647 0.814 0.773 0.425�� 0.435�� 0.358��

SEC 3.954 0.741 0.709 0.670�� 0.465��

TRUST 3.926 0.672 0.694 0.567��

LOY 3.646 0.830 0.731

��Corrlation is significant at 0.01 level (2-tailed).

USAB ¼Website usability; EPP ¼ Expected Product performance; PRIV ¼ Privacy;

SEC ¼ Security; TURST ¼ Trust; LOY ¼ Loyalty. Diagonal elements represent the square

root of the average variance extracted (AVE) between the constructs. For discriminant

validity, diagonal elements should be larger than off-diagonal elements.


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The findings provide support for hypotheses 1, 2, 3, and 4. Theresults also indicated that trust (TRUST) is an antecedent of loyalty(LOY) with a standardized path coefficient of 0.654 (p < 0.01), sup-porting hypothesis 5. Refer to Table 4 and Figure 2 for the results.

TABLE 4. Standardized Path Coefficient and T-Value for the Base Model

Parameter estimates

structural paths

Standardized path

coefficients

Critical value

(p)

Hypotheses

supported

H1: USAB!TRUST 0.303�� 4.160 Supported

H2: PPP!TRUST 0.191�� 1.834 Supported

H3: PRIV!TRUST 0.109� 1.635 Supported

H4: SEC!TRUST 0.403�� 4.843 Supported

H5: TRUST! LOY 0.654�� 8.413 Supported

�p < 0.10; ��p < 0.05; ��p < 0.01.

FIGURE 2. Base Model


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Testing of Moderation Effect

To test if e-CRM moderates the relationship between trust andloyalty, a summated scale of all the items measuring trust ande-CRM was created. The summated scale of trust, e-CRM and itsinteraction effect (trust * e-CRM) was then regressed on thesummated scale of loyalty. Results indicated that the interactioneffect was non-significant (p ¼ 0.566), thus indicating that e-CRMwas not a moderator between trust and loyalty. Thus hypothesis6 is not supported. In other words, it can be said that therelationship between trust and loyalty is direct and is not moderatedby e-CRM.

Model Modification

The results of Lagrange and Wald test indicated the addition of apath from privacy (PRIV) to Loyalty (LOY) to further improve thefit indices of the revised model. The result of the R2 statistics alsosupported the addition of this path in the revised model. FromTable 6, it can be seen that the addition of this path increased thetotal variance explained by approximately 8 percent, thus suggestingthat the revised model is better than the base model. Table 5, Table 6and Figure 3 provide the results of the revised model.

TABLE 5. Model Modification for Purifying the Theoretical Model

Model CFI NFI NNFI RMSEA Model Modifications

Base model 0.927 0.836 0.918 0.045 None

Revised

model

0.932 0.84 0.923 0.044 Drop: Privacy!Trust

Add: Privacy! Loyalty

TABLE 6. R-Square Statistics for Structural Model

Latent Factors Base Model Revised Model

Web usability, Expected Product

performance, Privacy, and SecurityTrust

0.701 0.69

Trust!Loyalty 0.428

Trust and Privacy!Loyalty 0.503

}


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Validation

In an effort to demonstrate model validation, the data was dividedinto two halves, a calibration sample and a validation sample. Theresult of the validation sample indicated very similar results as thoseobtained from the calibration sample. Thus, it provides strongerevidence for the findings. The result of the validation sample is shownin Table 7.

FIGURE 3. Revised Theoretical Model

TABLE 7. Validation Result for the Base Model

Parameter estimates

structure paths

Standardized path

coefficients

Critical value

(p)

Hypotheses

supported

H1: USAB!TRUST 0.30�� 4.20 Supported

H2: EPP!TRUST 0.19�� 1.80 Supported

H3: PRIV!TRUST 0.11�� 1.64 Supported

H4: SEC!TRUST 0.40�� 4.84 Supported

H5: TRUST! LOY 0.654�� 8.41 Supported

��p < 0.05; ��p < 0.01.


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DISCUSSION

The current study empirically examined three research questions.The first was to examine the impact of consumer uncertainties ononline trust. The determinants of online trust in this study were web-site usability, expected product performance, privacy, and security.These variables are believed to be highly related to the reduction ofuncertainty. The results suggest that these variables have a signific-ant and positive relationship with trust. The strongest relationshipswere security, website usability, perceived performance, and privacy,respectively. Overall the results are consistent with previous studies.

Security was the strongest predictor of trust. Belanger, Hiller, andSmith (2002) found that consumers value security features more thanprivacy statements, third party privacy seals, and third party securityseals. Ranganathan and Ganapathy (2002) found that security wasa stronger predictor of consumer purchase than privacy, design,and information content.

The second research question investigated the relationship betweentrust and loyalty. The results indicated that security, website usabil-ity, expected product performance, and privacy collectively explained70% of the variance in online trust, and that online trust explains42.8% variance in loyalty. When the privacy to trust path wasremoved, the collective explanatory power was 69%. However, onlinetrust and privacy explained 50.3% of variance in loyalty, an increaseof 7.5%. This finding suggests that privacy is a more important deter-minant of customer retention than online trust. This finding isconsistent with Morgan and Hunt’ s (1994) work. They found thatopportunistic behavior (similar to privacy) did not impact trust butdid impact relationship commitment. However, the findings of thisstudy are not consistent with other research in online settings. Thoseresults suggested that privacy has a significant impact on trust (Suh& Han, 2003), trust mediates the relationship between privacy andbehavioral intention to make an online transaction (Liu, Marchewka,Lu, & Yu, 2004), and privacy concern impacts negatively on purchaseintent both directly and indirectly through trust (Eastlick, Lotz, &Warrington, 2006).

The third research question was to investigate the relationshipbetween e-CRM, and trust and loyalty. The results of the study didnot provide support of the hypothesized relationship. This findingis consistent with previous studies that found that there is notmuch a firm can do to affect customer loyalty in consumer market


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(Dowling, 2002), and that successful implementation of CRMrequires careful consideration on issues of trust and privacy (Boulding,Staelin, Ehret, & Johnston, 2005). This suggests that e-CRM in facthas direct relationships with online trust and privacy, which in turnlead to loyalty. Thus, e-CRM implementation should be very careful.The wrong implementation can reduce rather than increase customertrust and loyalty.

LIMITATION AND FUTURE RESEARCH

The present paper purposely focused on specific elements that havebeen suggested as significant predictors of trust. Thus, we did notconsider variables that may be related to trust or loyalty develop-ment. For example, we did not take into consideration how dispo-sitional trust of customers can influence the development of trust,or customer loyalty. Future research should include these additionalvariables and investigate those relationships.

The present study does not include satisfaction and commitmentvariables, which many studies have suggested as predictors of trustand loyalty. For example, Flavi�aan, Guinalıu, and Gurrea (2005)found that trust was partially dependent on the degree of websitesatisfaction. They also found positive relationships between websiteusability and trust, and between website usability and satisfaction.In the offline B2B literature, Morgan and Hunt (1994) found supportfor commitment-trust theory. According to the theory, commitmentmediates trust and loyalty. Eastlick, Lotz, and Warrington (2006)argued that trust and commitment are the core elements of servicese-tailers. These studies show the importance and different impactsof satisfaction and commitment on trust and loyalty. Thus, a morecomplete study should include dispositional trust, commitment, andsatisfaction. The inclusion of these additional variables could provideovercome the inconsistent empirical findings of the relationshipbetween privacy, trust, and loyalty.

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RECEIVED: June 3, 2007REVISED: August 19, 2007

ACCEPTED: October 2, 2007


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APPENDIX

Constructs Items Source

Web site usability 1. it was easy to use this website the first

time I used it,

Flavian et al. (2005).

2. it is easy to find the information that I

need from this website.

3. it is easy to navigate within the

website.

4. downloading pages from this website

is quick.

Expected product

performance

1. the company that owns this website

provides quality after sales service

and

2. the company has no-question-asked

return policy

Privacy 1. the company that owns this website

never sells my personal information to

other companies.

Suh and Han (2003), and

Ranganathan and

Ganapathy (2002)


will remove my personal information if

I request it to do so.

3. I was informed about what information

the company would collect about me.


explained how they would use the

information collected.

Security 1. this website implements security

measures to protect its online

shoppers.

Ranganathan and

Ganapathy (2002)

and Koufaris and

Hampton-Sosa

(2004)

2. this website ensures that transactional

information is protected from being

accidentally altered during

transmission on the Internet.

3. this website ensures that transactional

information is protected from being

accidentally destroyed during

transmission on the Internet.

4. this website employs secure modes

for transmitting information.

Trust 1. the company that owns this website

will keep promises it makes to me.

Flavian et al. (2005)

2. this website is characterized by the

frankness and clarity of the services

that it offers to the customer.

(Continued )


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APPENDIX Continued

Constructs Items Source

3. most customers would like to deal

with this website=company

4. I think that this website is concerned

with the present and future interests of

its customers.

5. I think that this website takes into

account the desires and needs of its

users in its offers.

6. this company is fair in its customer

service policies following a transaction

7. this company keeps its customers’

best interest in mind during most

transactions.

Loyalty 1. I seldom consider switching to

another website.

Anderson and

Srinivasan (2003)

2. as long as the present service

continues, I doubt that I would switch

websites

3. I try to use the website whenever I

need to make a purchase.

4. when I need to make a purchase, this

website is my first choice would not

easily switch to other providers.

5. to me this site is the best retail website

to do business with. All these items

were measured by a five point scale

anchored by strongly disagree to

strongly agree.

e-CRM 1. this website provides two-way

communication with its customers.

Reinartz, Kraft, and

Hoyer (2004)

2. this website seeks to improve

relationships with its customers.

3. this website integrates customer

information into its design.

4. this website provides personalized

attention to customers.

5. this website actively promotes

customer retention programs.

6. this website attempts to provide

customized services or product to its

customers

7. this website seeks to build long term

relationship with its customers.


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a parsimonious model of the antecedents and consequence of online trust: an uncertainty perspective

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