The Behavioral Implications of Consumer Trust Across Brick-And-Mortar and Online Retail Channels

8/3/2019 The Behavioral Implications of Consumer Trust Across Brick-And-Mortar and Online Retail Channels

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The Behavioral Implicationsof Consumer Trust

Across Brick-and-Mortarand Online Retail Channels

Qimei ChenDavid A. Griffith

Fang Wan

ABSTRACT. Changes in the retail environment have stimulated retail-ers to develop strategies aimed at synchronizing multiple, complemen-tary channels to service an increasingly diverse consumer marketplace.In this research, two studies are presented to test a model of the behav-ioral implications of trust in brick-and-mortar and online retail channels.Results from Study1 indicate that trustwas influenced primarilyby chan-nel interactivity, and that trust, in turn, influenced behavioral loyalty tothe channel as well as intention to purchase from the channel in bothbrick-and-mortar and online retail channels. Results from Study 2 dem-

onstrate carry-over effects from a retailers online channel to its brick-

QimeiChen is AssistantProfessor of Marketing,University of Hawaii, Departmentof Marketing, College of Business Administration, C303, 2404 Maile Way, Honolulu,HI 96822 (E-mail: [email protected]). David A. Griffith is Assistant Professor ofMarketing, Michigan State University, Department of Marketing and Supply ChainManagement, The Eli Broad Graduate School of Management, 370 North BusinessComplex, EastLansing, MI 48824-1122 (E-mail: [email protected]). Fang Wan isAssistant Professor of Marketing, University of Manitoba, Department of Marketing,I.H. Asper School of Business, 181 Freedman Crescent, Winnipeg, MB, R3T 5V4,Canada.

Authors arelisted inan alphabeticalorder andcontributed to themanuscript equally.

Journal of Marketing Channels, Vol. 11(4) 2004Available online at http://www.haworthpress.com/web/JMC

2004 by The Haworth Press, Inc. All rights reserved.doi:10.1300/J049v11n04_05 61
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and-mortar channel. Academic and practitioner implications related tomulti-channel retailing are presented. [Article copies available for a fee

from The Haworth Document Delivery Service: 1-800-HAWORTH. E-mail ad-dress: Website: 2004 by The Haworth Press, Inc. All rights reserved.]

KEYWORDS.Online retailing, channel integration, e-commerce, cross-channel synchronization

INTRODUCTION

The retail environment continues to evolve, increasing competitionand compelling existing retailers to develop innovative strategies. Al-though currently a minor segment of the $3.2 trillion U.S. retail envi-ronment, the online retail ($35.9 billion) channel is projected to growsubstantially faster than traditional brick-and-mortar channel retailing(Department of Commerce, 2002; National Retail Federation, 2002).The development and growth of the online retail channel has increasednot only inter-channel retail competition (i.e., competition betweenbrick-and-mortar and online channels) (Brynjolfsson and Smith, 2000;Palmer, 1997), but also has stimulated retailers to develop strategiesaimed at synchronizing multiple, complementary channels (i.e., wherea single retailer employs both brick-and-mortar and online retail chan-nels to provide value to consumers) (Mathwick, Malhotra, & Rigdon,

2001; Rubin, 2001; Schoenbachler & Gordon, 2002). For example, es-tablished brick-and-mortar retailers, such as Wal-Mart, Macys, JCPenney, etc., recognizing the importance of the online retail channel,have integrated online storefronts into their channel strategy.

The employment of a multi-channel retail strategy generates a numberof retail strategy issues, such as multi-channel pricing (Keegan, 1998;Mathwick et al., Mathwick, Malhotra, & Rigdon, 2001), cross-channelbranding (Reda, 2000; Rubin, 2001) and cross-channel strategy integra-tion. Fundamental to an effective multi-channel retail strategy is the devel-opment of strong customer relationships. Only after a retailer understandsthe fundamental factors related to the development of strong relationshipsin multiple channels can they effectively develop appropriate retail strate-gies for each channel to maximize overall channel strategy effectiveness.

Central to the study of relationships has been the issue of trust (Hart &Johnson, 1999; Merrilees & Fry, 2002; Sirdeshmukh, Singh, & Sabol,2002). While prior research has added toour understanding of trust in retail

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channels, it hasnotexplored theapplicability of trust across retailchannels,thus providing retail academics and practitioners with limited insights intothis important area. More importantly, research has not explored if a con-sumers trust of a retailer in one channel influences consumer behavioralimplications in the retailers other channels. As understanding these issuesis necessary to the development of theories in the area of multi-channel re-tailing, the conceptual and empirical study of these issues are important ar-eas of inquiry in retail research. These issues are also manageriallyimportant, as failure to understand the behavioral implications of trust inconsumer relationships in a multi-channel retail setting could hinder a re-tailers performance.

In this article, we present a two-study examination of multi-channel

retailing. In the first study, we examine how a consumers trust of a re-tail channel is influenced by both the interactivity of the channel and theconsumer characteristic of risk aversion and how trust in a channel re-sults in consumer behavioral implications in both brick-and-mortar andonline retail channels. We then present a second study to explore thecarry-over effect of a retailers online trust on the behavioral implica-tions in the retailers brick-and-mortar retail channel.

THEORETICAL RATIONALE AND HYPOTHESES

Retailing necessitates an integration of channel and customer ap-proaches (Schoenbachler & Gordon, 2002; Sheth, 1983). As such, in

this research, we employ a channel-and-customer focused conceptualframework by investigating the influence of a key channel characteris-tic (i.e., channel interactivity) and a key consumer characteristic (i.e.,risk aversion) on trust and trusts subsequent influence on behavioralimplications in multiple channels (see Figure 1).

The conceptual framework developed is in no way intended to repre-sent a complete causal nexus of the antecedents and consequences oftrust in retail channels, as trust is not the primary focus of the study.Rather, we wish to examine a rudimentary model of trust across retailchannels to provide insights for multi-channel retailing. As such, the fo-cus of this article is on behavioral implications (i.e., channel loyalty andpurchase intentions) in brick-and-mortar and online retail channels inthe context of multi-channel retailing. We begin our discussion of the

model by conceptualizing trust and then discussing how channelinteractivity and consumer risk aversion influence trust in retail chan-nels and the resulting behavioral implications.

Chen, Griffith, and Wan 63


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TrustTrust is an important element of success in both brick-and-mortar

and online retail channels (e.g., Hart & Johnson, 1999; Hoffman,Novak, & Peralta, 1999; Merrilees & Fry, 2002; Sirdeshmukh, Singh,&Sabol, 2002). The value of trust is derived from a reduction in risk andcost to ones exchange partner (Arrow, 1974). In social science, trusthas been construed predominantly in terms of ones beliefs about themotives or intent of another party (Blau, 1964; Rempel & Holmes,1986). Luhmann (1979, p. 42) argues, one fundamental condition oftrust is that it must be possible for the partner to abuse the trust; indeed itmust not merely be possible for him to do so but he must also have aconsiderable interest in doing so. Similarly, Morgan and Hunt (1994)

conceptualize that trust exists when one party has confidence in the reli-ability and integrity of its exchange partner. Common to these concep-tualizations is the willingness of one party to rely upon the other. Assuch, consistent with prior conceptualizationsof trust, consumer trust isconceptualized in this study as the dependability, competence, and in-tegrity a consumer perceives in a retailer or retail channel.

Channel Interactivity (Channel Characteristic)

Interactivity is an important aspect of retail channels (Hoffman &Novak, 1996; Merrilees & Miller, 2001; Novak, Hoffman, & Yung,2000;Srinivasan, Anderson,& Ponnavolu, 2002). Interactivity refers tothedynamic natureof theengagement that occurs between a retailer and

its customers (Srinivasan, Anderson, & Ponnavolu, 2002).In a brick-and-mortar, channel interactivity could be in the form of inter-

actions with a retailers employeesor friends andacquaintances (Underhill,


ChannelCharacteristic

ConsumerCharacteristic

TrustBehavioral

Implications

FIGURE 1. A Channel-and-Customer Focused Conceptual Framework


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2000; Woodruffe-Burton, Eccles, & Elliott, 2002). Further, Mathwick etal. (2001) argue that the brick-and-mortar retail channel is being trans-formed into a retail interactive theater, staffed to offer advice, cookinglessons, beauty makeovers and fashion shows, thus enhancing channelinteractivity. Similarly, in an online retail channel, interactivity has beenviewed as a critical element driving consumer experiences (Merrilees &Fry, 2002; Hoffman & Novak, 1996; Novak, Hoffman, & Yung, 2000).For example,Albaetal. (1997)argue that interactivity inonlineretail chan-nels influences consumer response. They argue that an online channelsinteractivity in a search/query process can reduce dependence on detailedconsumer memory thus increasing the perceived value that the consumerderives from the transaction. Building on this researchwe theorize that one

outcome of channel interactivity is trust.Researchers theorize that consumer trust is contingent upon the con-

sumers perceived level of interactions with a retailer that provide theconsumer information (Sultan & Mooraj, 2001; Yoon, 2002). Here wetheorize that interactivity in a retail channel increases consumer infor-mation acquisition, e.g., through the dynamic, bi-directional flow of in-formation. Retailers efforts in encouraging information flows viainteractivity signal to consumers a concern and willingness of the re-tailer to involve the consumer in the purchase decision.Thesignalingofconcern and openness for information flows build a consumers trust.Therefore,we theorize that higher levelsof channel interactivity will re-sult in higher levels of trust in both brick-and-mortar and online retail

channels. More formally stated:

H1: Channel interactivity positively influences trust in both brick-and-mortar and online retail channels.

Risk Aversion (Consumer Characteristic)

Risk aversion refers to a consumers avoidance of uncertainty (e.g.,Campbell & Goodstein, 2001; Dowling & Staelin, 1994; Rogers, 1983;Szymanski & Busch, 1987). Risk aversion, as well as consumers percep-tions of the risk inherent in certain retail channels, is commonly studied inthe retailing literature (e.g., Berkowitz, Walton, & Walker, 1979; Cox &Rich, 1964; Donthu & Garcia, 1999; Donthu & Gilliland, 1996; Hawes &

Lumpkin, 1986; Keaveney & Parthasarathy, 2001; Spence, Engel, &Blackwell, 1970; Tan, 1999). Research indicates that highly risk averseconsumers avoid uncertainty in their retail behavior whereas those con-



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sumers with a greater risk-taking propensity tolerate greater uncertainty intheir retail behavior.

Researchers theorize that consumers are vulnerable when they rely onanother partys goodwill (Rousseau, Sitkin, & Ronald, 1998). Intrinsically,trust implies a willingness to accept vulnerability, but with an expectationor confidence that one can rely on the goodwill of the other party (Lewicki,McAllister, & Bies, 1998; Moorman, Zaltman, & Deshpande, 1992). Asrisk averse consumers are unwilling to accept risk, we theorize that con-sumers withhigher risk aversion tendtobe less trusting ofa retailer, or a re-tail channel. Bestowing of trust to a retailer or retail channel increasesconsumeruncertaintyand therefore increases the consumersvulnerability.Thus:

H2: Consumer risk aversion negatively influences trust in both brick-and-mortar and online retail channels.

Interaction Effects of Interactivity and Risk Aversion

The channel-and-consumer focused conceptual framework employedhere suggests interactivity between channel and consumer characteris-tics. For example, Sheth (1983) argues that choice calculus and shop-ping predisposition are outcomes of the interactive effects of consumerand retailer determinants. As such, we theorize that channel interactionand consumer risk aversion will jointly influence trust. For example, it

can be theorized that consumers higher in risk aversion would preferhigher levelsof interactivity in a channel as higher levelsof interactivitywould enhance the bi-directional flow of information thus reducing un-certainty. Therefore, we theorize:

H3: The interaction between interactivity and consumer risk aver-sion positively influences trust in both brick-and-mortar and on-line retail environments.

Behavioral Implications of Trust

Researchsuggests that trust influences behavioral intent (e.g., Geyskens,Steenkamp, & Kumar, 1999; Macintosh & Lockshin, 1997; Singh &

Sirdeshmukh, 2000). For example, conceptualizing trust as a relationshipquality dimension, Smith and Barclay (1997) reported a positive effect oftrust on forbearance fromopportunism.Similarly, MorganandHunt (1994)



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found empirical support for the relationship between a trust and coopera-tive behavior.

In the retailing literature, Sirdeshmukh et al. (2002) found a direct rela-tionship between consumer trust and consumer loyalty. Similarly, in on-line retail research,Lynch,Kent andSrinivasan (2001)note that given theabsence of physical exposure and contact, trust may be particularly im-portant in influencing behavioral implications. It is argued here that trustin a retailer diminishes consumer uncertainty (e.g., false advertising, nothonoring policies, privacy concerns,etc.), thus enhancingpositive behav-iors such as loyalty and purchase intentions. Therefore, we theorize:

H4: Trust is positively related to behavioral implications in brick-

and-mortar and online retail channels.

RESEARCH DESIGN

To enable us to examine the central issues proposed, we cast our investi-gation in the retail context of the apparel industry. We selected this settingfor four key reasons. First, the apparel industry was selected given its over-all importance in the retail sector. Second, the context of apparel was se-lected given the nature of the product. Klein (1998) and Shim et al. (2001)argued that the Internet facilitates information search is particularly usefulfor search goods because the perceived costs of providing and assessingobjective data are low in the online retailing setting.Our choice ofan expe-

rience good is intended to complement the contexts adopted by previousresearch. Third, apparel retailing was determined, via pre-testing, to be anappropriate product category for the intended subjects (i.e., undergraduatestudents). Finally, apparel retailing allows for the direct examination ofmulti-channel retailing as most apparel retailers are currently engaged inthis strategy. Because we know relatively little about multi-channel retail-ing and most notably the carry-over effects across retail channels, we usedthis context as an opportunity to extend knowledge in this area.

STUDY ONE

Sample

One hundred and thirty-eight undergraduate students participated in asurvey at a large mid-western university. Respondents received extracredit in return for their participation. The majority of respondents (65%)



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reported that they had purchased clothes from both brick-and-mortar andonline stores in the six months preceding the research. All 138 respon-dents were Internet users. Sixty percent of respondents indicated greaterthan seven hours of online activity per week. Ninety-three percent of re-spondents were between the ages of 17 and 25, with fifty-eight percentbeing female. Thirty-five percent of respondents reported annual familyincome between $20,000-$59,999, with sixty percent of the respondentsreporting annual family income as over $60,000 and five percent report-ing annual family income at less than $20,000.

Measures

A structured questionnaire was developed. To ensure content validityof the measures, a review of the relevant academic and practitioner litera-ture was conducted. In this study, we conceptualized behavioral implica-tions as consisting of behavioral loyalty andpurchase intentions related toa retail channel. We operationalized behavioral loyalty as a compositemeasurebased on a consumerspurchasing frequencyandamountspent ina retail channel in accordancewith suggestions made by Sirohi, McLaughlin,and Wittink (1998) and Pritchard, Havitz, and Howard (1999). Purchasefrequency for each channel was measured by asking: About how manytimes the respondents purchased clothes from the channel (brick-and-mortar or online) in the past three months (response categories were:never, 1-2 times, 3-4 times, 5-7 times, 8-10 times, and more than 10times). Amount spent in each channel wasmeasured by asking: About the

total amount a respondent spent on buying clothes from the channel(brick-and-mortar or online) in the past three months (the response cate-gories were: $0-$50, $51-$150, $151-$250, $251-$350, $351-$450,$451-$550, and $551 or more). Confirmatory factor analysis of thetwo-item factor structures for behavioral loyalty yielded adequate good-ness of fit measures (brick-and-mortar: chi-square/degree of freedom =2.80, p > .10, NFI = .997, CFI = .998, RMSEA = .12, AIC = 55.43; on-line: chi-square/degree of freedom = 1.80, p > .10, NFI = .992, CFI =.996, RMSEA = .13, AIC = 52.43). In addition, correlations between thebehavioral loyalty items were acceptable (r brick-and-mortar = .78; r online =.91). Each two-item scale was summed to create an index of in-store andonline behavioral loyalty. In addition, factor analysis of the two behav-ioral loyalty items yielded one factor structure, explaining an adequateamount of explained variance (brick-and-mortar = 70.1%; online =76.7%). In thepath analysis, each behavioral loyalty index wasentered asseparate endogenous variables. Purchase intention in each channel was



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measured via a one-item, seven-point scale asking: How likely is it thatyou would consider purchasing clothes from a brick-and-mortar (online)store in the next few weeks? (from very unlikely to very likely).

Trust wasassessed using a four-item, seven-point semantic differentialscale similar to the scales used by Ganesan (1994) and Sirdeshmukh et al.(2002). Respondents were asked to rate their overall trust toward the on-line retail channel or brick-and-mortar retail channel: (1) very undepend-able-very dependable, (2) very incompetent-very competent, (3) of verylowintegrity-ofvery high integrity, and(4) very unresponsive to custom-ers-very responsive to customers (abrick-and-mortar = .76; aonline = .82). Ex-ploratory factor analysis of the four items yielded a single factor solutionforboth onlineandbrick-and-mortar channel with an adequate amount of

explained variance (brick-and-mortar = 62.1%; online = 64.8%). Confir-matory factor analysis of the four-item factor structures for trust yieldedadequate goodness of fit measures (brick-and-mortar: chi-square/de-gree of freedom = 3.14, p < .05, NFI = .997, CFI = .998, RMSEA = .232,AIC = 30.29; online: chi-square/degree of freedom = .43, p > .10, NFI =.999, CFI = .998, RMSEA = .132, AIC = 24.87). For each channel, acomposite index was created.

Channel interactivity was conceptualized as the potential for immedi-ate feedback within a retail channel. Channel interactivity was measuredvia two, four-item, seven-point Likert scales (one for each channel) simi-lar to Jee and Lee (2002) and Li, Kuo and Russell (1999). The scales as-sessed the respondents perception of (1) the interactivity of the brick-

and-mortar/online channel, (2) the responsiveness of the brick-and-mortar/online channel, (3) the availability of the brick-and-mortar/on-line channel, and (4) the sensitivity of the brick-and-mortar/onlinechannel (abrick-and-mortar = .77;aonline = .82). Exploratory factoranalysis ofthe four items yielded a single factor structure for both online andbrick-and-mortar channel with adequate amount of variance explained(brick-and-mortar = 62.8%; online = 60.8%). Confirmatory factor analy-sis of the four-item factor structures for channel interactivity yielded ade-quate goodness of fit measures (brick-and-mortar: chi-square/degree offreedom = 2.48, p > .10, NFI = .997, CFI = .998, RMSEA = .223, AIC =28.96; online: chi-square/degree of freedom = .51, p > .10, NFI = .999,CFI = .996, RMSEA = .139, AIC = 25.02). For each channel, a compositeindex of channel interactivity wascreated forusein subsequent analysis.

Risk aversion was conceptualized as a consumers cognitive and be-havioral disposition toward avoiding uncertainty. Risk aversion wascaptured using a four-item, seven-point Likert scale derived from Raju



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(1980). The scale consisted of (1) I am the kind of person who wouldbuy any new product once (reverse coded), (2) Even for an importantdate or dinner, I wouldnt be afraid of trying a new or unfamiliar restau-rant (reverse coded), (3) I never buy something I dont know about atthe risk of making a mistake, and (4) If I buy a product, I will buy only

well-established brands (a = .75). Exploratory factor analysis of thefour items yielded one factor with 61.22% of total variance explained.Confirmatory factor analysis of the four-item factor structure for riskaversion yielded adequate goodnessof fitmeasures (chi-square/degreeof freedom = 3.44, p < .05, NFI = .995, CFI = .996, RMSEA = .245,AIC = 30.87). A composite index was created by summing up the fouritems and treated as exogenous variables in subsequent analysis.

A multiplicative term incorporating channel interactivity and riskaversion was created to test the interaction effect of channel interactivityand risk aversion for each channel model. We first deducted the meanfrom each index and then multiplied the two difference scores to mini-mize multicollinearity in the path analysis (Aiken & West, 1991). The in-teraction terms were entered in the final path analysis as exogenousvariables.

Results

Totestour hypotheses, wereconfiguredour dataand stacked the parallelmeasures for brick-and-mortar and online retail channels. A dummy vari-

able (0 = online and 1 = brick-and-mortar) was then created; the a pathmodel was assessed for each group using AMOS 4.0 using summatedscales of indicators. This approach is similar to researchers who haveadopted a two-stageprocedure(e.g., Baker,Parasuraman,Grewal, & Voss,2002; Homburg, Hoyer, & Fassnacht, 2002; Sirdeshmukh, Singh, & Sabol,2002; Voss, Parasuraman, & Grewal, 1998). In the path models, channelinteractivity, risk aversion and the interaction term were entered as exoge-nous variables. We allowed two correlational pathsbetween interactivityand the interaction term and between risk aversionand the interaction term.Trust was enteredas an endogenous variable that was predicted by all threeexogenousvariables. In addition, trust wasalso predicting twoendogenousvariablesbehavioral loyalty and purchase intentions. The error terms of

the final two endogenous variables were allowed to correlate. Means ofeach variable in the model and correlations are reported in Table 1a andTable 1b. The path results are presented in Figure 2.



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The general model specified for both channels yielded adequate fit

measures with chi-square/degree of freedomindex being less than 1 (c2 =

12.14, df = 14, p > .10), CFI (.998), NFI (.997) and RMSEA (.05) meet-ing the standards established by Marsh, Balla and Hau (1996), i.e., CFIand NFI exceeding .95 and RMESEA of .08 or lower. The relationshipsamong variables for both brick-and-mortar and online models were simi-lar. As theorized in H1, channel interactivity positively influenced con-sumer trust in both brick-and-mortar (path coefficient = .27, p < .01) andonline (path coefficient = .23, p < .05) retail channels. The results did notsupport risk aversion (H2: path coefficient brick-and-mortar =.04, p > .10;path coefficient online = .19, p > .10) or the interaction of interactivityand risk aversion (H3: path coefficient brick-and-mortar = .05, p > .10; pathcoefficient online = .09,p > .10). However, as theorized inH4, trust signifi-cantly influenced thebehavioral implications of both loyalty (path coeffi-

cient brick-and-mortar = .22, p < .01; path coefficient online = .27, p < .01) andpurchase intentions (pathcoefficient brick-and-mortar = .16,p < .05; path coef-ficient online = .43, p < .01) in both retail channels.


TABLE 1a. Brick-and-Mortar Model Variables: Descriptive Statistics and Cor-

relations (Study One)

Model Variables Mean Std. Dev. Correlations

1 2 3 4 5

1. Channel I nteractivity: Brick-and-Mortar 14.75 2.65 --

2. Risk Aversion 15.65 3.41 .085 --

3. Brick-and-Mortar Trust 10.87 2.81 .269** .019 --

4. Brick-and-Mortar Behavioral Loyalty 8.58 3.13 .159# .166# .199* --

5. Brick-and-Mortar Purchase Intention 4.33 1.04 .147# .065 .166# .521** --

TABLE 1b. Online Model Variables Descriptive Statistics and Correlations (Study

One)

Key Variables in the Model Mean Std. Dev. Correlations

1 2 3 4 5

1. Channel Interactivity: Online 6.85 2.78 --

2. Risk Aversion 10.87 2.81 .065 --

3. Online Trust 11.41 3.31 .238** .274** --

4. Online Behavioral Loyalty 2.75 1.73 .085 .115 .274** --

5. Online Purchase Intention 2.08 1.23 .077 .151# .438** .567** --

Note: **Correlation is significant at the 0.01 level (2-tailed).*Correlation is significant at the 0.05 level (2-tailed).#Correlation is significant at the 0.10 level (2-tailed).


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Further analysis was conducted to assess comparability across brick-and-mortar and online retail channels. To compare the path coefficientsacross models, we followed the procedure recommended by Cohen andCohen (1983) and Jaccard, Turrisi, and Wan (1990). Here, we treated thechannel as a dummy variable andtestedwhether retailchannel moderatedthe relationships in the model. We first took the difference between thecorrespondent path coefficients in the brick-and-mortar and online chan-nel and then subjected the difference to a test of statistical significancewith the following equation:

tb b

S E b S E b=

+

1 2

1 22 2( . . ) ( . . )


ChannelInteractivity

.02

Risk Aversion

.82**

Interaction

ChannelInteractivity

.12**

Risk Aversion

.83**

Interaction

.27**

.04

.05

.23*

.19

.09

Error variance= 6.442**

Trust Brick-and-Mortar

(R explained: 7.3%)2

Error variance= 9.522**

TrustOnline


.22**

.16*

.27**

.43**

Brick-and-Mortar

BehavioralLoyalty


Brick-and-Mortar

PurchaseIntention


OnlinePurchaseIntention


9.047**

.50**

1.053**

2.745**

.51**

1.226**

9.047**

OnlineBehavioral

Loyalty


FIGURE 2. Path Analysis Results (Standardized Path Coefficients) (Study One:

Brick-and-Mortar Model vs. Online Model)

Goodness of Fit Measures:c2= 12.14, df = 14, c2/df = 0.868, p > .10; NFI = 0.995; CFI = .998; RMSEA = .05; AIC = 92.150**Standardized Path coefficients significant at p < .001; *p < .05; # p < .10.


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Where b1 and b2 refer to the unstandardized regression coefficients forgroup 1 and 2 (split the sample on the median of moderator X2); S.E. b1and S.E. b2 refer to the standard error of the unstandardized regressioncoefficient.

As indicated in Table 2, the only significant path difference related tothe trust to purchase intentions relationship, thus indicating cross-chan-nel applicability of the model. Further, differences in explained vari-ance were identified. Comparing across models we note that the modelfor online retail channel explains greater variance of the endogenousvariables than the brick-and-mortar retail channel model (trust: 11.9%for online vs. 7.3% for brick-and-mortar; behavioral loyalty: 7.5% foronline vs. 4.6% for brick-and-mortar; purchase intentions: 18.9% for

online vs. 2.7% for brick-and-mortar).

Discussion

Findings from Study One indicate partial support for the model. Spe-cifically, perceived interactivity was found to be an important influenc-ing variable in the development of trust in both brick-and-mortar andonline retail channels. Second, in both brick-and-mortar and online re-tail channels, a consumers trust in the channel significantly influencedthe behavioral implications of loyalty and purchase intentions. Thefindings suggest that interactivity may serve as a trust building mecha-nism in both brick-and-mortar and online retail channels. Further, andmore importantly, the findings demonstrate the importance of trust in


TABLE 2. Comparing PathCoefficients in Brick-and-Mortar and OnlineModels

(Study One)

Specific Paths Compared In-Store Web t-Value

Channel InteractivityTrust .21**(.064)

.275**(.098)

.555

Risk AversionTrust .037(.134)

.22(.171)

.842

InteractionTrust .009(.023)

.021(.035)

.287

TrustBehavioral Loyalty .254**(.098)

.143**(.043)

1.037

TrustPurchase Intention .064*(.003)

.162**(.029)

3.361**

Note:1.Cells represent unstandardized path coefficients with standard errors in the parentheses.2.Pathcoefficientsare significant atvariouslevels **p< .01; *p< .05, #p< .10. Both unstandardizedand standardizedcoeffi-cients were reported in the cell. Standardized coefficients were included in parentheses.3. T values with ** s ignificant at p < .01.


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both retail channels. These findings indicate the fundamental impor-tance of trust, whether in a brick-and-mortar or online channel, and itsinfluence on consumer behavioral implications.

However, comparison of brick-and-mortar and online retail channelpath models suggests that while consumer trust was a major factor signif-icantly influencing consumer behavioral implications in both brick-and-mortar andonline channels, theeffect of consumer trust was significantlystronger in online retail channels. This indicates the challenge that onlineretailing channel faces. It can be argued that given this channels recentdevelopment, consumers may be wary of its use, thus necessitatinggreater development of trust. Clearly, issues such as privacy, fraud, andpost-transaction delivery services further prohibit consumers from ren-

dering ready trust and faith in this new channel. As theorized by previousresearch (Nohria & Eccles, 1992; Yoon, 2002), this study empiricallydemonstrates that online trust is different from brick-and-mortar trust.Our findings indicate that trust is more critical in stimulating behavioralimplications in an online channel when compared to a brick-and-mortarchannel.

In Study One, consumer trust was operationalized as trust towardeach retail channel (at a general channel level), as opposed to a specificretailers channel alternatives. As such, several additional issues arise.First, will trust still be a significant factor impacting consumers pur-chase intentions when thecontext is restricted to a brand-specific onlineretailer? Second, will channel interactivity and risk aversion influenceconsumer trust in an online brand-specific context? Third, will trust

with a brand-specific retailer in an online channel have behavioral im-plications in the retailers brick-and-mortar channel? Study Two wasdesigned to address these questions.

STUDY TWO

In Study Two, an experiment was designed to investigate two effectsin a brand-specific environment: (1) the effect of interactivity and riskaversion on trust, and (2) the carry-over effect of online trust on in-storebehavioral implication for a multi-channelbusiness.As indicated previ-ously, the employment of a multi-channel retail strategy generates anumber of retail strategy issues. One such issue relates to the stimula-tion of behavioral implications across a retailers channels. As has beenindicated, fundamental to an effective retail strategy is the developmentof strong customer relationships. However, in a multi-channel retail set-



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ting theissue becomes more complicated as each of a retailers channelsmay influence behavioral implications in its other channels. At a prod-uct/brand level, Aaker (1996) argues that a firms brand equity can beleveraged as a firm expands its product line. Extending the branding lit-erature to the retail channel it can be theorized that trust developedwithin one of a retailers channels will generate positive behavioral im-plications for another of the retailers channel (i.e., leveraged to a re-tailers other channels). For example, trust developed with a consumerin an online channel will carry-over to the retailers brick-and-mortarretail operations. As such, building on the rudimentary model hypothe-sized previously, we add the following hypothesis:

H5: In a brand-specific retail setting, consumer trust in an onlineretailer will positively influence consumer behavioral implica-tions in the retailers brick-and-mortar retail channel.

Sample

Sixty-nine students (32 male and 37 female) in undergraduate market-ing courses at a large Western university participated in an experiment inStudy Two. Ninety-two percent of the subjects were between the ages of20 and 25 with the balance between the ages of 26 and 35. Seventy-sevenpercent of subjects had purchased products online. Among them, nearlyhalf of the subjects had purchased online at least once a month (42%),with 24% routinely purchasing clothes online. Subjects spent an average

of 11 hours online per week. Subjects were assigned randomly to twotreatment conditions (i.e., degree of interactivitylow/high) resulting incell sizes of 35 and 34.

Experiment Procedure

Several Web-based search engines (e.g., Yahoo,Netscape,Altavista,etc.) were used to identify apparel retailers targeting the 18-35 market.Careful attention was paid to the interactivity offered in each retailersonline channel. After careful assessment, Lands End(the largest onlineapparel retailer, generating $1.462 billion in revenue in 2001) was se-lected as the context for use in Study Two.

One week prior to the experiment subjects were randomly assignedto two treatment conditions (high/lowinteractivity) and asked to fill outa short-survey regarding their body features (body feature informationwas neededto createvirtual models for thehigh interactivity treatment).



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Although only the body feature information from the subjects assignedto the high interactivity treatment was used, all participants were askedto fill out the short-survey to minimize the confounding effect of thetask. The online shopping task employed necessitated subjects to selectcasual pants and a casual shirt for personal use. In the low-interactivitytreatment condition only color palette and fabric choices were used tostimulate interactivity (with opportunity to increase viewing size ofeach).Thehigh interactivity treatmentconditionconsistedof (1)a colorpalette and fabric choices for the clothing presented (with opportunityto increase viewing size of each) and (2) a virtual model built for eachsubject. Each virtual model was personalized to share the same bodyfeatures as the subject. Further, each virtual model could be manipu-

lated by thesubject (e.g., rotating view, changing color of clothing, etc.).Both experimental treatments constrained subjects from accessing ad-ditional product or company information. Efforts were taken to ensurethat male and female subjects were exposed to comparable apparel interms of price, style, color and fabric.

Experimental sessions were conducted in a computer lab in groupsranging from 8 to 12 participants. Male and female subjects were as-signed to different sessions to avoid cross-gender confounding effects.Experiment administrators gender matched the subjects in each ses-sion. Experiment administrators read the instructions from a script de-scribing the procedures. Subjects were first asked to complete thequestions measuring their pre-exposure to the brand and their risk aver-sion characteristics. Next, subjects were directed to the computers,

preloaded for each treatment. After examining the retailers apparel,subjects were asked to complete the questionnaire with the manipula-tion measures and dependent measures. Subjects were then debriefed.

Measures

As the focus of Study Two was the carry-over effect in multi-channelretailing, behavioral implications were assessed as purchase intentionsin multiple channels. To assess purchase intentions, we employedscales similar to Griffith, Krampf and Palmer (2001), and Baker andChurchill (1977). Online purchase intention was assessed using aone-item seven-point scale (ranging from not likely to very likely)capturing the subjects intention to buy the clothes from the online re-tailer. Brick-and-mortar purchase intention was assessed using atwo-item, seven-point scale (ranging from not likely to very likely)capturing the subjects intention to (1) buy the product if they saw it in



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store, and (2) actively seek out the product in store to purchase it. Thecorrelation for the scale was .92.

As in Study One, trust was assessed using a four-item, seven-point se-mantic differential scale similar to the scale proposed by Ganesan (1994)and Sirdeshmukh et al. (2002). Respondents were asked to rate theiroverall trust toward the online retailer: (1) very undependable-very de-pendable, (2) very incompetent-very competent, (3) of very low integ-rity-of very high integrity, and (4) very unresponsive to customers-veryresponsive to customers (a = .94). Exploratory factor analysis of the fouritems yielded one factor with 83.20%of total variance explained. Confir-matory factor analysis of the four-item, one-factor structure for trustyielded adequate goodness of fit measures (chi-square/degree of free-

dom = .144, p > .10, NFI = .998, CFI = .998, RMSEA = .125, AIC =24.29). A composite index was created (a = .93).

A scale similar to the channel interactivity scale employed in Study One(Jeeand Lee,2002; Li, Kuo,and Russell, 1999) was employedas a manipu-lationmeasure in StudyTwoto assess theinteractivity, responsiveness,sensitivityand availabilityof the low/high interactivity treatments.Thescale was modified to fit into the brand-specific online apparel retail envi-ronment. The four-item, seven-point Likert scale asks: (1) Interacting withthis site is like having a conversation with a sociable, knowledgeable andwarm representative from the company, (2) I felt as if this Web site talkedback to me while I was navigating, (3) I perceive the Web site to be sensi-tive to myneeds for product information, and (4) All of the attributes about

clothes I want to know have been successfully digitized online (a = .92).Exploratory factoranalysis of thefour items yielded a singlefactorsolutionwith 80.66% of the total variance explained. Confirmatory factor analysisof the four-item, one factor structure for channel interactivity yielded ade-quate goodness of fit measures (chi-square/degree of freedom = 5.43, p .05, NFI = .992, CFI = .995, RMSEA = .153, AIC =

29.16). A composite index of risk aversion was created.The interaction term of channel interactivity and risk aversion was de-

veloped similar to Study One. The interaction term was entered as one of



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the exogenous variables in the analysis. Further, as a pre-existing brandwas used in the experiment, pre-exposure attitude toward the brand wasassessed.Attitude toward thebrand wasmeasured by a three-item, seven-point semantic differential scale (a = .93): (1) bad/good, (2) dislike/like,and (3) unfavorable/favorable (Griffith, Krampf, & Palmer, 2001; Smith,1993; Kempf & Smith, 1998).No differences were observed across treat-ment conditions (Xhigh = 4.67,Xlow = 4.21, t = 1.95, p > .10).

Results

Hypotheses were examined through thedevelopmentof a model sim-ilar to that in Study One. However, as the focus of Study Two was toex-

plore the influence of trust on behavioral implications in multi-channelretailing, to address H5, trust was modeled to influence both online andbrick-and-mortar purchase intentions.

A manipulationcheckwas conducted. Results indicated that subjectsin the high-interactivity condition reported a significantly higher levelof channel interactivity than in the low-interactivity treatmentcondition(Xhigh = 8.82,Xlow = 3.11, df = 67, p < .001). The descriptive statisticsand correlations are presented in Table 3.

The proposed model adequately fits the data: chi-square/degree offreedom = 6.43, p < .01; NFI = .96, CFI = .96, RMSEA = .28 and AIC =85.22. Results, supportive of H1 (however, at the retailer level as op-posed to the retail channel level), indicate that channel interactivity intheonline channel positively influenced trust (path coefficient = .92, p

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The Behavioral Implications of Consumer Trust Across Brick-And-Mortar and Online Retail Channels

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