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Introducing Media Richness into an Integrated Model of Consumers'Intentions to Use Online Stores in Their Purchase ProcessEric Brunelle a

a Department of Management, HEC Montréal, Montréal, Québec, Canada

Online publication date: 19 December 2009

To cite this Article Brunelle, Eric(2009) 'Introducing Media Richness into an Integrated Model of Consumers' Intentions toUse Online Stores in Their Purchase Process', Journal of Internet Commerce, 8: 3, 222 — 245To link to this Article: DOI: 10.1080/15332860903467649URL: http://dx.doi.org/10.1080/15332860903467649

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Introducing Media Richness into an IntegratedModel of Consumers’ Intentions to Use Online

Stores in Their Purchase Process

ERIC BRUNELLEDepartment of Management, HEC Montreal, Montreal, Quebec, Canada

The objective of this study was to develop and empirically test aconceptual framework designed to explain consumers’ intentionsto use online stores in their purchase process. The proposed modelintegrates the variables that were identified from a literaturereview and introduces a new dimension: perceived media rich-ness. An online survey was carried out and data from 749 consu-mers was collected and analyzed by applying structural equationmodeling techniques. The results provide empirical support formedia richness theory in a commercial context and for causalrelationships explaining consumers’ intentions to use online storesin their information search and transaction tasks. Managerial andtheoretical implications are discussed.

KEYWORDS electronic commerce, media richness theory, onlineconsumer behavior, online store use intention

INTRODUCTION

The technological developments that have occurred in the last two decadeshave profoundly transformed business practices. These technologies haveallowed a new business space, known as the marketspace, to emerge(Rayport and Sviokla 1994). This electronic business space opens up numer-ous opportunities to firms. In particular, it offers the possibility of reducing,and even eliminating, the spatio-temporal constraints that generally limitthe marketplace. More specifically, the marketspace enables companies togain access to a larger pool of consumers, to be accessible at all times and

Address correspondence to Eric Brunelle, Assistant Professor, Department of Manage-ment, HEC Montreal, 3000, chemin de la Cote-Sainte-Catherine, Montreal, Quebec H3T 2A7,Canada. E-mail: [email protected]

Journal of Internet Commerce, 8:222–245, 2009Copyright # Taylor & Francis Group, LLCISSN: 1533-2861 print=1553-287X onlineDOI: 10.1080/15332860903467649

222

Downloaded By: [Brunelle, Eric] At: 09:04 19 December 2009

from everywhere, to develop new promotional approaches, to save on thecost of disseminating and exchanging information, to reduce payment time,to facilitate the customization of communications and products, to implementmass customization practices, etc. (Quader 2006). Businesses are striving tobenefit from these new technologies and the advantages they create. How-ever, communicating over long distances in time and space raises manymanagement challenges. Among other things, the nature of the informationpresented to consumers is modified (Kirkman and Mathieu 2005; Shekhar2006), which creates a new business relations dynamic. It is, therefore,imperative for companies to fully understand the impact of this new dynamicso they can properly manage relationships and set up efficient consumerinterfaces. In light of these considerations, this study has the goal of proposingand testing a model to explain consumers’ intentions to use online stores toperform information searches and transactions.

A number of studies done in the last few years have shown that a goodunderstanding of consumer behavior on the Internet is essential if firms areto implement effective electronic commerce strategies and deploy functionalconsumer interfaces (Dellarocas 2006; Hansen 2008; Pitta, Franzak, andFowler 2006; Schibrowsky, Peltier, and Nill 2007; Weinberg, Parise, andGuinan 2007). As well, numerous studies have attempted to explain consu-mers’ choice to engage in online shopping (Chang, Cheung, and Lai 2005;Constantinides 2004; Saeed, Hwang, and Yi 2003; Zhou, Dai, and Zhang2007). Nevertheless, although several studies suggest that consumers’behavior can be explained by the media richness theory (Black et al. 2002;Korgaonkar, Silverblatt, and Girard 2006; Pavlou and Fygenson 2006), noresearch has been found that proposes an explanatory model of consumerbehavior based on this theory. With this study, therefore, making two impor-tant contributions to the literature is anticipated. First, the introduction of thevariable ‘‘perceived media richness’’ into an integrated model to explainconsumers’ intentions to use online stores is likely to make a significant con-tribution to the understanding of consumers’ behavior online and to have aconsiderable impact on future research in the field. Second, because thereare practically no integrated models, and still fewer such models that havebeen empirically validated, the results of this research will provide newinsight into this subject and empirically support certain relationships thatcan only be detected when the subject is considered as a whole.

With this in mind, the following sections present a review of the literatureconcerning the media richness theory and the factors that explain consumerintentions to use online stores. This literature review enabled the develop-ment of the model examined in this study, which is presented in the sectionon the conceptual framework. The research program created to test the modelis discussed in the methodology section, after which the results of these ana-lyses are examined. Finally, the implications of this study, its limitations, andavenues for future research are presented in the discussion and conclusion.

Introducing Media Richness into an Integrated Model 223


CONCEPTUAL FRAMEWORK

In order to develop the conceptual framework of this study, literatureconcerning the media richness theory and studies investigating the factors thatexplain consumer intentions to use online stores was reviewed. This enabledthe identification of numerous variables, such as consumer experience,consumer confidence, perceived risk, consumer attitude, consumer motives,and consumer product involvement, and the development of an integratedmodel that relates these variables to each other and introduces a new variable:perceived media richness. Figure 1 presents this model. On the basis ofprevious research, this model presents the hypotheses formulated regardingthe relationships between these variables. The following sections present aliterature review and explain the hypotheses formulated in the model.

Media Richness Theory

The media richness theory suggests that individuals’ performance in acommunication context will be a function of the fit between the characteris-tics of the medium—media richness—and the characteristics of the task to beachieved—task analyzability (Daft and Lengel 1986). Media richness refers toa medium’s ability to convey certain types of information and is determinedby its capacity for immediate feedback, the multiple cues and sensesinvolved, language variety, and personalization (Lengel and Daft 1988).Along these dimensions, media are ranked on a continuum describing theirrelative richness from richest to leanest: face-to-face is the richest medium,followed by telephone, voice messaging, electronic mail, and Web sites

FIGURE 1 Conceptual model—the intention to use an online store.

224 E. Brunelle


(Rice, 1992). Task analyzability refers to the degree to which tasks involve theapplication of objective, well-understood procedures that do not requirenovel solutions. An analyzable task corresponds to a task with clear, precisepredetermined responses to potential problems; an unanalyzable task, on theother hand, requires individuals to think about, create, or find satisfactorysolutions to problems outside the domain of facts, rules or procedures. Asa result, in a commercial context, it is anticipated that richer media, such asface-to-face meetings, are more appropriate for unanalyzable tasks, whereasleaner media, such as Web sites, are more appropriate for analyzable tasks.

As Carlson and Zmud (1999) and King and Xia (1997) pointed out, themedia richness theory has been investigated in a number of studies. Gener-ally, the results supported the theory when tested on so-called traditionalmedia, such as face-to-face meetings, telephone calls, letters, and memos.However, inconsistent empirical findings have resulted from the introductionof new media such as electronic mail and voice mail. These inconsistencieshave encouraged a reconsideration of the media richness theory to handlenew media. As a result, an extension of the theory has been formulated:the channel expansion theory. The channel expansion theory suggests thatpast experiences influence how an individual develops richness perceptionsfor a given channel (Carlson and Zmud 1999). The experience acquired byindividuals allows them to develop knowledge bases that may be used tomore effectively encode and decode rich messages on a channel. Thus,individuals with more experience with the channel and with the topic areable to participate more easily in rich communication via lean channels. Inthe context of this study, it is hypothesized that the more experience aconsumer has with online stores and with the product purchasing process(information search and transaction tasks), the more likely he or she is toperceive online stores as being rich media.

The media richness and channel expansion theories have primarilybeen used to study intra-organizational communications. More specifically,these theories have been relied upon to study the use of media by workers(Daft and Lengel 1986; Trevino, Lengel, and Daft 1987), teleworkers (Higaet al. 2000), virtual teams (Wijayanayake and Higa 1999), and managers(Lengel and Daft 1988; Markus 1994). As well, they have been applied tostudy the use of electronic mail (Adria 2000; Dawley and Anthony 2003;Lee 1994; Marginson, King, and McAulay 2000; Markus), the use of multi-media (Lim, O’Connor, and Remus 2005), the execution of negotiation tasks(Purdy, Nye, and Balakrishnan 2000), teamwork (Alge, Wiethoff, and Klein2003; Lowry and Nunamaker 2003), workers’ performance (Mennecke,Valacich, and Wheeler 2000; Suh, 1999), the quality of organizationalcommunication (Byrne and Lemay 2006; Cable and Yu 2006), the transferof knowledge (Hasty, Massey, and Brown 2006), the accuracy of distanceeducation programs (Shepherd and Martz 2006), the quality of service(Froehle 2006; Vickery et al. 2004), and the impact of media on product



development (Banker, Bardhan, and Asdemir 2006; Ganesan, Malter, andRindfleisch 2005). Moreover, these theories have also been applied to betterexplain users’ disappointment in a computer-assisted communication context(Carlson and George 2004; Zhou et al. 2003) and to compare the differencesbetween men’s and women’s choices to use one medium rather than another(Dennis, Kinney, and Hung 1999).

In addition, the findings of consumer behavior studies show that consu-mers are likely to adopt different behaviors depending on which channel isused (Korgaonkar et al. 2006; Teo 2006). In accordance with the mediarichness and channel expansion theories, it is proposed that these differentbehaviors may be explained by consumers’ perception of media richness.

Along the same lines, Degeratu, Rangaswamy, and Wu (2000) demon-strated empirically that there is a distinction between two kinds of informa-tion that affect consumers’ decision-making process. These are sensoryinformation, such as information resulting from the evaluation of the productby seeing it, touching it and smelling it, and non-sensory information, such asthe factual description of the product and the presentation of its technicalfeatures. These two kinds of information are closely related to the conceptof information richness. Rich information is personalized, flexible, andquickly adaptable to the individual’s needs and requirements (Rice 1992).Consequently, a channel that provides rich information allows an individualto obtain sensory information more easily than a channel that offers leaninformation. Conversely, a channel that provides lean information enablesa person who is looking for non-sensory information to avoid beingswamped by a sea of useless data and to find the desired information moreefficiently (Daft and Lengel 1986). Thus, it is hypothesized that the perceivedmedia richness of an online store will positively explain consumers’intentions to use the online store in a non-sensory information search taskor a sensory information search task. Moreover, it is hypothesized thatconsumer channel experience and consumer product purchase experiencewill positively explain the perceived media richness.

Factors that Explain Consumer Intentions to Use Online Stores

Past studies have identified several factors that constitute vital input inbuilding a conceptual framework to explain consumers’ intentions to useonline stores to perform information search tasks and transaction tasks. Inthis section, therefore, these factors and their anticipated relationships basedon earlier research are presented.

RELATIONSHIP BETWEEN INFORMATION SEARCH AND TRANSACTION CHANNEL

As already noted, two steps in the buying process are most affected by onlineshopping: the information search task and the transaction task (Anderson

226 E. Brunelle


and Anderson, 2002). Past studies found a relationship between the intentionto use an online store to perform an information search and the intention touse such a store to conduct the transaction (Verhoef, Neslin, and Vroomen,2007). Thus, based on past studies, it is hypothesized that the intention to usean online store to perform a non-sensory information search task or asensory information search task would positively explain the intention touse an online store to perform the transaction task.

CONSUMER CHANNEL CONFIDENCE AND PERCEIVED CHANNEL RISK

Consumer channel confidence and perceived channel risk play an importantrole in decision-making by online consumers (Chang et al. 2005; Devaraj,Fan, and Kohli 2006; Garbarino and Strahilevitz 2004; Lee and Tan 2003;Montoya-Weiss, Voss, and Grewal 2003). This is because e-commerceactivities are technology-intensive and, therefore, entail only limited humancontact. This feature of e-commerce makes it essential to build consumerconfidence and reduce perceived risk (Molesworth and Suortti, 2002). Blackand colleagues (2002) and Bhatnagar, Misra, and Rao (2000) stated that theprobability of a consumer’s preference for a particular channel increases sig-nificantly if the level of confidence in that channel is high and the perceivedrisk is low. Montoya-Weiss and others established that the perceived riskassociated with the use of a Web site has a negative impact on consumers’preference for and satisfaction with that channel. Moreover, the confidencelevel for a channel is mostly a matter of perceived risk (Garbarino andStrahilevitz). Based on past studies, it is hypothesized that consumer channelconfidence would positively explain the intention to use an online store toperform a non-sensory information search task, negatively explain the inten-tion to use an online store to perform a sensory information search task, andpositively explain the intention to use an online store to perform a transac-tion task. It is also hypothesized that perceived channel risk would negativelyexplain the intention to use an online store to perform a non-sensory infor-mation search task, positively explain the intention to use an online store toperform a sensory information search task, and positively explain the inten-tion to use an online store to perform a transaction task. Finally, it is hypothe-sized that perceived channel risk would negatively explain consumerchannel confidence.

CONSUMER CHANNEL ATTITUDE

Many studies link consumer attitude toward an online store with theintention to use this channel (Goldsmith 2002; Jarvenpaa and Staples 2000;Madlberger 2006; So, Wong, and Sculli 2005). A positive attitude towardonline shopping strongly influences the intention to buy online. It also hasa positive impact on the desire to browse e-merchants’ Web sites and a



negative impact on the intent to change channels (Fayawardhena 2004).Consequently, consumer channel attitude should be part of any explanationof consumer online store use. Moreover, Balabanis and Reynolds (2001)showed that consumers’ attitude toward online stores is explained by theperceived channel risk. This leads to the formulation of the following hypoth-eses. Based on past studies, it is hypothesized that consumer channel attitudewould positively explain the intention to use an online store to perform anon-sensory information search task, the intention to use an online store toperform a sensory information search task, and the intention to use anonline store to perform a transaction task. Moreover, it is hypothesized thatperceived channel risk would negatively explain consumer channel attitude.

TYPES OF CONSUMER MOTIVES

The type of consumer motivation influences consumers’ intentions to useonline stores in their information searches (Sanchez-Franco and Rolden2005; Sivaramakrishnan, Wan, and Tang 2007). Black and colleagues (2001)pointed out that consumers who are driven by instrumental motives tend toprefer convenience channels such as the Internet, whereas consumers drivenby social motives prefer face-to-face channels, such as physical stores. Donthuand Garcia (1999), Li, Kuo, and Russel (1999), and Swaminathan,Lepkowska-White, and Rao (1999) obtained similar results for convenience-oriented consumers, who prefer the Internet, versus social-oriented consu-mers, who prefer physical stores. Kaufman-Scarborough and Lindquist(2002) and Degeratu and others (2000) show a connection between Internetuse and the perception of convenience provided by that channel. Joines,Scherer, and Scheufele (2003) emphasized that consumers who are drivenby economic motives (an instrumental motive) spend more time searchingfor product and service information and are much more likely to use multiplechannels in their shopping process. Rohm and Swaminathan (2004) observefour factors motivating online shoppers: (1) search for convenience, (2) disin-clination to go out to a store, (3) desire for information for planning and shop-ping purposes, and (4) search for product and brand alternatives offered byretailers. All of these motivating factors are instrumental. Based on paststudies, therefore, it is hypothesized that consumer instrumental motiveswould positively explain the intention to use an online store to perform anon-sensory information search task, negatively explain the intention to usean online store to perform a sensory information search task, and positivelyexplain the intention to use an online store to perform a transaction task. Itis also hypothesized that consumer social motives would negatively explainto the intention to use an online store to perform a non-sensory informationsearch task, positively explain the intention to use an online store to perform asensory information search task, and negatively explain the intention to usean online store to perform a transaction task.

228 E. Brunelle


CONSUMER PRODUCT INVOLVEMENT

Many studies link consumer product involvement with the intent to use onlinestores in the information search process (Demangeot and Broderick 2006;Eroglu, Machleit, and Davis 2003; Floh and Treiblmaier 2006; Kolodinsky,Hogarth, and Hilgert 2004; Nysveen and Pedersen 2005; Wu 2002). Thesestudies show that the higher the consumer’s product involvement, the moreinformation of all kinds the consumer needs. Moreover, studies show thatconsumer channel confidence and perceived channel risk explain consumerproduct involvement (Bart et al. 2005; Elliott and Speck 2005; Pires, Stanton,and Eckford 2004). Thus, past studies led to the hypothesis that consumer pro-duct involvement would positively explain both the intention to use an onlinestore to perform a non-sensory information search task and the intention touse an online store to perform a sensory information search task. Moreover,they also led to the hypothesis that consumer channel confidence wouldnegatively explain consumer product involvement, while perceived channelrisk would positively explain consumer product involvement.

DEMOGRAPHIC CHARACTERISTICS

Several studies have assessed the impact of consumers’ demographic charac-teristics on their intention to use online stores. The results in this regard arenot conclusive. In fact, different studies have shown that younger consumersare likely to be more receptive to online stores (Black et al. 2002); that,despite the fact that older consumers use the Internet less in their informationsearches, they buy more online than young people (Sorce, Perotti, andWidrick 2005); that people who buy online have higher incomes (Keaveneyand Parthasarathy 2001; Li et al. 1999); and that men and women differ in thisregard (Garbarino and Strahilevitz 2004; Van Slyke, Comunale, and Belanger2002). On the other hand, other studies have generated contradictory resultsand provide no statistical support for positing a difference based on age,income, or sex (Bhatnagar and Ghose 2004; Joines et al. 2003; Karayanni,2003). To sum up, the results of studies of demographic profile are inconclu-sive and suggest that it is necessary to do more research to improve knowl-edge of this question and to verify the relationships among these variables.

METHODOLOGY

Data Collection and Sample

To ensure that respondents possessed a minimum knowledge of Internetuse, an online survey was designed to carry out this study and test theconceptual model. In accordance with theory application research (Calder,Phillips, and Tybout 1981, 1982, 1983; McGrath and Brinberg 1983), high



internal validity was the goal. Thus, the study was limited to consumers’intent to buy a personal computer online. This product appears to be per-fectly suitable because consumers can execute the whole shopping process,including the transaction, via the online store and because personal compu-ters are among the most frequently purchased products over the Internet.1

To obtain high internal validity, use of a homogeneous sample is recom-mended (Calder et al. 1981). Thus, e-mail lists provided by four differentuniversity students’ associations to recruit students coming from the same citywere used. A draw for a cash prize of $500 was offered to boost the responserate. A total of 9,880 students were solicited, and 749 usable questionnaireswere recorded in the database and retained to test the hypotheses. Asexpected, the profile of the sample was homogeneous and fairly representa-tive of the universities’ student population: 58.1 percent of the respondentswere between 18 and 25 years of age, 29.4 percent were between 26 and35, and the remainder were age 36 and older. Regarding education, 78percent of the respondents had a university education (48 percent at theundergraduate level and 30 percent at the graduate level), 14.6 percenthad a college education2 (these probably represented the newly admitteduniversity students), 3.1 percent had less than a college-level education,and 5.6 percent did not answer. As for gender, 53.8 percent of the respon-dents were female and 43.3 percent were male, while 2.9 percent did notanswer. Regarding income, 54.2 percent of the respondents made less than$20,000, 24.7 percent had an income of between $20,001 and $40,000, 12.1percent had between $40,001 and $60,000, 4.9 percent made more than$60,000, and 4 percent did not answer. Finally, 36.3 percent of the respon-dents had already bought one computer, 49 percent had already boughttwo or three computers, 5.4 percent had already bought more than threecomputers, 6.3 percent had never bought a computer, and 3.1 percent didnot answer.

Measures

Measures were developed following the procedures proposed by Churchill(1979). Multi-item scales were generated based upon previous measures.To operationalize the online survey, respondents were presented withpictures and a description of the online store of a well-known nationalretailer of electronic goods. This national retailer was selected because itsWeb site was ranked in first place among the most appreciated Canadianretailers’ Web sites by RedFlagDeals.com (December 2004).

Thus, the literature was searched to identify measurement instrumentsand scales that had already been validated, which were then used to collectdata. The 2-item scale developed by Gupta, Su, and Walter (2004a) was usedto measure the intention to use the online store to perform the transaction.Also adapted were the 4-item consumer channel confidence scale developed

230 E. Brunelle


by Bhattacherjee (2002), the 4-item perceived risk scale from Gupta, Su, andWalter (2004b), the 3-item channel attitude scale from Mathwick and Rigdon(2004), the 4-item channel experience level scale from Carlson and Zmud(1999), the 3-item instrumental motives scale and the 3-item social motivesscale from Li et al. (1999), the 6-item consumer product involvement scalefrom Mathwick and Rigdon, and the 4-item perceived media richness scalefrom Carlson and Zmud. Finally, participants were asked, ‘‘Overall, howdo you evaluate your level of experience with the use of computer?’’ in orderto measure consumer product experience. All of these items are presented inTable 1.

An empirically validated measurement instrument was not able to beidentified for the variable intention to use the online store to execute an infor-mation search. Thus, to measure this variable, a scenario approach wasadopted, as done by Gehrt and Yan (2004) and Keen and others (2004). Basedon the definition of Degeratu and colleagues (2000) for non-sensory andsensory information, three scenarios that corresponded to a non-sensoryinformation search task were used, as well as three others that correspondedto a sensory information search task. A 7-point Likert scale anchored by ‘‘notvery probable that I would use the online store’’ to ‘‘very probable that Iwould use the online store’’ was used to measure the consumer’s intentionto use the online store to perform the information search.

Reliability and Validity

Before conducting statistical analyses to test the proposed model, it wasessential to ensure the quality of the measures used and verify their reliabilityand validity (Hair et al. 1998). Therefore, the approach suggested by Bagozziand Yi (1988) was applied, which tested the unidimensionality, reliability,convergent validity, and discriminant validity of the measures. The resultsof this analysis is presented below.

First, as recommended, an exploratory factor analyses was run using aprincipal component analysis to determine the psychometric properties ofthe items composing the different scales. All items loaded as theoreticallyexpected, and no items needed to be removed. Second, as Hair andco-workers (1998) proposed, Cronbach’s alpha was used to verify the reliabil-ity of the measures. As Table 1 shows, Cronbach’s alphas of the scales for allmeasures ranged from .68 to .96, supporting the reliability of the measures.Convergent validity was then assessed by running confirmatory factoranalyses, as Anderson and Gerbing (1988) recommended. As can be seenTable 1, the goodness-of-fit statistics indicate the unidimensionality of themeasures. All factor loadings were highly significant (p< .001), and all theestimates for the average variance extracted (AVE) were higher than the .50level, with the exception of the two scales measuring the intention to usethe online store to perform the information search task (Fornell and Larcker



TABLE 1 Confirmatory Factor Analysis

Construct=items Item loading a

Averagevarianceextracted

Consumer channel confidence .92 .73Online stores are trustworthy .828Online stores allow me to make secure payments .865The information presented by online stores is reliable .832I can shop on online stores with complete confidence .922

Perceived channel risk .84 .57Offer good business opportunities=Do not offer goodbusiness opportunities

.734

Offer a great potential for benefit=Offer a greatpotential for loss

.853

Present a positive situation=Present a negativesituation

.777

Allow me to save money=Do not allow me to savemoney

.646

Consumer channel attitude .89 .73High quality=Low quality .760Good=bad .926Dislike very much=Like very much .873

Consumer channel experience .96 .86I am experienced with online store use .887I feel competent using online stores .968I feel comfortable using online stores .917I feel that online stores are easy to use .935

Consumer instrumental motives .84 .64I like to get accurate information before I buy aproduct

.661

I like to compare prices before I buy a product .793I like to compare product characteristics before I buya product

.923

Consumer social motives .90 .75I like to touch products before I buy them .865I like to see products before I buy them .814I like to try products before I buy them .885

Consumer product involvement .89 .58Means nothing to me=Means a lot to me .637Worthless=Valuable .675Boring=Interesting .873Exciting=Unexciting .884Fascinating=Mundane .706Involving=Uninvolving .488

Perceived media richness .86 .57. . . give and receive timely feedback .850. . . tailor the messages exchanged .903. . . communicate a variety of different cues (such asemotional tone or attitude)

.592

. . .use rich and varied language .637Intention to use an online store for non-sensoryinformation search

.68 .46

Get the product price .706

(Continued )

232 E. Brunelle


1981). In this case, because these two measures were specifically and newlydeveloped for this study, an AVE higher than .40 is acceptable (Menguc andAuh 2006; Zhou, Yim, and Tse 2005). Finally, as Anderson and Gerbingsuggested, discriminant validity was assessed among all measures by using2-factor confirmatory factor analysis (CFA) models. An unconstrained and aconstrained model for each possible pair of constructs were run andcompared. In all cases, the Chi-Square value of the unconstrained modelwas significantly less than that of the constrained model, supporting thediscriminant validity of the measures. Overall, the results showed adequatereliability and validity levels for the measures.

RESULTS

The descriptive statistics and the correlation matrix are presented in Table 2.Following the method described by Byrne (2006), structural equation modelswere employed using EQS 6.1 software to validate the causal relationshipsamong variables in the search model. As Figure 2 shows, the goodness-of-fit statistics are within the range of the recommended level, indicating thatthe proposed model could be tested. To test the model, the standardizedpath coefficient had to be observed. The Beta and the T-value of all the pathsare presented in Appendix A. As can be seen in Figure 2, which presents thesignificance levels for the relationships, the results of this study empiricallysupport the following claims: the perceived media richness of the onlinestore, the type of consumer motive (instrumental vs. social), consumer chan-nel confidence, perceived channel risk, consumer channel attitude, consumer

TABLE 1 Continued

Construct=items Item loading a

Averagevarianceextracted

Get the product characteristics .733Get the brand names that the retailers sell .564

Intention to use an online store for sensory informationsearch

.71 .41

Get a feel for the product use .663Get a feel for the product look .609Get a feel for the retailers .639

Intention to use an online store to perform thetransaction

.94 .90

How willing would you be to buy [the product] fromthe online store?

.941

How probable is it that you would buy [the product]from the online store?

.952

Goodness-of-fit statistics: X2¼ 1,187.122; df¼ 634; X2=df¼ 1.87; D Bentler-Bonett¼ 0.934; CFI¼ 0.968;

IFI¼ 0.968; GFI¼ 0.914; RMSEA¼ 0.036.



TABLE2

Mean

s.Stan

dardDeviation.an

dInter-Correlation

1p

2p

3p

4p

5p

6p

7p

8p

1Consumerch

annelexperience

1.000

2Consumerproduct

experience

0.446

0.00

1.000

3Perceivedmedia

rich

ness

0.166

0.00

0.132

0.00

1.000

4Consumerch

annelco

nfidence

0.513

0.00

0.485

0.00

0.071

0.05

1.000

5Perceivedch

annelrisk

�0.265

0.00

�0.355

0.00

�0.028

0.44

�0.438

0.00

1.000

6Consumerch

annelattitude

0.360

0.00

0.220

0.00

0.193

0.00

0.380

0.00

�0.260

0.00

1.000

7Consumerinstrumental

motives

0.213

0.00

0.040

0.29

�0.057

0.12

0.155

0.00

�0.138

0.00

0.107

0.00

1.000

8Consumersocial

motives

�0.244

0.00

�0.295

0.00

�0.019

0.60

�0.289

0.00

0.289

0.00

�0.095

0.01

0.069

0.06

1.000

9Consumerproduct

involvement

0.331

0.00

0.206

0.00

0.174

0.00

0.195

0.00

�0.155

0.00

0.205

0.00

0.245

0.00

0.056

0.13

10

Age

�0.139

0.00

�0.018

0.63

�0.025

0.50

�0.134

0.00

0.018

0.63

�0.036

0.33

�0.040

0.28

0.017

0.65

11

Gender

0.239

0.00

0.197

0.00

0.103

0.01

0.135

0.00

�0.100

0.01

0.051

0.17

0.000

1.00

�0.247

0.00

12

Inco

me

0.126

0.00

0.229

0.00

�0.032

0.40

0.152

0.00

�0.168

0.00

0.079

0.04

0.035

0.35

�0.167

0.00

13

Non-sensorial

search

over

onlinestore

0.402

0.00

0.353

0.00

0.067

0.07

0.371

0.00

�0.279

0.00

0.329

0.00

0.195

0.00

�0.196

0.00

14

Sensorial

search

overonline

store

0.047

0.20

0.120

0.00

0.331

0.00

0.052

0.16

�0.014

0.71

0.128

0.00

�0.188

0.00

�0.089

0.02

15

Realizationofatran

saction

overonlinestore

0.369

0.00

0.599

0.00

0.199

0.00

0.462

0.00

�0.416

0.00

0.247

0.00

0.014

0.71

�0.491

0.00

Mean

s5.717

3.446

3.162

5.118

3.336

4.714

6.466

4.722

Stan

darddeviation

1.369

1.290

1.421

1.355

1.163

1.204

0.785

1.725

(Continued

)

234


9p

10

p11

p12

p13

p14

p15

p

1Consumerch

annelexperience

2Consumerproduct

experience

3Perceivedmedia

rich

ness

4Consumerch

annelco

nfidence

5Perceivedch

annelrisk

6Consumerch

annelattitude

7Consumerinstrumental

motives

8Consumersocial

motives

9Consumerproduct

involvement

1.000

10

Age

�0.047

0.21

1.000

11

Gender

0.168

0.00

0.087

0.02

1.000

12

Inco

me

�0.008

0.83

0.313

0.00

0.088

0.02

1.000

13

Non-sensorial

search

over

onlinestore

0.231

0.00

�0.140

0.00

0.095

0.01

0.057

0.13

1.000

14

Sensorial

search

overonline

store

0.149

0.00

0.106

0.00

0.107

0.00

0.041

0.27

0.078

0.03

1.000

15

Realizationofatran

saction

overonlinestore

0.182

0.00

0.020

0.60

0.255

0.00

0.179

0.00

0.344

0.00

0.250

0.00

1.000

Mean

s5.362

25.93

0.446

23.811

5.843

2.217

3.681

Stan

darddeviation

1.105

8.11

0.497

19.376

1.344

1.361

2.067

TABLE2

Continued

235


product involvement, and age have a significant effect on a consumer’s inten-tion to use an online store in an information search task. The consumer’sintention to use an online store to search for information, the type of consumermotive (instrumental vs. social), consumer channel confidence, perceivedchannel risk, and gender have a significant impact on the consumer’s intentionto use an online store in a transaction task. Moreover, the consumer’s experi-ence with the Web and with the purchase of the product has a positive andsignificant effect on the perceived richness of the online store, and the per-ceived risk related to the use of the online store has a significant negative effecton the consumer’s confidence in and attitude toward the online store.

It should be noted that the results of this analysis did not support therelationship between the consumer’s attitude toward the online store andthe consumer’s intention to buy online. Thus, the results show that therelationships are at a significant level, except those between attitude andintention and between age and intention.

DISCUSSION AND CONCLUSION

The purpose of this article was to propose and empirically test a model thatexplains consumers’ intentions to use an online store and to introduce per-ceived media richness into this model. The results of this study have certainimplications. First, although further studies will have to be done to improvethe model, the results obtained provide empirical support for an integratedmodel in which all the variables identified in the literature are included.

FIGURE 2 Results.

236 E. Brunelle


There is no denying that this is an important step in the development ofknowledge of the subject and one which will serve as the basis for futureresearch.

Second, these analyses empirically support the media richness theory ina commercial context. These findings open up a new way of explainingconsumers’ intentions to use online stores to perform information searchand transaction tasks. Consumers’ perception of the richness of an onlinestore will have to be considered when analyzing their intentions regardingonline use. Thus, consumers prefer channels that appear to them to offerthe kind of information that best corresponds to what they are searching.Companies must, therefore, endeavor to offer information that correspondsto what consumers seek. Consequently, properly understanding the informa-tion needs of consumers at each step of the consumption process becomes atop priority. Businesses must be able to answer the following question: Whatinformation are consumers looking for and what is the nature of this informa-tion? Once a firm knows this, the key is to determine how to offer such infor-mation most effectively to consumers. Viewed in this way, the challenge forcompanies is to find the arrangement of channels that will be most efficientand effective in supporting consumers as they search for information andcarry out transactions.

Third, these results support the idea that the best approach in develop-ing electronic commerce strategies is to establish them according to thecompany–consumer interface as a whole and not to limit them to the Website itself. This study underscores how important it is for companies to gaina deep understanding of how consumers perceive the richness of their onlinestores. This knowledge will allow companies to efficiently adapt the waythey use their online stores and how they deploy their multi-channel strate-gies in order to better meet consumers’ needs. According to the mediarichness theory, an online store will be more efficient for analyzable tasksand a bricks-and-mortar store for unanalyzable tasks. Companies, therefore,need to understand the nature of the consumer’s task in the shoppingprocess and the consumer’s perception of an online store’s richness. Furtherstudies will have to be undertaken to provide a more complete explanationof the perception of online stores’ richness, explain the consumer’s task indetail, and understand how companies can enhance this perception andinduce consumers to buy online.

Limitations on the Study and Avenues for Future Research

The results of this study lead to the belief that other studies will have to beconducted to develop a better understanding of consumers’ intentions tobuy online. In accordance with theory application research, high internalvalidity was the focus. It is important to remember that this study was con-ducted on a homogeneous sample with specific characteristics. For example,



the mean income of the respondents was low, their level of education wasquite high, and they constituted a fairly young sample. It is very possible thatthese characteristics of the sample influenced the results obtained. The sameis true of the context of the study, which examined only a single product—apersonal computer. Consequently, the results cannot be generalized.Replicating the study in different contexts (with more products from differentcategories, testing the model with different online stores, etc.) and with amore representative sample are relevant research avenues that will improveits external validity.

Moreover, recall that the objective of this research is to explainconsumers’ intent to buy online. However, it must be emphasized that suchan intention does not necessarily guarantee an online purchase. Futureresearch must be carried out to better understand the relationship betweena consumer’s intention to buy online and the actual use of an online store.Other more extrinsic factors, such as the ease with which an online storeversus a bricks-and-mortar store can be accessed, time constraints, social pres-sures, and so on, might bemore relevant in explaining the use of online stores.

NOTES

1. Source: Nielsen Online, ‘‘MegaView Retail,’’ as cited by Marketing Charts, February 4, 2008, http://

www.marketingcharts.com/direct/top-10-online-retail-categories-by-order-size-october-2009-11209/

2. In the province of Quebec, students must study for two or three years at a college after leaving high

school and before going to university.

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APPENDIX A Results of the SEM Analysis

Links Beta T value p

Perceived channel risk! consumer channel attitude �.343 �7.614 ��

Perceived channel risk! consumer channel confidence �.605 �14.013 ��

Perceived channel risk! consumer product involvement �.107 �1.804 ��

Consumer channel confidence! consumer productinvolvement

.135 2.389 ��

Consumer channel experience!perceived media richness .136 2.914 ��

Consumer product experience!perceived media richness .187 4.032 ��

Perceived media richness! search non-sensoryinformation over Web store

.066 1.457 �

Consumer channel confidence! search non-sensoryinformation over Web store

.166 2.799 ��

Perceived channel risk! search non-sensory informationover Web store

�.184 �2.726 ��

Consumer channel attitude! search non-sensoryinformation over Web store

.191 3.726 ��

Consumer instrumental motives! search non-sensoryinformation over Web store

.186 3.983 ��

Consumer social motives! search non-sensoryinformation over Web store

�.109 �2.165 ��

Consumer product involvement! search non-sensoryinformation over Web store

.109 2.374 ��

Age! search non-sensory information over Web store �.066 �1.478 �

Gender! search non-sensory information over Web store .006 0.150 ns

(Continued )

244 E. Brunelle


APPENDIX A Continued

Links Beta T value p

Income! search non-sensory information over Web store .034 0.748 nsPerceived media richness! search sensory informationover Web store

.354 6.154 ��

Consumer channel confidence! search sensoryinformation over Web store

.030 0.463 ns

Perceived channel risk! search sensory information overWeb store

.133 1.791 ��

Consumer channel attitude! search sensory informationover Web store

.059 1.076 ns

Consumer instrumental motives! search sensoryinformation over Web store

�.206 �3.991 ��

Consumer social motives! search sensory informationover Web store

�.091 �1.647 ��

Consumer product involvement! search sensoryinformation over Web store

.182 3.762 ��

Age! search sensory information over Web store .151 3.018 ��

Gender! search sensory information over Web store .044 0.915 nsIncome! search sensory information over Web store .020 0.393 nsConsumer channel confidence!performance of atransaction over Web store

.118 2.474 ��

Perceived channel risk!performance of a transaction overWeb store

�.307 �5.310 ��

Consumer channel attitude!performance of a transactionover Web store

�.053 �1.256 ns

Non-sensory search over Web store!performance of atransaction over Web store

.115 2.428 ��

Sensory search over Web store!performance of atransaction over Web store

.420 4.283 ��

Consumer instrumental motives!performance of atransaction over Web store

.018 0.410 ns

Consumer social motives!performance of a transactionover Web store

�.31 �7.322 ��

Age!performance of a transaction over Web store �.004 �0.107 nsGender!performance of a transaction over Web store .061 1.721 ��

Income!performance of a transaction over Web store .021 0.581 ns

Goodness-of-fit statistics: X2¼ 1,866.943; df¼ 781; X2=df¼ 2.39; DBentler-Bonett¼ 0.904; CFI¼ 0.940;

IFI¼ 0.940; GFI¼ 0.880; RMSEA¼ 0.046.�Significant at p< .10.��Significant at p< .05.��Significant at p< .01.��Significant at p< .001.

ns¼not significant.



Journal of Internet Commerce Introducing Media...

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