an empirical investigation of antecedents of b2b websites

AN EMPIRICAL INVESTIGATION OF

ANTECEDENTS OF B2B WEBSITES’

EFFECTIVENESS

G o u t a m C h a k r a b o r t yV i s h a l L a l a

D a v i d W a r r e n

f

A B S T R A C TThe purpose of this study was to identify the factors that influencecustomers’ perceptions of the effectiveness of business-to-businessWebsites and to test empirically the significance of these factors.Based on a review of academic and trade press literature, weidentified eight factors that are thought to influence business-to-business Website effectiveness. Following standard scaledevelopment procedures, we developed valid and reliable scales formeasuring each of these eight factors. A Web survey-based fieldstudy was conducted in which 540 business customers of a powertool company gave their opinions about one of eight constructionindustry Websites with which they were most familiar. Wesimultaneously tested the significance of these eight factors in

© 2002 Wiley Periodicals, Inc. and

Direct Marketing Educational Foundation, Inc.

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JOURNAL OF INTERACTIVE MARKETING

VOLUME 16 / NUMBER 4 / AUTUMN 2002

Published online in Wiley InterScience (www.interscience.wiley.com).

DOI: 10.1002/dir.10044

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GOUTAM CHAKRABORTY is anAssociate Professor of Marketing atOklahoma State University,Stillwater, Oklahoma.

VISHAL LALA is a doctoralstudent in marketing at OklahomaState University, Stillwater,Oklahoma.

DAVID WARREN is an MBAstudent at Oklahoma StateUniversity, Tulsa, Oklahoma.

This research was conducted withsupport from a large power-toolcompany in the Midwest regionthat wishes to remain anonymous.An earlier version of this articlewon the conference-wide bestpaper award at the DMEF’s 13thAnnual Educators’ Conference. Theauthors acknowledge helpfulcomments and suggestions fromthe editor and an anonymousreviewer of JIM, two anonymousreviewers of the DMEF’s 13thAnnual Educators’ Conference,Loren Zeller, and Tracy Suter.

explaining the effectiveness of Websites. Ourresults suggest that of the eight factorsconsidered, informativeness, organization,transaction-related interactivity, andpersonalization are significant predictors ofWebsite effectiveness. We found no directrelationship between the other factors (non-transaction-related interactivity, privacy/security,accessibility, and entertainment) and Websiteeffectiveness.

The growth of and the hype associated withInternet commerce in both business-to-business(herein referred to as B2B) and business-to-consumer (herein referred to as B2C) domainshave been discussed extensively in both aca-demic and trade press literature (Day, 1998;Lohse, Bellman, & Johnson, 2000; Peppers &Rogers, 2001; Peterson, Balasubramanian, &Bronnenberg, 1997; Porter, 2001; Seybold,2001). Although commerce may be conductedon the Internet in a multitude of ways, one ofthe commonly used methods for generatingcommerce involves selling goods and servicesthrough a company’s Website. While some re-searchers have expressed concerns about com-moditization of undifferentiated products onthe Internet (Alba et al., 1997; Bakos, 1997;Lynch & Ariely, 2000), others have found thatWebsites play important roles in overcomingcommoditization and introducing price hetero-geneity (Brynjolfsson & Smith, 2000). Beyondcommerce, a company’s Website is also used forcommunicating, entertaining, and interactingwith customers, prospects, and other stakehold-ers. Thus, one of the important issues for man-agers is to be able to understand, measure, andtrack the different factors that influence theeffectiveness of their Websites.

Normative prescriptions abound in the pop-ular press literature for what makes a Websiteeffective. Some of these prescriptions are basedon sound communication principles, while oth-ers are common-sense approaches to Websitedesign (Nielsen, 2000). While we are aware that

many consulting companies have conductedextensive empirical research on Website effec-tiveness, the results from such studies are, un-fortunately, not available in the public domain.Published studies in academic literature haveattempted the following: determination of fac-tors that affect consumers’ evaluation of a Web-site (Chen & Wells, 1999; Eighmey, 1997), im-portance of specific features such as interactivityin a Website (Ghose & Dou, 1998; Olson &Widing, 2002), personalization agents (Ansari,Essegaier, & Kohli, 2000; Iacobucci, Arabie, &Bodapati, 2000), privacy and security issues(Milne & Boza, 1999; Phelps, D’Souza, &Nowak, 2001; Yoon, 2002), importance of delayin accessing of Websites (Dellaert & Kahn, 1999;Weinberg, 2000), and importance of Websitebackground (Stevenson, Bruner, & Kumar,2000). Unfortunately, most published academicstudies are somewhat limited by their use ofB2C Websites, student/faculty samples, andsmall sample sizes.

Although B2C Websites have received morethan their fair share of media and researchattention, a recent study by the U.S. Depart-ment of Commerce claimed that B2B Websitesoutperformed B2C Websites in terms of com-merce by more than three times in the year2000 (www.ecommerce.gov). Recognition ofthe operational efficiencies and effectivenessthat emerges from utilizing the Internet is driv-ing an increasingly large number of B2B mar-keters to switch to the Internet for conductingtransactions (Sharma, 2002). Furthermore, theimpact of the Internet in international market-ing is expected to be much greater for B2B thanfor B2C (Samiee, 1998). In general, forecastersagree that the gap between B2B and B2C com-merce will only widen over the next three to fiveyears (Forrester Research, The Gartner Group,and The Boston Consulting Group).

Given the consensus about the importance ofB2B e-commerce, it is surprising that little aca-demic research has empirically demonstratedwhat factors lead to the success of B2B Websites.This gap in knowledge is critical because thefindings from B2C Website research may nottranslate well to B2B Websites due to differ-ences between the two types of sites. For exam-

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ple, Sawhney and Kaplan (1999) as well as Pep-pers and Rogers (2001) argued that most B2Bsituations differ markedly from B2C situationswith respect to transaction volume, averagetransaction amount, number of customers, thenature of relationship between buyer and seller,logistics, and fulfillment issues associated withtransactions, etc.

The objective of our research is to addressthe voids in the knowledge as described above.Specifically, we want to develop a reliable andvalid scale for measuring and understandingthe factors that lead to effective B2B Websites.In the next section, we first briefly review priorresearch about predictors of Website effective-ness. Based on this review, we develop opera-tional definitions of constructs that influenceWebsite effectiveness and propose hypothesesby relating these constructs to Website effective-ness. This is followed by a discussion of thedesign of our survey instrument and method ofdata collection. The results from a survey usinga large sample of B2B customers in the con-struction industry are discussed in the next sec-tion. Finally, we discuss the implications of ourresearch, consider its limitations, and identifyfuture research directions.

CONCEPTUAL FRAMEWORK ANDHYPOTHESES DEVELOPMENTWe view a B2B Website as an interface betweena company and its prospects, customers, andother stakeholders. This differs from B2C Web-sites in that the prospects and customers areother businesses rather than consumers. Ourgoal is to understand the effects of prospects’and customers’ perceptions about different di-mensions of a company’s Website on its effec-tiveness. In considering the perceptions of pros-pects and customers of a B2B Website, we focusmore on broader perceptual constructs (such asorganization, personalization) than very spe-cific Website design features (such as exactcolor combinations, font size). Consequently,our literature review ignores academic and pop-ular press literature that has focused on veryspecific Website design features. Instead, we

build our conceptual model by drawing uponacademic and popular press literature that pro-poses relationships among broader perceptualconstructs as shown in Figure 1. Each of theeight perceptual constructs in Figure 1 has beenidentified by prior researchers (Table 1 pro-vides a summary of prior research) as anteced-ents that influence Website effectiveness as dis-cussed next.

Antecedents of Website Effectiveness

Personalization. Personalization in the con-text of a Website involves treating each visitor asan individual, recognizing visitors when theyrevisit a site, and serving up information basedon his/her explicit or implicit preferences(Peppers & Rogers, 1999). In general, person-alization helps screen out unwanted informa-tion or product options, reduces user effort by

F I G U R E 1Antecedents of Website Effectiveness

A N T E C E D E N T S O F B 2 B W E B S I T E S ’ E F F E C T I V E N E S S


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eliminating the need to provide personal infor-mation or preferences, improves the accuracyof searches, and speeds up the completion oftransactions. Two related sets of issues are rep-resented in this construct (Redmond, 2002;Wind & Rangaswamy, 2001). The first set ofissues relates to the idea that personalized Web-sites attempt to treat a visitor not as a faceless

statistic but as an individual by recognizingwhen a visitor returns to a site, often by address-ing a person by name. This is usually achievedby the use of a registration tool (user ID andpassword) in conjunction with cookie technol-ogy. The second set of issues relates to the ideaof customization of content in a Website. Thatis, one is allowed a greater degree of control

T A B L E 1Antecedent Constructs and Corresponding Literature

Construct Related Issues Relevant Literature

Personalization ● Recognizing visitors● Customization● Tailoring● Customerization● Explicit and implicit personalization● Collaborative filtering

Huffman & Kahn, 1998; Peppers & Rogers, 1999;Ansari, Essegaier & Kohli, 2000; Iacobucci,Arabie, & Bodapati, 2000; Waltner, 2000;Holland & Baker, 2001; Seybold, 2001; Wind &Rangaswamy, 2001; Redmond, 2002

Interactivity ● Machine-mediated communication● Real-time communication● Dialogue● Interactive decision aids● Transaction-related vs. non-transaction-

related interactivity

Berthon, Pitt, & Watson, 1996; Deighton, 1996;Hoffman & Novak, 1996a; Hoffman & Novak,1996b; Deighton, 1997; Ghose & Dou, 1998;McKenna, 1995; Peterson, Balasubramanian, &Bronnenberg, 1997; Day, 1998; Hagel, 1999;Iacobucci & Hibbard, 1999; Hanson, 2000;Haubl & Trifts, 2000; Novak, Hoffman, & Yung,2000; Bickart & Schindler, 2001; Coviello,Milley, & Marcolin, 2001; Holland & Baker,2001; Rayport & Jaworski, 2001; Bauer, Grether,& Leach, 2002; Olson & Widing, 2002

Informativeness ● Product/company details● One-way communication

Hoffman & Novak, 1996a; Bakos, 1997; Eighmey,1997; Chen & Wells, 1999; Keeney, 1999;Brynjolfsson & Smith, 2000; Lohse, Bellman, &Johnson, 2000; Peppers & Rogers, 2001;Seybold, 2001; Sheehan & Doherty, 2001

Organization ● Arrangement of content/links/graphics● E-comprehension● Readability● Chunking● Complexity

Eighmey, 1997; Chen & Wells, 1999; Keeney, 1999;Stern, 2000; Stevenson, Bruner, & Kumar, 2000;Coyne & Hurst, 2001; Nielsen, 2001; Bauer,Grether, & Leach, 2002; Leong, Ewing, & Pitt,2002

Privacy/Security ● Personally identifiable information● Transmission of transactional

information● Lack of control● Trust● Assurance seals● Disclosure statements

Hoffman, Novak, & Peralta, 1999; Korgaonkar &Wolin, 1999; Milne & Boza, 1999; Sheehan,1999; Sheehan & Hoy, 2000; Miyazaki &Fernandez, 2000; Lala, Arnold, Sutton, & Guan,2001; Phelps, D’Souza, & Nowak, 2001; Luo,2002; Yoon, 2002

Accessibility ● Delays● Access lags● Server crash

Dellaert & Kahn, 1999; Hanson, 2000; Weinberg,2000; Bauer, Grether, & Leach, 2002

Entertainment ● Fun● Excitement

Eighmey, 1997; Chen & Wells, 1999; Bruner &Kumar, 2000; Peppers & Rogers, 2001



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over the type of information one is exposed towhen one visits a Website. Consequently, indi-viduals get more relevant and targeted news,information, and even ads. These in turn en-hance the visitor’s experience with the Websiteand increase the effectiveness of the Website(Peppers & Rogers, 1999; Seybold, 2001).

The explicit preference method of customi-zation allows visitors to select both the type ofinformation they want to see and how suchinformation is displayed on the site. This issimilar to the concept of customerization wherelittle prior information about customers exists,but the Website is tailored based on the explicitpreferences of the customer (Wind & Ran-gaswamy, 2001). Explicit personalization iswidely practiced by many Websites, includingportals (such as Yahoo, MSN, etc.), financialservices (such as Schwab and E*Trade), largeretailers (such as Amazon and Wal-Mart), andB2B sites (such as Cisco, Dell, Grainger). Con-cerns about the inability of intelligent agents tomimic human behavior, their tendency to dis-tort information (Redmond, 2002), reductionin level of consumer control (Hoffman, Novak,& Schlosser, 2000), and privacy issues (Milne &Boza, 1999; Phelps et al., 2001; Yoon, 2002) areimportant reasons for the continued use of ex-plicit personalization.

The implicit preference method of customi-zation usually involves serving up different in-formation to different visitors to the same Web-site in either of the two ways. First, informationat a Website may be changed on the fly based onthe past behavior of a visitor at the Websitealong with business rules established by thecompany (Peppers & Rogers, 1999; Waltner,2000). Personalization software engines fromcompanies such as BroadVision are based onthis principle and have been used in both B2C(American Airline’s AA.com) and B2B (Rock-well’s PTplace.com) Websites. Second, informa-tion at a Website can also be changed on the flybased on recommendations systems that usecollaborative filtering applications (Iacobucci etal., 2000) or Bayesian preference models (An-sari, Essegaier, & Kohli, 2000).

Case study-based evidence from corporateWebsites has suggested that personalization in-

creases the likelihood of users revisiting a Web-site (Holland & Baker, 2001). Empirical evi-dence also suggests that consumers are moresatisfied when they are allowed to specify theirattribute preferences in selecting products(Huffman & Kahn, 1998). In summary, whetherthe explicit or the implicit methods of person-alization (or a combination) are employed by aWebsite, the fundamental goal of personaliza-tion is to increase visitors’ quality of experienceat a Website, which in turn increases the Web-site’s effectiveness. Based on this review, we pro-pose the following hypothesis.

● H1: The greater the level of perceived per-sonalization in a Website, the higher is theWebsite’s effectiveness.

Interactivity. Many researchers have arguedthat the uniqueness of the Web as a communi-cation medium over other mediums such astelevision and radio stems from the interactivenature of the Web (Coviello, Milley, & Marco-lin, 2001, Deighton, 1997; Peterson et al., 1997).Interactivity reflects the ability of an organiza-tion to use information technology to addressan individual, gather and remember the re-sponse of an individual, and address the indi-vidual once more in a way that takes into ac-count his/her unique response (Deighton,1996). Machine-mediated interactivity, the formof interactivity seen on the Internet, has theadded advantage of ensuring real-time commu-nication (Hoffman & Novak, 1996a). In gen-eral, interactivity frees customers from their tra-ditional passive roles as receivers of marketingcommunication, gives them greater controlover the information search and acquisitionprocess, and allows them to be active partici-pants in the marketing process (Hoffman &Novak, 1996b). This leads to a more satisfyingWebsite experience, which in turn increases theeffectiveness of Websites (Novak, Hoffman, &Yung, 2000).

While most considerations of interactivity in-volve a dialogue between a company and a cus-tomer or prospect (e.g., McKenna, 1995), pub-lished studies are beginning to appear aboutinteractivity among consumers at a company’s



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Website (Bickart & Schindler, 2001, Holland &Baker, 2001). It has also been noted that inter-activity lies on a continuum with certain Web-sites being more interactive than others. Basedon the degree of interactivity, the marketingpractices of companies have been broadly clas-sified as transaction, database, e-marketing, in-teraction, or network marketing (Coviello et al.,2001; Day, 1998; Hagel, 1999; Iacobucci & Hib-bard, 1999). Our conceptualization of interac-tivity takes these characteristics into consider-ation.

Berthon, Pitt, and Watson (1996) suggestedthat the level of interactivity of a Website iscritical in converting site visitors from “lookers”to “buyers.” In other words, interactivity at aWebsite influences Website effectiveness. Ghoseand Dou (1998) studied interactive functions inWebsites and found that the greater the degreeof interactivity in a Website, the higher is theWebsite’s attractiveness. They found that inter-activity in a Website can take many forms, in-cluding customer support activities (such as or-der status tracking, feedback options), marketresearch activities (such as taking product sur-veys), personal choice helpers (such as key wordsearches, dealer locators), advertising/promo-tion/publicity activities (such as sweepstakes,multimedia shows, and user groups), and evenentertainment activities (such as playinggames). Much research on interactivity focuseson one or more of these interactive functions.Customer service and market research func-tions through a company’s Website are conve-nient, easy, available 24/7, and capable of sav-ing the consumer time and money (Hanson,2000; Rayport & Jaworski, 2001). Personalchoice helpers such as interactive decision aidshave been found to reduce the consumer’s con-sideration set, improve decision quality (Haubl& Trifts, 2000), and result in better overall eval-uation of the decision task (Olson & Widing,2002). User groups or online communities pro-vide a forum for interactions among consumers.The information obtained from such online in-teractions has been found to generate higherlevels of interest than information obtainedfrom the Website (Bickart & Schindler, 2001).Furthermore, these interactions seem to influ-

ence the level of satisfaction (Bauer, Grether, &Leach, 2002) and site loyalty (Holland & Baker,2001).

Although the interactive functions studied byGhose and Dou (1998) cover most facets of inter-activity, we believe their study placed relatively lessemphasis on e-commerce activities as a form ofinteractivity due to the nature of the study (allconsumer goods companies) and the time periodwhen the research was conducted. Specifically, webelieve the ability of a Website to perform trans-action-related tasks, such as purchase and ordertracking, are an important facet of interactivity.This is confirmed by a recent study in the B2Bdomain by Bauer et al. (2002), who found theability of a site to perform transaction-related tasksto influence consumer satisfaction with the site,albeit negatively.

In view of the preceding discussion, we con-ceptualized two dimensions of interactivity:transaction-related and non-transaction-related.Transaction-related interactivity focuses onprospects’ and customers’ activities (at a Web-site) that culminate directly into commerce. Ex-amples of such activities include the ability toplace orders, make payments, and track orders.Non-transaction-related interactivity focuses onactivities that do not lead directly to e-com-merce, such as the ability to interact with otherusers and the ability to compare competitor’sproduct features. Regardless of whether inter-activity leads directly to commercial transac-tions, based on a review of prior research weposit that such activities will enhance custom-ers’ experiences at a Website and consequentlylead to higher Website effectiveness. Thus, wehypothesize the following.

● H2: The greater the perceived transaction-related interactivity in a Website, thehigher is the Website’s effectiveness.

● H3: The greater the perceived non-transaction-related interactivity in a Web-site, the higher is the Website’s effective-ness.

Informativeness. Marketing practitioners andacademic researchers contend that one of theprimary purposes of a company’s Website is to



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provide information to prospects, customers,and other stakeholders (Chen & Wells, 1999;Eighmey, 1997; Lohse et al., 2000; Peppers &Rogers, 2001; Seybold, 2001). In fact, a recentcontent analysis revealed that Websites are be-ing used by firms as a part of an integratedcommunication strategy to serve higher objec-tives such as creating desire and action (Shee-han & Doherty, 2001). This suggests thatmanagers attribute high importance to Websiteinformation. Consequently, the ability of a Web-site to make a visitor feel that the Website hascommunicated something of value is viewed asone of the most important predictors of Web-site effectiveness.

Although informativeness and interactivitymight seem to be similar constructs, it must benoted that informativeness is the ability of aWebsite to make information available. In thissense, informativeness may be looked upon asstatic information available on a Website (Hoff-man & Novak, 1996a). Interactivity, on theother hand, reduces consumer search costs byhelping to access the information available on aWebsite more efficiently (Bakos, 1997; Brynjolf-sson & Smith, 2000). Furthermore, it must benoted that a site may score high on informative-ness regardless of the manner in which is pre-sented. Thus concerns of information overloador formatting are unrelated to the ability of thesite to provide information of value. Finally, weconceptualize informativeness as a perceptualconstruct. Therefore, informativeness is not thesame as the actual amount of information avail-able on a Website, even though we would ex-pect them to be correlated.

In an exploratory study by Keeney (1999), aneed to maximize product information was ex-pressed as one of the main objectives related toe-commerce. Eighmey (1997) conducted a pilotstudy and a field study of Website perceptionsand concluded that effective Websites demon-strate the productive intersection of informa-tion and entertainment. Chen and Wells (1999)found perceived informativeness of a Website tobe the second most important factor in explain-ing variance in visitors’ attitudes toward theWebsite. Lohse et al. (2000) found that thesearch for product information is the most im-

portant predictor of whether someone wouldmake an online purchase. Therefore, we pro-pose the following hypothesis.

H4: The higher the level of perceived infor-mativeness in a Website, the higher is theWebsite’s effectiveness.

Organization. In the early days of Internetadoption by the business world, Website design-ers had little concern for organization of infor-mation (Nielsen, 2001). Over time, good Web-site design practices evolved. These include thechunking of information, the effective use ofhyperlinks, the use of contrasting backgroundto increase legibility of text, etc. (Coyne &Hurst, 2001; Stern, 2000). Today, most Websitedesigners agree that having a lot of informationon a site may be of little value unless visitors tothe Website find the arrangement of informa-tion logical and easy to understand. This is par-ticularly relevant for B2B sites that rely on muchwritten text to convey information (Leong, Ew-ing, & Pitt, 2002). We view organization as theability of a Website to arrange content, informa-tion, images, graphics, etc., in a manner thatincreases clarity of information and makes iteasy for a visitor to find the needed informa-tion. Consequently, a well-organized Websitewill be perceived as being less complex, moreuser friendly, and will increase the quality of avisitor’s experience at the site. This in turn willincrease the effectiveness of the Website.

An exploratory study revealed ease of use asone of the concerns of Website users. Specifi-cally, maximizing ease of user interface, makingaccess easy, and simplifying finding a desiredproduct were mentioned as main objectives(Keeney, 1999). Clearly, an efficiently executedWebsite design that enhances ease of use is animportant factor in determining Website effec-tiveness (Eighmey, 1997). On the other hand,factors such as web page complexity that lowerease of use also lower attitude toward the site(Stevenson, Bruner, & Kumar, 2000). Similarly,in a study based on B2B sites, navigability wasfound to improve commitment toward the sitebut not satisfaction (Bauer et al., 2002). Finally,Chen and Wells (1999) found organization to



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be one of the significant factors in explainingthe variance in visitors’ attitudes toward a Web-site. Therefore, we propose the following hy-pothesis:

● H5: The higher the level of perceived or-ganization in a Website, the higher is theWebsite’s effectiveness.

Privacy and Security. The Pew Internet andAmerican Life Project, in a large-scale study,documented that American Internet users havegreat concerns over how Websites are collect-ing, using, and sharing personally identifiableinformation (www.pewinternet.org). Consum-ers feel a growing lack of control over how theirpersonal information is used by companies andfind it unacceptable for marketers to sell infor-mation about them. In general, privacy con-cerns are a result of lack of control, especiallyover secondary use of information (Phelps etal., 2001; Sheehan & Hoy, 2000), lack of trust inthe Website (Milne & Boza, 1999; Sheehan &Hoy, 2000), and knowledge of informationpractices by companies (Milne & Boza, 1999;Phelps et al., 2001). Gender and age-based dif-ferences also exist with women being more con-cerned about privacy than men and older peo-ple being more concerned than younger people(Milne & Boza, 1999; Sheehan, 1999). Conse-quences of such concerns may vary from notpurchasing at the Website (Phelps, D’Souza, &Nowak, 2001), requesting to be taken off themailing list, spreading negative word of thecompany, complaining to a third party such asan Internet Service Provider, to providing in-complete personal information when register-ing at the Website (Sheehan & Hoy, 1999).

In view of the negative impact of privacy con-cerns not only on sales but also on overall im-age, Websites are interested in alleviating pri-vacy concerns. Steps that may be taken in thisdirection include gaining consumer trust (Hoff-man, Novak, & Peralta, 1999; Luo, 2002; Yoon,2002), providing compensation in exchange forinformation (Sheehan & Hoy, 1999, 2000),posting online disclosure statements (Miyazaki& Fernandez, 2000), providing a statement ofhow the information would be used (Hoffman

et al., 1999), or use of third-party assuranceseals such as Truste. Among these solutions, thefocus of much research has been on developinga good relationship with consumers andthereby enhancing feelings of trust in the Web-site (Luo, 2002; Yoon, 2002). To summarize,privacy concerns are associated with purchasingbehavior (Korgaonkar & Wolin, 1999; Phelps etal., 2001) and satisfaction with the Website(Yoon, 2002), therefore we expect that reduc-tion of these concerns will improve Websiteeffectiveness.

Security issues are centered on transmissionand storage of transactional information by aWebsite. As in the case of privacy, consumersexperience a lack of control over the paymentinformation provided to a Website. Such con-cerns over security issues grow with increases inonline proficiency, probably from greater expo-sure to stories of security lapses on Websites(Hoffman, Novak, & Peralta, 1999).

Security issues raised by environmental con-trol are shared by the Websites and consumers.In contrast, the secondary use of information isa source of conflict between commercial Web-sites and consumers (Hoffman et al., 1999).This has led many Websites to resolve the tech-nical issues related to security. In spite of thesemeasures, visitors may still perceive the Websiteto be unsafe. Other steps taken to lower percep-tions of security concerns include online re-tailer disclosure statements, providing onlinecredit card security guarantees (Miyazaki & Fer-nandez, 2000), and use of third-party assuranceseals such as Verisign or BBBOnline (Lala, Ar-nold, Sutton, & Guan, 2001). Reduction in per-ceptions of security concerns affects web usage(Korgaonkar & Wolin, 1999) and also enhancessatisfaction with the Website (Yoon, 2002).Based on the above review, we propose the fol-lowing hypothesis:

H6: The greater the perceived privacy andsecurity of a Website, the higher is the Web-site effectiveness.

Accessibility. Accessibility refers to the easewith which a visitor can reach a Website. Poordownload speeds due to access lags, transmis-



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sion lags, or server lags can be a source ofirritation to users (Hanson, 2000). Long waitingtime for a Website to download has been shownto negatively affect evaluations of the Website(Dellaert & Kahn, 1999; Weinberg, 2000). Ac-cessibility of a site in general has a positive effecton trust that customers have in a Website(Bauer et al., 2002). Popular media have alsopublicized many incidents where long waitingtimes to load a Web page (due to congestion onthe net) or inability to access a Website tempo-rarily (due to server breakdown or server capac-ity constraints) resulted in user frustration, lostsales, and negative publicity. Therefore, we hy-pothesize the following:

H7: The greater the perceived accessibility ofa Website, the higher is the Website’s effec-tiveness.

Entertainment. Many researchers have sug-gested that the effectiveness of a Website de-pends on whether visitors to a Website feel thatit is able to engage their attention by being fun,exciting, pleasurable, enjoyable, or entertaining(Bruner & Kumar, 2000; Chen & Wells, 1999;Eighmey, 1997). The basic idea is that if visitorsperceive their experience with a Website as en-tertaining, they are more likely to credit theWebsite with positive attributes and are alsomore likely to conduct business with such sites.A Website may score high on entertainment bybeing funny. Amusing animations, jokes, satires,and humorous remarks are often used to makea Website funny. A Website does not, however,have to be laughter-provoking to be entertain-ing; in fact, most entertaining Websites are not.Use of interesting themes, flashy graphics, orappealing site design may contribute to a Web-site experience being perceived as entertaining.

As mentioned earlier, Eighmey (1997) foundthat effective Websites demonstrate the produc-tive intersection of information and entertain-ment. Chen and Wells (1999) factor analyzed anumber of adjectives used to describe Websiteexperiences and found that the entertainmentdimension explained a third of the variance inthe attitude toward the Website. Bruner andKumar (2000) found that animation and graph-

ics make a Web page more interesting, which inturn influences attitude toward the site. Al-though all three studies reported above usedprimarily B2C Websites, marketing practitio-ners have suggested that entertainment valuemay be important even for B2B Websites (Pep-pers & Rogers, 2001). On the basis of this re-view, we propose the following hypothesis:

H8: The higher the level of perceived enter-tainment in a Website, the higher is the Web-site effectiveness.

Website EffectivenessBoth behavioral (such as hit rate or number ofunique visitors) and perceptual metrics havebeen proposed to measure the effectiveness ofWebsites. Many of the commonly used behav-ioral measures (such as hit rate or unique visi-tors) suffer from problems due to the wide-spread availability and use of online robots,non-uniqueness of IP addresses, and caching ofWeb pages by Internet browsers (Dreze & Zu-fryden, 1998). Thus in this research we decidedin favor of a perception-based measure of effec-tiveness of a Website as developed by Chen andWells (1999). They developed a valid and reli-able rating scale to measure overall effective-ness of B2C Websites. We adopted this measure(with appropriate modifications for B2B Web-sites).

METHOD

Study Context and Sample SelectionData for the main study were obtained fromcompanies in the construction industry. Thisindustry was selected for three reasons. First,one of the authors has a lot of domain expertisein this industry and knows the characteristics ofthe main Websites in this industry very well.Second, most companies in this industry inter-act directly with other businesses and not con-sumers. Third, a large power tool company inthe Midwest sponsored this study and allowedus to sample from its customer base for datacollection. A sample of 3,000 companies wasrandomly selected from this power tool compa-



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ny’s opt-in customer e-mail list. These custom-ers were sent an e-mail asking them to partici-pate in a Web-based survey. As an incentive, therespondents were offered a chance to win ahigh-end cordless drill/driver (retail priceabout $300) manufactured by the power toolcompany.

Of the 3,000 e-mails sent, 101 were returnedas undeliverable. A total of 609 responses wereobtained during the two weeks (in March 2001)the survey was hosted on the Website of a mar-ket research company. However, 52 responseshad to be dropped because of problems (aban-donment by respondents, server crash, etc.)during data collection. Thus, we were left with557 completed surveys and an effective re-sponse rate of approximately 19.2%. The char-acteristics of the final sample follow.

About 24% of the respondents belonged tocompanies that had fewer than 10 employees,32% belonged to companies that had between10 and 100 employees, and the remaining 37%belonged to companies that had more than 100employees (7% of the respondents didn’t an-swer this question). These percentages comparefavorably with known characteristics of the cus-tomers for the power tool company. Of therespondents’ job functions, 35% were engi-neers, 11% were managers, 10% were owners,7% were purchasing agents, and the remaining37% performed other functions. It was alsofound that the Internet was most often used bysample respondents to identify, compare, pur-chase, and specify products. The use of theInternet by sample members for managing andbidding for construction projects was less fre-quent.

Scale Development ProcedureWe developed scales for each of the nine con-structs in Figure 1 by using a four-phase iterativeprocedure (Churchill, 1979). In developing ourmeasures, we used items from published scales(with appropriate modifications) wherever pos-sible for measuring constructs used in thisstudy. However, as pointed out in the literaturereview, most published studies used B2C sitesand consumers as their sample. Thus, many ofthe construct operationalizations in the pub-

lished studies had to be modified substantiallybecause of the differences between B2B sitesand B2C sites.

In the first phase, based on the literaturereview, we defined the constructs (see Table 2).Then items from all available measures of sim-ilar constructs in published studies were col-lated to generate a large pool of items. This wassupplemented by additional items developedindependently by the authors. Care was taken totap the domain of each construct as closely aspossible.

In the second phase, the items generated inthe first phase were subjected to a face validitytest by academicians in the fields of Internetmarketing and electronic commerce as well asmanagers from the power tool company. Theywere asked to critically evaluate the items fromthe standpoint of domain representativeness,item specificity, clarity, uniqueness, and appli-cability in the context of the construction indus-try. Based on the feedback received, some itemswere dropped and others were modified to im-prove specificity and precision. In the thirdphase, two focus groups were conducted by thepower tool company with a small sample (12respondents in each focus group) of its custom-ers. In addition, 20 in-depth interviews wereconducted with the customers of this power toolcompany. Again, based on the feedback re-ceived, suggestions from the focus groups, andin-depth interviews, some items were eliminatedand others revised to improve their specificityand precision. The final phase involved testingthe measures, and this is described in detail inthe results section. A brief description of theitems corresponding to each measure remain-ing at the end of the third phase is describednext. All the eight antecedents were measuredusing a 7-point scale ranging from “not at allapplies” to “very much applies.”

Measures and Items

Personalization. We could not find any pub-lished scale for this construct. Thus, the itemsfor this construct were chosen based primarilyon input from focus group participants anddomain experts. At the end of the third phase



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of construct development, we had a four-itemscale for this construct. It contained items suchas “Website recognizes return visitors” and“Website allows customization of content.”These items are identified in Table 2 as P1–P4.

Interactivity. Interactivity as conceptualized byGhose and Dou (1998) is a very broad con-struct. We conceptualized two dimensions ofinteractivity that might impact Website effec-tiveness. Transaction-related interactivity wasoperationalized using four items (identified asTRI1–TRI4 in Table 2). A representative item is“ability to make purchases.” The nontransac-tion-related interactivity is operationalized withseven items (identified as NTRI1-NTRI7 in Ta-ble 2). A representative item is “Website allowscomparisons with competitor’s product fea-tures.”

Informativeness. Chen and Wells (1999) usedthe adjectives “informative,” “intelligent,”“knowledgeable,” “resourceful,” “useful,” and“helpful” for operationalizing informativenessof a B2C Website. Respondents in our focusgroups and in-depth interviews questioned thesuitability of these adjectives in describing B2BWebsites. Based on the feedback from theseinterviews, we developed four items for differ-ent types of information typically offered by acompany in a B2B Website. These items areidentified as I1–I4 in Table 2. A representativeitem is “detailed technical information aboutproducts.”

Organization. Chen and Wells (1999) foundthe adjectives “not messy,” “not cumbersome,”“not confusing,” and “not irritating” to be asso-ciated with organization. Focus group respon-dents and domain experts questioned theappropriateness of all negatively worded adjec-tives in describing B2B sites. Based on theirfeedback, we developed a three-item measurefor the organization including items such as“Website is well organized.” These items areidentified as O1–O3 in Table 2.

Privacy and Security. Although it seems thepopular press is awash with this issue, we could

not find a published scale for measuring con-sumers’ perceptions about this construct in aWebsite context. Consequently, we had to de-velop our own measures (based on commonusage of these terms as well as feedback fromfocus groups and domain experts) for this con-struct. Three items were used to tap into priva-cy- and security-related issues. These items areidentified as PS1–PS3 in Table 2. A representa-tive item is “Website has a posted privacy policy.”

Accessibility. Similar to the privacy/security is-sues, there was no published scale for measur-ing accessibility. Therefore, we developed a two-item measure to tap into the domain of thisconstruct that spans Website loading quicklyand functioning continuously. These items areAC1 and AC2 in Table 2.

Entertainment. Chen and Wells (1999) foundsix adjectives to describe the entertainment fac-tor that came out of the factor analyses of all theadjectives in their study. However, feedbackfrom domain experts and customer interviewsindicated that the adjectives “cool” and “flashy”used by Chen and Wells are less likely to be usedby a mature audience and even less likely to beused to describe B2B site. Similarly, “imagina-tive” was another adjective thought to be moreappropriate for a B2C site by our focus grouprespondents. These three items were thereforedropped. We incorporated the other three ad-jectives from the Chen and Wells study to de-velop a three-item measure for entertainment.These items are identified as E1–E3 in Table 2.A sample item is “Website is fun.”

Website Effectiveness. We used a five-itemmeasure to tap into the domains of this con-struct. These items were adapted from Chenand Wells (1999). Focus group respondentsconsidered one of the items from the Chen andWells scale, “I feel comfortable in surfing thissite,” inappropriate for B2B sites. This item wasdropped. The final scale has five items. Four ofthese items were measured using a 7-point Lik-ert scale with end anchors as “strongly disagree”and “strongly agree.” A representative item is “Iam satisfied with the service provided by this



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T A B L E 2Construct Measures and Validity

Construct Definitions and Items*

Estimation Sample Holdout Sample

Std.Loading

CompositeReliability AVE

Std.Loading


Personalization(P)

Ability of a Website to treat each visitoras an individual and to serve upinformation based on his/her explicit orimplicit preferences

0.83 0.72 0.85 0.74

● The site encourages registration (P1) 0.73 0.80● The site recognizes return visitors (P2) 0.95 0.90

Transaction-Related (TRI)Interactivity

Ability of a Website to engage in two-waycommunication with a visitor for the solepurpose of conducting a transaction

0.87 0.62 0.92 0.74

● The site allows you to make purchases(TRI1)

0.68 0.80

● The site allows you to make payments(TRI2)

0.64 0.78

● The site allows you to check order/shipment status (TRI3)

0.90 0.95

● The site allows you to see in-stockavailability of items (TRI4)

0.92 0.91

Non-transaction-Related(NTRI)Interactivity

Ability of a Website to engage in two-waycommunication with a visitor forpurposes such as offeringrecommendations and productcomparisons, which may not lead to atransaction

0.86 0.61 0.91 0.71

● The site allows customization ofcontent (P3)

0.75 0.80

● The site allows you to configureproduct/pricing options (P4)

0.82 0.79

● The site makes recommendations usingcustomer’s input and preferences(NTRI3)

0.80 0.91

● The site allows online exchange ofinformation with other users (NTRI5)

0.78 0.82

● The site allows comparison withcompetitor’s product features (NTRI6)

0.76 0.84

● The site allows comparison withcompetitor’s product prices (NTRI7)

0.79 0.81

Informativeness(I)

Ability of a Website to provide a visitorwith information of value

0.79 0.51 0.82 0.55

● The site provides detailed technicalinformation about products (I1)

0.84 0.82

● The site provides application or tradespecific usage information (I2)

0.88 0.88

● The site provides general informationabout the company (I3)

0.48 0.46

● The site provides industry-relatednews/information (I4)

0.54 0.73



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T A B L E 2Continued

Construct Definitions and Items*

Estimation Sample Holdout Sample

Std.Loading


Std.Loading


Organization(O)

Ability of a Website to arrange content,information, hyperlinks, images, andgraphics in a manner that increasesclarity of information and makes it easyto find needed information

0.86 0.67 0.91 0.77

● The site is well organized (O1) 0.92 0.96● The site is not cumbersome tonavigate (O2)

0.88 0.91

● The site looks appealing (O3) 0.66 0.74Privacy/Security

(PS)Ability of a site to protect personal,financial, and transaction-relatedinformation of a visitor

0.93 0.82 0.92 0.81

● The site has posted a privacy policy(PS1)

0.86 0.80

● The site has third-partyprivacy/security seal (PS2)

0.93 0.91

● The site emphasizes security of data(PS3)

0.91 0.97

Accessibility(AC)

Ease with which a visitor can reach thesite

0.76 0.61 0.72 0.56

● The site loads quickly (Activity) 0.80 0.76● The site functions continuously (24/7)

(AC2)0.76 0.74

Entertainment(E)

Ability of a Website to engage attentionof a visitor by being fun, exciting,pleasurable, and enjoyable

0.98 0.93 0.98 0.94

● The site is fun (E1) 0.96 0.97● The site is exciting (E2) 0.99 0.99● The site is entertaining (E3) 0.95 0.96

Website (WSE)effectiveness

Overall evaluation of the goodness orbadness of a Website

0.87 0.59 0.88 0.61

● Compared to other Websites in theconstruction industry, I rate thisWebsite as (WSE1)

0.50 0.58

● The site makes it easy to build arelationship with the company (WSE2)

0.71 0.69

● I like to visit this site often (WSE3) 0.66 0.71● I am satisfied with the service provided

by this site (WSE4)0.89 0.89

● I feel it is useful to spend my time atthis site (WSE5)

0.87 0.86

Note. This table shows the final assignment of items to constructs based on the scale purification. Our original conceptualization forassignment of each item to constructs can be inferred by the alphanumeric code. For instance, we conceptualized P1–P4 to load onpersonalization (P). In this table “Std. Loading” is “Standardized Loading” and “AVE” is “Average Variance Extracted.”



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site.” A fifth item, “compared to other Websitesin the construction industry, I rate this Websiteas” was measured on a 5-point scale with end-anchors as “one of the worst” and “one of thebest.” These items are identified in Table 2 asWSE1–WSE5.

Survey InstrumentRespondents were given a drop-down list of tenWebsites (the most common sites in the con-struction industry) and asked to indicate onesite with which they were most familiar. (Re-questing the respondents to pick the most fa-miliar Website didn’t bias response variation. Inother words, the Website selected was equallylikely to be good or bad.) They were then askedto indicate their perceptions of the site chosenfrom the drop-down list on a battery of ques-tions as described in the measures. At the end,they were asked a few questions about theircompany and their job descriptions.

RESULTS

Analysis OverviewAlthough we had 557 completed surveys, two ofthe ten Websites were chosen by very few re-spondents, (Website number four was picked by11, and Website number six was picked by sixrespondents). Because the sample sizes forthese two sites were so small, we decided to dropthese observations from the analysis. Thus, ourdata set was reduced to 540 observations. Wefollowed the commonly accepted procedure ofrandomly dividing the data set into two subsets,an estimation sample (n � 286) and a validationor holdout sample (n � 254). We then usedexploratory factor analysis (EFA), reliabilityanalysis (using Cronbach’s alpha and item-to-total correlation), and confirmatory factor anal-ysis (CFA) to purify our measurement modelusing only the estimation sample data. The mea-surement model was then validated using con-firmatory factor analysis on the holdout sampledata. Finally, we used regression analysis on theentire data set to test our hypotheses about therelationships among the constructs. We notethat this procedure closely follows the approach

recommended by many researchers (Breckler,1990; Bullock, Harlow, & Mulaik, 1994).

Purification of Measurement Model onthe Estimation Sample Data

An index of Kaiser’s measure of sampling ade-quacy (overall MSA � 0.882) and Bartlett’s testof sphericity (�2 � 6729.11, p � 0.00) suggestedthe data in the estimation sample were suitablefor factor analysis (Stewart, 1981). We stronglyfeel that many of these constructs are correlatedconceptually, and therefore we ran a factoranalysis with oblique rotation. Data from all 35questions (tapping the eight independent andthe one dependent construct) were analyzedsimultaneously by a common factor analysis us-ing Oblimin rotation. Based on the eigenvaluegreater than one and scree-plot criteria, wechose an eight-factor model that captured68.64% of the total variance. Note that ourconceptualization indicated a nine-factor solu-tion. We tried a nine-factor model, but the load-ings of the eight-factor model presented acleaner and more interpretable solution thanthe nine-factor model. The results of the EFAfor the eight-factor model are shown in Table 3.

Purification Based on EFA Results. All butone of the non-transaction-related interactivityitems (NTRI4) in the EFA had maximum factorloadings greater than 0.4. Gorsuch (1983) sug-gested removal of items that fail to meet thiscriterion. Churchill (1979) recommended thatitems that produce a substantial or sudden dropin item-to-total correlations should also be con-sidered for deletion during scale purification.All but two non-transaction-related interactivityitems were uniform in the item-to-total correla-tions. The item “the site helps locate dealersnear you” (NTRI4) had a maximum factor load-ing of 0.29, produced a sudden drop in item-to-total correlation and had a low communality of0.37. The item “the site provides quick responseto e-mail inquiries” (NTRI2) had an item-to-total correlation of 0.40 and produced a suddendrop in the item-to-total correlation for thenon-transaction-related interactivity construct.Based on this evidence, these two items weredeleted from the construct non transaction-



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related interactivity. We also note that in theEFA, two of the personalization items (P3 andP4, capturing customization of content) loadedon the non-transaction-related interactivity con-struct. Post hoc, this made sense for the follow-

ing reason. For any explicit customization ofcontent, users typically have to provide detailedinformation to the Website. That is, users willhave to interact with the Website. All of theeight Websites included in this study used pri-

T A B L E 3Exploratory Factor Analysis (with Oblimin Rotation)*

Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Factor 7 Factor 8

E2 �.901E3 �.872E1 �.868PS3 �.942PS2 �.831PS1 �.748WSE5 .857WSE2 .757WSE4 .754WSE3 .701WSE1 .496I3 .842I4 .734I2 .559I1 .505TRI1 .826TRI3 .804TRI2 .745TRI4 .700P2 .732P1 .731NTRI7 �.849NTRI5 �.821NTRI6 �.798NTRI3 �.652P3 �.604P4 �.566NTRI2 �.404NTRI4O1 .833O2 .792AC1 .675AC2 .653NTRI1 .482O3 .422

Note.* Loadings � 0.40 have been suppressed in this table. Items NTRI1, NTRI2, and NTRI4 were deleted based on scale purificationmentioned in the text. The exact description of each item is contained in Table 2.



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marily the explicit method of customization.This may have produced strong correlations be-tween respondents’ ratings about the customi-zation elements with their ratings about thenon-transaction-related interactivity items.

In the exploratory factor analysis, most of theitems in Table 3 loaded on separate factors aswe conceptualized. However, items belongingto the constructs “organization” and “access-ibility” loaded onto the same factor in theexploratory factor analysis. Based on our con-ceptualization, these two constructs should betheoretically distinct from each other, and weconjecture that their confounding in the ex-ploratory factor analysis is a chance finding.Although we cannot substantiate this, it is pos-sible that there is an ecological correlation be-tween Website organization and accessibility inthe eight Websites used in our study. That is, weconjecture that for these eight Websites, thosethat are well organized are also highly acces-sible. In any case, we subsequently test thefactor-structure (eight versus nine) in the con-firmatory factor analysis as described next.

Purification Based on CFA Results. First, wetested an eight-factor model (with item assign-ment to factors as found in EFA) against a nine-factor model (the only change is that organiza-tion and accessibility items were separated intotwo factors). The results of this test suggest thatthe nine-factor model outperforms the eight-factor model (improvements in the chi-squaredifference test was significant at the 5% level).Thus, all subsequent discussion is based on thenine-factor model.

In the CFA, one of the items measuring non-transaction-related interactivity, “the site allowssearch capability” (NTRI1), was found to have alow squared multiple correlation (0.03) and alow standardized loading (0.17). The modifica-tion indices also suggested freeing up crossloadings for this item. Further, dropping thisitem improved the overall fit of the model. Thisitem was therefore deleted. The overall fit mea-sures of the CFA model suggest a good fit forthe data (�2 � 880.96 with 418 df; CFI � 0.92,GFI � 0.81; RMSEA � 0.068), particularly given

the attenuation in the fit measures for largemodels and large sample sizes.

Because many of the measures developedwere new, they needed to be tested for con-struct validity. Measures of the level of internalconsistency between items of a single construct,the differences between items of different con-structs, and convergent validity were assessedfor each of the nine constructs. We examineditem reliabilities, tests of composite reliability,and average variance extracted. The compositereliabilities were acceptable and ranged from0.76 to 0.98 (Fornell & Larcker, 1981). Averagevariance extracted measures the amount of vari-ance captured by a construct in relation to thevariance due to random measurement error. Allestimates of average variance extracted ex-ceeded the 0.5 minimum cutoff suggested byBagozzi and Yi (1988). These values are re-ported in Table 2. In addition, all the standard-ized loadings in the measurement model weresignificant at the 5% level.

The first test of discriminant validity was toassess whether pairs of constructs were suffi-ciently different from each other or whetherthey could be collapsed into single factors toyield a more parsimonious model. In order toachieve this, a two-factor confirmatory factoranalysis of pairs of constructs was conductedtwice, once by constraining the correlation be-tween the latent variables to unity and once byfreeing up the parameter. A chi-square differ-ence test was then used to test whether thechi-square value of the unconstrained modelwas significantly lower, in which case discrimi-nant validity would be upheld (Anderson &Gerbing, 1988). The results from this test indi-cated that the discriminant validity was upheldin all pairwise tests. The second test of discrimi-nant validity involves comparing the varianceextracted estimates of each measure with thesquare of the parameter estimate between themeasures. If the variance extracted estimatesexceed the square of the correlation betweenthe two constructs, evidence of discriminant va-lidity exists (Fornell & Larcker, 1981). The vari-ance extracted estimates reported in Table 2 foreach of the constructs exceeded the square ofthe correlations between the constructs (i.e.,



66

square of the corresponding number in thephi-matrix from the LISREL output). A thirdand final test was whether the 95% confidenceintervals of the phi’s (i.e., the correlations be-tween the constructs) contain the value of unity(Anderson & Gerbing, 1988). This test was alsofound to be satisfactory for all the constructs.Therefore, the measurement model seems to beacceptable based on the estimation sampledata.

Test of Measurement Model on theHoldout Sample Data

A more rigorous test of the measurement modelis to force the CFA model (developed on theestimation sample data) on the holdout sampledata. Achieving a comparable fit would offerevidence of robustness of the measures devel-oped. Accordingly, we ran a maximum likeli-hood confirmatory factor analysis by forcing thefinal measurement model developed from theestimation sample data on the data from theholdout sample. The overall fit measures forthis forced confirmatory factor analysis (�2

� 970.82 with 418 df; CFI � 0.91, GFI � 0.77;RMSEA � 0.079) demonstrate a fairly good fit

and help to cross-validate the model. In addi-tion, the composite reliabilities and the averagevariance extracted in the measurement modelare very similar between the estimation sampledata and the holdout sample data (as shown inTable 1). Finally, all loadings were statisticallysignificant at the 5% level for the holdout sam-ple. Thus, we feel confident about the measure-ment model developed in this study.

Regression Analysis Using theEntire Sample

Having validated the measurement model, weformed summated rating scales for each con-struct by averaging the scores on the items be-longing to each construct. We tested our hy-potheses using a multiple regression model withWebsite effectiveness as the dependent variableand the eight antecedents in Figure 1 as theindependent variables. We ran the regressionmodel by simultaneously forcing all the inde-pendent variables in the model. The overallresults from the regression using the entire sam-ple data are reported in Table 4. As the num-bers in the Table 4 show, the overall regressionmodel is statistically significant (p value for the

T A B L E 4Regression Results of Relationship between Antecedents and Website Effectiveness

Antecedents

Dependent Variable: Website Effectiveness

Zero-Order CorrelationbStandardized Regression

Coefficientsa t Statistic

Personalization 0.388 (0.138) 0.13* (0.03) 3.13Transaction-related interactivity 0.338 (0.156) 0.15* (0.03) 3.54Non-transaction-related interactivity 0.353 (�0.060) �0.07 (0.03) �1.35Informativeness 0.513 (0.287) 0.28* (0.04) 6.72Organization 0.597 (0.280) 0.37* (0.05) 6.54Privacy/Security 0.366 (�0.058) �0.06 (0.03) �1.29Accessibility 0.471 (0.036) 0.04 (0.04) 0.81Entertainment 0.388 (0.028) 0.03 (0.03) 0.62Overall R2 0.452Adjusted R2 0.443F8,503 51.84

Note.* Indicates that the coefficient is statistically significant (p values � 0.05).a The numbers in parentheses are standard errors.b The numbers in parentheses are partial correlation coefficients.



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ANOVA F statistic is less than 0.001). The inde-pendent variables together explained 45% ofthe variance in Website effectiveness. Based onthe t tests of regression coefficients in themodel, informativeness, organization, transac-tion-related interactivity, and personalizationhad statistically significant and positive effectson Website effectiveness. These findings offersupport for hypotheses H1, H2, H4, and H5.While the other hypotheses, H3, H6, H7, and H8,

were not supported, they need to be viewed inthe context of the full regression model. That is,given the significant independent variables inthe model, the other independent variables(accessibility, privacy/security, nontransaction-related interactivity, and entertainment) do notsignificantly add to the predictive power of themodel.

We note that the four nonsignificant inde-pendent variables had reasonably large zero-order correlations (as reported in Table 4) withthe dependent variable. Additionally, as we con-ceptualized, these nonsignificant independentvariables are also correlated with the significantindependent variables. However, the correla-tions among the independent variables are notso large as to make the regression results sus-ceptible to the problems of multicollinearity.For example, the highest variance inflation fac-tor (VIF) is 2.97, and this value is much lowerthan the value of 10 commonly used as a cut-offfor identifying multicollinearity problems in re-gression models. Thus, while no direct relation-ship is found between the non significant inde-pendent variables and Website effectivenessgiven the other significant variables in themodel, it is possible that these nonsignificantindependent variables have some indirect ef-fects (due to their correlation with the signifi-cant independent variables) on the dependentvariable.

Recall that each respondent in this studyrated one of the eight Websites (the one withwhich he/she was most familiar) in this study.We were concerned about the potential biasingeffects of any single Website in our tests ofhypotheses using regression. To explorewhether a bias was introduced due to the idio-syncratic feature of any one Website in our sam-

ple, we reran the regression model leaving outone Website at a time. The results from thisleave-one-site-out analysis are reported in Table5. All the regressions generated essentially thesame pattern of results, including statistical sig-nificance of overall model, percent variance ex-plained, statistical significance of the coeffi-cients, and the directionality of the coefficientsof the independent variables. Therefore, weconclude that our results are fairly robust andnot unduly influenced by any one Website inour data.

DISCUSSIONThe purpose of this study was to develop andvalidate measurement scales for factors that in-fluence customers’ perceptions of the effective-ness of B2B Websites and to empirically test thesignificance of these factors. Based on our re-view of academic and trade press literature, weidentified eight factors that might influenceWebsite effectiveness. Using a four-phase scaledevelopment procedure, we developed validand reliable scales for measuring each of theseeight factors for B2B Websites. We then tested(simultaneously) the significance of these fac-tors in explaining B2B Website effectiveness.The relative importance of the significant fac-tors in explaining the variability of Website ef-fectiveness, as captured by the partial correla-tion coefficients, indicate that in the context ofthis study informativeness is the most importantfactor. This is followed by organization, transac-tion-related interactivity, and personalization.No direct relationship could be found betweenthe other factors (non-transaction-related inter-activity, privacy/security, accessibility, and en-tertainment) and Website effectiveness.

This study represents the first effort in theacademic literature to determine the factorsthat contribute to the effectiveness of B2B Web-sites. While qualitative arguments have beenpropagated in the literature, this study takes ona quantitative approach in testing hypothesesrelating each factor to Website effectiveness. Tothe best of our knowledge, no published empir-ical study has attempted to test the significanceof all of these factors simultaneously in a B2B



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setting. Thus, this study contributes to advanc-ing our knowledge about what factors influenceB2B Website effectiveness. In addition, the rig-orously developed and tested scales generatedin this study could be used to advance moreresearch in this area. Before discussing the man-agerial implications of the results from ourstudy, we first describe its limitations so thatreaders can interpret the implications withinthe context of the study limitations.

Limitations and Further ResearchOur objective in this study was to develop andvalidate measures and test a simple direct-effects model in the B2B Website context. Ow-ing to this aim, this study has a few limitations.First, this study is based on customer percep-tions of Websites in the construction industry.Therefore, the findings from this study need tobe interpreted with caution, as they may notgeneralize to other industries. Replicating thesefindings in future research using Websites fromother industries would be extremely importantfor increasing the generalizability of these re-

sults. Second, as compared to other Web-basedsurveys, the response rate in our study was onthe lower end. While respondents and nonre-spondents were found to be similar in terms ofdemographics, this still leaves open the possibil-ity of a nonresponse bias. Third, the framing ofthe Website choice question may have biasedresponses to the higher end of the scale. Spe-cifically, respondents were asked to give theirresponses for the Website they were most famil-iar with. It is possible that the most familiar sitewas also the most preferred Website. Althoughresponses were uniformly spread over the entirescale for the variables measured, in the absenceof comparable data regarding average effective-ness scores of Websites, it is not possible to ruleout the possibility of an upward bias in re-sponses. Fourth, even though we did not ad-dress indirect effects in our model, it is possiblethat a hierarchical relationship exists amongthe eight antecedent variables investigated inthis research. These types of models need to beinvestigated in future research. Related to thisissue is the possibility of the existence of inter-

T A B L E 5Multiple Regression Results of Leaving Out One Site at a Time

Independent Variables

Site Left Out in the Analysis

Site 1 Site 2 Site 3 Site 5 Site 7 Site 8 Site 9 Site 10

Personalization 0.13* 0.16* 0.13* 0.12* 0.12* 0.13* 0.13* 0.12*Transaction-related

interactivity 0.15* 0.14* 0.15* 0.12* 0.14* 0.15* 0.16* 0.17*Non-transaction-related

interactivity �0.07 �0.03 �0.06 �0.09 �0.06 �0.05 �0.10 �0.07Informativeness 0.27* 0.33* 0.27* 0.30* 0.28* 0.28* 0.27* 0.26*Organization 0.38* 0.24* 0.38* 0.38* 0.37* 0.38* 0.41* 0.39*Privacy/Security �0.06 �0.11 �0.08 �0.01 �0.05 �0.07 �0.04 �0.07Accessibility 0.05 0.10 0.04 0.02 0.06 0.03 0.01 0.04Entertainment 0.03 0.10 0.04 0.01 0.02 0.01 0.02 0.04R2 0.44 0.49 0.44 0.45 0.45 0.45 0.46 0.46Adj. R2 0.43 0.47 0.43 0.44 0.44 0.44 0.45 0.45F 47.22* 35.10* 47.06* 43.58* 49.13* 50.78* 43.11* 48.48*N (after listwise

deletion) 483 309 490 437 489 498 419 459

Note.* p value � 0.05.



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action effects among the independent variablesused in this study. While we did not hypothesizeor test for any interaction effect, such effectsneed to be investigated in future research. Fi-nally, we used only perceptual measures forboth independent and dependent variables.While perceptual measures are clearly impor-tant for generating insights into how customersperceive and value B2B Websites, the bottomline orientation of direct marketers demandsthat future research use other constructs suchas actual revenue generated by a Website orROI of a Website for the dependent measure.

Managerial ImplicationsWeb managers are often faced by resource allo-cation questions; and more often than not theirdecisions are guided by intuition, especially inthe area of Website design. Our results offerthem some help in deciding on how to allocatefunds. Based on the results of this study, man-agers for B2B sites will be well advised to im-prove the perceptions of their sites with respectto informativeness, organization, transaction-related interactivity, and personalization. It isinteresting to note that although entertainmentis often cited as the most important factor inB2C Website effectiveness, this factor was notsignificant in our study. Thus, it appears thatutilitarian aspects of B2B sites are more impor-tant to the customers. Also interesting is thenonsignificance of privacy/security and accessi-bility issues. Again, popular press and B2C re-search have consistently played up the impor-tance of these factors. But these seem to playrelatively smaller roles in evaluation of B2B sitesin the construction industry.

Another interesting outcome from this studyis the operationalization and significance of thepersonalization dimension. As we discussed inthe scale development, we first thought person-alization went beyond just recognizing each vis-itor individually and should contain more “bellsand whistles” such as the Website’s ability tocustomize content on the fly. It turns out that,in this sample, personalization simply meansaddressing visitors as individuals and remem-bering them when they return to the site. Thispersonalization factor was significant in explain-

ing variability in Website effectiveness. The cus-tomization items loaded on non-transaction-related interactivity, which turned out to benonsignificant in the regression model. There-fore, it appears that for this industry, managerstrying to influence Website effectivenessthrough personalization can do so simply bytreating each visitor as an individual and recog-nizing them personally when they come back tothe site. Of course, only future research canpoint out whether this result is specific to theB2B sites in the construction industry orwhether it will hold for other B2B sites as well.

In conclusion, it is clear that for B2B Web-sites some factors (such as informativeness) in-fluence Website effectiveness more than otherfactors (such as entertainment), and managerswould do well to keep these in mind whileallocating resources to Website design. Thegrowth of B2B sites on the Web demands thatfurther academic studies should attempt to rep-licate these findings and explore more compli-cated (such as hierarchical or mediational)models.

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an empirical investigation of antecedents of b2b websites

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