predicting intentions to return to the web site ...

PREDICTING INTENTIONS TO RETURN TO THE WEB SITE: EXTENDING THE DUAL MEDIATION HYPOTHESIS ERIC J. KARSON AND ROBERT J. FISHER

ERIC J. KARSON is Assistant Professor of Marketing at the College of Commerce and

M

acKenzie, Lutz, and Belch (1986) test four advertising attitude

Finance, Villanova University,

models and find that the Dual Mediation Hypothesis is the best. This research

Villanova, PA;

proposes an extended model within an online context, using intentions to

e-mail: [email protected]

return (Ir) to a Web site versus purchase intentions, with a direct path between

ROBERT J. FISHER

attitudes toward the Web site (Asite) and Ir. This path is hypothesized as Web

is the R. A. Barford Professor in

sites contain informative or entertaining content that attracts subsequent

Marketing Communications at the

visits, and Ir depends on other non-brand-related factors such as security, ease

Richard Ivey School of Business, University of Western Ontario,

of use, transactional capabilities, etc. Data from visitors to three actual Web

London, Ontario, Canada;

sites—digital cameras, watches, and a charity—demonstrate significant rela-

e-mail: [email protected]

tionships between Asite and Ir. In support of this perspective, when Asite was decomposed into its claim and non-claim components, the non-claim compo-

The authors would like to thank Villanova University’s Center for Instructional Technologies for their

nent had a significant effect on Ir for all three sites. Implications for online researchers and advertisers are discussed.

significant technical support. The second author would like to acknowledge the support of the Social Sciences and Humanities Research

© 2005 Wiley Periodicals, Inc. and Direct Marketing Educational Foundation, Inc.

Council of Canada. The authors would also like to thank the reviewers whose many suggestions aided in the development of this article.

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JOURNAL OF INTERACTIVE MARKETING VOLUME 19 / NUMBER 3 / SUMMER 2005 Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/dir.20040

INTRODUCTION The almost geometric growth of the key enabling technologies—home PC penetration, “broadband” communication (cable modems, ISDN and DSL lines, satellite links), information technologies for database creation, maintenance, and manipulation—along with an equally startling decrease in the costs of these activities, has helped fuel an explosive growth in Internet advertising. Mangalindan (2004) cites a 20% increase in 2003 online ad spending from 2002 up to $7.2 billion, with Forrester Research expecting as much as 23% growth in 2004. Internet advertising is most frequently associated with the venerable banner ad, in essence a billboard in cyberspace. More recently, the banner ad has spawned a rich variety of related forms including button ads, interactive HTML banners, interstitials, pop-up and pop-under ads, skyscrapers, and vertical banners, to name a few. Other innovative developments in Internet advertising include BMW’s six to eight minute action films (www.bmwfilms.com) and the recent viral marketing campaign by Burger King called the “subservient chicken” (www. subservientchicken.com) (Ulbrich, 2004). Because of its household penetration, consumers’ demand for information and other factors, Silk, Klein, and Berndt (2001) argue that Internet advertising is a potential substitute or complement for all of the major categories of existing media. Advertising found on Company Sponsored Web Sites (CSWS) clearly falls within the traditional definition of advertising as a form of impersonal communication designed to promote the product offerings of an identified sponsor. However, the Internet has some significant differences from traditional advertising media that make it unique (see Karson & Korgaonkar, 2001 for a review). In particular: • The Internet is a rich medium that provides matchless breadth, depth, and availability of information (Bruner & Kumar, 2000; Peterson, Balasubramanian, & Bronnenberg, 1997). • Web site visitors are “in control”—they not only choose the sites they wish to visit and when they want to visit them, but also the length of time they spend on each element of the site and how many times they return (Mohammed, Fisher, Jaworski, & Cahill, 2001).

• Internet-based advertising has the potential for interactive and personalized communication flows between businesses and consumers on a scale that was previously impossible (cf. Evans & Wurster, 2000). Further, CSWS have become much more than simply an advertising medium. Most CSWS facilitate multiple aspects of marketing including serving as a repository for almost limitless amounts of information on companies, brands, products, and even competitors; enabling direct dialogue between customers and organizations; and facilitating the customization, sale, and distribution of products and services. CSWS are designed to generate and reinforce positive brand and product messages, and have become a primary source of information for consumers whether they purchase on- or offline (Klein & Ford, 2003). Given these factors, it is not clear that current models of attitude toward the ad (Aad) developed in offline contexts apply online. Although the fundamental attitude formation and change processes that underlie traditional Aad models are thought to be context independent, we propose that modifications are necessary to make them better suited for online contexts. The objective of the present research is to develop and test the validity of an extended version of the Dual Mediation Hypothesis (DMH) model developed by MacKenzie, Lutz, and Belch (1986). Specifically, we propose three modifications to the DMH which make it more applicable for online contexts while preserving the model’s theoretical roots. After reviewing the models and presenting our modifications, our expanded model is tested with a sample of visitors to three actual Web sites from three product categories: digital cameras, watches, and a charity.

THE DUAL MEDIATION HYPOTHESIS MacKenzie et al.’s seminal 1986 article, “The Role of Attitude Toward the Ad as a Mediator of Advertising Effectiveness: A Test of Competing Explanations” (henceforth MLB) firmly established the importance of ad attitudes on consumer purchase intent. MLB drew on previous work in consumer behavior and psychology to develop four plausible alternatives for the effects of advertising on intention to buy (Ib). Common among the four models are the following

PREDICTING INTENTIONS TO RETURN TO THE WEB SITE

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Cad Cb

attractiveness, music, the ad’s visual elements, or the number of arguments lead to temporary attitude changes that have been found to be moderately predictive of behavior (Petty & Cacioppo, 1981).

Aad Ab

Ib

Where: Cad = ad cognitions Cb = brand cognitions Aad = attitude toward the ad Ab = attitude toward the brand Ib = intention to buy Source: MacKenzie et al. 1986

FIGURE 1 Dual-Mediation Hypothesis (DMH)

paths relating ad cognitions (Cad), brand cognitions (Cb), brand attitudes (Ab), Aad, and Ib: 1) Cad S Aad, 2) Cb S Ab, and 3) Ab S Ib. The first two links represent how thoughts (cognitions) are believed to influence attitudes as predicted by the Theory of Reasoned Action. MLB also incorporate the link from Ab to Ib in all models, citing the “considerable evidence in support of the linkage . . . under the rubric of the ‘extended’ Fishbein model” (p. 131). MLB examined the four competing models of advertising effects and found that the DMH (see Figure 1) best fits the data from tests conducted with both developmental and validation samples. As a result of extensive verification, the DMH remains unchallenged as “the” model of advertising effects some 14 years after its introduction (Moore & Lutz, 2000). The DMH incorporates the central and peripheral routes to attitude change proposed by Petty and Cacioppo (1981) in the Elaboration Likelihood Model (ELM) (Moore & Lutz, 2000). The central route to persuasion is represented by the indirect path operating through brand cognitions (i.e., Aad S Cb S Ab). In the first step of the central route, exposure to an ad is presumed to lead to brand-relevant thoughts that are both evaluative and non-evaluative in nature (cf. MacInnis & Jaworski, 1989). In turn, cognitions related to the brand affect brand attitudes. In contrast, the direct Aad S Ab path represents the peripheral route of persuasion. Cues such as endorser

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Numerous studies in marketing and consumer behavior have found support for the central and peripheral routes to persuasion contained within the DMH for the prediction of Ib across various media and product categories (Brown & Stayman, 1992). Given that the fundamental processes of attitude formation and change should not vary between on- and offline contexts, we expect that the general DMH model will hold for CSWS with three modifications: 1) attitude toward the site (Asite) is substituted for Aad to reflect the broader context of Web site advertising; 2) intention to return to the site (Ir) is substituted for Ib to reflect the importance of site visits for advertising revenue, brand awareness and equity, and relationship building for many CSWS; and 3) the addition of a path between Asite and Ir because of the expected impact of Web site design and content factors on subsequent visits. We examine each of these modifications in more detail next.

Modification 1: Asite Substituted for Aad As discussed previously, Web sites are used for purposes beyond the simple persuasive messages found in banner advertising and its variants. CSWS include a variety of features that are not necessarily captured by the Aad measure including interactivity, the potential for variations in the Web site experience because of personalization technologies and visitors’ control over navigation, perceptions of site security, ease of use, and so forth. Because Web sites include direct advertising claims that are designed to affect brand and product beliefs, as well as provide a variety of benefits that are non-claim related (e.g., entertainment and information on the sponsor), Web sites contain non-claim components that have persuasive effects on visitors. As a result, we propose that it is more relevant to use attitude toward the Web site (Asite) rather than the traditional Aad measure when considering online advertising. Whereas the traditional Aad measure is designed to capture consumers’ overall reactions to an

advertisement in traditional print or television media (Brown & Stayman, 1992), Asite reflects the entire Web site experience. It is necessary to use a broader measure in order to accurately predict brand attitude effects, intentions to purchase a specific product, and the willingness to return to the site. No matter what advertising claims are made on a CSWS, visitors will not purchase or return unless the site is, among other things, easy to navigate, easy to use, and secure. We therefore propose that A site is a more useful predictor of A b and intentions than A ad in online contexts.

Modification 2: Ir Substituted for Ib It is apparent that many Web sites are not designed to stimulate immediate sales. CSWS in a variety of industries including consumer packaged goods (e.g., Kraft.com); automobiles (e.g., Chevrolet.com); sports (e.g., NHL.com); and news (e.g., USAToday.com) are not primarily designed for e-commerce transactions. These and many other CSWS are designed to build strong buyer–seller relationships, build brand equity, and deliver an audience to advertisers. While early Web sites were often characterized as rather unimaginative “brochureware” (searchwebservice.com 2004), CSWS continue to endure and evolve. It could be argued that CSWS are a dominant business model on the Web, and serve multiple goals beyond e-commerce transactions. As such, the focus for many CSWS is to maximize site visitation within specific demographic segments and communities of interest. Further, recent research has established a significant relationship between an individual’s frequency of visits to a site, and likelihood of buying (Moe & Fader, 2004). As a consequence, Ir is a dependent variable that is of critical importance to online marketers and, increasingly, a research focus. For example, Palmer (2002) uses “likelihood of return” as a key measure of Web site success, along with “frequency of use” and satisfaction, while Rosen and Purinton (2004) use “likelihood of revisit” in developing a Web site preference scale. Similarly, citing low switching costs for Web users, Koufaris (2002, p. 207) states “. . . intention to return is a satisfactory approximation of actual customer retention” in his application of the widely accepted Technology Acceptance Model developed by Davis (1989). Other studies, such as Raney, Arpan, Pashupati, & Brill’s (2003) research on site interactivity and entertainment value include both Ib and Ir.

Modification 3: Addition of the Asite S Ir Link Our substitution of Ir for Ib in the DMH should not affect the paths leading into A b. As demonstrated elsewhere (e.g., Raney et al., 2003), visitors’ brand evaluations remain a significant predictor of the desire to return to the Web site. The more attractive the product or brand being evaluated, the more likely the visitor is to return. However, Ab does not capture aspects of the Web site such as its entertainment value (Raney et al., 2003), functional utility or “shopping enjoyment” (Koufaris, 2002), or its overall performance (Palmer, 2002). To illustrate, many financial sites include mortgage calculators that help visitors determine the implications of making early payments. This functional benefit is open to all visitors regardless of whether they purchase from the sponsoring site or not. Further, software sites for products such as snagit.com offer free trial periods so potential buyers can evaluate products prior to purchase (techsmith.com). In each of these examples, Ir is, to some degree, independent of A b. Further, the “shopping environment” is likely to be particularly important in online contexts for brands or sellers that are unknown to consumers (Mohammed et al., 2001). Web site design becomes a critical factor in not only deciding whether or not to purchase on a current visit, but also whether to return to a site at a later time. As a result, site visitors are highly interested in non-product related cues such as the type of online security used at the site (CNET.com, December 20, 2001), and other facets of the Web site such as payment procedures and terms, site design, and the ease of making the transaction. Overall, the preceding arguments present a compelling case for a modified DMH model we call the Extended Dual Mediation Hypothesis (EDMH) model, presented in Figure 2. We propose that after substituting Asite for Aad and Ir for Ib, we will find a significant Asite S Ir link. We further propose that the rationale for the path from A site S Ir implies that when A site is decomposed into its claim and non-claim components (cf. Miniard, Bhatla, & Rose, 1990) the non-claim component will have a significant effect on Ir. In this instance, the non-claim components of A site include all aspects of the Web site that are not direct claims about the product or brand being presented.

PREDICTING INTENTIONS TO RETURN TO THE WEB SITE

5

Csite

␥11

␤41

Cb

A site ␤31

␤21 ␤24

Ab

␤32

Ir

Where: Csite = site cognitions Cb = brand cognitions Asite = attitude toward the site Ab = attitude toward the brand Ir = intention to return to the Web site

FIGURE 2 Extended Dual-Mediation Hypothesis (EDMH) for Intentions to Return to the Web site

METHOD Internet users were recruited from the student population at a private mid-Atlantic university. Participants were sought from introductory business classes and members of a campus organization. A total of 325 students responded out of the total of 851 students who were e-mailed, for a response rate of 38.2%. After eight respondents were eliminated for missing data, 317 remained for an effective response rate of 37.3%. Students were either entered in a drawing to win $300 or they received extra credit for participating in the study.

Procedures Three Web sites were selected in product categories that were identified by students as relevant and interesting in a pre-test: digital cameras (www. SiPixdigital.com)1, watches (www.fossil.com), and a charity (www.specialolympics.com). Participants were randomly assigned to one of the three Web sites, and directed to a Web page to complete the study. After welcoming participants to the study they were asked preliminary questions related to the relevant product category and brand. Then, participants

1 Since the administration of the survey, SiPix no longer maintains its camera Web site.

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assigned to the digital camera and watch sites were asked to act as if they were shopping for a specific product contained on their assigned Web site. (e.g., “You are looking for a relatively inexpensive digital camera with around a 2 Mega Pixel resolution. A friend has mentioned SiPix, so you go to check it out.”) Participants assigned to the Special Olympics site were asked to consider making a donation to that charity. After visiting the actual Web site, which appeared in a frame (taking, on average, two minutes and 4.8 seconds, s.d. ⫽ 10.03 seconds), participants were directed to answer questions about the site they had just visited.

Measures All measures used seven-point scales, with higher scores reflecting more or higher levels of the construct. Also, scale means were calculated as the sum of the individual items divided by the number of items in the scale. Means, standard deviations and coefficient alpha for each scale can be found in Table 1. Cognitive Responses (Csite and Cb). After responding to a series of items about computer and Internet use and viewing the assigned Web site, participants were asked to provide brand and site cognitions in the form of a thought listing task. The thought listing instructions were: “. . . list all thoughts, ideas, and images that occurred to you while looking at the Web site.” Response coding procedures followed those established by Wright (1980). A coder, unaware of the study’s intent, first divided responses into separate thoughts. Next, each thought was classified as either a brand- or site-related thought, a global evaluative thought (e.g., “Stylish for the parts I glanced at”), or irrelevant. Global evaluative and irrelevant thoughts were disregarded. Each thought was then classified as positive, neutral, or negative. A favorability index was calculated for Csite and Cb based on the number of positive minus negative thoughts. In the small number of cases where the coder was unable to categorize a thought, one of the authors made the determination. Unlike MLB, we treat Csite as a single latent construct measured by the favorability index. MLB modeled Cad as three latent constructs each with a single indicator variable (execution, source bolstering/derogation, and repetition-related cognitive responses). Table 2 shows the cognitive response categories and frequencies.

TABLE 1

Means, Standard Deviations, and Coefficient Alpha

DIGITAL CAMERAS (SiPix) (n ⴝ 85) STD. MEAN DEV. ALPHA

VARIABLE Attitude Toward the Site (Asite) Attitude Toward the Brand (Ab) Claim Attitude (Asite-c) Non-claim Attitude (Asite-nc) Intentions to Return to the Site (Ir) Brand Cognitions (Cb)* Site Cognitions (Asite)*

MEAN

WATCHES (FOSSIL) (n ⴝ 133) STD. DEV.

ALPHA

CHARITY (SPECIAL OLYMPICS) (n ⴝ 99) STD. MEAN DEV. ALPHA

4.91 5.03

1.19 0.98

.96 .93

5.35 5.30

1.18 1.23

.97 .96

6.00 6.54

1.00 0.62

.94 .86

4.96 4.85 3.30 0.29 ⫺0.07

0.98 1.27 1.73 1.09 1.20

.83 .96 .94 na. na.

5.08 5.29 4.08 0.67 0.35

1.02 1.22 1.90 1.22 0.90

.92 .96 .94 na. na.

5.74 5.93 4.29 0.30 0.53

0.96 0.99 1.56 0.60 1.25

.91 .95 .92 na. na.

* ⫽ favorability indices.

TABLE 2

Cognitive Response Categories and Frequencies

DIGITAL CAMERAS (SiPix) (n ⴝ 85) CATEGORY Site Cognitions (Csite) Positive Neutral or global Negative Brand Cognitions (Cb) Positive Neutral or global Negative

WATCHES (FOSSIL) (n ⴝ 133)

CHARITY (SPECIAL OLYMPICS) (n ⴝ 99)

FREQ

%

FREQ

%

FREQ

%

29 43 35

27.1 40.2 32.7

58 82 12

38.2 53.9 7.9

64 55 12

48.9 42.0 9.2

42 45 17

40.4 43.3 16.3

106 59 17

58.2 32.4 9.3

29 75 0

28.6 71.4 0

Attitude Toward the Site (Asite). Consistent with MLB’s Aad measure we used a global Asite measure. The question stem was, “How did you find the overall Web site?” followed by three semantic items (unfavorable/ favorable, bad/good, and negative/positive). Attitude Toward the Brand (Ab). The Miniard et al. (1990) Ab measure was adapted for this study. For the digital camera and watch Web sites, the

semantic differential scale was preceded by the question: “Please rate the specific product you were asked to find on the Web site.” The three items in the scale were anchored with: unfavorable/favorable, bad/good, and negative/positive. For the Special Olympics site, because no specific product was evaluated, participants were asked “Please rate your impression of the Special Olympics brand presented on the Web site.” Claim and Non-Claim Components of Web Site Attitude (Asite-c and Asite-nc). We also collected measures of both claim and non-claim components of Asite using the approach designed by Miniard et al. (1990) to decompose Aad. The measures of these constructs were preceded by the following explanation: In obtaining your further reaction to the Web site, we would like you to distinguish between two basic components of the Web site. The first component involves claims made about the product or brand. The second component involves the remaining elements within the Web site such as the format, pictures or illustrations, layout, and so forth. For Aad-c, participants were then asked: “Concerning the first component, evaluate the claims made about (the brand),” followed by three semantic differential items anchored by uninformative/informative, not persuasive/persuasive, and weak/strong. The nonclaim measure asked participants: “Concerning the

PREDICTING INTENTIONS TO RETURN TO THE WEB SITE

7

second component, how would you evaluate the remaining elements (everything except the claims) within the Web site?” followed by three semantic differential items anchored by unfavorable/favorable, bad/good, and negative/positive.

space, we present the correlations between the summed scales rather than the individual measures. Consistent with MLB, the measurement scale was established by setting one l for each construct equal to 1.0. For constructs measured with a single item, the l was set to one and the error term (d1 and e9) fixed at zero. This approach creates a latent construct that is equivalent to the manifest variable assigned to measure it in the model (i.e., j1 and h4 become equivalent to X1 and Y9, respectively). In all models, the measures load on the desired constructs with t values in excess of 3.00.

Intentions to Return to the Site (Ir). Participants were asked, “How likely are you to return to this site at a later date?” We used the MLB Ib scale anchors: unlikely/likely, improbable/probable, and impossible/ possible given their strong measurement properties.

As recommended by Bollen (1989), we report the CFI (Comparative Fit Index) to enable comparisons across models with varying degrees of freedom and differences in model structure. As can be seen from Tables 4–6, each model produces a reasonable fit,

RESULTS The four competing models considered by MLB were tested with AMOS 4.0. The correlation matrices for the three samples can be found in Table 3. To conserve

TABLE 3

Digital Cameras (SiPix) Ir Asite Ab Asite-c Asite-nc Cb Csite Watches (Fossil) Ir Asite Ab Asite-c Asite-nc Cb Csite Charity (Special Olympics) Ir Asite Ab Asite-c Asite-nc Cb Csite

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Correlation Matrices

Ir

Asite

Ab

Asite-c

Asite-nc

Cb

1.00 0.42 0.32

1.00 0.68

1.00

0.30 0.35 0.37

0.57 0.74 0.38

0.26

0.55

1.00 0.52

1.00

0.49 0.41 0.45 0.37 0.16

0.57 0.51 0.46

1.00 0.40 0.34

1.00 0.22

1.00

0.33

0.25

0.44

0.23

1.00

0.64 0.65 0.75 0.34 0.31

1.00 0.68 0.64 0.44 0.29

1.00 0.62 0.25 0.30

1.00 0.39 0.35

1.00 0.29

1.00

1.00 0.33 0.09 0.32 0.26 0.00

1.00 0.64 0.63 0.69 0.19

1.00 0.70 0.66 0.20

1.00 0.60 0.22

1.00 0.05

1.00

0.20

0.29

0.12

0.12

0.21

⫺0.02

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C site

1.00

TABLE 4

Predicting Intentions to Return to Site: Digital Camera Site (SiPix) N ⫽ 85

INDEPENDENT INFLUENCES HYPOTHESIS (IIH) MODEL STD. EST.

PARAMETER b12 (Ab S Asite) b21 (Asite S Ab) b31 (Asite S Ir) b32 (Ab S Ir) b41 (Asite S Cb) b24 (Cb S Ab) g11 (Csite S Asite)

S.E.

t

AFFECT TRANSFER HYPOTHESIS (ATH) MODEL STD. EST.

S.E.

RECIPROCAL MEDIATION HYPOTHESIS (RMH) MODEL

DUAL MEDIATION HYPOTHESIS (DMH) MODEL

t

STD. EST.

S.E.

t

STD. EST.

S.E.

t

.15 .09

3.42 2.46

.50 (.63)

.07

.50 (.66)

.07

7.22

.53 (.42) .28 (.35)

0.43 (ns)

.60 (.30)

.22

2.68

.60 (.31)

.21

2.79

.09

4.62

.21 (.27)

.07

3.17

.31 (.37)

.08

4.14

.60 (.63) .35 (.38) .21 (.24)

.09

5.90

.54 (.55)

.09

5.87

.40 (.43)

.07

5.49

.54 (.55)

.55 (.37)

.16

3.52

.09 (.05)

.20

.41 (.48) .54 (.55)

x2 ⫽ 103.88 df ⫽ 42, p ⫽ .000 CFI ⫽ .93

x2 ⫽ 69.51 df ⫽ 42, p ⫽ 0.005 CFI ⫽ .97

x2 ⫽ 63.34 df ⫽ 41, p ⫽ 0.014 CFI ⫽ .975

EXTENDED DUAL MEDIATION HYPOTHESIS (EDMH) MODEL STD. EST.

S.E.

t

6.82

.50 (.63) .57 (.38)

.07 .24

6.73 2.42

.21 .10 .07

2.87 3.70 2.83

.05 (.03) .35 (.38) .21 (.24)

.30 .10 .08

.17 (ns) 3.73 2.73

.09

5.88

.54 (.55)

.09

5.89

x2 ⫽ 56.84 df ⫽ 41, p ⫽ 0.051 CFI ⫽ .98

x2 ⫽ 51.19 df ⫽ 40, p ⫽ .111 CFI* ⫽ .99

DUAL MEDIATION HYPOTHESIS (DMH) MODEL

EXTENDED DUAL MEDIATION HYPOTHESIS (EDMH) MODEL

STD. EST.

ESTIMATE (STD. EST.) S.E.

* All paths significant at p ⬍ .01 unless otherwise indicated.

TABLE 5

Model Comparisons: Watch Site (Fossil) N ⫽ 133

INDEPENDENT INFLUENCES HYPOTHESIS (IIH) MODEL PARAMETER b12 (Ab S Asite) b21 (Asite S Ab) b31 (Asite S Ir) b32 (Ab S Ir) b41 (Asite S Cb) b24 (Cb S Ab) g11 (Csite S Asite)

STD. EST.

S.E.

t

.53 (331) .47 .(30)

.13 .13

4.10 3.71

.45 (.46) .42 (.32)

.08 .11

5.70 3.81

AFFECT TRANSFER HYPOTHESIS (ATH) MODEL

RECIPROCAL MEDIATION HYPOTHESIS (RMH) MODEL

STD. EST.

S.E.

t

STD. EST. .53 (5.52)

S.E. .14

.58 (.60)

.07

8.34

.14 (.13)

.19

.81 (.47)

.14

5.78

.81 (.49)

.13

6.08

.26 (.28) .43 (.32)

.06 .11

4.07 3.83

.41 (.42) .22 (.17)

.08 .09

4.87 2.46

x2 ⫽ 95.96 df ⫽ 42, p ⫽ 0.00 CFI* ⫽ .939

x2 ⫽ 52.10 df ⫽ 42, p ⫽ 0.14 CFI ⫽ .988

t 3.85

.72 (ns) .58 (.57)

x2 ⫽ 51.26 df ⫽ 41, p ⫽ 0.13 CFI ⫽ .988

.81 (.49) .36 (.35) .25 (.26) .43 (.33)

S.E.

t

.07

7.88

.13 .09 .07 .11

6.11 4.22 3.69 3.86

x2 ⫽ 52.84 df ⫽ 41, p ⫽ 0.10 CFI ⫽ .992

.58 (.57) .53 (.31) .46 (.28) .37 (.35) .25 (.26) .43 (.33)

.07 .18 .17 .09 .07 .11

t 7.78 3.01 2.67 4.25 3.64 3.86

x2 ⫽ 44.07, df ⫽ 40, p ⫽ .30 CFI ⫽ .987

* All paths significant at p ⬍ .01 unless otherwise indicated.

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9

TABLE 6

Model Comparisons: Charity Site (Special Olympics) N = 99

INDEPENDENT INFLUENCES HYPOTHESIS (IIH) MODEL STD. EST.

PARAMETER

S.E.

t

AFFECT TRANSFER HYPOTHESIS (ATH) MODEL STD. EST.

S.E.

RECIPROCAL MEDIATION HYPOTHESIS (RMH) MODEL STD. EST.

t

b12 (Ab S Asite)

.47 (.28) .32 (.13)

.17 .27

2.75 1.19 (ns)

b41 (Asite S Cb) b24 (Cb S Ab)

.03 (.03)

.11

g11 (Csite S Asite)

.23 (.30)

.08

.27 (ns) 2.95

.29 (.45)

x2 ⫽ 63.57 df ⫽ 42, p ⫽ .017 CFI ⫽ .969

.66 (.25)

.07 .28

⫺.05 (⫺.05) .10 .23 (.30)

.08

4.11 2.34

.44 (.67) .66 (.26)

.23 .28

⫺.51 ⫺.11 (⫺.10) .11 (ns) 2.94 .27 (.34) .12

x2 ⫽ 52.55 df ⫽ 42, p ⫽ 0.128 CFI ⫽ .985

EXTENDED DUAL MEDIATION HYPOTHESIS (EDMH) MODEL

t

STD. EST.

S.E.

t

ESTIMATE (STD. EST.) S.E.

⫺.61 (ns) 1.96

.29 (.45)

.07

4.05

.28 (.44)

.07

3.97

2.33

.47 (.25) .31 (.12)

.19 .30

2.42 1.01 (ns) 1.86 ⫺.54 (ns) 2.95

S.E.

⫺.49 (⫺.32) .81

b21 (Asite S Ab) b31 (Asite S Ir) b32 (Ab S Ir)

DUAL MEDIATION HYPOTHESIS (DMH) MODEL

2.38

.66 (.25)

.28

.12 (.19) .06 ⫺.98 ⫺.06 (⫺.06) .11 (ns) 2.20 .23 (.30) .08

x2 ⫽ 52.28 df ⫽ 41, p ⫽ 0.11 CFI ⫽ .984

1.86 .12 (.19) .06 ⫺.56 ⫺.06 (⫺.05) .11 (ns) 2.93 .23 (.30) .08

x2 ⫽ 49.12 df ⫽ 41, p ⫽ 0.18 CFI ⫽ .988

t

x2 ⫽ 43.42 df ⫽ 40, p ⫽ .33 CFI ⫽ .995

* All paths significant at p ⬍ .05 unless otherwise indicated.

although the Independent Influences Hypotheses (IIH) is clearly less adequate than the other three models. Consistent with past research, the DMH emerges as the best fitting of the four models tested by MLB for each of the three Web sites studied here based on the p value and CFI for each of the models. Next, we contrasted the EDMH to the DMH. As the DMH model is “nested” within the EDMH model, (essentially the DMH is an EDMH with a zero value for the Asite S Ir path), we performed a ⌬x2 test to determine the better fitting model for each of the three sites. The test between the DMH and EDMH produces a significant ⌬x2, df ⫽ 1 in each case (SiPix ⌬x2 ⫽ 5.65, p ⬍ .05; Fossil ⌬x2 ⫽ 8.77, p ⬍ .01; Special Olympics ⌬x2 ⫽ 5.68, p ⬍ .05). Moreover, in each model the Asite S Ir path is significant and in the expected direction (SiPix b31 ⫽ .38, t ⫽ 2.42, p ⬍ .05; Fossil b31 ⫽ .31, t ⫽ 3.01, p ⬍ .05; Special Olympics b31 ⫽ .25, t ⫽ 2.42, p ⬍ .05). Thus, the EDMH significantly improves model fit over the DMH by adding a

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direct path from Asite to Ir, with significant b31 paths in all cases. A comparison of the parameter estimates of the DMH and the EDMH in Tables 4 through 6 reveals little difference between the two models for the estimated values of b21, b41, b24, and ␥11. The only significant difference in the models appears to be a substantial reduction in the magnitude of the Ab S Ir path. Indeed, the addition of the Asite S Ir path results in a non-significant Ab S Ir path in two of the three models (the exception is the Fossil site). The result suggests that the use of the DMH to predict intentions to return to the Web site may overestimate the effect of Ab on Ir because it does not include Asite S Ir. Finally, we argue that the Asite S Ir path exists because of the rich content of online advertising environments, and specifically because CSWS include non-product related elements that affect intentions. We test this assertion with a model that decomposes

TABLE 7

Structural Estimates and Goodness-of-Fit Indices for the Asite-nc and Asite-c Model

DIGITAL CAMERAS (SiPix) PARAMETER b31 (Asite-c S Ir) b32 (Asite-nc S Ir) b31 (Asite-c S Ab) b32 (Asite-nc S Ab) b43 (Ab S Ir) b51 (Asite-c S Cb) b52 (Asite-nc S Cb) b35 (Cb S Ab) g11 (Csite S Asite-c) g21 (Csite S Asite-nc)

WATCHES (FOSSIL)

ESTIMATE (STD. EST.)

S.E.

t

.62 (.35)

.24

.30 (.21) .36 (.41) .23 (.33)

.17 .09 .06

⫺.07 (⫺.03) .41 (.38) .05 (.06) .21 (.26) .29 (.35) .46 (.45)

.30 .12 .09 .08 .09 .10

CHARITY (SPECIAL OLYMPICS)

ESTIMATE (STD. EST.)

S.E.

t

ESTIMATE (STD. EST.)

S.E.

t

2.61

.08 (.04)

.19

0.44 (ns)

.74 (.40)

.25

2.92

1.80 3.99 3.69

.35 (.21) .53 (.48) .35 (.36)

.16 .08 .07

2.13 6.87 4.83

.61 (.36) .51 (.52) .44 (.48)

.21 .09 .07

2.86 6.02 6.14

.52 (.30) ⫺.01 (⫺.05) .44 (.41) .23 (.25) .31 (.27) .46 (.36)

.20 .10 .09 .07 .10 .11

⫺.76 (⫺.41) .13 (.19) ⫺.02 (⫺.04) .16 (.11) .11 (.15) .18 (.23)

.29 .07 .06 .11 .07 .08

⫺.23 (ns) 3.51 .59 (ns) 2.72 3.19 4.44

␹2 = 117.21 df = 69, p = .000 CFI = .953

␹2 = 113.90 df = 69, p = .001 CFI = .976

2.63 ⫺.06 (ns) 5.01 3.49 3.09 4.28

⫺2.63 1.87 ⫺.34 (ns) 1.42 (ns) 1.48 (ns) 2.31

␹2 = 115.93 df = 69, p = .000 CFI = .962

*p ⬍ .05.

Asite into its claim (Asite-c) and non-claim (Asite-nc) components. The model includes all of the paths in the EDMH such that Asite-c and Asite-nc are both influenced by Csite, and both influence Cb, Ab, and Ir. The models fit the data adequately for the three sites, though not as well as either the simpler EDMH model or the original DMH. All CFI indices are in excess of .95, and the x2 statistics are less than twice the degrees of freedom for the three models. Consistent with our argument, the Asite-nc S Ir paths in all three models are significant (SiPix b32 ⫽ .21, t ⫽ 1.80, p ⬍ .05; Fossil b32 ⫽ .21, t ⫽ 2.13, p ⬍ .05; Special Olympics b32 ⫽ .36, t ⫽ 2.86, p ⬍ .05). In contrast, the Asite-c S Ir path (i.e., b31) is significant in only two of the three models (SiPix and Special Olympics). The fit indices and parameter estimates for the Asite-nc and Asite-c models are presented in Table 7.

DISCUSSION AND CONCLUSIONS The results replicate the MLB findings related to the IIH, Affect Transfer Hypotheses, Reciprocal Mediation Hypothesis, and DMH models for all three Web sites. Consistent with previous work, we found

that the IIH model had the poorest fit. The IIH hypothesizes that Asite and Ab have independent effects on intentions, but unlike the other four models tested, does not propose that Asite and Ab are causally related. The relatively poor fit of this model adds to the substantial evidence of the Asite to Ab path. Also consistent with MLB and other previous work, we found that the DMH was the best fitting of the four original models in an online context. The present research found a significant and large Asite S Ir effect, and this effect was independent of brand-related cognitions or evaluations. Not only did the addition of the Asite S Ir path significantly improve overall model fit, but in two of the three models, the inclusion of the path made the Ab S Ir link insignificant. The importance of the Asite S Ir path suggests that Internet advertising environments can have a direct effect on intentions to return to the site in addition to the traditional routes via brand attitudes. We found evidence to suggest that the Asite S Ir path occurred because the Web sites we tested included information that was independent of the claims made about the product or brand being considered. When

PREDICTING INTENTIONS TO RETURN TO THE WEB SITE

11

Asite was decomposed into its claim and non-claim components, the non-claim component had an independent effect on intentions in all three Web sites. These results suggest that the participants in our study used non-claim related information on the site to form intentions to return at a later time. The results highlight the importance of Web design features and content in driving return visits. A further look at the Asite and Ab measures provides some support for the preceding interpretation. The term “attitude toward the brand” in previous research implies that the Ab measure reflects brand-related associations that originate from advertising but also purchase experience, distribution, sales interactions, etc. (cf. Keller, 1993). However, traditional Ab measures are designed to reflect respondents’ evaluations of one specific product. For example, the Ab measure used by Miniard et al. (1990) reflects participants’ evaluations of one member of the fictional Sunburst product line (i.e., “the Sunburst soft drink”) (p. 295). Consistent with this approach to measuring Ab, participants in the present research evaluated a specific

TABLE 8

product within the SiPix and Fossil Web sites. Although Ab is affected by brand equity, and product characteristics including aesthetics, ergonomics, price, and features, it is unlikely to capture other aspects of the Web site that should drive the willingness to return. To demonstrate this, measures were gathered on two constructs important in the evaluation of Web sites: interactivity (Luna, Peracchio, & de Juan, 2005) and perceived ease of navigation (Salisbury, Pearson, Pearson, & Miller, 2001). The significant effect of these measures on brand cognitions and thoughts, as well as Ir are evident in Table 8, providing some evidence of the importance of Web-specific elements’ impact on behavioral intentions. Traditional measures of Aad are likewise unable to capture the full range of Web site characteristics that affect intentions to return to the site because they focus on adverting claims rather than the visitor’s overall experience. As demonstrated by our results, non-product or brand specific elements were captured by the Asite measure and appear to have been the

Structural Estimates and Goodness-of-Fit Indices for the Component Models

CHARITY

WATCHES

DIGITAL CAMERAS

ESTIMATE (STD. EST.)

S.E.

t

ESTIMATE (STD. EST.)

S.E.

t

ESTIMATE (STD. EST.)

S.E.

t

0.38* 0.51*

0.09 0.09

4.27 5.66

0.20* 0.14

0.11 0.10

1.93 1.42

0.29* 0.15

0.10 0.09

2.87 1.54

⫺0.14* 0.15* ⫺0.01

0.06 0.05 0.08

2.41 2.83 ⫺0.02

0.43* 0.03 ⫺0.04

0.11 0.10 0.09

3.99 0.25 ⫺0.40

0.52* ⫺0.30* 0.21*

0.11 0.11 0.09

4.62 ⫺2.67 2.38

EON→Ab INT→Ab Ab→Ir EON→Ir

0.11* 0.03 0.37 0.35*

0.05 0.05 0.38 0.15

2.45 0.64 0.97 2.43

⫺0.08 0.61* 0.39* ⫺0.21

0.10 0.12 0.14 0.15

⫺0.82 5.09 2.74 ⫺1.38

0.11 ⫺0.08 0.21 ⫺0.10

0.09 0.10 0.25 0.19

1.20 ⫺0.77 0.86 ⫺0.54

INT→Ir

0.39*

0.15

2.61

0.92*

0.19

4.86

0.19

2.95

PARAMETER Csite→INT Csite→EON INT→Cb EON→Cb Cb→Ab

2

x = 104.12 df = 69, p = .001

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2

x = 187.85

x = 160.56

df = 69, p = .001

df = 71, p = .000

NOTE: INT = a three item measure of site interactivity (Luna et al. 2002). EON = a three item measure of the site’s perceived ease of navigation (Salisbury et al. 2001). * p ⬍ .05.

12

0.57*

reason behind the significant Asite S Ir paths we found. It is important to note that many non-product related factors (e.g., ease of use, site responsiveness, and security) are likely to be processed centrally. These factors are extremely important in online environments because of both the lack of brand equity for new brands, and the absence of physical (i.e., offline) locations for many online firms. It is possible, however, that some peripheral cues, such as the color and layout of the Web site, may also influence this relationship. Global measures of Asite used in the present study provide an overall assessment of the Web site that incorporates both types of non-product specific attributes that influence intentions to return. More fully understanding how all aspects of Web sites are processed, and under what conditions, certainly bears further investigation.

Future Research In order to fully understand the impact of Web site attitudes on intentions, we need to be able to identify which executional elements contain non-product related information. These components are likely to be online advertising elements that affect trust and satisfaction with the purchasing experience—two factors that have been identified as factors that affect relationship quality in sales environments (e.g., Crosby, Evans, & Crowles, 1990). Also important is a better understanding of how online environments affect the relationship between intentions and purchase behaviors. Online shopping environments reduce the spatial and temporal barriers that often exist in traditional retail environments, perhaps increasing the potential for impulsive consumption behaviors (cf. Rook & Fisher, 1995). It would also be useful to replicate our results using brand choice rather than intentions, given that consumers may use different decision-making approaches for each (Biehal, Stephens, & Curlo, 1992). As online environments allow for direct testing of purchase behaviors, these relationships should come into clearer focus. Further, we acknowledge the limitation of using primarily offline brands for this study. Whether the brand and the medium interact is an effect that needs investigation. Finally, much like MacKenzie and Lutz (1989) explore the antecedents of Aad, we need to more fully explore the antecedents of Asite, with particular attention to

paid to situational and individual factors influencing each potential antecedent.

MANAGERIAL IMPLICATIONS That “what you say,” and “how you say it” are key aspects of marketing communications has been accepted for years. While, previously, the “how” component was thought to operate indirectly on viewers intentions only through brand cognitions and attitudes, we have demonstrated that, online, when the goal is to get Web users to return to a site for future visits, the “how” does indeed have a direct effect. Identifying this direct effect places further importance on the design, and function, of Web sites. As we continue to refine our understanding of what makes Web viewers return to a site (crucial for any e-tailer who has invested up to $75 per customer for acquisition) (Blankenhorn, 2002), demonstrating a direct link between these design issues should lead to a greater understanding of consumers’ “cognitive landscapes” (Rosen & Purinton, 2004), and reactions to design cues. Hoffman (cited in Blackman, 2004, p. R11) indicates the importance of brand image for a traditional retailer moving online—J&R Electronics. Hoffman states, “People think ‘If it’s [J&R Electronics] on Amazon, it must be good,’” supporting the link between Web sites and intentions we propose.

CONCLUSION This research replicates findings in traditional offline media that the Dual Mediation Hypothesis is superior to three other attitude toward the ad models in an online context. Moreover, the research found that by modifying existing models by using attitude toward the Web site instead of attitude toward the ad (reflecting the greater complexity and dynamism of advertising on the Web), and by substituting intention to return to a Web site for intentions to buy, that attitude toward the site had a direct effect on intentions in three significantly different product categories. Support for the general theory that the significant attitude toward the site to intention to return path occurred because of non-claim aspects of the Web site was found by models that decomposed attitude toward the site into its claim and non-claim elements.

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REFERENCES Biehal, G., Stephens, D., & Curlo, E. (1992). Attitude Towards the Ad and Brand Choice. Journal of Advertising, 21, 19–36. Blackman, A. (2004). A Strong Net Game. Wall Street Journal, October 25, R6, R11. Blankenhorn, D. (2002). Estimate Customer Lifetime Value Using Campaign Results. B to B, 87 (4), 20. Bollen, K.A. (1989). Structural Equations with Latent Variables. New York: John Wiley & Sons. Brown, S.P., & Stayman, D.M. (1992, June). Antecedents and Consequences of Attitude Toward the Ad: A Meta Analysis. Journal of Consumer Research, 19, 34–51. Bruner, G.C., & Kumar, A. (2000, January–April). Web Commercials and Advertising Hierarchy of Effects. Journal of Advertising Research, 40, 35–42. Crosby, L.A., Evans, K.R., & Cowles, D. (1990), Relationship Quality in Services Selling: An Interpersonal Influence Perspective. Journal of Marketing, 54(3), 68–81. Davis, F.D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319–340.

Advertising Effectiveness: A Test of Competing Explanations. Journal of Marketing Research, 13, 130–143. Mangalindan, M. (2004). After Wave of Disappointments, The Web Lures Advertisers Back. The Wall Street Journal, February 25, A1, A6. Miniard, P.W., Bhatla, S., & Rose, R.L. (1990, August). On the Formation and Relationship of Ad and Brand Attitudes: An Experiment and Causal Analysis. Journal of Marketing Research, 27, 290–303. Moe, W.W., & Fader, P.S. (2004). Capturing Evolving Visit Behavior in Clickstream Data. Journal of Interactive Marketing, 18(1), 5–19. Mohammed, R., Fisher, R.J., Jaworski, B.J., & Cahill, A. (2001). Internet Marketing: Building Advantage in a Networked Economy. New York: McGraw-Hill. Moore, E.S., & Lutz, R.J. (2000, June). Children, Advertising, and Product Experiences: A Multimethod Inquiry. Journal of Consumer Research, 27, 31–48. Palmer, J.W. (2002, June). Web Site Usability, Design, and Performance Metrics. Information Systems Research, 13, 151–167.

Evans, P., & Wurster, T.S. (2000, September–October). Strategy and the New Economics of Information. Harvard Business Review, 71–82.

Peterson, R.T., Balasubramanian, S., & Bronnenberg, B.J. (1997, Fall). Exploring the Implications of the Internet for Consumer Marketing. Journal of the Academy of Marketing Science, 25, 329–346.

Karson, E.J., & Korgaonkar, P.K. (2001, Fall). An Experimental Investigation of Internet Advertising and the Elaboration Likelihood Model. Journal of Current Issues and Research in Advertising, 23, 53–72.

Petty, R.E., & Cacioppo, J.T. (1981). Attitudes and Persuasion: Classic and Contemporary Approaches. Dubuque, IA: Brown

Keller, K.L. (1993, January). Conceptualizing, Measuring, and Managing Customer-Based Brand Equity. Journal of Marketing, 57, 1–22. Klein, L.R., & Ford, G.T. (2003, Summer). Consumer Search for Information in the Digital Age: An Empirical Study of Prepurchase Search for Automobiles. Journal of Interactive Marketing, 17, 29–49. Koufaris, M. (2002, June). Applying the Technology Acceptance Model and Flow Theory to Online Consumer Behavior. Information Systems Research, 13, 205–223. Luna, D., Peracchio, L.A., & de Juan, M.D. (2005). Smoother Surfing Across Cultures: Bilinguals on the Web. In C. Haugtvedt, K. Machleit, & R. Yalch (Eds.), Online Consumer Psychology: Understanding and Influencing Behavior in the Virtual World. Mahwah, NJ: Lawrence Erlbaum. MacInnis, D.J., & Jaworski, B.J. (1989, October). Information Processing from Advertisements: Toward an Integrative Framework. Journal of Marketing, 53, 1–23. MacKenzie, S.B., & Lutz, R.J. (1989, April). An Empirical Examination of the Structural Antecedents of Attitude Toward an Ad in an Advertising Pretesting Context. Journal of Marketing, 53, 48–65. MacKenzie, S.B., Lutz, R.J., & Belch, G.E. (1986, May). The Role of Attitude Toward the Ad as a Mediator of

14

JOURNAL OF INTERACTIVE MARKETING

Raney, A.A., Arpan, L.M., Pashupati, K., & Brill, D.A. (2003, Autumn). At the Movies On the Web: An Investigation of the Effects of Entertaining and Interactive Web Content on Site and Brand Evaluations. Journal of Interactive Marketing, 17, 38–53. Rook, D.W., & Fisher, R.J. (1995, December). Normative Influences on Impulsive Buying Behavior. Journal of Consumer Research, 22, 305–313. Rosen, D.E., & Purinton, E. (2004, July). Website Design: Viewing the Web as a Cognitive Landscape. Journal of Business Research, 57, 787–794. Salisbury, W. D., Pearson, R.A., Pearson, A.W., & Miller, D.W. (2001). Perceived Security and World Wide Web Purchase Intention. Industrial Management and Data Systems, 101(4), 165–176. SearchWebServices.com. (2004). Retrieved April 26, 2004, from http://searchwebservices.techtarget.com Silk, A.J., Klein, L.R., & Berndt, E.R. (2001). The Emerging Position of the Internet as an Advertising Medium. Netnomics, 3(2), 129–148. Ulbrich, C. (2004). Porno Hen Hawks for Burger King. Retrieved April 14, 2004, from www.wired.com/news Wright, P. (1980, September). Message-Evoked Thoughts: Persuasion Research Using Thought Verbalizations. Journal of Consumer Research, 7, 151–175.