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On-Line Product Presentation: Effects on Mood, Perceived Risk, and Purchase Intention Jihye Park Iowa State University

Sharron J. Lennon The Ohio State University

Leslie Stoel The Ohio State University

ABSTRACT Because the Internet purchase of apparel is risky, there is a strong need to develop better visual product presentation on-line that may give some sense of fit and other tactile experience to reduce perceived risk and create pleasurable shopping experiences. Toward this end, the effect of product presentation on consumer responses was examined here. In addition, the relationships among variables were investigated to provide details of the nature of the effect of product presentation. This study employed a 2 ⴛ 2 between-subjects factorial design: product movement (product in motion vs. product not in motion) ⴛ image size (large vs. small). Mock Web sites were created to closely mimic the design of actual Web sites. Two hundred forty-four female undergraduates logged on and evaluated two pairs of pants under the same treatment conditions. The present research showed (a) main effects for product movement on mood, perceived risk, and apparel purchase intention; (b) an interaction between product movement and image size on apparel purchase intention; (c) a negative relationship between mood and perceived risk; (d) a positive relation-

Psychology & Marketing, Vol. 22(9): 695–719 (September 2005) Published online in Wiley InterScience (www.interscience.wiley.com) © 2005 Wiley Periodicals, Inc. DOI: 10.1002/mar.20080 695

ship between mood and apparel purchase intention; (e) a negative relationship between perceived risk and apparel purchase intention; and (f) mediating relationships among variables. Based on the results, apparel e-tailers are advised to create positive mood using product rotation to decrease shoppers’ perceived risk and increase purchase intent. © 2005 Wiley Periodicals, Inc.

E-commerce sales at domestic on-line retailers totaled 56 billion dollars in 2003 and accounted for about 2% of all retail sales. E-commerce sales continue to grow. For example, in the first quarter of 2004, U.S. retail ecommerce sales were about $15.5 billion, with an increase of 28% over the first quarter of 2003 (U.S. Census Bureau, 2004). As e-commerce sales became more important consumers looked for ways to reduce cognitive effort associated with on-line shopping, the importance of Website technical support has been recognized by retailers (Peters, 2004). Major issues relating to the use of technology to enhance Web-site performance include product presentation techniques such as three-dimensional (3D) views, enlargement, and control of image resolution. Use of such technology enhances Web-site performance to sustain the site and to enhance Web-site success in a competitive marketplace. According to Retail Forward (2001), about 85% of on-line shoppers identified threedimensional images of products useful for understanding product features and functions. One product that sells surprisingly well on-line is apparel (Internet Retailer, 2004). In 2001, apparel was the third largest e-commerce category, with 10% of market share, following travel (e.g., airline tickets) and computer-related goods (e.g., software, hardware) (Mudd, 2002). Although apparel sells well on-line, consumers still perceive risks associated with on-line apparel shopping. Sensory attributes such as fabric hand, garment fit, color, and quality are important criteria for all types of in-home apparel shopping (McCorkle, 1990), but are difficult to evaluate on-line. For example, a study by Cyber Dialogue indicated that 30% of on-line shoppers had not purchased apparel on-line because the color of the item was in question (Elliot & Fowell, 2000). Because sensory attributes cannot be completely evaluated when people shop on-line, risk associated with on-line apparel purchases may prevent consumers from purchasing. According to Ernst & Young (2001), over 60% of on-line shoppers have not yet purchased apparel on-line because of perceived risk. Because apparel products may be even more risky to purchase on-line than other products, it is important for e-merchants to develop strategies to reduce that risk. One way to reduce risk is to create an attractive visual product presentation (Bhatti, Bouch, & Kuchinsky, 2000; Szymanski & Hise, 2000) that gives some sense of fit and other tactile experiences. Furthermore, aspects of visual product presentation may make shopping on-line pleasurable and may increase purchase intention. Therefore, the purpose of this study was to examine the effect of product presentation on mood, perceived 696

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risk, and purchase intention. In addition, relationships among those variables were also investigated.

CONCEPTUAL BACKGROUND Product Presentation As increases in on-line shopping and in the number of on-line retailers have created a competitive market place, the importance of Web-site design has been emphasized (e.g., Elliot & Fowell, 2000). Consumers may reduce cognitive effort and save time through on-line shopping. For example, Web-site designs that use fast presentations, uncluttered screens, and easy search paths support a pleasurable and effective shopping experience by reducing shopping time and the cognitive effort of shopping. In one study, Then and DeLong (1999) emphasized the importance of the layout and design of apparel Web sites. They suggested that the more information a retailer can offer through the visual display of apparel, the more interested the consumer will be in purchasing apparel on-line. Three important visual aspects of product presentation were suggested for success in selling on-line: Images of the product (a) in its closest representation of end use, (b) displayed in conjunction with similar items, and (c) from various angles such as front and back. About 89% of respondents preferred a realistic human model to display the garment and to show how it fit the body. Displaying apparel on a three-dimensional model may minimize uncertainties of shopping for apparel on-line. However, even though the importance of displaying apparel on a three-dimensional model or with different angles was emphasized by researchers, in fact, a content analysis of the top 31 apparel Web sites revealed that only one front view was available on 30 of those apparel Web sites (Park & Stoel, 2002). Consumer Response System in the Computer-Mediated Environment Consumer perceptions and responses have been a mainstream of consumer behavior research in the past 25 years. Holbrook and Hirschman (1982) integrated existing consumer behavior models and conceptualized a comprehensive model of a consumer response system that describes how a consumer responds to products, services, or marketing objectives. Based on the model, the response system involves cognition, affect, and behavior. Cognitive responses involve memory, knowledge structures, imagery, beliefs, and thought (Holbrook & Hirschman, 1982). Types of affective responses typically measured are mood or feelings evoked by marketing stimuli (Batra & Ray, 1986; MacKenzie & Lutz, 1989). For instance, Li, Daugherty, and Biocca (2001) found that consumers expeON-LINE PRODUCT PRESENTATION

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rience emotions when interacting with 3D visual products. Behavior (i.e., conative response) as a result of cognition and/or affect is explained by purchase and/or consumption. Instead of actual behavior, behavioral intention, such as purchase intention, is often used to assess marketing effectiveness (Hoch & Ha, 1986). Cognitive, emotional, and conative responses can be elicited by product experience (Hirschman, 1984). Traditionally, psychologists have identified two types of experiences—direct and indirect experiences—based on the degree of sensory interactions with the product. According to Gibson (1996), an unmediated interaction between the consumer and a product, wherein all of a person’s sensory capacities—sight, hearing, taste, smell, and touch—can occur through direct experience. However, indirect experiences are usually evoked from a representation of physical products in media such as advertising. Direct product experience is generally viewed as increasing confidence, and indirect experience is able to influence attitude for search goods (Marks & Kamins, 1988). Technological advances have created another type of indirect experience, the so-called virtual experience. A virtual experience is a mediated experience, a simulation such as 3D virtual on-line stores or product presentations (Li et al., 2001). For example, visual inspection of a product may be simulated on-line with enlargement or zoom functions, or by virtual rotation of the product. Li et al. (2001) found that virtual experience is accompanied by psychological and emotional states when consumers interact with products in a 3D environment. Virtual experience often occurs through interactive media such as the Internet or computer simulations. Because the Internet is a limited environment and customers cannot obtain tactile and physical product experiences, the importance of virtual experience has emerged. Visual presentation such as product movement may provide a virtual experience and help consumers overcome the drawbacks of limited information and limited experience with on-line shopping. In addition, through visual presentation consumers may approximate product functionality (Li, Daugherty, & Biocca, 2002). The combination of the direct visual experience and the simulated tactile and behavioral experiences evokes cognitive, affective, and conative responses in the e-commerce environment. The virtual experience can be developed via presence of product presentation in the computer-mediated environment. Presence is the experience that is established in a representational environment via media (Steuer, 1992); the individual has sensations of being present in the environment (Biocca & Delaney, 1995). When presence occurs the user of a medium perceives an illusion that simulates sensory experiences such as an illusion that a perceived product is present in the environment (Biocca & Delaney, 1995). Biocca and Delaney said that presence can be developed through a variety of imaginary characters in the media or in the content of the media that are controlled by computer-generated graphical properties, including shape, color, sound, and motion. Vividness, the rep698


resentational richness of a mediated environment, is one of the major components of presence in the communication system and influences the computer-mediated virtual experience (Steuer, 1992). Sensory breadth and depth such as high-resolution quality, large image size, and movement are aspects of stimuli that make it vivid (e.g., Biocca & Delaney, 1995; Choi, Miracle, & Biocca, 2001; Lombard, 1995). According to T. Kim and Biocca (1997), presence in a computer-mediated environment positively influenced buying intention for advertised products. Similarly, Choi et al. (2001) found that media content such as moving advertising agents on a Web site developed to create presence had a positive effect on purchase intention for the brand and revisit intention for the Web site. However, there is still limited research on the extent to which imagery content contributes to presence and its effects on advertising effectiveness and consumer responses such as user enjoyment, mood, persuasion, and memory in the current advertising literature. Several past studies (e.g., Fiore, Yah, & Yoh, 2000; Spies, Hesse, & Loesch, 1997) have emphasized mood as a central consumer response over cognitive responses that results from the interaction with store atmosphere. Music, product display, or fragrance could evoke positive mood and result in greater in-store purchase intention. Accordingly, this study examines cognitive responses (perceived risk), affective responses (mood), and conative responses (purchase intention) as affected by product presentation. In addition, based on the past literature, attention may be an alternative explanation for the effect of product presentation on affective responses. Product Presentation and Mood Mood is defined as “a type of affective state which is transient and particular to a specific time and situation” (Jeon, 1990, p. 24). Both positive and negative moods are important determinants of human impressions and behaviors. Positive moods have been found to enhance the performance of behaviors that lead to positive outcomes such as greater personal power and greater freedom to act as one wishes. People in positive moods tend to feel more confident, optimistic, and unconstrained (Forest, Clark, Mills, & Isen, 1979). Consumers may experience a wide range of subjective moods or feeling states while searching for, choosing, and using products, as well as while interacting with service providers (Cohen & Areni, 1991; Ruth, Brunel, & Otnes, 2002). Mood is affected by visual merchandise presentation (Spies et al., 1997; Swinyard, 1993), including apparel product presentation (Fiore et al., 2000). One way to manipulate product presentation is to increase the size of product images. Such a manipulation could play an important role in creating positive mood. Empirical studies by K. K. Cox (1970) and Finn (1988) found that size of shelf space and size of print advertisements influenced attention. When products were displayed on a large-size shelf or when a print advertisement was larger, people were likely to ON-LINE PRODUCT PRESENTATION

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pay attention. Because size of stimuli increases attention and attention increases mood (Langer, 1989), it is possible that larger illustrations or pictures may affect attention and thereby evoke more positive feelings. Furthermore, Jeandrain (2001) found that a three-dimensional presentation on-line created positive mood, and thus could provide an entertaining shopping experience. Thus, a moving or large object may evoke positive mood in the context of on-line shopping. Based on the literature, the following hypothesis was developed (see Figure 1): H1: Product presentation affects mood. (a) As compared to people exposed to Web sites with products not in motion, those exposed to Web sites with products in motion will exhibit more positive mood. (b) As compared to people exposed to Web sites with smaller product images, those exposed to Web sites with larger product images will exhibit more positive mood. (c) Size and movement interact to affect mood.

Product Presentation and Perceived Risk Product presentation may serve as visual product information. For example, apparel color presented on screen may function as product information, but may not be perceived as accurate enough to make judgments regarding the product. This may increase perceived risk, which is defined as the nature and amount of uncertainty perceived by consumers in contemplating a particular purchase decision (D. F. Cox & Rich, 1964) and make shoppers avoid purchasing apparel from the Internet (Ernst & Young, 2001). Because visual inspection to check fit, texture, and color is required for apparel selection (D. F. Cox & Rich, 1964), a large image and/or an image of the product in motion may provide descriptive and visual product information that is helpful in decision

Figure 1. The conceptual model for the effect of product presentation on e-shopping behavior.

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making. A large image may enable consumers to see well enough to inspect color, texture, and garment details (Then & Delong, 1999). Even though there is no empirical literature exploring the direct effect of image size and product movement on perceived risk, it is reasonable to expect that enhanced product presentation (e.g., large-sized image, product movement, or a combination of both) on Web sites will increase consumer confidence in judging apparel quality and thus, reduce perceived risk of shopping from the Internet. Therefore, the following hypothesis was developed (see Figure 1): H2: Product presentation affects perceived risk. (a) As compared to people exposed to Web sites with products not in motion, those exposed to Web sites with products in motion will perceive less risk. (b) As compared to people exposed to Web sites with smaller product images, those exposed to Web sites with larger product images will perceive less risk. (c) Size and movement interact to affect perceived risk.

Product Presentation and Purchase Intention Sensory proximity such as visual presentation of a product may produce an emotional response when purchasing a product. Thus, the manner of product display in a store may increase purchase intention. Consumers who had a good store experience (e.g., attractive and interesting store display) had much more favorable intention to purchase than consumers who had bad shopping experiences (Swinyard, 1993). An appealing visual presentation of products may accelerate consumers’ intention to purchase products (Then & Delong, 1999). So more attractive, interesting, and/or appealing visual displays of apparel may affect intent to purchase on-line. Visual aspects of product presentation such as images of the online product in its closest representation of end use, displayed in conjunction with similar items, and from various angles such as front and back, may generate higher purchase intention for consumers and, in turn, increase sales for e-business (Then & Delong, 1999). Visual presentation (e.g., product movement, a large-sized image) can provide product information that is known to influence consumer purchase intention (M. Kim & Lennon, 2000) and on-line sales (Then & Delong, 1999). Because visual inspection to check the fit, texture, and color is required for apparel selection (D. F. Cox & Rich, 1964), a large product image and product movement may provide descriptive visual product information and may play an important role in purchase decision making (i.e., by allowing people to inspect detail, color, and texture) (Then & Delong, 1999). Even though there is no empirical literature exploring the direct effect of image size and product movement on purchase intention, it seems likely that presenting apparel from various angles ON-LINE PRODUCT PRESENTATION

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(Then & Delong, 1999), either by rotating the product or displaying a large image, will help facilitate making purchase decisions. In addition, a combination of product movement and large image may present the optimal impact on purchase decisions. Therefore, the following hypothesis was developed (see Figure 1): H3: Product presentation affects purchase intention. (a) As compared to people exposed to Web sites with products not in motion, those exposed to Web sites with products in motion will have greater purchase intention. (b) As compared to people exposed to Web sites with smaller product images, those exposed to Web sites with larger product images will have greater purchase intention. (c) Size and movement interact to affect purchase intention.

Mood and Perceived Risk A mood state may function to reduce or increase perceptions of risk (Johnson & Tversky, 1983). Individuals who face the difficult task of evaluating unknown risks may cope with the judgmental tasks by consulting their current feelings. In a study by Johnson and Tversky (1983), a depressed and anxious mood was induced by having participants read reports of negative events such as descriptions of a cancer case. People in negative moods evaluated a large number of risks as more threatening than subjects in positive moods. People who feel depressed and anxious may conclude that the task they are asked to evaluate is depressing and threatening. Positive mood generated from buying stimuli or a store environment may reduce perceived risk. According to the consumer decision-making model developed by Blackwell, Miniard, and Engel (2001), internal information retrieved from memory (e.g., prior experience, mood, familiarity) could reduce perceived risk. Schwarz (1990) claimed that when evaluating situations, a positive mood serves as a source of information that sometimes replaces the evaluation of the objective information of the target. If a person feels good, he or she may attribute this positive feeling to characteristics of the present situation and thus evaluate the situation more favorably. In fact, Gorn, Goldberg, and Basu (1993) found effects for mood on new-product evaluation. Consistent with Schwarz’s (1990) claim, people who were in positive moods evaluated the new product more favorably than those who were in negative moods. This illustrates that positive feelings may decrease perceived risk and result in positive evaluation. Based on the literature, it is reasonable to expect that positive mood in Internet shopping may serve as internal information reducing perceived risk and enhancing positive product evaluation to make purchase decisions. Therefore, the following hypothesis was generated (see Figure 1). 702


H4: There is a negative relationship between positive mood and perceived risk.

Mood and Purchase Intention Shopping for apparel from the Internet may be considered a complex task for three reasons. First, apparel is considered an experience good (Ekelund, Mixon, & Ressler, 1995), one that typically must be physically experienced (i.e., examined, tried on, and used) in order to evaluate; thus, purchasing apparel via the Internet is likely to be especially complex. Second, mastery of the computer interface is required for Internet shopping and poses difficulty for shoppers with little computer expertise. Third, there are some general uncertainties associated with in-home shopping of all kinds (e.g., concerns about product returns, credit card security) and Internet shopping in particular (e.g., concerns about loss of privacy, merchant legitimacy) that make purchase decisions more difficult and can increase the complexity of the Internet shopping task (Elliot & Fowell, 2000; Jarvenpaa & Todd, 1997; Jones & Vijayasarathy, 1998; Maignan & Lukas, 1997; Maney & Dugas, 1997; Novak, Hoffman, & Peralta, 1998; Rowley, 1996). Research has shown that when faced with complex tasks, mood may be used as heuristic information in facilitating decision making (Kelly, 1972; Schwartz & Clore, 1983). Therefore, in the Internet shopping context, positive moods may increase Internet purchase intentions. Relationships between consumers’ mood and purchase intent have been found in several empirical studies in marketing and psychology. Mood states are present in virtually every shopping encounter and are likely to influence what is purchased and when, how much is purchased, how carefully one compares products before making a selection, and even one’s intent to repurchase a brand or product (Babin, Dardin, & Griffin, 1994). According to Swinyard (1993), when the consumer was in a good mood during shopping, he or she was more likely to spend extra time shopping in the department and store and to purchase more products. Babin et al. (1994) also found that strong positive feeling states such as “good,” “happy,” “satisfied,” and “wonderful” can lead to increased time spent in the store, spending, and judgments of liking for the store. Bitner (1992) also found that positive moods resulted in more favorable evaluations of the store and influenced customers to buy more things. The relationship between consumer moods and purchase intention was also found in the work of Alpert and Alpert (1990) and Spies et al. (1997). The positive moods affected by a pleasant store environment (e.g., background music, store layout, and signs) influenced greater purchase intention as compared to the negative moods. Therefore, the following hypothesis was developed (see Figure 1): H5: There is a positive relationship between positive mood and apparel purchase intention.

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Perceived Risk and Purchase Intention Howard and Sheth (1969) proposed that one of the determinants of purchase intention is confidence, which is the inverse of perceived risk. Bennett and Harrell (1975) suggested that confidence might play an important role in predicting intentions to purchase. Confidence about the brand is positively related to intention. This suggests that lower perceived risk may be related to higher purchase intention. In Internet shopping, Vijayasarathy and Jones (2000) found that consumers’ perceived risk was an important factor that influenced intention to shop online. Shoppers’ confidence in judging quality of products or in making decisions to purchase products may reduce perceived risk, as consumers develop shopping experience from the Internet (Yoh, Damhorst, Sapp, & Lazniak, 2003). Therefore, the following hypothesis was generated (see Figure 1): H6: There is a negative relationship between perceived risk and apparel purchase intention.

METHOD Pretest A pretest was conducted to select appropriate multiple stimuli (two pairs of pants) for stimulus sampling purposes (Fontenelle, Phillips, & Lane, 1985). The first objective was to select pants that would minimize variability due to garment style. Nine pairs of khaki pants were purchased from retail stores and photographed on a size six female human model. Two versions of a questionnaire were developed. One version contained large photos of all nine pairs of pants and the other contained small photos of the nine pairs of pants. Small and large photo sizes were operationally defined as 306 ⫻ 186 pixels and 612 ⫻ 372 pixels, respectively, based on findings of Park and Stoel (2002). Eleven unipolar 5-point Likert-type scales developed by D. Cox and Cox (2002) were used to rate the pants (e.g., fashionable, attractive, practical). The second objective of the pretest was to determine if participants perceived the manipulation as intended. Accordingly, another 5-point item instructing participants to rate the size of the photos was included. Sixty female students participated in the pretest; 30 were exposed to the large images and 30 were exposed to the small images. Results of between-subjects multivariate analysis of variance revealed no significant multivariate effect for garment style on the dependent variables, F(11, 49) ⫽ 0.924, p ⫽ .963. The two pairs of khaki pants with the most similar ratings on the 11 adjectives were selected for the main study. Perceptions of image size (large vs. small) were also analyzed. The results of univariate analysis of variance revealed that image size had a signif704


icant effect on size perceptions; [F(1, 59) ⫽ 93.619, p ⬍ .001], such that the large images (M ⫽ 33.17) were perceived as larger than the small images (M ⫽ 15.77). Procedure Sample. Two hundred forty-four female undergraduate students in a large midwestern university volunteered to participate in this study. Incentives for participation included extra course credit and participation in a cash prize drawing. According to M. Lee and Johnson (2002), college students are especially likely to be potential Internet shoppers based on Internet shopper demographics. Design and Experimental Manipulation. The main part of this study employed a 2 ⫻ 2 between-subjects factorial design: product movement (product in motion vs. product not in motion) by image size (large vs. small). The two pair of khaki pants selected in the pretest were displayed separately in each treatment condition. Four mock Web sites were created to closely mimic the design of actual Web sites in accordance with the four experimental conditions with the use of Microsoft PowerPoint. Static products (or products not in motion) were presented by front, side, and back photos placed together within one horizontal image in order to provide a consistent amount of visual information across the two movement conditions. The clarity of the photos was controlled with the use of Photoshop Premiere. For the product-in-motion condition, the same model wearing the same khaki pants was smoothly rotated on a round plate and videotaped on a digital video camcorder. To transfer the rotated images to PowerPoint, the videotaped images were transferred into Media Player software and reorganized and imported into PowerPoint. The number of pieces of verbal information associated with each garment also was controlled and consistent across the mock Web sites. This information included fiber content, fabric construction, color, price, item care, item quality, sizing availability, item measurement, country of origin, texture/fabric hand, and quantity available. The product information provided was typical for an on-line retailer (Park & Stoel, 2002).

Materials and Procedure. A laboratory setting was established in a computer lab furnished with Pentium III computers. The use of PowerPoint software eliminated the effect of speed of picture downloading. Participants were randomly assigned to treatments in groups; they were first asked to rate their current mood (premood) and then to answer some general questions regarding the extent of their Internet shopping. After completing these items, they were asked to read general instructions about the experiment on the computer screen. Participants were ON-LINE PRODUCT PRESENTATION

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then presented with a scenario asking them to browse with the intention of purchasing. They were exposed to two pairs of khaki pants under the exact same treatment condition and were allowed to browse for 2 min (1 min for each product) in the same treatment combination (e.g., product in motion and small image for two pairs of khaki pants). The length of browsing time allowed was based on observations and estimations of Web-site browsing time. Each Web site was timed for a 1-min exposure, but participants could move forward by clicking during that minute or wait for the automatic transition to the next Web site. Moving backward to the prior Web site was not allowed, so that the participants were exposed to each Web site only once. In the product-in-motion condition, the product movement continued for the full minute unless the participant forwarded to the second Web site. After exposure to the treatment condition, participants rated their mood again (postmood), completed manipulation checks, completed the dependent variables, and provided some demographic information. Instrument Scales for Hypotheses. Mood was assessed with the use of a six-item mood scale (e.g., happy, delighted) developed by Izard (1972). Izard reported two subscales, Joy and Distress, with reliabilities of 0.80 and 0.90, respectively (Izard, 1972). In the current study, scores from the three negative mood items (discouraged, sad, downhearted) were reversed; then scores from all six mood items were summed for an overall positive mood score. A 24-item perceived risk scale, developed for television shopping by M. Kim and Lennon (2000), was revised for the on-line shopping context. M. Kim and Lennon factor analyzed the scale and reported reliabilities ranging from 0.74 to 0.91. In the current study, the scores of the 24 items were summed for an overall measure of perceived risk. Three items originally designed to assess purchase intention (Okechuku & Wang, 1988) were revised to reflect the Internet apparel-shopping context. The overall reliability found by Okechuku and Wang (1988) was 0.82. Four additional items tapping purchase intent in a television apparel-shopping context (M. Kim & Lennon, 2000) and revised to reflect an Internet shopping context also were used to measure purchase intention in this study. M. Kim and Lennon reported a reliability of 0.90. These seven items were summed for an overall measure of purchase intent. All mood, perceived risk, and purchase intention items used 5-point Likert-type scales ranging from 1 (strongly disagree or unlikely) to 5 (strongly agree or likely). Attention was measured in an attempt to determine the mechanism by which product presentation might affect mood. To measure attention, a free-recall measure developed by A. Y. Lee and Sternthal (1999) was adopted and used to assess the number of pieces of information recalled. Participants were asked to “list and describe what you saw on the Web sites.” The number of pieces of information recalled was summed. 706


Demographic Information, Internet Shopping Behavior, and Manipulation Checks. Participants provided some demographic information, including age, ethnic background, and sex. Participants also indicated whether or not they had previously purchased apparel or other products from the Internet, the type of apparel they had previously purchased, the amount of money they had spent for apparel on the Internet in the last 6 months, and the number of apparel items they had purchased via the Internet in the last 6 months.1 There were two manipulation checks. First, the perception of image size was assessed on a 5-point scale with end points strongly disagree (1) and strongly agree (5). Second, four 5-point items (active, dynamic, static, and passive) in the same agree/disagree format were used to assess the perception of product movement. After reversing the scores for static and passive, scores from the four items were summed for the measure of perceived movement. Finally, the 11 attributes of garment style (e.g., garment attractiveness, fashionability) were also assessed with the use of the same 5-point unipolar scales (e.g., fashionable–not fashionable) (D. Cox & Cox, 2002) that were used in the pretest.

RESULTS Preliminary Analyses Sample. The mean age of the 244 participants was 22 years. Approximately 70% of participants were Caucasian Americans, 13% were Asian/Asian Americans, 12% were African Americans; the remaining 5% self-identified as Hispanic American (3%), Native American (0.4%), or other. Approximately 78% of participants had purchased something from the Internet, which supports the M. Lee and Johnson (2002) observation that college students are likely to be Internet shoppers. About 53% (n ⫽ 130) of participants had purchased apparel via the Internet and of those, 95% had purchased women’s clothing over the Internet. In terms of apparel purchased from the Internet, 20% of those 130 apparel purchasers had not purchased in the last 6 months, 47% had spent less than $100 in the last 6 months, and 32% had spent from $100 to $500 in the last 6 months. In terms of number of apparel items so purchased, 21% had purchased one apparel item, 39% had purchased from 2 to 4 apparel items, 16% had purchased 5 to 7 apparel items, and 2% had purchased more than 10 apparel items in the last 6 months. Reliability Analysis. With the use of Cronbach’s alpha, reliabilities for the 6-item measure of positive mood were found to be 0.81 (N ⫽ 244) for premood and 0.76 (N ⫽ 244) for postmood, respectively. Reliability for the 1

There is some discrepancy in numbers because not everyone completed all items.


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24-item measure of perceived risk was 0.88 (N ⫽ 244). Reliability for the 7-item apparel purchase intention measure was 0.89 (N ⫽ 244). Those reliabilities were considered adequate. Manipulation Checks. Manipulation checks provided evidence of successful manipulations of image size and of movement. With the use of univariate analyses of variance, main effects were found for (a) image size on size perceptions [F(1, 243) ⫽ 46.211, p ⬍ .001, Eta2 ⫽ 16%] and (b) product movement on the summed measure of perceived movement [F(1, 243) ⫽ 43.700, p ⬍ .001, Eta2 ⫽ 15.3%]. Those exposed to large images perceived them to be larger (M ⫽ 3.56, SD ⫽ 0.93) than those exposed to small images (M ⫽ 2.70, SD ⫽ 1.02). People exposed to moving images perceived more movement (M ⫽ 13.63, SD ⫽ 2.89) than people exposed to nonmoving images (M ⫽ 11.14, SD ⫽ 3.00). Hypotheses Testing Multivariate Analysis. The effects of product presentation on mood, perceived risk, and purchase intention (Hypotheses 1–3) were tested by multivariate analysis of covariance. The covariate in each case was the measure of premood. Bonferroni’s test was used to adjust for multiple comparisons, when post hoc comparisons across product size and movement were performed. In this analysis, the covariate was significantly related to the dependent variables; F(3, 237) ⫽ 21.57, p⬍ .001. After the effects of the covariate were removed, a significant multivariate main effect for product movement on the dependent variables was found; F(3, 237) ⫽ 14.00, p ⬍ .001. However, there was no significant multivariate effect for image size on the dependent variables; F(3, 237) ⫽ 1.17, p ⫽ .32. Thus, H1(b), H2(b), and H3(b) were not supported. Image size and product movement interacted to affect the dependent variables, F(3, 237) ⫽ 3.71, p ⬍ .05. Next, univariate analyses of covariance (ANCOVAs) were calculated with premood as a covariate to determine which dependent variables contributed to the significant multivariate effects. Effects that were nonsignificant at the multivariate level were not probed at the univariate level. In what follows, the term mood was used to mean postmood. Univariate Analyses: Product Presentation and Mood. Univariate analysis of covariance was performed to test H1(a). After adjustment for premood, there was a significant main effect for product movement on mood; F(1, 243) ⫽ 37.480, p ⬍ .001, Eta2 ⫽ 0.136. The results revealed that people who were exposed to Web sites with products in motion (M ⫽ 22.98, SD ⫽ 3.63) exhibited more positive mood than people exposed to Web sites with products not in motion (M ⫽ 20.55, SD ⫽ 3.90). Therefore, H1(a) was supported. In addition, univariate analysis of covariance was performed to test H1(c). After adjusting for premood, there was no significant interaction effect for image size and product movement on 708


mood, F(1, 243) ⫽ 0.032, p ⫽ .859, Eta2 ⫽ 0.000. Therefore, H1(c) was not supported. Univariate Analyses: Product Presentation and Perceived Risk. Univariate analysis of covariance was performed to test H2(a). After adjustment for premood, there was a main effect for product movement on perceived risk; F(1, 243) ⫽ 7.364, p ⬍ .01, Eta2 ⫽ 0.030. The results revealed that people who were exposed to Web sites with products in motion (M ⫽ 79.41, SD ⫽ 14.35) exhibited less perceived risk as compared to those exposed to Web sites with products not in motion (M ⫽ 84.51, SD ⫽ 14.10). Therefore, H2(a) was supported. In addition, univariate analysis of covariance was performed to test H2(c). After adjustment for premood, the interaction for image size and product movement on perceived risk was nonsignificant; F(1, 243) ⫽ 2.654, p ⫽ .105, Eta2 ⫽ 0.011. Therefore, H2(c) was not supported. Univariate Analysis: Product Presentation and Purchase Intention. Univariate analysis of covariance was performed to test H3(a). After adjusting for premood, there was a main effect for product movement on apparel purchase intention; F(1, 243) ⫽ 6.950, p ⬍ .01, Eta2 ⫽ 0.028. The results revealed that people who were exposed to Web sites with products in motion (M ⫽ 18.13, SD ⫽ 6.21) exhibited greater apparel purchase intention compared to those exposed to Web sites with products not in motion (M ⫽ 15.97, SD ⫽ 5.99). Therefore, H3(a) was supported. In addition, univariate analysis of covariance was performed to test H3(c). After adjusting for premood, there was a significant interaction for image size and product movement on apparel purchase intention; F(1, 243) ⫽ 9.943, p ⬍ .01, Eta2 ⫽ .040. Therefore, H3(c) was supported. Bonferroni’s test was performed for post hoc multiple comparisons to examine differences in purchase intention across the four treatment combinations of image size and product movement. Results revealed significant differences in purchase intention (a) between people who saw large images with movement and those who saw large images without movement (mean difference ⫽ 4.46, p ⬍ .001), (b) between people who saw large images with movement and those who saw small images with movement (mean difference ⫽ 3.43, p ⬍ .01), and (c) between people who saw large images with movement and people who saw small images without movement (mean difference ⫽ 3.09, p ⬍ .05). No other comparisons were significantly different. Mood and Perceived Risk. Simple regression analysis was used to determine the relationship between perceived risk and mood. Results of the analysis revealed that mood was significantly related to perceived risk; F(1, 242) ⫽ 5.284, p ⬍ .05, R2 ⫽ 2%. People in a more positive mood perceived less risk associated with purchasing apparel from the Internet, b ⫽ –0.146, p ⬍ .05. Therefore, H4 was supported. ON-LINE PRODUCT PRESENTATION

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Relationship of Apparel Purchase Intention to Mood and Perceived Risk. Stepwise multiple regression analysis was used to assess relationships among mood, perceived risk, and apparel purchase intention. A significant negative relationship between perceived risk and apparel purchase intention was found; F(1, 242) ⫽ 20.606, p ⬍ .001, R2 ⫽ 7.8%, b ⫽ –0.11. Mood was positively related to purchase intention; F(1, 241) ⫽ 18.307, p ⬍ .001, R2 ⫽ 5.4%, b ⫽ 0.37. Thus, perceived risk and mood were related to apparel purchase intention, indicating that people who felt less perceived risk and were in a more positive mood were likely to have greater apparel purchase intention. Therefore, both H5 and H6 were supported by this analysis. Post-Hoc Test 1: Testing Mediating Relationships The integration and synthesis of existing empirical studies in psychology and marketing suggest additional mediating relationships to the tested hypotheses above. Several studies have found that marketing stimuli such as salient moving objects or product display influenced positive or negative mood states (e.g., Bitner, 1992; Fiore et al., 2000) and, in turn, increase or decrease perceptions of risk (e.g., Johnson & Tversky, 1983). Following Schwarz (1990), a positive mood induced by product movement may serve as a source of information that replaces the evaluation of the objective information of the product. If a person feels good, he or she may attribute this positive feeling to characteristics of the present situation and thus evaluate a risky situation more favorably (e.g., Gorn et al., 1993). As a result of positive mood states and less perceived risk induced by product movement display, consumers may spend extra time shopping and purchase more products (e.g., Babin et al., 1994; Swinyard, 1993). This implies not only a direct relationship between product movement and purchase intention but also an indirect relationship through mood and perceived risk. Furthermore, a sequence of those linear relationships among variables can yield three possible mediating relationships: (a) both mood and/or perceived risk mediate the relationship of product movement and purchase intention, (b) mood mediates a relationship between product movement and perceived risk, and (c) perceived risk serves a mediating role in the relationship between mood and purchase intention (see Figure 2). Therefore, mediating analyses were conducted to assess the nature of the relationships among the variables. As in Baron and Kenny (1986), mediating regression analyses were used to test if mood and perceived risk mediated the relationship between product movement and purchase intention. In this analysis, the independent variable was product movement and the dependent variable was purchase intention. Mood and perceived risk were treated as mediators. This requires three steps: (a) regressing mood and perceived risk separately on product movement; (b) regressing purchase 710


Note. The dotted line indicates a relationship supported by the MANCOVA, but not by the mediating analysis.

Figure 2. Supported hypotheses among product movement, mood, perceived risk, and purchase intention.

intention on product movement; and (c) regressing purchase intention on product movement, mood, and perceived risk. Results of simple regression analyses were as follows: (a) both mood [F(1, 242) ⫽ 25.51, p ⬍ .001, b ⫽ 0.31] and perceived risk [F(1, 242) ⫽ 7.83, p ⬍ .01, b ⫽ –0.18] were significantly related to product movement, (b) a significant positive relationship between purchase intention and product movement was found [F(1, 242) ⫽ 7.55, p ⬍ .01, b ⫽ 0.17], and (c) multiple regression examined the influence of product movement, perceived risk, and mood on purchase intention. In that analysis, only perceived risk (b ⫽ –0.34) and mood (b ⫽ 0.22) were related to purchase intent; F(1, 242) ⫽ 12.56, p ⬍ .001. Therefore, perfect mediation of the two mediators (perceived risk, mood) holds because the independent variable (product movement) had no effect when the two mediators were controlled (entered into the regression equation). Presentation (i.e., movement) influenced purchase intent indirectly through mood and perceived risk. Next, the second mediating regression analysis was performed to determine if mood mediated the relationship between product movement and perceived risk (see Figure 1). In this analysis, product movement was the independent variable and perceived risk was the dependent variable. Mood was a mediating variable. The results of three regressions were as follows: (a) a significant positive relationship between product movement and mood was found [F(1, 242) ⫽ 25.51, p ⬍ .001, b ⫽ 0.31], (b) a significant negative relationship between product movement and perceived risk was found [F(1, 242) ⫽ 7.83, p ⬍ .01, b ⫽ –0.18], and (c) the combination of product movement (b ⫽ –0.15) and mood (b ⫽ –0.10) on risk was significant [F(1, 242) ⫽ 5.01, p ⬍ .001]. However, a comparison of the standardized coefficient from the second regression for product movement (b ⫽ –0.18) the one from the third regression for product movement (b ⫽ –0.15) indicates that mood functioned as a mediator in ON-LINE PRODUCT PRESENTATION

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the relationship between product movement and perceived risk, but was not a perfect mediator. Product movement influenced perceived risk indirectly through mood, but also influenced perceived risk directly. Last, the third mediating regression analysis was performed to determine if perceived risk mediated the relationship between mood and purchase intention (see Figure 1). In this analysis, mood was the independent variable and purchase intention was the dependent variable. Perceived risk was a mediating variable. The results of three regressions were as follows: (a) a significant negative relationship between mood and perceived risk was found [F(1, 242) ⫽ 5.28, p ⬍ .05, b ⫽ –0.54], (b) a significant positive relationship between mood and purchase intention was found [F(1, 242) ⫽ 18.97, p ⬍ .001, b ⫽ 0.42], and (c) the combination of perceived risk (b ⫽ –0.11) and mood (b ⫽ 0.37) on purchase intention was significant [F(1, 242) ⫽ 18.31, p ⬍ .001]. However, the standardized coefficient from the second regression for mood (b ⫽ 0.42) was greater than the one from the third regression for mood (b ⫽ 0.37). Based on the results of these mediating regression analyses, perceived risk functioned as a mediator in the relationship between mood and purchase intention, but was not a perfect mediator. Mood influenced purchase intent indirectly through perceived risk, but also influenced purchase intent directly. Post-Hoc Test 2: The Effect of Product Movement on Recall Based on the literature, it is possible that movement per se did not affect the variables but rather that attention as a result of movement had those effects. Previous research on attention (e.g., Folk et al., 1992; Johansson, 1973; Maljkovic & Nakayama, 1994; Morrin & Ratneshwar, 2000; Nakayama & Silverman, 1986; Todd & Van Gelder, 1979; Yantis, 1993) emphasized the importance of stimulus movement in influencing visual selection and attention in many tasks. Langer (1989) pointed out that when people were engaged in a complex task such as tennis or sewing and paid attention to the task, they were likely to be actively involved in the task and tended to enjoy the task more than when people did not pay attention to the task. Thus, it is possible that attention due to motion may influence mood and enjoyment of a task. In this study, because product movement captures attention during browsing, participants may not have attended to other product and/or customer service information available through the Web site. For example, people who were exposed to the Web sites with movement may recall less information (except visual information), as compared to people who were exposed to the static Web sites. This would provide evidence that attention is responsible for the effects. Participants listed and described what they saw on the Web sites. Inaccurate or incorrect information was excluded, as was visual information related to product presentation (e.g., “interesting rotation of the products”). A one-way univariate analysis of variance on number of pieces 712


of information correctly recalled yielded a significant main effect for product movement on recall, F(1, 243) ⫽ 6.407, p ⬍ .05, Eta2 ⫽ 2.6%. The results revealed that people who were exposed to Web sites with products in motion (M ⫽ 2.12, SD ⫽ 0.16) recalled less information than people exposed to Web sites with products not in motion (M ⫽ 2.71, SD ⫽ 0.17). These results support the explanation that attention as a result of movement caused the main effects for movement. This suggests that product movement had an indirect effect on mood and risk; it captured attention and in turn, attention influenced mood and perceptions of risk.

DISCUSSION AND IMPLICATIONS In this study, image size and product movement were manipulated in the context of simulated apparel Web sites. Both manipulations in the computer-mediated environment were expected to create virtual experiences affecting mood, perceived risk, and purchase intent in that context. However, none of the main effects for image size was significant. There are several possible explanations for the nonsignificance. Image size was based on those currently available on Internet apparel Web sites, which means that Internet apparel shoppers (53% of participants) may be familiar with seeing different sizes of images at Web sites. It is also possible that both the small- and large-sized images were clear enough to provide a similar amount of information so that participants perceived a similar amount of risk, regardless of image size. If the small image size was clear enough for participants to evaluate the garments, then purchase intention may not be affected by image size. Another possible explanation for the nonsignificant effects for image size is the between-subjects design of the experiment. Had participants been exposed to both sizes of images in a mixed design, for example, it is more likely that the difference in size might have affected the dependent variables. The present study provides evidence of a difference in both mood and perceived risk as a function of product movement on the Web sites. This supports previous literature (e.g., Folk et al., 1992) that consistently found a relationship between movement and mood. The analysis of recall data suggests that the mechanism by which this occurs is through attention. Thus, apparel e-tailers may find it useful to attract browsers’ attention and create a positive mood by using product rotation, which this study shows serves to decrease shoppers’ concern about the uncertainties of on-line apparel product purchase. Analysis of covariance results show that product movement affected purchase intent. This is consistent with Swinyard (1993) who found that an appealing visual display of products increased consumers’ intention to purchase products. Consumers may experience pleasure from movement, perceive less risk, and have greater intent to purchase. It is possible that the ability to view garments from various angles may provide ON-LINE PRODUCT PRESENTATION

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product information and in turn, may generate higher purchase intent. However, a mediating analysis indicated that mood and perceived risk functioned as perfect mediators between product movement and purchase intent. Although product simulations with movement ultimately create strong purchase intent, such simulations may initially evoke affective (mood) and/or cognitive responses (perceived risk) that precede purchase intent. Results of the mediating analysis support the model of the consumer-response system (Holbrook & Hirschman, 1982), which posits that consumers respond to marketing stimuli through a series of sequential responses with affective and/or cognitive responses preceding conative responses to marketing stimuli (e.g., product presentation). The results also showed that perceived risk mediated the relationship between mood and purchase intention. This supports previous research by Johnson and Tversky (1983), Gorn et al. (1993), Swinyard (1993), and Bitner (1992). Positive moods led to positive on-line apparel browsing, less perceived risk, and increased purchase intention. Therefore, it is important for e-tailers to create a pleasurable on-line shopping environment that will evoke positive mood. This research suggests that product presentations using movement attract attention and generate good mood in on-line shoppers. Future research could investigate the effects of other attention-getting features such as complementary music on mood and purchase intent. Future research should also examine the effect of attention-getting or vivid movement in product presentation versus movement of other Web-site content (e.g., logos, banners) on mood and purchase intent. It is conceivable that too much movement on a Web site could be distracting and could adversely affect mood and purchase intent. The present study revealed a negative relationship between perceived risk and apparel purchase intention, which is consistent with Vijayasarathy and Jones (2000). Internet shopping may be avoided due to uncertainties and negative consequences. If people feel confident about their judgments in shopping on the Internet, their purchase intent may increase. Thus, it is important for e-tailers to make the shopping environment less risky to help shoppers make purchase decisions with confidence. Accurate and detailed visual information on the screen (e.g., color, clothing from various angles) can be made available to decrease perceived risk and facilitate decision making. Finally, image size and product movement were expected to interact in affecting mood, perceived risk, and purchase intent. However, only purchase intent was so affected. People who were exposed to Web sites with large moving images exhibited greater purchase intention than people in any of the other three treatment conditions. Previous research demonstrates that amount of information positively affects apparel purchase intent (M. Kim & Lennon, 2000), so it is possible that the large moving image provided more information to participants than any other treatment combination, which in turn affected purchase 714


intent. However, if that explanation were true, then perceived risk should have been similarly affected, because amount of information also affects perceived risk. The nature of the interaction on purchase intent can be explained with the use of the concept of vividness. Although this research did not set out to study vividness, the presentation variables can be interpreted in those terms. Aspects of stimuli that make it vivid include large image size and movement (e.g., Biocca & Delaney, 1995; Choi et al., 2001; Lombard, 1995). Thus, a large-sized image that is moving would be more vivid (has two aspects of vividness) than a large-sized image that is nonmoving (has one aspect of vividness), or a small moving image (has one aspect of vividness), or a small nonmoving image (has no aspect of vividness). An interaction of movement by image size is then simply an assertion that more-vivid imagery has a greater effect on dependent variables than less-vivid imagery. Because the large moving image has more characteristics of vividness, it might be expected to evoke greater purchase intent than the other conditions. This study adds valuable empirical findings to the literature on the relationship between product movement and mood, perceived risk, and purchase intention. Because the past literature on movement focused on theory building in psychology, it may be important to test the effect for movement in a real shopping situation, especially in the Internet shopping context. This study also contributes to knowledge about Website design for apparel e-tailers. Because product information immediately available to on-line apparel shoppers is limited to product presentation on screen and does not enable shoppers to physically inspect the products, it is important to provide detailed and accurate information on screen. Products revolving around a Y axis may simulate a shopping experience at retail stores in terms of judging fit. Product rotation allows shoppers to see all possible views of a garment and captures the attention of consumers. However, at the same time, Web-site designers should control image complexity to minimize download time when product movement is presented. Although a laboratory experiment has numerous advantages such as uniformity of procedures, manipulation of selected variables, and control of environmental circumstances and other extraneous variables (Touliatos & Compton, 1988), it also has limitations. In this study, participants were homogeneous in terms of age, they were not all real Internet shoppers, and the situation was simulated. Although a scenario describing a potential purchasing situation was given to increase the reality of the experiment, participants were probably aware of its artificial nature. Several factors related to the experimental procedure, such as sitting in a computer lab for a limited amount of time and following the instructions given by the researcher, may reduce reality in the shopping situation. In addition, motivations to participate in an experiment (e.g., extra credit, drawings) may also decrease reality. ON-LINE PRODUCT PRESENTATION

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