Shopping on Social Networking Web Sites: Attitudes toward Real versus Virtual Items   

Jiyoung Cha

University of North Texas  

Abstract

Assuming that shopping is a business area into which U.S. social networks can expand, this study explores whether and how factors affecting shopping attitudes on social networking sites may differ according to product type. This study focuses on two types of items that social networking sites carry: real and virtual. It reveals that shopping services have different target consumers and factors according to product type. Age, usefulness, ease of use, security, and fit are critical in establishing favorable attitudes toward shopping for real items. For virtual items, gender, social networking site experience, ease of use, and fit influence the attitudes.  

Keywords: Social networking Web sites, online shopping, virtual, real, technology acceptance model.

Introduction

Most social networking Web sites based in the United States have relatively short histories. The growth of these sites is, however, exponential. According to ComScore Media Metrix, MySpace ranked second in visitor page views, behind only Yahoo, by May 2006 (Knowledge @ Wharton 2006). As of August 2007, six social networking Web sites ranked in the top 20 of U.S. Web site traffic lists: MySpace was ranked 6th, Orcut 8th, Facebook 10th, Hi5 11th, and Friendster 18th (Alexa.com 2007). Moreover, major U.S. social networking Web sites have seen a rapid increase in the numbers of their registered users. As of May 2009, Facebook and MySpace had more than 307 million and 123 million users worldwide, respectively (Albanesius 2009).

Social networking Web sites have succeeded in attracting not only users but investors too. Media conglomerates have tried to acquire or invest in social networks. Despite some doubt about the stable revenue structures of social networks in their nascent business stage, News Corp. acquired MySpace, the largest U.S. social network, for $580 million in 2005. Yahoo and Viacom both offered $1 billion to buy Facebook in 2006. Venture capital firms recently put up $25 million in funding for Facebook (Rosenbush and Mullaney 2006; Vara 2007). Several venture capitalists also invested from $1 million to $10 million in Friendster (Kawamoto 2006).

Despite the success of social networking sites with respect to attracting users and investors, academics and industry observers are concerned about their potentially vulnerable business models (Knowledge @ Wharton 2006; Olsen 2004). Specifically, the profitability of some social networks is highly questionable (Claburn 2006; Tedeschi 2004) because the growing number of users and explosive Web traffic do not necessarily mean that the social networks will make a profit. The finances of social networking Web sites also depend substantially on revenues from outside advertisers. For example, Friendster's revenues come solely from advertisers (Hopkins 2003). According to Framingham, a media research firm, MySpace generated about $125 million in advertising revenue in the fourth quarter of 2006 (Havenstein 2007) and $440 million in revenue in fiscal year 2007. There is little doubt that the primary source of revenue is advertising (Williamson 2007). Facebook generated $150 million in revenue and $30 million in profit during calendar year 2007, through August; an advertising deal with Microsoft accounted for half of that revenue (Vara 2007). 

For community-oriented Web proprietors, network externalities are a critical factor for success. Researchers suggest that companies with the greatest installed network bases will dominate the market (Arthur 1996; Brynjolfson and Kemerer 1996; Katz and Shapiro 1985; Lee and O'Connor 2003). As an increasing number of people register on popular Web social networking sites, they may eventually turn away from smaller social networks—even though these smaller social networks serve as niche sites that gratify specific needs. Such a trend seems to be occurring in the United States, where as of August 2007, six social networking sites ranked in the top 20 list of most visited sites, but two years later, only Facebook and MySpace remained on that list (Alexa.com, 2009). In addition, Facebook has started to outperform MySpace in terms of traffic and the number of unique visitors (Albanesius 2009). Given the importance of network externalities in the context of social networking sites, it is imperative for social networks to develop stable revenue structures that utilize their massive user bases.

In retrospect, myriad Internet companies have drawn huge numbers of users and received substantial attention from the press, but then disappeared within a short period of time—largely due to their lack of solid business models that generate profits. Advertisers and investors remain in flux as interesting new technologies emerge. For example, e-Marketer, an online business research firm, has cut its ad spending estimates for social networking Web sites; advertisers were thus predicted to spend $1.4 billion on U.S. social networking Web sites in 2008, a significant drop from its previous estimate of $1.6 billion. Specifically, the company cut revenue estimates for MySpace and Facebook, predicting MySpace would take an 11.2% hit and decline from $850 million in advertising income to $775 million. Facebook was projected to suffer a 12.5% drop, from $305 million to $265 million (Nicole 2008; Sanders 2008).

The economic downturn is one plausible reason for these lowered advertising spending estimates, but the industry pinpoints another primary cause, namely, the lack of advertising and business models customized for social networking Web sites. The U.S. social networking sites are still trying to figure out how to turn their massive audiences into advertising dollars (Sanders 2008). From a managerial perspective, the key to success lies in learning how to monetize their mass user bases by building stable and relevant business models. This need reflects the intense competition in the social networking site market segment, as well as the lack of existing solid business models in the overall online industry. In that regard, this study suggests that shopping services may provide an area of possible business growth for social networks.

Operators of social networking sites must establish user bases, which gives them potential shoppers in hand. It would not be necessary for social networks to invest a huge amount of money in promoting and advertising shopping services or attracting potential shoppers; large social networks already have potential shoppers in hand. In addition, whatever it is that keeps users coming back to a particular social networking site should be pivotal for shopping services in general. The socially interactive nature of social networking sites also likely increases the exposure of these returnees to the goods and services that the sites market.

This study also considers shopping services attractive for social networking sites because of the user demographics of social networking sites. Audiences of traditional media spread across all age groups, whereas social networks are highly concentrated on teenagers and people in their 20s and 30s. As of July 2009, users between the ages of 13 and 34 years accounted for nearly 65% of Facebook users. The largest age group of users, between 18 and 25 years, constitutes 30% of all Facebook users (Inside Facebook 2009). In addition, 85% of U.S. college students use at least one social networking Web site, and 60% and 85% log on to social networking sites daily or once a week, respectively (Arrington 2005). A more recent industry report confirms these trends remain valid, in that more than 80% of U.S. college students use social networking sites on a regular basis (The Info-Shop 2007). Users who range in age from their teens through their 30s are particularly attractive targets for sellers of goods and services; they also are more likely to purchase products or services online than are older consumers (Akhter 2003; He and Mykytyn 2007).

Due to the unique characteristics of social networks, items sold through social networking sites may differ from those sold in other online stores, whose businesses focuses entirely on shopping services. Online shopping sites mostly sell "real goods" or "real services," whereas social networks can carry not only real items but also "virtual items." Real items refer to goods or services that can be used offline, regardless of whether the goods or services are bought online or offline, such as books, furniture, clothes, flight tickets, DVDs, and so on. Virtual items instead are products or services whose use and purchase are constrained to a particular Web space. Profile layouts, avatars, virtual gift items, and music that can be only used on specific Web sites are examples of virtual items. Despite the considerable amount of buzz around social networking sites in the press, academia has paid scarce attention to them, and most existing studies consider social aspects or privacy issues (e.g., Barnes 2006; Ellison, Steinfield, and Lampe 2006; Tong et al. 2008) rather than investigating social networks from a managerial perspective.

By integrating the technology acceptance model (TAM) with other perceptions of social networks and characteristics of individual users, this study aims to investigate whether and how the factors that affect attitude toward shopping on social networking sites differ according to product type (i.e., real versus virtual items). This investigation therefore sheds light on the similarities and differences between the drivers of consumers' online shopping for real and virtual items. Also, it offers insights into whether widely accepted theories in the e-commerce context apply to shopping on social networking sites as well. Unlike shopping-driven sites, such as Amazon and eBay, shopping services are not the primary business domain for social networking sites, so consumers may perceive their shopping services differently. From the perspectives of social network operators, launching shopping services represents a category extension, and this study may help them find ways to boost the chances of success for their shopping services venues. 

Shopping on Social Networking Web Sites

Social media play increasingly important roles as a marketing platform. More and more retailers use social media to target teens and young adults, and social networking sites are a central venue in that trend (Market Watch 2008). A survey commissioned by the American Marketing Association reveals a positive outlook for likelihood of e-commerce on social networking sites, in that 47% of consumers said they would visit social networking sites to search for and discuss holiday gift ideas, and 29% said they would buy products there (Horovitz 2006).

Some U.S. social networks have geared up to provide shopping services. Facebook added a shopping application that enables users to search for products they want to buy, then share their opinions of those products with other Facebook members (Forbes 2007). Facebook has supplied virtual gifts, valued at $1 apiece, since April 2007. The addition of shopping services to social networking sites is in the nascent stage in the United States, but social networking sites in some other countries employ shopping services aggressively. For example, Cyworld.co.kr, a popular Korean social network that attracts more than one-third of the country's population and 90% of people in their 20s, carries both real and virtual items. It generates approximately $300,000 daily from individual users by selling virtual items such as music, avatars, and customized profile layouts (Schonfeld 2006). Although U.S. social networking sites provide users with similar items, such as music and profile layouts, they usually are provided for free. Cyworld also generates revenues from selling real items, such as clothes and fashion accessories.

Research suggests that consumers rely on two different sets of values in making their shopping decisions: hedonic and utilitarian (Babin and Darden 1995; Babin, Darden, and Griffin 1994). Batra and Ahtola (1990, p. 159) define these values as follows: "(1) consummatory affective (hedonic) gratification from sensory attributes, and (2) instrumental, utilitarian reasons." Hedonic shopping value thus reflects the value received from the multisensory, fantasy-related, and emotive feeling a consumer receives from a particular product, whereas utilitarian shopping value focuses on the acquisition of products and/or information in an efficient manner, which reflects a more task-oriented, cognitive, unemotional outcome (Babin, Darden, and Griffin 1994; Holbrook and Hirschman 1982). Utilitarian value therefore is more associated with cognitive aspects of attitudes, such as economic benefit (Zeithaml 1988), convenience, and time savings (Jarvenpaa and Todd 1997; Teo 2001).   

Online shoppers tend to seek utilitarian values rather than hedonic values (Reibstein 2002), because online shopping services lack multisensory attributes. The primary utilitarian values that online shoppers seek include the convenience of locating and comparing merchants, evaluating price/quality ratios, and conserving temporal and psychological resources (Grewal et al. 2003; Mathwick, Malhotra, and Rigdon 2001). Adding virtual items to social networking sites could expand the value of online shopping. Shopping for virtual items also is more relevant to hedonic than to utilitarian values, because consumers would not purchase virtual items out of necessity. Thus, whether a social networking site sells real or virtual items may determine consumers' attitudes toward shopping on that site. Considering the different nature of real and virtual items in a shopping context, this study explores the differences and similarities between factors that affect shopping for real and virtual items on social networking sites.

Perceived Usefulness, Ease of Use, and Enjoyment

The technology acceptance model (TAM) posits that the perceived usefulness and perceived ease of use of a particular information technology drive users' attitudes and intentions to adopt that technology (Davis 1989; Davis, Bagozzi, and Warshaw 1989). According to empirical tests in different technologies and settings, the TAM is a parsimonious, robust model for predicting technology acceptance intentions (Gefen and Straub 2003). Perceived usefulness is "the degree to which an individual believes that using a particular system would enhance his/her job performance" (Davis 1989, p. 320), whereas perceived ease of use refers to "the degree to which an individual believes that using a particular system would be free of real and mental efforts" (Davis 1989, p. 323). Recent studies show that perceived usefulness and ease of use both affect consumers' intentions to use e-commerce (Gefen and Straub 2000; Lee, Park, and Ahn 2001).

Another construct added to the model is perceived enjoyment. Perceived enjoyment is defined as "the extent to which the activity of using the computer is to be perceived enjoyable in its own right, apart from any performance consequences that may be anticipated" (Davis, Bagozzi, and Warshaw 1992, p. 1113). Enjoyment usually emerges as important for shopping experience, along with convenience and social interactions (Javenpaa and Todd 1997). Thus, perceived enjoyment, perceived usefulness, and perceived ease of use may be able to predict attitudes toward shopping services offered on social networks.  

Perceived Fit

The perceived fit construct often appears when a brand introduces a new product or service in different product or service categories. In marketing literature, perceived fit refers to the degree of similarity between an extension product category and existing products affiliated with the brand (DelVecchio and Smith 2005). Previous studies suggest that perceived fit between parent brands and their extensions can enhance the performance of the latter. If new products or services are perceived as similar to their parent brand, consumers are more likely to evaluate the new product favorably (Boush et al. 1987; Papadmitriou, Apostolopoulou, and Loukas 2004). The fit between the brand and the extension category also can reduce uncertainty triggered by a particular extension category (Smith and Andrews 1995). Papadmitriou, Apostolopoulou, and Loukas (2004) confirm the significant impact of perceived fit on intention to purchase the extended products or services.

Even though some U.S. social networking Web sites have introduced shopping services and applications, the extension is considered fledgling; these sites are not yet very aggressive about selling goods and services in general. Therefore, introducing and developing shopping services can be considered category extensions from a managerial standpoint. In that regard, consumers' perception of the fit between social networking sites and the individual items they sell would influence their attitudes toward the shopping services. Therefore, 

Perceived Security

Security is a salient issue for e-commerce because users must submit sensitive information to purchase goods or services online. Salisbury and colleagues (2001, p. 166) define perceived security on the Web as "the extent to which one believes that the World Wide Web is secure for transmitting sensitive information." They also find empirically that perceived security on the Web positively affects purchase intentions online. That is, the less secure someone perceives the Web to be, the lower the probability that he or she will make a purchase through that channel. Yenisey, Ozok, and Salvendy (2005) assert that this barrier causes increasing numbers of people to hesitate when asked to submit sensitive information over the Web.

Perceived security also may be critical for social networks that introduce e-commerce. Unlike online shopping malls, such as Amazon, shopping services are not the primary business area offered by social networking Web sites. If people doubt the transactional security of social networks, they may not shop for or purchase things on social networking Web sites. Therefore, 

Experience with Social Networking Web Sites 

Zajonc (1968) suggests the influence of a "mere exposure effect," such that continuous exposure tends to increase people's liking for given stimuli. As a person experiences more exposure to a particular stimulus, he or she establishes a more positive attitude toward that stimulus (Monroe 1976; Wilson 1979; Zajonc 1968). The more familiar they are with a medium, due to their frequent use of it, the more favorably people feel toward that medium. Several prior studies specifically focus on the relationship between overall Internet experience and purchase intentions and behaviors on the Internet. Aldridge, Forcht, and Pierson (1997) assert that the likelihood of buying online increases as overall use of the Internet increases, and Hoffman, Novak, and Peralta (1999) empirically find that Internet experience has a positive association with purchase behaviors on the Internet. Applying this theory to social networking sites, 

Online Shopping Experience

Despite promising outlooks for online shopping in its nascent stage, Forrester Research projects that online shopping will account for only 9% of overall U.S. retail sales in 2010 (Linn 2007). Although online shopping has grown rapidly in recent years, some Internet users remain reluctant to purchase goods on the Internet because they are skeptical of how much privacy and security they have in doing so (Aldridge, Forcht, and Pierson 1997; Wang, Yeh, and Jiang 2006). Others may hesitate to shop online because they would miss the social interaction or direct experience with products. Online shopping analysts argue that people who have not purchased online tend to continue to buy goods or services offline (Linn 2007). Rogers (1995) also explains that people are more likely to adopt an innovation they are comfortable with and that is compatible with other technologies they already use. Therefore, 

Gender

More men used the Internet in its nascent years than did women, so online shopping was more prevalent among men than among women in the late 1990s (Ernst and Young 1999; Pew Internet 1998). Research also indicates that male consumers spend more money and buy more frequently online than do female consumers (Graphics, Visualization, and Usability Center 1999; Li, Kuo, and Russell 1999). Yet the gender gap has decreased in recent years; according to the Pew Internet (2001) survey, 58% of men and 54% of women were Internet users as of 2001. More recent surveys, such as the Pew Internet (2002) and Sky News (2002), indicated that women are more dominant than men when it comes to e-commerce. Focusing on expenditures online, women accounted for 58% of online shopping, whereas men were responsible for 42% between April 2004 and March 2005, according to comScore (Maguire 2006). Nevertheless, men still report higher levels of online purchase intentions than do women (Doolin et al. 2005).

Dittmar, Long, and Meek (2004) maintain that differences in conventional shopping motivations between men and women may explain why women are less likely to buy online. Because the online shopping environment does not offer emotional involvement or social interaction, women may be less likely to shop online. However, the situation could differ on social networking sites. Unlike other e-commerce sites that tend to mitigate opportunities for social interaction during shopping, social networking sites enable users to interact with their friends. For example, Facebook's shopping application allows users to rate and discuss products they want to purchase with their friends. Therefore, users of social networks can obtain their online friends' opinions about the products they want to buy.

Meanwhile, Girard, Korgaonka, and Silverblatt (2003) find that online shopping preferences depend on product types. Men are more likely to shop online for books, computers, and other "utilitarian experience" goods (e.g., cell phones, televisions). Women instead shop online for hedonic experience goods, such as perfume and clothing. The unique characteristics of social networks as venues for shopping and product types suggest the following hypothesis:

Age

Previous studies indicate that age and technology adoption have an inverse relationship in various technology contexts. Older people tend to exhibit more negative perceptions of new technologies and feel greater reluctance to adopt them (Gilly and Zeithaml 1985; Pommer, Berkowitz, and Walton 1980). Madden and Savage (2000) specify that age is negatively associated with Internet use, and the Pew Internet (2004) project supports this relationship. These findings extend to the adoption of specific Internet-related technologies, such as online chat rooms and Webcasting (Lin 2004; Peter, Valkenburg, and Schouten 2005). Age also has a negative relationship with the adoption of e-commerce. Akhter (2003) suggests that younger people are more likely than older consumers to purchase products or services using the Internet, and He and Mykytyn (2007) reveal a negative relationship between age and the intention to adopt online payment methods. Therefore, 

Method

Sample and Procedures

The data for this study come from a survey. Before the main test, two pretests, using two samples of 38 and 40 college students, were conducted. On the basis of the pretests, the questions and wordings for the questionnaire were carefully refined. For the main survey, a total of 167 students at a large university located in the southeast part of the United States participated. Although the use of college students can be viewed as convenient, Basil (1996) suggests this sample is valid if their demographic group is of interest to the topic of study. A college student sample is reasonable to study shopping services on social networking sites, because college students are the primary users of social networks (Arrington 2005; The Info-Shop 2007) and represent a significant portion of the demographic age group that social networking sites and related retailers target for marketing (Market Watch 2008).

The sample for this study consists of students enrolled in two large introductory mass communication courses. The courses were open to all of the majors across campus, so the participants' majors were heterogeneous. The sample consists of 77.2% women (n = 129) and 22.8% men (n = 38); 6% of them where first-year students (n = 10), 13.8% second year (n = 23), 17.4% junior (n = 29), and 62.9% senior (n = 105). Their ages range17 to 30 years, though more than 95% of the participants were between the ages of 17 and 25 years, and the average age was 20.71 years (SD = 1.52). Of the participants, 99.4% used at least one social networking site (n = 167). With respect to time spent on social networking sites per week, 57.7% said that they spent 1 to 5 hours per week on average; 29.8% spent 6 to 10 hours; and 9.6 % spent 11 to 20 hours.  

The participants were first asked to indicate the social networking site with which they were most familiar; they answered the remaining questions on the questionnaire with regard to that social networking site. This approach was taken because their perceptions (e.g., perceived security) of and familiarity with social networks vary. For real and virtual items that social networking sites could carry in their shopping services, this study considers 10 products or services: computers and computer accessories, DVDs, video games, books, tickets, clothes and accessories, profile layouts, avatars, virtual gifts, and playable music. The selection of real items reflects the products college students purchase most often online (Pew Internet 2001), and the virtual items include items available on social networks that already offer shopping services in the United States and other countries. 

Measures

Appendix 1 shows the measurement items and reliabilities for the constructs. Appendix 2 indicates the descriptive statistics for the constructs. Three items are adapted from Davis (1989) and Davis, Bagozzi, and Warshaw (1989) to assess perceived ease of use; four items from the same sources measure perceived usefulness. The measures for perceived enjoyment come from Davis, Bagozzi, and Warshaw (1992). Two items that measure perceived security are adapted from Vijayasarathy (2003). To measureperceived fit, one item captures the holistic similarity between items that a site might sell and the social networking site that the respondent uses. Morrin (1999) and Tauber (1988) suggest that the similarity between existing products affiliated with the brand and the extension category can be construed holistically. The one item for the perceived fit comes from Keller and Aaker (1992). All these measurement items use a seven-point Likert scale, ranging from 1 (strongly disagree) to 7 (strongly agree). In contrast, respondents indicate their experience with social networking sites and Internet purchasing, on the basis of their frequency of using the social networking site and online purchasing, on seven-point scales ranging from 1 (never) to 7 (all the time). Respondents also specify their age in years. To measure attitude toward shopping for the 10 real and virtual items on social networking sites, an item from Goby (2006) provides the measure, which uses a seven-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree).

A principal component factor analysis using Varimax rotation was conducted first to examine whether attitude toward shopping for individual items differs according to product type. Bartlett's test shows that the overall correlations for individual shopping items are significant (χ² = 900.66, p < .001), which suggests factor analysis is an applicable method for classifying the shopping items. As Table 1 summarizes, the factor analysis successfully yields two factors: real items (α = .89) and virtual items (α = .81). The first factor (attitude toward shopping for real items) refers to six items: computers and accessories, tickets, clothes and accessories, books, DVDs, and video games. The second factor (attitude toward shopping for virtual items) includes four items: profile layouts, music that can be played on social networks, virtual gifts, and avatars. The real and virtual items account for 53.08% and 12.87% of the total variance, respectively. The means of attitude toward shopping for real items (M = 4.16, SD = 1.37) and virtual items (M = 4.10, SD = 1.41) are similar.

Table 1. Factor analysis results for attitude toward shopping on social networks

Attitude toward Shopping for Items Real Items Virtual Items
Book .84 .09
Ticket .82 .17
DVD .81 .31
Clothing and accessories .71 .26
Computers and accessories .68 .44
Video game .61 .52
Profile layouts .10 .84
Virtual gifts .18 .77
Avatars .42 .69
Music played on profile .28 .69
 
Notes. The first factor (real items) achieves an eigenvalue of 5.31. The second factor (virtual items) reaches an eigenvalue of 1.29.

Results

Two separate multiple regressions assess the research question and hypotheses. Tables 2 and 3 show the results from the regressions. The conceptual models explain 76.4% (adjusted R² = .75) and 58.0% (adjusted R² = .55) of the variance for the attitude toward shopping for real items and virtual items, respectively. Neither model suffers from multicollinearity problems. The variance inflation factor (VIF) values range from 1.07 to 3.25.

Table 2. Multiple regression for attitude toward shopping for real items on social networking Web sites

  B SE β
Perceived usefulness .15 .06 .16**
Perceive ease of use .27 .05 .26***
Perceive enjoyment -.12 .07 -.12*
Perceived security .07 .04 .07*
Perceived fit between social   networks and real items .67 .05 .66***
Experience with social networks  -.09 .06 -.07
Internet purchasing experience .05 .05 .04
Age -.10 .04 -.11**
Female .23 .14 .07
 
Notes: R = .87; R² = .76.

* p < .05.

** p < .01.

*** p < .001 (one-tailed tests).         

 

Table 3. Multiple regression for attitude toward shopping for virtual items on social networking Web sites

  B SE β
Perceived usefulness -.06 .08 -.06
Perceive ease of use .15 .08 .14*
Perceive enjoyment .12 .09 .13
Perceived security -.01 .05 -.02
Perceived fit between social networks and real items .64 .06 .63***
Experience with social networks  -.17 .08 -.13*
Internet purchasing experience -.01 .07 -.01
Age -.04 .05 -.04
Female .55 .20 .16**
 
Notes: R = .76; R² = .58.

* p < .05.

** p < .01.

*** p < .001 (one-tailed tests).

Hypothesis 1a postulates that perceived usefulness, ease of use, and enjoyment are positively associated with attitude toward shopping for real items on social networking sites. The hypothesis receives partial support, because perceived usefulness (β = .16, p < .01) and perceived ease of use (β = .26, p < .001) are positively associated with attitude toward shopping for real items on social networking sites. However, perceived enjoyment (β = -.12, p < .05) indicates a negative association with attitude toward shopping for real items. Hypothesis 1b postulates that perceived usefulness, ease of use, and enjoyment are positively associated with attitude toward shopping for virtual items on social networks. Perceived ease of use (β = .14, p < .05) is positively associated with attitude toward shopping for virtual items, but perceived usefulness and enjoyment do not statistically affect this attitude.

Hypotheses 2a and 2b propose that perceived fit is positively associated with attitude toward shopping for real and virtual items on social networks, respectively. Both of the hypotheses receive support. That is, if college students perceive a good fit between real items and social networking sites (β = .66, p < .001), their attitude toward shopping for those items on social networking sites is more favorable. The same logic applies to virtual items (β = .63, p < .001). Hypotheses 3a and 3b suggest a positive association between perceived security of social networks and attitude toward shopping for real and virtual items on the social networks, respectively. Hypothesis 3a is supported, but hypothesis 3b is not. Perceived security (β = .07, p < .05) associates positively with attitude toward shopping for real items, but it has no statistically significant association with attitude toward shopping for virtual items on social networks.

Hypotheses 4a and 4b suggest that experience with social networking sites is positively associated with attitude toward shopping for real and virtual items on social networks, respectively. Neither hypothesis receives support; rather, experience with the social networks has a negative association with attitude toward shopping for virtual items (β = -.13, p < .05). Hypotheses 5a and 5b presume that online purchasing experience is positively associated with attitude toward shopping for real and virtual items on social networking sites, respectively. Online purchasing experience does not exhibit a positive association with attitude toward shopping for either real or virtual items on social networking sites. Neither of the hypotheses receives support.

Hypothesis 6a and 6b propose a positive association between female gender and attitude toward shopping for real and virtual items on social networks, respectively. Hypothesis 6a is not supported, but hypothesis 6b is supported. Although the results do not indicate a positive association between female consumers and attitude toward shopping for real items, this study indicates that women (β = .16, p < .01) are more likely than men to have a favorable attitude toward shopping for virtual items on social networks. Hypotheses 7a and 7b posit that age and the attitude toward shopping for real and virtual items on social networks have negative associations, respectively. Hypothesis 7a is supported. Age is a significant predictor of attitude toward shopping for real items on social networking sites, such that younger people are more likely than older people to shop for real items on social networks (β = -.11, p < .01), even with the fairly narrow age range of the subjects in this study. However, age has no association with attitude toward shopping for virtual items on social networking sites. Therefore, hypothesis 7b does not receive support. 

In response to RQ1, the factors that affect attitude toward shopping are quite different for real versus virtual items. Perceived usefulness, ease of use, security of shopping services, and perceived fit between social networking sites and real items have positive impacts, whereas perceived enjoyment and age exhibit negative associations with attitude toward shopping for real items on social networks. With respect to virtual items, perceived ease of use, perceived fit, and female gender predict favorable attitudes, but experience with social networks has a negative association.

Discussion

Noting the dearth of research investigating shopping possibilities on social networking sites and purchase intentions toward virtual items online, this study attempts to identify predictors of attitude toward shopping for real and virtual items on social networking sites by expanding the TAM. Although the TAM serves as a prevalent explanation of attitude, intentions, and actual use of new systems, it may be too parsimonious, which implies it should be supplemented and extended with other constructs (Venkatesh and Davis 2000). Therefore, this exploratory study integrates the primary constructs of TAM-perceived usefulness, ease of use, and enjoyment-with other constructs to predict attitude toward shopping for real and virtual items on social networking sites. The proposed conceptual models add more variables pertaining to subjects' perceptions of and experience with social networking sites (i.e., perceived security, fit, and experience with social networking sites), as well as other consumer characteristics (i.e., experience with online purchasing, gender, and age) to the TAM.

The proposed models explain much of the variance in attitude toward shopping in a social networking site context, namely, 76% of the variance for real items and 58% of the variance for virtual items. However, these proposed models remain quite simple. In addition, the model for the real items explains more variance than models in prior studies that center on shopping for real items in e-commerce sites (e.g., Limayem, Khalifa, and Frini 2000; Pavlou 2003; Shih 2004).

The findings help identify how valid the critical predictors of attitude toward shopping-oriented sites are in the context of social networking sites that operate shopping services as an additional business area. The proposed models explain the results for real items better than they do those for virtual items. More unexplored factors remain regarding attitude toward shopping for virtual items. This finding should be expected, because the conceptual models are based on existing studies focused on online purchasing of real items, whereas little existing research examines virtual items in an online shopping context. 

In line with most prior studies on Internet technology adoption (Gefen, Karahanna, and Straub 2003; Suh and Han 2002), this study reveals that the more people perceive shopping services on social networking sites as useful and easy to use, the more favorable they feel toward shopping for real items on those social networks. Perceived enjoyment has a negative association with shopping attitude toward real items, which appears to contradict Atkinson and Kydd's (1997) suggestion that perceived enjoyment strongly influences the entertainment purposes of the Web. The shopping values sought through online shopping may offer a possible explanation for this result.

According to Reibstein (2002), cost savings are a primary reason for purchasing products and services through online channels. Some people consider shopping a recreational activity, yet consumers' overarching tendency to seek utilitarian values from online shopping seems to overshadow the possible recreational value of shopping services on social networking sites-if the items sold are real items. Therefore, perceived usefulness and perceived ease of use of shopping services, which represent utilitarian values, determine the attitude toward shopping for real items. In contrast, the perceived enjoyment of shopping on social networks, a hedonic shopping value, presumably hinders the efficiency of shopping for real items.

Unlike the findings for real items, only perceived ease of use, among the TAM constructs, has a positive impact on attitude toward shopping for virtual items on social networks. Perceived usefulness instead appears rather negatively associated with attitude shopping for virtual items on social networking sites, though it is not statistically significant. The inherent nature of virtual items, including their usage limited to virtual spaces and the emotional gratifications they may provide, could explain why perceptions of usefulness of shopping services do not matter, in parallel with Heijden's (2004) study, which shows perceived ease of use is a stronger predictor than perceived usefulness of intentions to adopt hedonic systems.

Regarding the role of perceived ease of use, its effect is stronger than that of perceived usefulness for both real and virtual items, whereas the effect of perceived usefulness traditionally has appeared stronger in the context of e-commerce-oriented sites (Gefen, Karahanna, and Straub 2003; Pavlou 2003). A meta-analysis of the studies that have employed TAM confirms that perceived usefulness has a greater effect than perceived ease of use on the adoption of various new technologies or systems (Ma and Liu 2004). Shopping services on social networking sites represent a category extension and a new feature for the site users; the findings in this study imply that by placing more emphasis on establishing and promoting an easy interface for searching and transactions, merchants can lure customers to shop on social networking sites.

Perceived security of shopping services on social networking sites is one of the salient factors that is positively associated with attitude toward shopping for real items but not with shopping for virtual items. According to a report by Nielsen (2008) report, 94% of U.S. Internet users have shopped online. Internet users may become increasingly comfortable with online transactions in general. As the number of online shoppers increases, the impact of the security concerns about online shopping might decline. Yet this study also reveals that such concerns still pose a barrier for social networking sites that wish to offer shopping services for real items.

The findings show that security is a critical factor affecting the attitude toward shopping for real items on social networking sites—but not so for shopping for virtual items on social networking sites. A plausible reason for this finding is the perceived price difference between real and virtual items. Because many virtual items on U.S. social networking sites are free or cost very little, people may not have a concept of paying for virtual items on social networking sites. Apparently, they recognize that real items are more costly than virtual items in e-commerce, so the impact of the security issue suggests that social networking sites should boost their levels of security when selling real items and increasing the price of virtual items on social networking sites.  

Furthermore, this study illustrates that perceived fit is a common and the strongest predictor of attitudes toward shopping for both real and virtual items on social networking Web sites. Social networks' expansion into shopping services therefore appears to represent a category extension to consumers. Because such shopping services are just beginning and peripheral business units from users' perspectives, selecting and introducing product or service categories that fit well with their existing brand images are keys to success. 

Strikingly, this study indicates that experience with social networking sites has an inverse association with attitude toward shopping for virtual items. On the surface, this finding contradicts prior studies, but it also reflects that people who frequently use U.S. social networking sites are accustomed to receiving many free virtual items. Thus, frequent social network users should be more reluctant to shop for virtual items because they possess the strong belief that virtual items on social networking sites are or should be complimentary.

This finding offers another critical insight: Consumers still have strong perceptions that intangible goods available on the Web are free, whereas they accept that they must pay for tangible goods, regardless of the channel through which they purchase them. Therefore, Web proprietors should take care in their long-term decisions regarding whether to provide consumers with a particular service for free or require payment for a new service. The decision is even more important when the service is innovative and new to the market. 

Although the participants indicate similarly favorable attitudes toward shopping for real and virtual items, they evaluated virtual items, rather than real items, as better fits for social networking sites overall. Papadmitriou, Apostolopoulou, and Loukas (2004) find that a good perceived fit increases the likelihood that consumers will purchase the products or services. Launching virtual items could help social networking sites reduce their investment risks when they expand their business into shopping services. However, U.S. social networking sites must determine how they can change the perceptions of low value of virtual items among frequent social networking site users and to encourage willingness to pay. As the online music industry has, social networking sites might need to transition to alter consumers' perception of the value of intangible virtual products. This issue is also important for the copyright protection of intangible products. 

The finding that women are more likely than men to support shopping for virtual items on social networks builds on previous studies that suggest women tend to shop online for hedonic experience goods (Girard, Korgaonkar, and Silverblatt 2003). The characteristics of virtual items categorize them as hedonic experienced goods. The interactivity of social networking sites also may mitigate the flaws of online shopping for female consumers, such as the lack of social interaction and emotional involvement. Interestingly, female gender also has a positive association with attitude toward shopping for real items, but a statistical significance is not detected. In that regard, further research should explore how the addition of social interaction functions in an online shopping context might increase female consumers' shopping intentions and behaviors.

Despite the narrow age range of the subjects in this study, they reveal that age is negatively associated with attitude toward shopping for real items on social networks. This finding aligns with prior studies that indicate an inverse association between age and intentions to shop online. Some e-commerce sites, such as eBay, have launched social networking functions; the finding from this study also implies that such social shopping is likely to attract more young people to shopping venues online for real items.

From a managerial perspective, the findings of this study indicate that the target consumers and social networking site features should differ according to product type, if the sites want to expand their businesses to include shopping services. That is, younger people with positive perceptions of the usefulness, ease of use, and security of shopping services on social networks, as well as those who recognize the fit between social networks and the real items to be sold, will have more favorable attitudes toward shopping for real items. Women with have less experience with social networking sites but adopt a positive perception of the ease of use of shopping services and the fit between shopping services and virtual items on social networking sites will exhibit more positive attitudes toward shopping for virtual items.

This study also suggests social networking sites can act as unique venues that combine social interaction, emotional involvement, and hedonic experience items and thereby boost female consumers' online shopping. Attitude toward shopping for virtual items includes even more unexplored driving forces, so further studies should examine other factors that may influence the purchase likelihood of virtual items.

As an exploratory study, this investigation provides a starting point for determining how the sale of virtual items might contribute to the growth of the Internet as a shopping channel and create a unique shopping experience. It also suffers some limitations. Most social networking sites based in the United States do not actively offer shopping services. To measure college students' attitudes toward shopping services on social networks, this study uses an assumption that social networking sites provide shopping services, because some participants in the survey may not be familiar with shopping services on social networks. For the same reason, the focus remains on attitudes toward shopping rather than attitude toward purchase, purchase intentions, or actual purchase. Other studies should investigate purchase intentions and actual purchase behaviors in other countries in which social networking sites actively offer shopping services.

Finally, the selected shopping items all reflect popular items in existing online shopping stores and social networking sites that already offer shopping services. The survey data also come from students at only one university, and though the participants represent different majors, the results must be interpreted with caution. Despite the narrow age range, the results indicate that age has a negative relationship with attitude toward shopping for real items. The increasing trend of introducing shopping services on various online venues, including virtual communities and game sites, should enable additional studies to examine the effect of age on the intention to purchase real and virtual items among more diverse age groups. Further studies also should pay more attention to issues related to how the unique features of social networking sites (e.g., network size) may influence users' intentions to purchase virtual items.  

References

Akhter, Syed H. (2003), "Digital Divide and Purchase Intention: Why Demographic Psychology Matters,"  Journal of Economic Psychology, 24 (3), 321-327.

Albanesius, Chloe (2009), "More Americans Go To Facebook Than MySpace," PC Magazine, June 16, http://www.pcmag.com/article2/0,2817,2348823,00.asp(accessed August 21, 2009).

Aldridge, Alicia, Karen Forcht, and Joan Pierson (1997), "Get Linked or Get Lost: Marketing Strategy for the Internet," Internet Research: Electronic Networking Applications and Policy, 7 (3), 161-169.

Alexa.com (2007), "Top U.S. Web Sites by Traffic," http://www.alexa.com/site/ds/top_sites?cc=US&ts_mode=country⟨=none (accessed August 10, 2007).

--- (2009), "Top U.S. Web Sites by Traffic," http://www.alexa.com/topsites/countries/US (accessed August 21, 2009).

Arrington, Michael (2005), "85% of College Students Use Facebook," TechCrunch, September 7, http://www.techcrunch.com/2005/09/07/85-of-college-students-use-facebook/ (accessed June 18, 2008).

Arthur, W. Brian (1996), "Increasing Returns and the New World of Business," Harvard Business Review, 74 (July-August), 100-11.

Atkinson, MaryAnne and Christine Kydd (1997), "Individual Characteristics Associated with World Wide Web Use: An Empirical Study of Playfulness and Motivation," Database for Advances in Information Systems, 28 (2), 53-62.

Babin, Barry J. and William R. Darden (1995), "Consumer Self Regulation in a Retail Environment," Journal of Retailing, 71 (1), 47-70.

---, ---, and Mitch Griffin (1994), "Work and/or Fun: Measuring Hedonic and Utilitarian Shopping Value," Journal of Consumer Research, 20 (4), 644-656.

Barnes, Susan B. (2006), "A Privacy Paradox: Social Networking in the United States," First Monday, 11 (July), 9.

Basil, Michael D. (1996), "The Use of Student Samples in Communication Research," Journal of Broadcasting & Electronic Media, 40, 431-440.

Batra, Rajeev and Olli T. Ahtola (1990), "Measuring Hedonic and Utilitarian Sources of Consumer Attitudes," Marketing Letters, 2 (2), 159-170.

Boush, David, Shannon Shipp, Barbara Loken, Esra Gencturk, Susan Crockett, Ellen Kennedy, Bettie Minshall, Dennis Misurell, Linda Rochford, and Jon Strobel (1987), "Affect Generalization to Similar and Dissimilar Line Extensions," Psychology & Marketing, 4 (3), 225-241.

Brynjolfsson, Erik and Chris F. Kemerer (1996), "Network Externalities in Microcomputer Software: and Econometric Analysis of the Spreadsheet Market," Management Science, 42 (12), 1627-47.

Claburn, Thomas (2006), "Social Networks Connect Businesses," Information Week, March 14, http://www.informationweek.com/internet/showArticle.jhtml?articleID=181503346&subSection=E-Business (accessed August 25, 2007).

Davis, Fred D. (1989), "Perceived Usefulness, Perceived Ease of use, and User Acceptance of Information Technologies," MIS Quarterly, 13 (3), 319-340.

---, Richard P. Bagozzi, and Paul R. Warshaw (1989), "User Acceptance of Computer Technology: A Comparison of Two Theoretical Models," Management Science, 35 (8), 982-1003.

---, ---, and ---  (1992), "Extrinsic and Intrinsic Motivation to Use Computers in the Workplace," Journal of Applied Social Psychology, 22 (14), 1111-1132. 

DelVecchio, Devon and Daniel C. Smith (2005), "Brand-Extension Price Premiums: The Effects of Perceived Fit and Extension Product Category Risk," Journal of Academy of Marketing Science, 33 (2), 184-195.

Dittmar, Helga, Karen Long, and Rosie Meek (2004), "Buying on the Internet: Gender Differences in On-line and Conventional Buying Motivations," Sex Roles: A Journal of Research, 50 (5-6), 423-445. 

Doolin, Bill, Stuart Dillon, Fiona Thompson, and James L. Corner (2005), "Perceived Risk, the Internet, Shopping Experience, and Online Purchasing Behavior: A New Zealand Perspective," Journal of Global Information Management, 13 (2), 66-88. 

Ellison, Nicole B., Charles Steinfield, and Cliff Lampe (2006), "The Benefits of Facebook ‘Friends:' Social Capital and College Students' Use of Online Social Network Sites," Journal of Computer Mediated Communication, 12 (4).

Ernst and Young (1999), "The Second Annual Ernst and Young Internet Shopping Study," http://www.e-consultancy.com/knowledge/whitepapers/9/the/second-annual-Internet-shopping-study.html (accessed August 10, 2007).

Forbes (2007), "New Shopping Application Allows Users to Shop Online with Friends," July 20, http://www.forbes.com/businesswire/feeds/businesswire/2007/07/20/businesswire20070720005396r1.html (accessed September 10, 2007).

Gefen, David, Elena Karahanna, and Detmar W. Straub (2003), "Trust and TAM in Online Shopping: An Integrated Model," MIS Quarterly 27 (1), 51-90.

--- and Detmar W. Straub (2000), "The Relative Importance of Perceived Ease-of-Use in IS Adoption: A Study of E-Commerce Adoption," Journal of the Association for Information Systems, 1 (8), 1-30.

---and --- (2003), "Managing User Trust in B2C E-Services," e-Service Journal, 2 (2), 7-24.

Gilly, Mary C. and Valarie A. Zeithaml (1985), "The Elderly Consumer and Adoption of Technologies,"  Journal of Consumer Research, 12 (3), 353-357.

Girard, Tulay, Pradeep Korgaonkar, and Ronnie Silverblatt (2003), "Relationship of Type of Product, Shopping Orientations, and Demographics with Preference for Shopping on the Internet," Journal of Business and Psychology, 18 (1), 101-120.

Goby, Valerie Priscilla (2006), "Online Purchases in an Infocomm Sophisticated Society," CyberPsychology & Behavior, 9, 423-431.

Graphics, Visualization, and Usability Centre (1999), "10th World Wide Web User Survey," http://www.cc.gatech.edu.lp.hscl.ufl.edu/gvu/user_surveys/User_Survey_Home.html (accessed September 10, 2007).

Grewal, Dhruv, Gopalkrishnan R. Iyer, R. Krishnan, and Arun Sharma (2003), "The Internet and the Price Value-Loyalty Chain," Journal of Business Research, 56 (5), 391-8.

Havenstein, Heather (2007), "Social Network Sites: Popular but not Profitable," Computerworld, September 3, http://www.pcworld.com/article/id,136686-page,1/article.html (accessed September 15, 2007) 

He, Fang and Peter P. Mykytyn (2007), "Decision Factors for the Adoption of an Online Payment System by Customers," International Journal of E-Business Research, 3 (4), 1-32.

Heijden, Hans van der (2004), "User Acceptance of Hedonic Information Systems," MIS Quarterly, 28 (4), 695-704.

Hoffman, Donna L., Thomas P. Novak, and Marcos Peralta (1999), "Building Consumer Trust Online,"  Communications of the ACM, 42 (4), 80-85.

Holbrook, Morris B. and Elizabeth C. Hirschman (1982), "The Experiential Aspects of Consumption: Consumer Fantasies, Feelings, and Fun," Journal of Consumer Research, 9 (2), 132-40.

Hopkins, Jim (2003), "Investors Court Social Networking Sites," USA Today, December 9, http://www.usatoday.com/tech/news/2003-12-09-meet_x.htm (accessed August 19, 2008).

Horovitz, Bruce (2006), "Survey: Social Network Sites Could Also Lure Shoppers," USA Today, November 23, http://www.usatoday.com/tech/news/2006-11-23-social-shopping_x.htm (accessed November 30, 2007).

Inside Facebook (2009), "College Students' Facebook Use Easing Up Over the Summer, While Parents Logging On in Record Numbers," July 6,http://www.insidefacebook.com/2009/07/06/college-students-facebook-use-easing-up-over-the-summer-while-parents-logging-on-in-record-numbers/ (accessed August 21, 2009). 

Jarvenpaa, Sirkka L. and Peter A. Todd (1997), "Consumer Reactions to Electronic Shopping on the World Wide Web," International Journal of Electronic Commerce, 1 (2), 59-88.

Katz, Michael L. and Carl Shapiro (1985), "Network Externalities, Competition, and Compatibility,"  American Economic Review, 75(3), 424-40.

Kawamoto, Dawn (2006), "Friendster Scoops up $10 Million in Funding," August 21, http://www.news.com/Friendster-scoops-up-10-million-in-funding/2100-1038_3-6107860.html (accessed July 10, 2007).

Keller, Kevin Lane and David A. Aaker (1992), "The Effects of Sequential Introduction of Brand Extensions," Journal of Marketing Research, 29 (February), 35-50.

Knowledge @ Wharton (2006), "MySpace, Facebook, and Other Social Networking Sites: Today Hot and Gone Tomorrow?" Managing Technology, May 3,http://knowledge.wharton.upenn.edu/article.cfm?articleid=1463 (accessed August 20, 2007)

Lee, Dongwon, Jinsoo Park, and Joongho Ahn (2001), "On the Explanation of Factors Affecting E-Commerce Adoption," Proceedings of the 22nd International Conference on Information Systems, V. Storey, S. Sarker, and J. I. Degross, eds. New Orleans, LA, pp. 109-120.

Lee, Yikuan and Gina Colarelli O'Connor (2003), "New Product Launch Strategy for Network Effects Products," Journal of the Academy of Marketing Science, 31 (3), 241-55.

Li, Hailong., Cheng Kuo, and Martha G. Russell (1999), "The Impact of Perceived Channel Utilities, Shopping Orientations and Demographics on Consumer's On-line Buying Behavior," Journal of Computer-Mediated Communication, 5, http://www.ascusc.org/jcmc/vo15/issue2/hairong.html (accessed August 1, 2008).

Limayem, Moez., Mohamed Khalifa, and Anissa Frini, (2000), "What Makes Consumers Buy from Internet? A Longitudinal Study of Online Shopping," IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 30 (4), 421-432. 

Lin, Carolyn (2004), "Webcasting Adoption: Technology, User Innovativeness, and Media Substitution,"  Journal of Broadcasting & Electronic Media, 48 (3), 446-465.

Linn, Allison (2007), "Online Shopping Growth to Slow in the Next Decades," MSNBC, September 4, http://www.msnbc.msn.com/id/20321999/ (accessed November 10, 2008).

Ma, Qingxiong and Liping Liu (2004), "The Technology Acceptance Model: A Meta-Analysis of Empirical Findings," Journal of Organizational and End User Computing, 16 (1), 59-72.

Madden, Gary and Scott J. Savage (2000), "Some Economic and Social Aspects of Residential Internet Use in Australia," Journal of Media Economics, 13 (3), 171-185.

Maguire, James (2006), "Men, Women, and E-Commerce," E-commerce Guide, http://www.ecommerce guide.com/solutions/building/article.php/3577891 (accessed March 25, 2007).

Market Watch (2008), "JupiterResearch Finds That Social Media Has Emerged as Important Marketing Platform for Retailers During Back-to-School Shopping Season,"Market Watch, August 18, http://www.marketwatch.com/news/story/jupiterresearch-finds-social-media-has/story.aspx?guid=%7B5D4FA471-3AB4-453B-A15D-7174BD0D3D93%7D&dist=hppr (accessed August 18, 2008).

Mathwick, Charla, Naresh Malhotra, and Edward Rigdon (2001), "Experiential Value: Conceptualization, Measurement, and Application in the Catalog and Internet Shopping Environment," Journal of Retailing, 77 (1), 39-56.

Monroe, Kent B. (1976), "The Influence of Price Differences and Brand Familiarity on Brand Preference," Journal of Consumer Research, 3 (1), 42-49.

Morrin, Maureen (1999), "The Impact of Brand Extensions on Parent Brand Memory Structures and Retrieval Processes," Journal of Marketing Research, 36 (November): 517-525.

Nicole, Kristen (2008), "Projections: Ad Spending on Social Networking Websites to Drop," Mashable Social Networking News, May 13,http://mashable.com/2008/05/13/emarketer-projections-revise/ (accessed August 18, 2008).

Nielsen (2008), "Trends in Online Shopping a Global Nielsen Consumer Report," February , http://www.nielsen.com/solutions/GlobalOnlineShoppingReportFeb08.pdf  (accessed October 25, 2008)

Olsen, Stefanie (2004), "Google Spawns Social Networking Service," CNET News, January 22, http://www.news.com/2100-1026_3-5146006.html (accessed August 25, 2007).

Papadimitriou, Dimitra, Artemisia Apostolopoulou, and Ilias Loukas (2004), "The Role of Perceived Fit in Fans' Evaluation of Sports Brand Extensions," International Journal of Sports Marketing and Sponsorship, 6 (1), 31-48.

Pavlou, Paul A. (2003), "Consumer Acceptance of Electronic Commerce: Integrating Trust and Risk with the Technology Acceptance Model," International Journal of Electronic Commerce, 7 (3), 101-134.

Peter, Jochen, Patti Valkenburg, and Alexander Schouten (2005), "Characteristics and Motives of Adolescents Talking with Strangers on the Internet and Its Consequences," Paper presented at the annual meeting of the International Communication Association, New York, NY.

Pew Internet (1998), "Basic Patterns of Internet Use," http://www.people-press.org/tech98mor.htm  (accessed August 15, 2007).

--- (2001), "More Online, Doing More," http://www. pewinternet.org/reports/index.asp/ (accessed September 10, 2007).

--- (2002), "Pew Internet and American Life Data Memo," September, http://www.pewinternet.org/pdfs/PIP_College_Memo.pdf (accessed September 15, 2007).

--- (2004), http://www.cnn.com/SPECIALS/2005/online.evolution/interactive/chart.online.life/age.html (accessed March 10, 2007).

Pommer, Michael D., Eric N. Berkowitz, and John R. Walton (1980), "UPC Scanning: An Assessment of Shopper Response to Technological Changes," Journal of Retailing, 56 (2), 25-44.

Reibstein, David J. (2002), "What Attracts Customers to Online Stores, and What Keeps Them Coming Back?" Journal of the Academy of Marketing Science, 30 (4), 465-73.

Rogers, Everett (1995), Diffusion of Innovations, 4th ed. New York: The Free Press.

Rosenbush, Steve and Timothy J. Mullaney (2006), "Social Networking's Gold Rush," BusinessWeek, April 16, http://www.businessweek.com/technology/content/apr2006/tc20060419_514268.htm (accessed September 10, 2007).

Salisbury, W. David, Rodney A. Pearson, Allison W Pearson, and David W Miller (2001), "Perceived Security and World Wide Web Purchase Intention," Industrial Management & Data Systems, 101 (4), 165-76.

Sanders, Holly M. (2008), "Online Ad Spending Estimates Drop for Social Nets," New York Post, May 14, http://www.nypost.com/seven/05142008/business/online_ad_spending_estimates_drop_for_so_110826.htm (accessed August 18, 2008).

Schonfeld, Erick (2006), "Cyworld Ready to Attack MySpace," Business 2.0, July 27, http://money.cnn.com/2006/07/27/technology/cyworld0727.biz2/index.htm(accessed September 10, 2007).

Shih, Hung-Pin (2004), "An Empirical Study on Predicting User Acceptance of E-Shopping on the Web, Information & Management, 41 (3), 351-368.

Sky News (2002), "U.K. Survey Sponsored by Barclays Bank," May 21, http://www.nua.ie/surveys/?f=VS&art_id=905357975&rel=true (accessed September 10, 2007).

Smith, Daniel C. and Jonlee Andrews (1995), "Rethinking the Effect of Perceived Fit on Consumers' Evaluations of New Products," Journal of the Academy of Marketing Science, 23 (1), 4-14.

Suh, Bomil and Ingoo Han (2006), "Effect of Trust on Consumer Acceptance of Internet Banking,"  Electronic Commerce and Applications, 1 (3), 247-263.

Tauber, Edward (1988), "Brand Leverage: Strategy for Growth in a Cost-Controlled World,"  Journal of Advertising Research, 28 (August/September), 26-30.

The Info-Shop (2007), "College Students Online: A Parallel Life on Social Networks," August, http://www.the-infoshop.com/study/em54126-student-online.html (accessed August 18, 2008).

Tedeschi, Bob (2004), "E-Commerce Report; Social Networks: Will Users Pay to Get Friends?" The New York Times, February 9,http://query.nytimes.com/gst/fullpage.html?sec=technology&res=9403E3DF173AF93AA35751C0A9629C8B63 (accessed September 10, 2007).

Teo, Thompson S. H. (2001), "Demographic and Motivation Variables Associated with Internet Usage Activities," Internet Research, 11 (2), 125-37.

Tong, Stephanie Tom, Brandon Van Der Heide, Linsey Langwell, and Joseph B. Walther (2008), "Too Much of a Good Thing? The Relationship between Number of Friends and Interpersonal Impressions on Facebook," Journal of Computer-Mediated Communication, 13 (3), 531-549.

Vara, Vauhini (2007), "Facebook Gets Personal with Ad Targeting Plan," The Wall Street Journal, August 23, B1, http://online.wsj.com/article/SB118783296519606151.html?mod=technology_main_whats_news (accessed September 10, 2007).

Venkatash, Viswanath and Fred D. Davis (2000), "A Theoretical Extension of Technology Acceptance Model: Four Longitudinal Studies," Management Science, 46 (2), 186-204. 

Vijayasarathy, Leo R. (2003), "Shopping Orientations, Product Types and Internet Shopping Intentions,"  Electronic Markets, 13 (1), 67-79.

Wang, Eric T.G., His-Yin Yeh, and James J. Jiang (2006), "The Relative Weights of Internet Shopping Fundamental Objectives: Effect of Lifestyle Differences,"Psychology & Marketing, 23 (5), 353-367.

Williamson, Debra Aho (2007), "Social Networks Ad Spending is Rising," E-Marketer, August 13, http://www.emarketer.com/Article.aspx?id=1005257 (accessed November 10, 2008).

Wilson, William R. (1979), "Feeling More than We can Know: Exposure Effects without Learning,"  Journal of Personality and Social Psychology, 37 (6), 811-21.

Yenisey, Mehmet M., A. Ant Ozok, and Gavriel Salvendy (2005), "Perceived Security Determinants in E-Commerce among Turkish University Students," in Behaviour and Information Technology. London: Taylor & Francis, 259-274.

Zajonc, Robert B. (1968), "The Attitudinal Effects of Mere Exposure," Journal of Personality and Social Psychology Monograph, Part 2, 9 (2), 1-27.

Zeithaml, Valarie A. (1988), "Consumer Perceptions of Price, Quality and Value: A Means-End Model and Synthesis of Evidence," Journal of Marketing, 52 (July), 2-22.  

Appendix 1. Constructs and items

Constructs Items 
Perceived ease of use

Cronbach's α = .88

 Shopping services on the social networking website will be easy to use.
Learning to shop on the social networking website will be easy for me.
It will be easy to get shopping services on the social networking website to do what I want to do.
 
Perceived usefulness

Cronbach's α = .93

 Shopping services on the social network will be useful for me. 
Shopping services on the social network will make me more efficient.
Shopping services on the social network will make my life easier.
 
Perceived enjoyment

Cronbach's α = .96

 I will find shopping on the social networking website to be enjoyable.  
The actual process of shopping on the social networking website will be pleasant. 
I will have fun shopping on the social networking website.
I will find shopping on the social networking website to be interesting.
 
Perceived security

Cronbach's α = .95

 Using credit cards to make purchases on the social networking website would be safe.
Making payments on the social networking website would be secure.
 
Perceived fit		 There is a good fit between the social networking website and each of the items.
 
Attitude toward shopping on social networking websites		 Given that the social network has access to the shopping system, using the social networking website to shop for each of the following items would be a good idea.

 

Appendix 2. Descriptive statistics

  M SD
Perceived ease of use 4.34 1.29
Perceived usefulness 3.11 1.43
Perceived enjoyment 3.43 1.45
Perceived security 3.90 1.50
Perceived fit between social networks and books 4.33 1.87
Perceived fit between social networks and DVDs 3.83 1.68
Perceived fit between social networks and clothing and accessories 3.10 1.65
Perceived fit between social networks and computer accessories and computers 3.82 1.67
Perceived fit between social networks and video games 3.63 1.80
Perceived fit between social networks and tickets 4.82 1.74
Perceived fit between social networks and profile layouts   4.70 1.90
Perceived fit between social networks and avatars  3.41 1.68
Perceived fit between social networks and virtual gifts 5.15 1.68
Perceived fit between social networks and music that can be played on your profile at the social network 5.14 1.79
Experience with social networking websites 6.07 1.10
Experience with online purchasing 3.78 1.24

 

About the Author

Jiyoung Cha (Ph.D., University of Florida) is an assistant professor in the Department of Radio, Television, and Film at the University of North Texas. Her research interests include the relationship between the media and the audience and the interaction between emerging new media and traditional media from management and marketing perspectives. She received her Ph.D. in mass communication with a minor in marketing from the University of Florida and her master's degree in Television, Radio, and Film at the S.I. Newhouse School of Communications at Syracuse University. E-mail: jcha@unt.edu.