How website socialness leads to website use - ProQuest Search

European Journal of Information Systems (2011) 20, 118–132 & 2011 Operational Research Society Ltd. All rights reserved 0960-085X/11 www.palgrave-journals.com/ejis/

How website socialness leads to website use Robin L. Wakefield1, Kirk L. Wakefield1, Julie Baker2 and Liz C. Wang3 1 Baylor University, Waco, U.S.A.; 2Texas Christian University, Fort Worth, U.S.A.; 3 University of Dallas, Irving, U.S.A.

Correspondence: Robin L. Wakefield, Baylor University, One Bear Place 98005, Waco, TX 76798, U.S.A. Tel: 254-710-4240; Fax: 254-710-1091; E-mail: [email protected]

Abstract Website designers are beginning to incorporate social cues, such as helpfulness and familiarity, into e-commerce sites to facilitate the exchange relationship. Website socialness elicits a social response from users of the site and this response produces enjoyment. Users patronize websites that are exciting, entertaining and stimulating. The purpose of our study is to explore the effects of website socialness perceptions on the formation of users’ beliefs, attitudes and subsequent behavioral intentions. We manipulate website socialness perceptions across two different online shopping contexts, one for functional products and the other for pleasure-oriented products, and draw from the responses of 300 Internet users. Our findings show that website socialness perceptions lead to enjoyment, have a strong influence on user intentions and these effects are invariant across shopping contexts. European Journal of Information Systems (2011) 20, 118–132. doi:10.1057/ejis.2010.47; published online 21 September 2010 Keywords: social response theory; socialness; TAM; enjoyment; ease of use; usefulness

Introduction

Received: 7 October 2008 Revised: 25 May 2009 2nd Revision: 8 February 2010 3rd Revision: 13 June 2010 4th Revision: 5 August 2010 Accepted: 9 August 2010

Converting retail shoppers to online buyers appears to be a daunting task. While U.S. B2C Internet sales continue to increase, the rate of increase in online sales is slowing (see www.e-marketer.com for trends) and Internet sales make up only a small percentage of retail sales (3.4% in 2008; www.census.gov/econ/estats/). Research has suggested that positive attitudes toward and acceptance of e-commerce may be inhibited by a lack of product ‘touch’ (Bhatnager et al., 2000) and lack of interaction with a representative from the organization ( Jahng et al., 2007). Jahng et al. (2007) suggests that richer interactions with e-commerce products and the seller’s representatives over existing technology systems may induce positive attitudes toward e-commerce, which should then influence intentions to use e-commerce systems. Indeed, a 2009 e-commerce survey reports that 51% of the online shoppers surveyed preferred an interactive online shopping experience as they perceived it as more informative (www.Allurent.com). Furthermore, 83% of respondents to an e-commerce survey indicated they would be more inclined to make a purchase from a website if the site offered increased interactive elements (Allurent, January 2008). However, most online shoppers (45%) say that all websites seem the same, with the main differentiators being price and merchandise availability (Endeca, January 2009). This difference between what online shoppers want in the online experience and what they get presents an opportunity for shrewd e-tailers. One approach to create richer interactive online experiences is to infuse websites with human-like traits such as recommendation agents that exhibit emotions, personalities and/or presentation skills (Prendinger & Ishizuka, 2004). A technology interface that incorporates life-like characters (e.g., avatars, anthropomorphic agents) that exhibit individual

How website socialness leads to website use

personality and emotion are part of the ‘social computing’ paradigm that supports the tendency of users to interact with computers as social actors (Prendinger & Ishizuka, 2004). An interactive online experience may induce a positive attitude toward an e-tailer’s website compared to websites lacking human-like traits. To the extent that online shoppers perceive that a website provides an innovative and social experience by incorporating human-like components, e-commerce acceptance and future use may be facilitated. Accordingly, we explore the idea that online shoppers’ website socialness perceptions will influence their beliefs, attitudes and intentions to use the website. Social response theory posits that people treat computers as social actors even when they know that machines do not possess human traits (Moon, 2000). Wang et al. (2007) empirically demonstrated that the use of social cues (i.e., language, voice, interactivity) on retail websites induced perceptions of socialness (i.e., the website was helpful, informative, polite), which in turn led to positive consumer response to the sites. Consistent with Wang et al. (2007), we use the term ‘socialness’ to describe the phenomenon of users treating technology or technology interfaces such as websites as social actors; that is, the user perceives that the interface exhibits life-like attributes associated with personality or emotion. Similarly, herein ‘website socialness perceptions’ refer to the extent to which consumers detect socialness on a website; specifically we examine perceptions of human-like traits such as friendliness, politeness and helpfulness. Socialness in face-to-face market transactions is evidenced by friendship, familiarity, personal recognition and support (Berry, 1995), and emphasizes a relational bond in addition to the transactional orientation between the buyer and seller. In the same way, rich media are able to transmit social cues such as friendliness and familiarity (Wang et al., 2007) and thus may create the perception of a personal connection between transacting parties. This, in turn, should facilitate the development of exchange relationships. The Technology Acceptance Model (TAM; Davis, 1989), as a by-product of the Theory of Reasoned Action (TRA) (Fishbein & Ajzen, 1975), has been a prominent starting point in the IS literature for examining the formation of attitude and behavioral intentions toward IT systems. Perceived usefulness (PU), perceived ease of use (PEOU) and enjoyment (Davis, 1989) determine attitudes, which in turn influence behavioral intentions to use an IT system. Others have identified additional variables important in attitude formation and the adoption and use of technology, including flow (Koufaris, 2002; Hsu & Lu, 2004; Finneran & Zhang, 2005), cognitive absorption (Agarwal & Karahanna, 2000; Saade & Bahli, 2005), playfulness (Webster & Martocchio, 1992; Jarvenpaa & Todd, 1996; Chung & Tan, 2004), and fun and/or enjoyment (Venkatesh & Speier, 1999; Van der Heijden, 2004; Wakefield & Whitten, 2006), among others. These variables are generally classified as intrinsic motivators

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that may provide the user internal pleasure or satisfaction (Vallerand, 1997), and in turn are positively related to technology use. In a similar vein, we examine the effect of website socialness perceptions in the context of TAM by focusing on the influence of these perceptions on user beliefs, attitudes and intentions to use e-commerce technology. The central question and purpose of our investigation is: How do website socialness perceptions contribute to attitude formation toward a website and intentions to use a website? Specifically, within the framework of two e-commerce retail contexts (one functionally oriented and the other pleasure-oriented), we examine the effects of users’ perceptions of website socialness on PEOU, PU, enjoyment and subsequent use intentions. Social cues that have been found to lead to perceptions of socialness (Wang et al., 2007) are manipulated via the presence or absence of an interactive shopping guide. Socialness perceptions elicit a social response from website users that leads to users’ impressions of an exciting, entertaining and enjoyable website experience. Data from the responses of 300 Internet users visiting the websites show that website socialness perceptions impact users’ attitudes through their effect on PEOU, PU and enjoyment beliefs, and they drive the dominant effects on user intentions. The findings have practical implications for the use of electronic interfaces, websites and e-commerce. Figure 1 illustrates our conceptual model.

Social response as a motivator Our study uses social response theory and the underlying bases of TAM, the TRA and motivation, to examine the overall research question. First, we review the basic tenets of these theories in relation to our study before turning to our hypotheses.

Social response theory As an interface or mediator between the organization and the consumer, the website operates as a social actor in that it is created for a specific purpose, can take actions, and may utilize resources on behalf of the organization

Perceived Ease of Use

Perceived Website Socialness

Enjoyment

Intentions to Use a Website

Perceived Usefulness

Figure 1

Research model.

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(Scott, 2003). Prior studies have found that individuals may respond to computers as social actors rather than only as a medium (e.g., Reeves & Nass, 1996; Moon, 2000). The basis for this phenomenon is social response theory, which posits that people apply social rules to respond to computers when computers possess humanlike characteristics or social cues, even though they know they are interacting with machines (Reeves & Nass, 1996). Previous research has found that people apply a variety of social rules when interacting with computers. For example, Nass et al. (1999) showed that when ‘interviewed’ by a computer, people exhibited the same type of polite behavior as they do when being interviewed by another person. People have applied gender stereotypes to computers (Nass et al., 1997), and have formed ‘teams’ with computers (Nass et al., 1996) as they do in human–human interactions. Moon (2000) discovered that it is possible to use computers to elicit intimate information from consumers when the computer behaves according to socially appropriate norms of interaction. Researchers suggest that mindlessness is one reason for these responses (e.g., Nass & Moon, 2000), which occurs because of unconscious attention to a subset of contextual cues such as the use of language, either text or audio, by the computer. These cues trigger individuals’ scripts, labels and expectations to fit their prior experiences (Langer, 1989). Thus, when a computer utilizes social cues, people tend to respond automatically based on their own simplistic social scripts. For example, people may reciprocate seemingly kind comments from a computer with the courtesy to listen further. Four types of social cues have been identified as being relevant to eliciting social responses (Nass & Steuer, 1993). The first cue is language, which refers to the words, their pronunciation and the methods of combining them, used and understood by a considerable community (Webster’s Ninth New Collegiate Dictionary, 1983). Nass & colleagues (1995) found that using strong or weak language in the text displayed on screen successfully created the perception of two different types of personalities. Traditionally, websites have incorporated language in the form of written text but not spoken language. The second cue is the human voice, which is appearing in a growing number of computer systems and websites (The Economist, 2002). Because humans are uniquely capable of speech, voice is likely to encourage the use of rules associated with human–human interactions (Reeves & Nass, 1996). Nass & Steuer (1993) discovered that people respond to different voices on the same computer as if they were different social actors. The third cue is interactivity, which Liu & Shrum (2002) contend is a construct that consists of active control, two-way communication and immediate feedback. Ha & James (1998) showed that the more communication in a user–machine interaction resembles interpersonal communication, the more interactive people consider the communication. The fourth cue is social role. The social



development literature states that people define other entities and themselves as humans by observing the roles that the entities play (Wallace, 1983). A social role could be filled by simply giving a label to an entity. For example, research has found that people may perceive computers as filling human-like roles and then respond to the computers in the context of those roles (e.g., ‘tutor’, or ‘salesperson’) (Nass & Steuer, 1993). It is important to note that prior studies have found that individuals’ automatic social responses to the human–computer interaction are based on their use of information heuristics and not on naı¨vete´ or initial curiosity about new technology (e.g., Reeves & Nass, 1996). Human–computer interaction elicits social response from even the most technologically experienced people (Reeves & Nass, 1996). Therefore, these effects are not likely to diminish as a result of increased familiarity with techniques that heighten perceptions of website socialness.

TRA and motivation theories Two theories have been advanced in the IS literature to help explain the acceptance and use of technology and are the basis for TAM. The TRA (Fishbein & Ajzen, 1975) focuses on the relationship between an individual’s beliefs and intentions. Motivation theory (Deci, 1975) posits that user attitudes are determined by both extrinsic and intrinsic rationales. In the context of technology acceptance, TRA suggests that the influence of external variables on behavior is mediated by cognitive beliefs, such as PEOU and PU in TAM. PEOU reflects the belief that interacting with technology is relatively free of cognitive effort and PU is the belief that using a technology enhances job performance (Davis, 1989). Both PEOU and PU influence user attitudes and exert a positive influence on intentions to use the technology in question. In our study, as online shoppers navigate websites incorporating virtual representatives with lifelike attributes, it is expected that users will perceive socialness. In turn, these perceptions are expected to influence the formation of users’ beliefs such as PEOU and PU, which ultimately influence their intentions to use the website. A user engages in e-commerce and uses a website because that user is motivated. If a situation contains a specific goal that provides a benefit independent of the activity itself, the behavior is extrinsically motivated. In contrast, an activity that is self-sustained and valued for its own sake is intrinsically motivated (Young, 1961, p. 171). Extrinsic motivation emphasizes goal achievement (Deci & Ryan, 1987) as the determinant of behavior, whereas intrinsic motivation involves expectations of fulfillment or enjoyment to be derived from the target behavior (Vallerand et al., 1997). For example, following social response theory, a user is likely to show affective responses toward technology that exhibits human-like traits. In this situation, a user may be intrinsically motivated to use a website as interaction


with a polite and helpful virtual representation provides the user some internal benefit such as enjoyment or satisfaction. Similarly, that same virtual representative may be informative and helpful to the user throughout the navigation and purchasing process. Thus, the user may be extrinsically motivated to return to the site because the virtual agent was useful for the completion of a purchase. In either case, the user’s attitude toward the website is influenced by the socialness perceptions acquired from the website, and these perceptions are generally automatic and unconscious (Langer, 1989), arise in response to social cues and result in the user making social attributions to the technology (social response theory).

Hypotheses The effects of website socialness perceptions Both intrinsic and extrinsic rationales contribute to behavioral intentions in TRA or intentions to use technology in TAM (Deci, 1975). For example, website use may be driven by socialness perceptions that engender feelings of satisfaction or loyalty in the user. If a user perceives favorable socialness aspects (e.g., helpfulness) conveyed by the technology interface, the user should react positively toward the website because social scripts are automatically engaged (social response theory) and the user may be intrinsically motivated to use the website primarily for the ‘social’ (interaction) experience. Likewise, if a user experiences positive affect from the interactivity with a polite and helpful interface, the user may respond in kind (Reeves & Nass, 1996) and use the website out of a sense of commitment or loyalty for the assistance received. Users may patronize a website because socialness perceptions, such as helpfulness, conveyed by the interface provide the user information or improve the purchase process. This utilitarian or extrinsic motivation for website use prompted by socialness perceptions should result in continued or ongoing interactions with the website. Prior empirical studies in other settings support the relationship between socialness perceptions and behaviors such as loyalty, interaction, decision making and commitment. For example, Oliver (1999) found that the quality of the social environment and social interactions led individuals to be loyal to an organization. Similarly, researchers discovered that a greater sense of socialness positively influenced the nature of the human– computer interaction (Short et al., 1976). More recently, scholars argue that social cues are important for both message comprehension and impression formation that affect a variety of processes, such as decision making (Hancock & Dunham, 2001). More specific to e-commerce, Fung (2008) finds that website customization employing human characteristics such as remembering customers leads to greater commitment to the website. As Lamb & Kling (2003) point out, users are apt to reciprocate interaction when they


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encounter social actors within the context of information and communication technologies. Taken together, these findings provide evidence that socialness derived from incorporating social components using an interactive shopping guide is likely to prompt behavioral responses to want to participate and interact with the website. H1

Website socialness perceptions will have a positive influence on users’ intentions to use a website.

The perception of socialness from computer technology induces an automatic social response from users (Nass et al., 1997, 1999; Nass & Moon, 2000), as we now explain. An e-commerce website might offer different capabilities on its site, such as a search engine, online chat, and/or customer product recommendations to guide the user or to locate information. According to social response theory, these applications may be infused with text, voice or other human-like interactions or social cues to which the user is apt to respond. If the user perceives these applications as ‘social’ and helpful, they might assign the human attribute of helpfulness or informative to the website, thinking, ‘that website was very informative’. Website capabilities such as online chat or a virtual salesperson take on the role of a sales representative, convey social cues and thus are likely to prompt socialness perceptions that influence the user’s beliefs regarding the ease of use and usefulness of the website. PEOU is a salient user belief in TAM which, along with PU, determines users’ attitudes and intentions to use a technology. Hence, a website that provides the user with navigation and purchase process helpers that are interactive and exhibit social cues would trigger automatic social scripts in the user. This type of interactivity with technology would be less cognitively burdensome on the user since social scripts are learned responses that may result without conscious thought. It follows then that such a website might be considered easier to use and more useful compared to a website void of such help. Interestingly, Nass et al. (1999) suggest that increased interactivity with technology increases the likelihood that users will automatically respond socially to the technology – as interfaces that make the technology easier to use may also serve as social cues. This implies that capabilities on websites meant to improve the usefulness and ease of use of the website may also increase users’ socialness perceptions. In sum, interactivity between the user and the website should result in a greater sense of socialness that influences the user’s beliefs about the website’s PEOU and PU. If the user responds positively to the social stimuli exhibited on the website, this may alleviate or offset anticipated technology difficulties associated with using the website. Jahng et al. (2007) found that greater interaction richness (i.e., social cues) in multimedia product presentations resulted in greater user perceptions of PEOU of the e-commerce systems.


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Similarly, recommendation agents on websites are described as engendering an agency relationship and after direct experience with the agents, users’ evaluation of both the PEOU and PU of the agents increased (Xiao & Benbasat, 2007). The technology acceptance literature supports a positive relationship between perceived socialness and the PU of technology. Karahanna & Straub (1999) found that e-mail that was regarded as ‘warm’, ‘personal’, ‘sociable’, and ‘sensitive’, positively influenced users’ beliefs that e-mail technology was useful. In an e-commerce context, Kumar & Benbasat (2006) suggest that it is the interactive component on websites that enables consumers to experience social perceptions during online shopping. When online shoppers were exposed to websites with recommendation agents or consumer reviews, online shoppers perceived the website as more social and more useful. Work by Rice (1993) indicates that users prefer media with high socialness characteristics across communication contexts, hence high socialness media are considered more useful than media with low socialness. Thus, we expect that a website that conveys socialness perceived by the user is more likely to be regarded as useful compared to a website void of socialness. In sum, it is expected that perceptions of website socialness will act to lessen users’ perceptions of the cognitive effort required to use the technology (increase ease of use perceptions) and will also influence users’ perceptions of the usefulness of the website, resulting in the following hypotheses: H2 Website socialness perceptions will have a positive influence on PEOU.


were influenced by game-based (i.e., fun or enjoyable) training interventions (Venkatesh, 1999), and the effect of enjoyment on Internet use (Atkinson & Kydd, 1997; Moon & Kim, 2001) and Internet shopping ( Jarvenpaa & Todd, 1996; Koufaris et al., 2001). In these studies, enjoyment was identified as a catalyst of greater intentions to use workplace technology and the Internet. Van der Heijden (2004) continued this stream by examining the role of enjoyment in the acceptance and use of websites, and defined enjoyment as the extent to which fun was derived from using a system (i.e., website). Internet use is typically expressed along two dimensions, transactional and social (Mathwick, 2002). Whereas a transactional orientation is functional or utilitarian and emphasizes the exchange-based nature of the relationship, social orientation is expressed by affect, or feelings of intimacy and warmth (Kumar & Benbasat, 2006) and a personal connection (Short et al., 1976). Some e-tailers have recognized the importance of consumer perceptions of socialness, and thus employ website tools (e.g., recommendation agents) intended to promote interaction and express helpfulness and familiarity. The enjoyment derived from social interaction has been found to be a dominant reason behind the frequency of playing online games (Choi & Kim, 2004). In bricksand-mortar transactions, research repeatedly shows that consumers seek out social interaction to enhance the enjoyment of the shopping experience (see Peterson et al., 1997). Similarly, Wang et al. (2007) found that website socialness perceptions had a positive relationship with pleasure. Therefore, we hypothesize the following: H4

H3 Website socialness perceptions will have a positive influence on PU.

Enjoyment In describing the role of intrinsic motivators, it is important to discuss how the enjoyment construct has been defined and operationalized in the literature as well as its relationship with perceived socialness. As an intrinsic motivator, enjoyment refers to the positive internal benefits derived from technology use or from pleasure-oriented websites that results in greater use or sustained use. For example, enjoyment has been identified as a motivator of intentions to use computers at work (Davis et al., 1992; Igbaria et al., 1996). The idea is that technology interactions perceived as ‘enjoyable’ or ‘fun’ create an expectation of an internal psychological reward sufficient to motivate sustained or extensive technology use. The enjoyment construct was originally defined as the extent to which using a computer was enjoyable in its own right, without consideration of performance consequences (Davis et al., 1992). IS research on the role of enjoyment expanded into examining how workplace technology use intentions


Website socialness perceptions will have a positive influence on enjoyment

PU represents salient beliefs that technology use will result in specific outcomes such as work performance (Davis et al., 1989), and these results may be affective in nature. For example, the usefulness of computers was positively related to the users’ enjoyment derived from using the computers (Compeau et al., 1999). When users realized greater efficiency, effectiveness and quality of work, they expressed greater enjoyment working with computers. In this case, Compeau et al. (1999) defined enjoyment as the affective response of ‘liking working with computers’ and ‘looking forward to using the computer’. The PU of online games has been shown to be significantly related to users’ positive attitude of liking the game (Hsu & Lu, 2004), in which users ‘felt good’ about playing. Similarly, the PU of an online store was significantly related to greater shopper satisfaction (Khalifa & Liu, 2007), a positive affective response. When value-added search mechanisms were added to a website, thereby creating greater usefulness, website users indicated greater shopping enjoyment in terms of an interesting, fun, enjoyable and exciting experience


(Koufaris, 2002). Users expressing positive affect (such as pleasure, delight, satisfaction, interest and fun) in response to a technology’s usefulness suggests that they are in a state of enjoyment. Thus, we expect that the PU of a website that engenders socialness perceptions will be positively related to the enjoyment experienced by the user interacting with the website. H5 PU will have a positive influence on enjoyment. Users are motivated to use e-commerce technology and websites because they expect an intrinsic and/or extrinsic benefit from the activity, which is a determinant of their beliefs and attitudes about the technology (PEOU, PU) and their intentions to use it (c.f. Deci, 1975; TRA). Empirical evidence supports the direct relationship between positive affect (enjoyment) and intentions to use technology. Davis et al. (1992) note the significant effect of enjoyment on intentions to use workplace computers. Koufaris (2002) finds that users’ affective or emotional response to a website is an important determinant of future visits. When online shoppers enjoy the website experience, they are as likely to return to the site. Hence, a website may be intrinsically ‘useful’ by fulfilling personal needs of the user such as providing fun or satisfaction, and the user may return to the website for non-utilitarian reasons. Similarly, the online shopping experience has been found to be positively related to consumer attitudes and intentions toward using a website ( Jarvenpaa & Todd, 1996). More specifically, work by Koufaris et al. (2001) showed that online shopping enjoyment resulted in greater intentions to return to the website, and enjoyment was the stronger motivator of website use intentions compared to PU (Van der Heijden, 2004). Thus, we expect that the enjoyment resulting from a website perceived as social will lead to greater user intentions regarding the website: H6 Enjoyment will have a positive influence on intentions to use the website. Other relationships (see dashed lines) among the variables are theoretically and empirically established in the IS literature and are not formally hypothesized in our study. PEOU consistently acts as a determinant of usage intentions across various technology contexts (e.g., Davis et al., 1989; Taylor & Todd, 1995; Gefen et al., 2003). When technology is easy to use, the relief of cognitive effort is related to greater usefulness when the user is extrinsically motivated (e.g.,Venkatesh, 2000; Gefen et al., 2003), or intrinsically motivated (c.f. Agarwal & Karahanna, 2000; Xiao & Benbasat, 2007). In general, when technology is perceived as easier to use, it is also regarded as more useful (e.g., Davis et al., 1989; Venkatesh & Morris, 2000) and more enjoyable (Van der Heijden, 2004). Lastly, PU is consistently related to behavioral intentions (e.g., Davis et al., 1989; Venkatesh,


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2000; Gefen et al., 2003; Van der Heijden, 2004; Khalifa & Liu, 2007).

Method Website socialness perceptions Prior research has used e-commerce and particularly websites to operationalize PU and PEOU beliefs (e.g., Koufaris, 2002; Gefen et al., 2003; Van der Heijden, 2004; Kumar & Benbasat, 2006; Jiang & Benbasat, 2007). Koufaris (2002) views the e-tailer’s website as a technology system, finding that the PU of the website positively influences users’ re-patronage intentions. A website is also described as an information technology (Gefen et al., 2003), and consumer intentions to use the website are based on their cognitive beliefs. Thus, precedent exists for using the website as the IT artefact, recognizing that technology or website ‘usefulness’ is fundamental to e-commerce. Working with a web-technology provider (www .rovion.com), we conducted a field study manipulating the socialness of two commercial websites combined with a survey-based approach to empirically test the relationships hypothesized in the model. Research has demonstrated that consumers respond to environmental stimuli differently depending upon the purpose of consumption (Wakefield & Inman, 2003; Van der Heijden, 2004). An individual motivated to visit a website for what is primarily a utilitarian product (e.g., home improvement products or supplies) may respond differently to social cues compared to another who visits a website for a hedonic product (e.g., entertainment tickets or merchandise) (c.f. Hirschman & Holbrook, 1982). We account for this possibility by presenting subjects with either a website offering principally utilitarian goods (i.e., custom window blinds) or a website featuring more hedonic goods (including tickets, DVDs, videos-on-demand and a broad selection of merchandise) associated with individual entertainers. To induce variability in perceived socialness for each of these websites (utilitarian/window blinds vs hedonic/ entertainers), subjects were exposed to the website either with an interactive streaming video guide vs the identical website that did not use the video guide. In keeping with the social response literature, the video guide reflected the four important aspects of social cues; social role (guide), human voice, language and interactivity. In essence, four different websites were used: a window blind website with/without a streaming video guide and an entertainment site with/without a streaming video guide. The same video technology (Rovion’s InPersonTM technology) was used in both cases; a videotaped human guide appropriate to the site was superimposed on the screen with a message to visitors to directly interact with the guide (for example, ‘Click on me to y’). The technology provider specifically promotes ‘rich media ads’ to enable ‘pages to come alive’. Figure 2 provides a


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Figure 2



Illustration of interactive shopping guide.

snapshot illustrating the appearance of this technology as it appears on a website. Subsequent pages on such websites employ the same video guide providing relevant product information and directing visitors to featured products. The video guide was hosted by the provider (Rovion) and displayed on both the entertainment and window blind websites without any other change to the website layout and design.

Sample frame and procedure Subjects were randomly selected from a national online panel of 1.5 million consumers. The company hosting the panel rewarded members with redeemable points for completing valid surveys. For each website, respondents were screened to make certain that subjects were in the likely target audience. Visitors to the window blind website were screened according to home ownership (non-homeowners were excluded). Visitors to the entertainment website were recruited based on having viewed on TV (or attended) a related entertainment event within the past 12 months. Following screening, subjects received the instructions shown in Appendix A for perusing the assigned website. Data were collected from a total of 300 online shoppers over a 48-h period. We randomly assigned 150 to each website (blinds or entertainment) of which 50 were randomly exposed to the website without the interactive shopping guide and the remaining 100 were exposed to the interactive shopping guide for that site. In the latter


case, a manipulation check confirmed that subjects viewed the guide. We measured subjects’ ‘liking’ of the video guide to determine whether the guides were viewed positively and equally across the two contextually different websites. Subjects’ perceptions were measured with three items (‘I like the guide very much’, ‘I like the guide’s voice very much’, ‘The guide looks pleasing’) on a 7-point disagree-agree scale. The three items were tested for reliability (alpha ¼ 0.973) and results indicated the respondents had very similar positive feelings regarding the video guides superimposed on the website (Mwindow blind ¼ 4.86; Mentertainment ¼ 4.83; F ¼ 0.06, ns). The 150 respondents representing homeowners viewing the window blind website were evenly split between males and females with represented ages of 21–35 (32.5%), 36–50 (42%) and over 50 (25.5%). The 150 visitors to the entertainment website were predominantly female (57.1%) with ages of less than 20 (12.9%), 21–35 (85.7%) and 36–50 (1.4%). In both cases, virtually all subjects (98%) had shopped online in the past year. The racial make-up of the subjects was predominantly Caucasian for the window blind website (80%) and the entertainment website (68%), but the percentage of African-Americans was higher for the entertainment website (12.1%) compared to the household blinds website (3.6%). However, neither race, nor gender, nor age produced significant differences in perceptions regarding the websites for any of the research variables.


Measures and manipulation check Multiple-item scales were used to measure each research variable (Appendix B). Socialness perception was measured with seven adjectives derived from scales drawn from the social response literature (Nass & Steuer, 1993; Nass et al., 1996; McMillan & Hwang, 2002). The enjoyment scale consisted of seven items adapted from Babin et al. (1994) that relate to enjoyment in the shopping experience. Four items measuring PEOU and four items measuring PU were adapted from (Davis, 1989) and modified to reflect the ease in making a website purchase and the usefulness of the site. Behavioral intentions toward the website were measured with three items developed by Baker et al. (2002). The descriptive statistics for each construct are shown in Table 1. We examined whether the manipulation of the websites with or without the interactive shopping guides produced significantly different perceptions of socialness on the two websites. Results of multivariate analysis reveals significant differences in socialness perceptions due to the shopping guide (F ¼ 4.37, P ¼ 0.037). Overall, the socialness perceptions associated with the websites using the shopping guides was higher (Mwith guide ¼ 5.11) than without (Mwithout guide ¼ 4.79). Interestingly, the perceived socialness for the utilitarian (blinds) website (Mutilitarian ¼ 5.19) was significantly higher than it was for the hedonic website (Mhedonic ¼ 4.71). There were no interaction effects (F ¼ 0.557, ns), suggesting that no differences existed in response to socialness manipulations across the two different websites. Measurement model Confirmatory factor analysis was performed using AMOS software. The standardized regression weights for each item (see Appendix B), which are analogous to factor loadings, showed evidence of construct validity and were greater than the recommended minimum 0.70 (Hair et al., 1998). The SEM fit indices indicated an acceptable model fit: X2260 ¼ 744.15 (Po0.000), goodness-of-fit index (GFI) ¼ 0.84, normed fit index (NFI) ¼ 0.91, incremental fit index (IFI) ¼ 0.94, comparative fit index (CFI) ¼ 0.94, root mean square error of approximation (RMSEA) ¼ 0.08. Most fit indicators were above the threshold of 0.90 and

Table 1 Construct

Descriptive statistics

Window blind website

Entertainment website

N ¼ 150

N ¼ 150

RMSEA met the recommended criterion of 0.050.08 (Hair et al., 1998). Each construct also demonstrated an acceptable level of internal consistency as measured by the composite reliability coefficient (a) and average variance extracted (AVE) in Table 2. All measures of Cronbach’s a exceeded the 0.70 criterion (Nunnally, 1978) and AVE measures exceeded the recommended 0.50 threshold (Hair et al., 1998). In addition, the inter-item correlations of the constructs were all below the 0.90 threshold (Bagozzi et al., 1991) indicating the distinctness of each construct. The square root of the AVE shown in bold on the diagonal in Table 3 also provided evidence of discriminant validity in that the average variance shared between each construct and its indicators is larger than the variance shared between each construct and other constructs. Discriminant validity indicates that each construct shares more variance with its measurement items than with other constructs in the model. Since the SEM fit statistics provide evidence of good model fit and each dimension showed acceptable reliability and validity, we proceeded to test the structural model.

Structural model Following acceptance of the measurement model, a direct-effect model and the research model were estimated using SEM techniques. Model results are shown in Table 4 and Figure 3. Individual user characteristics were included as control variables in the initial analysis. These variables included age, gender, frequency of online shopping and frequency of computer use. None of the control variables were significant and all were subsequently dropped from further analyses.

Table 2 Construct

Socialness PU Enjoy PEOU Intentions

5.4 5.2 4.3 5.0 4.9

1.05 1.25 1.45 1.27 1.22

Construct reliability and average variance extracted (AVE) # Items

Reliability (Cronbach’s a)

AVE

7 4 7 4 3

0.94 0.92 0.96 0.92 0.91

0.74 0.80 0.82 0.81 0.84

Socialness PU Enjoyment PEOU Intentions

Table 3

Video guide No video guide Video guide No video guide Mean S.D.

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Mean

SD

Mean

SD

Mean

SD

5.0 4.7 3.9 4.7 4.2

1.27 1.27 1.23 1.43 1.45

4.8 4.6 4.4 4.5 4.1

1.21 1.31 1.27 1.36 1.52

4.6 4.5 4.2 4.3 3.8

1.40 1.41 1.62 1.31 1.73

Socialness PU Enjoyment PEOU Intentions

Construct correlations

Socialness

PU

Enjoyment

PEOU

Intentions

0.86 0.685 0.681 0.708 0.701

0.89 0.701 0.691 0.683

0.91 0.677 0.695

0.90 0.752

0.92

All correlations are significant at Po0.01. Bold values on the diagonal represent the square root of the AVE.


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64.7% 64.2% 60.4% 88.7%

0.157** 0.777** 0.365** 0.279** 0.332** 0.150** 0.576** 0.483** 0.267** 0.139**

Std. coeff.

Perceived Website Socialness

Intentions to Use a Website R2 = 88.7%

H1: β = 0.157

The direct-effect model produced the following goodness-of-fit statistics: X2266 ¼ 1486.4 (P ¼ 0.000), GFI ¼ 0.71, CFI ¼ 0.85, NFI ¼ 0.82, IFI ¼ 0.85, RMSEA ¼ 0.12. These results are indicative of poor model fit to the data, and imply that the relationships in the data are not well described by the direct-effect model. This provides a small level of confidence for the efficacy of the larger research model and the additional hypothesized parameters. The estimated research model produced the following fit statistics: X2260 ¼ 744.15 (P ¼ 0.000), GFI ¼ 0.84, CFI ¼ 0.94, NFI ¼ 0.91, IFI ¼ 0.94, RMSEA ¼ 0.08. The fit statistics are indicative of a good model fit although GFI is below the recommended minimum of 0.90. Since GFI may be biased downward due to model complexity, GFI^ accounts for the number of parameters and was calculated as an acceptable 0.95. The results indicate that the standardized coefficients for all hypothesized paths in the structural model are significant (Po0.01). Socialness perceptions have a positive influence on usage intentions (H1, b ¼ 0.157), PEOU (H2, b ¼ 0.777) and PU (H3, b ¼ 0.365). Socialness perceptions lead to enjoyment (H4, b ¼ 0.279), as does PU (H5, b ¼ 0.332), which in turn influences user intentions (H6, b ¼ 0.150). Relationships not hypothesized but supported in the literature are also significant, as expected: PEOU has a positive influence on user intentions (b ¼ 0.576), PU (b ¼ 0.483) and enjoyment (b ¼ 0.267). Finally, PU has a direct influence on user intentions (b ¼ 0.139).

— — — 76%

0.297** — — — — 0.281** 0.726** — — 0.249** 5.97 — — — — 5.71 11.97 — — 5.04

Socialness-Intentions Socialness-PEOU Socialness-PU Socialness-Enjoyment PU-Enjoyment Enjoyment-Intentions PEOU-Intentions PEOU-PU PEOU-Enjoyment PU-Intentions R2 Enjoyment PU PEOU Intentions

Std. coeff.

Structural model.

2.42 13.24 4.95 3.82 4.54 2.51 7.60 6.28 3.35 2.11

Figure 3

t-value


**Po0.01.

H1 H2 H3 H4 H5 H6 H7 H8 H9 H10

Model fit

H6: β = 0.150

Perceived Usefulness R2 = 64.2%

Path coefficients (b)

X ¼ 1486.4, df ¼ 266 GFI ¼ 0.71, CFI ¼ 0.85 NFI ¼ 0.82, IFI ¼ 0.85 RMSEA ¼ 0.12

N ¼ 300

Enjoyment R2 = 64.7%

H3: β = 0.365

2

Direct effect model

SEM results Table 4

H4: β = 0.279

H5: β = 0.332

t-value

X ¼ 744.15, df ¼ 260 GFI ¼ 0.84, CFI ¼ 0.94 NFI ¼ 0.91, IFI ¼ 0.94 RMSEA ¼ 0.08

N ¼ 300

H2: β = 0.777

2

Research model

Perceived Ease of Use R2 = 60.4%

Utilitarian vs hedonic websites We also examined the possibility that the paths in the structural model might vary across the more utilitarian-oriented website for house blinds vs the more hedonic entertainment website (Van der Heijden, 2004). We ran a two-group analysis, first constraining all of the paths to be invariant across the two website samples (Nblinds ¼ 150; Nentertainment ¼ 150). Each path was then tested for significant differences by independently releasing it and comparing the resulting X2 statistic to the completely constrained model to determine if the model is improved


by allowing the selected path to vary across the two groups. With respect to any path related to socialness perceptions (H1–H4), no significant differences occurred between the utilitarian and hedonic-oriented websites. Otherwise, all paths were similar, with one exception. PEOU had a significantly stronger (DX2 ¼ 5.46, Po0.01) effect on user intentions for the entertainment website (t ¼ 7.45, 0.656) compared to the custom blinds website (t ¼ 5.88, 0.527). However, the relationship between PEOU and intentions was significant for both website types. Overall, we can conclude the model holds equally well in either of the contexts examined in this study.

Discussion The findings of our study provide insight into the important role of website socialness perceptions in determining technology acceptance, as our model explains about 89% of the variance in usage intentions. We detail key implications for theory, research and practice.

The pervasive effects of website socialness perceptions Website socialness perceptions lead to greater beliefs about PEOU, PU, enjoyment and user intentions. Respondents who considered the website friendly, helpful and informative (among other traits) indicated they believed the website was more useful, easier to use and enjoyable. As a result of these beliefs, these respondents indicated they would also be more likely to purchase from the website. Recall that the respondents were exposed to exactly the same website in each context (window blinds and entertainment products), except for the presence of the video shopping guide. Additional analysis shows that usage intentions are greater (F ¼ 6.85, Po0.01) for those exposed to the shopping guide (Mguide ¼ 4.9) than those who were not (Mno guide ¼ 4.2) in the window blind context, as well as in the entertainment context (Mguide ¼ 4.1, Mno guide ¼ 3.8). Those involved in e-commerce should take note that at this point in the evolution of retail websites, the cost of including interactivity or social elements is apt to be recouped through increased sales. However, the long-term effects are not known, as technology such as Rovion’s becomes more developed and widespread. One important finding is that the influence of website socialness perceptions results in significant effects on user intentions through its influence on other variables. Table 5 shows that website socialness perceptions have the strongest total effects on behavioral intentions toward the website (b ¼ 0.818) compared to PEOU (b ¼ 0.707), PU (b ¼ 0.189) and enjoyment (b ¼ 0.150). Interestingly, it is the indirect role of website socialness perceptions (b ¼ 0.660) that strongly accounts for the total effect, as it enhances PEOU, PU and enjoyment, all of which in turn influence user intentions.

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Table 5

Standardized total effects

Website socialness

PEOU

PU

Enjoyment

Indirect effects PEOU PU Enjoyment Intentions

0.376 0.453 0.660

0.160 0.131

0.050

Direct effects PEOU PU Enjoyment Intentions

0.777 0.365 0.279 0.157

0.483 0.267 0.576

0.332 0.139

0.150

Total effects PEOU PU Enjoyment Intentions

0.777 0.741 0.732 0.818

0.483 0.427 0.707

0.332 0.189

0.150

Care must be taken in generalizing these results beyond the specifics of this study that uses Rovions technologies. While the theoretical basis for website socialness perceptions is supported in this empirical study, the implementation of various types of ‘social’ technologies and in different e-commerce contexts requires further study. Future research is needed to determine the extent that website socialness perceptions may contribute to the use of evolving consumer technologies such as mobile phones, netbooks and interconnectivity equipment in automobiles.

Application in utilitarian and hedonic contexts The results of our two-group SEM analysis comparing the two types of websites revealed that the model effects held equally well across the utilitarian-oriented and the hedonic-oriented online shopping contexts. However, although each of the hypothesized path effects was significant in both contexts, the model explains 95.0% of the variance in usage intentions for the entertainment website compared to 81.5% for the window blinds website. It is likely that users visiting a more hedonic-oriented website are explicitly seeking emotional fulfillment. Consequently, the salience of the affective responses to social cues may produce relatively high levels of consistency between perceptions of website socialness and other perceptions of the exchange. While overall model effects are still very high in the utilitarian context, the user may be less attentive to affective or emotional aspects prompted by voice, language or interactivity when shopping for functional items. Future research that examines more clearly the utilitarian vs hedonic intent of the online visit may help delineate the effects.



For example, some individuals may view visiting a home supply website as an enjoyable diversion. Similarly, others may visit an entertainment website specifically to buy tickets to an event at a specific price as fast as possible (viz. highly functional exchange). How such individuals would respond to technology engendering website socialness perceptions may contrast with the general effects reported here.

Website socialness perception effects on user affect and intentions Our study results also indicate that technology employing website socialness is capable of eliciting an affective response from users participating in e-commerce. Recall that the respondents attributed lifelike traits to the websites such as friendliness, politeness, helpfulness, informative and intelligence, and respondents indicated their personal liking of the digital representative. Research that overlooks user-affective responses to social cues may neglect a key intrinsic motivator of technology use in e-commerce. For example, others have examined the service quality dimensions of websites, finding that online service quality dimensions for Amazon and Wal-Mart websites explain 49–52% of buying intentions (Parasuraman et al., 2005). Similarly, Koufaris (2002) explains 55% of the variance in intentions to return to Booksamillion.com when testing the influence of shopping enjoyment, concentration, PEOU, PU and control. Given the mixture of utilitarian and fun products offered by Amazon, Wal-Mart and Booksamillion, we might expect that the inclusion of website socialness perceptions would enhance the ability to predict beyond the dimensions previously studied. Future research may also find that other website service dimensions (viz. service quality factors) may produce social cues that influence the online experience. Our study results in explained variance of 89% across both utilitarian and hedonic product contexts. Clearly, the perception of website socialness is a critical element in an approach to explain and predict behavioral intentions in e-commerce. Using social cues The ability of technology to present social cues, such as an interactive shopping guide, to produce substantive affective responses is not necessarily positive for all users. Whereas respondents were generally favorable toward the Rovions guides used in this study, variance did exist across visitors to the websites. Figure 4 illustrates the relationship between the liking (mean score for the three items: ‘I like the guide very much’, ‘I like the guide’s voice very much’ and ‘The guide looks pleasing’) of the guide and website socialness perceptions. Liking of the guide explained 32% of the variance in website socialness perceptions, suggesting that care must be taken in selecting models (as with the Rovions technology that uses real people) or designing technology with social cues that are appealing to users. While this may seem obvious, it is a persistent issue/problem in other forms of media communications



8 7 6 Perceived Socialne

128

5 4 3 2 1 0 1

2

3

4

5

6

7

Liking of Digital Interface

Figure 4 guide.

The relationship between socialness and liking of the

(cf. Koernig & Page, 2002), and no less likely in computermediated environments. Accordingly, future studies could examine the effectiveness of different types of social technologies across online shopping contexts. The prevalence of human-like avatars in popular video games (viz. World of Warcraft, with over 11 million subscribers; www.wired.com/gamelife/2008), in virtual worlds such as Second Life (700,000 unique log-ins for March 2009; https://blogs.secondlife.com/community) and Eve Online (300,000 subscribers; www.pcworld.com), and to gather intelligence online from unsuspecting users (Lynch, 2009), suggests that users seek out and respond to this particular form of social technology. Future research is needed to determine the appropriate appearance Yee et al., 2009) and level of intrusiveness, including legality (see Jenkins, 2004) of avatars used in e-commerce.

Other future research on website socialness perceptions Whereas we successfully manipulated some aspects of website socialness via the digital shopping guide, it is interesting to note that variance exists in users’ perceptions of website socialness within each context (with or without the shopping guide; see Table 1). That is, the respondents associated other factors with website socialness that we did not conceptualize or measure. Given the magnitude of the effects found here, a fruitful line of research would identify other facets of online experiences that operate as antecedents or otherwise cue website socialness perceptions. For instance, auditory login recognition (‘Hello, Susan’) and levels of personalization/customization may influence socialness perceptions, as well as PEOU and PU.


Limitations Several issues are raised as limiting factors to the present study. These include the potential for common method bias to contribute as a statistical artefact as well as the generalizability of the results to other technologies and users. Common method bias is a potential obstacle when the data are self-reported with the dependent and independent data collected concurrently. This bias may result in detecting significant relationships that are merely remnants originating from systematic bias among the respondents. However, the use of four websites integrating two retailing contexts (i.e., window blinds and entertainment) and the presence/absence of the shopping guide limit the effect of this bias. Harman’s singlefactor test was also applied as a diagnostic indicator to test whether a single factor accounts for a majority of the covariance among the measures (Podsakoff et al., 2003). Results of exploratory factor analysis show five factors extracted, accounting for 81% of the variance, with the first factor accounting for 25%. This suggests that common method variance is limited since multiple factors emerged and no single factor accounts for a majority of the covariance, similar to Koh et al. (2004). However, the use of varied data collection methods is preferred and thus the possibility of common method bias cannot be overlooked when generalizing or replicating our results. It is important for future studies to incorporate additional remedies for this bias. The two different retail contexts serve to increase the external validity and generalizability of the findings. However, results cannot be generalized to all groups of online consumers, such as preteens and teenagers, who are heavy users of the Internet but were not included in the sample database. The customer segmentation issue provides a promising focus for future research as well as the study of the online buying behavior of business buyers. Some of the unique characteristics of the business buying process may result in different perceptions of website socialness and responses to interactive guides. Moreover, other aspects of social character such as personality, gender and age may be important variables (Nass et al., 1997), as well as the role culture may play in shoppers’ reactions to social cues in technology. In addition, our research examined the effects of website socialness perceptions on a limited number of constructs significant in e-commerce. Hence the findings


129

provide an incomplete picture of B2C online shopping behavior as well as online exchange relationships in the B2B context. Future research should consider whether and how social cues conveyed by websites influence other important user responses (e.g., trust, loyalty, satisfaction, service quality) and other online exchange scenarios (e.g., B2B, auctions, barters). It is also recommended that other website elements (e.g., videos, chat rooms, recommendation agents, FAQs) be examined for their efficacy in generating website socialness perceptions.

Conclusion Technology or websites rich with social cues may create efficiencies for organizations in reducing ambiguity and uncertainty and enhancing ongoing relationships with partners, suppliers and customers. Websites infused with cues that express familiarity, helpfulness and intelligence (for example) generate a social response from users of the site leading to greater enjoyment of the website. Users believe such websites are stimulating, exciting, adventurous and enjoyable. Users who enjoy the website experience have greater intentions of using the website, which may ultimately increase their personal connection with an e-tailer and could be a valuable source of competitive advantage if developed. E-tailers seeking to increase consumer attention, re-patronage and purchasing should examine the social aspects of the technology they use as well as the role of users as social actors (Lamb & Kling, 2003). Our study indicated that websites conveying greater website socialness perceptions had a significant effect on users’ beliefs and attitudes related to the website, whether the focus was window blinds or entertainment products. This may lead online shoppers to more highly value the online shopping experience for even mundane products and presents interesting opportunities for e-tailers. Website development firms should be encouraged to further develop interactive social components to enrich online relationships.

Acknowledgements The authors thank the senior editor and two anonymous reviewers for their constructive comments and guidance throughout the revision process. We also thank the Hankamer School of Business at Baylor University for their support of this project.

About the authors Dr. Robin Wakefield is an associate professor of MIS at the Hankamer School of Business at Baylor University. Her research appears in numerous IS journals including Information Systems Research, the European Journal of Information Systems, the Journal of Strategic Information Systems and Information & Management, among others.

Her main research interests include information security, technology acceptance and virtual teams. Dr. Kirk Wakefield is a professor of marketing at the Hankamer School of Business at Baylor University. His research in retailing focuses primarily upon consumer


130


response to pricing and promotional tools and appears in the Journal of Retailing, Journal of Marketing, and Journal of Consumer Research, among others. His research measuring consumer response to sports sponsorships can be found in the Journal of Advertising Research and Journal of Advertising. In addition, drawing from his experience working with scores of professional and collegiate sports teams and related research, Dr. Wakefield has written Team Sports Marketing, published by Elsevier (2006). Dr. Julie Baker is a professor of marketing at the Neeley School of Business, Texas Christian University. Her research focuses on retail and service atmospherics, customer perceptions of waiting time, services advertising and online retail shopping behavior. She has


published in the Journal of Marketing, Journal of the Academy of Marketing Science, Journal of Retailing and International Journal of Research in Marketing, among others. She serves on the editorial review boards of the Journal of Retailing, Journal of Service Research and Journal of Business Research. Dr. Liz C. Wang is currently a marketing consultant for an online company. She received her Ph.D. in marketing from the University of Texas at Arlington. She has taught at the University of Texas at Arlington and the University of Dallas. Her research interests include Internet marketing and retailing. Her recent work appears in the Journal of Marketing, International Journal of Internet Marketing and Advertising and International Journal of Electronic Marketing and Retailing.

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Appendix A See Table A1. Table A1 Respondent instructions Today we are interested in your thoughts about a website, particularly how easy you find this site to navigate for items that could be of interest to you. Please make certain the sound for your computer is turned on for this survey. By clicking on the link below, a new window should open on your computer with the browser set to this web site. Please spend a couple of minutes navigating around the site to look for items of interest to you. Please browse different areas of the home page first, spending some time getting familiar with the site. Click through to view several sections, including the ‘Products’ tab at the top of the page to more fully explore the different areas within the site. Once you have done so, please close the window for that site and return to this page, where you will click next to continue with the survey about your experience. You will not be able to click next until you have visited the site and have stayed there for a couple of minutes. CLICK HERE TO VISIT THE WEBSITE.

Appendix B See Table B1. Table B1

Measurement items and loadings

For each of the following adjectives, please indicate how well it describes the website you visited

Standardized item loadings

Socialness SO1 SO2 SO3 SO4 SO5 SO6 SO7

Friendly Helpful Polite Informative Likeable Intelligent Interactive

0.845 0.842 0.767 0.842 0.865 0.884 0.807

Ease of use PEOU1 PEOU2 PEOU3 PEOU4

If one were in a hurry, making a purchase could be completed quickly on this website It would not be time consuming to make a purchase on this website Making a purchase on this website would be an efficient way to manage my time Making a purchase on this website would not require a lot of effort

0.904 0.793 0.880 0.810

Enjoyment Enjoy1 Enjoy2 Enjoy3 Enjoy4 Enjoy5 Enjoy6 Enjoy7

During the navigating process, I felt the excitement of the hunt While navigating on this website, I felt a sense of adventure The enthusiasm of this website is catching; it picks me up I enjoyed this online shopping trip for its own sake, not just for the product/services that I might need Compared to other things I could have done, the time spent shopping on this website was truly enjoyable This website doesn’t just sell products/services – it entertains me I enjoyed being immersed in exciting new information on this website

0.857 0.875 0.914 0.887 0.899 0.856 0.893

Perceived usefulness PU1 If I want to make a purchase, I could accomplish just what I might need on this website PU2 I think of the company on this website as an expert in the product/services it offers PU3 Shopping from this website would fit my schedule PU4 If I want to make a purchase, the information and services on this website would be what I would look for

0.882 0.824 0.841 0.891

Intentions INT1 INT2 INT3

0.810 0.756 0.896

I would be willing to make a purchase on this website The likelihood that I would make a purchase on this website is very high The website would offer products/services that would be a good value for the money


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