Effects of specialization in computers, web sites, and web agents on e-commerce trust

Int. J. Human-Computer Studies 68 (2010) 899–912 www.elsevier.com/locate/ijhcs

Effects of specialization in computers, web sites, and web agents on e-commerce trust Yoon Jeon Koha,n, S. Shyam Sundarb,c a

Economics and Management Research Lab, KT, Gyeonggi-do, Republic of Korea Media Effects Research Laboratory, Pennsylvania State University, University Park, PA, USA c Department of Interaction Science, Sungkyunkwan University, Seoul, Republic of Korea

b

Received 5 July 2009; received in revised form 23 July 2010; accepted 13 August 2010 Available online 20 August 2010 Communicated by T.J. Hess

Abstract Suppose you went shopping online for wines and visited several sites, each recommending particular reds and whites. Which kind of site are you likely to trust more—costco.com or wine.com? The specialization implied by the latter suggests more expertise in the domain of wines. Does it mean that you are more likely to purchase wines recommended by sites such as wine.com and vintagecellars.com.au than those recommended by generalist sites such as costco.com and samsclub.com? Our study attempts to answer this question by experimentally investigating how specialization in media technology (specifically, web agent, web site, and computer) influences individuals’ perception and attitudes towards sources in online communication, particularly consumer trust and purchase behaviors in e-commerce. All subjects (N = 124) went to a specially constructed online site with a virtual shopping cart for a wine-purchasing task, as part of a 2 (specialist vs. generalist web agent)  2 (specialist vs. generalist web site)  2 (specialist computer vs. generalist computer) between-subjects experiment. Results indicate significant main effects and interactions of the agent, site, and computer specialization on trust and purchase decision time. Theoretical and practical implications are discussed. & 2010 Elsevier Ltd. All rights reserved. Keywords: Specialization; Media equation; Source layers in HCI; Web agent; Web site; Category-based perception; Generalist technologies; Domain expertise

1. Introduction Ever since the rise of cable television, specialization has become a staple of our media diet. With channels such as CNN, ESPN, MTV, and Cartoon Network staking out particular areas of programming on the cable spectrum, media users have come to expect television channels to specialize in certain content domains (e.g., news, sports, music, and comedy). Specialization is said to carry powerful psychological connotations pertaining to superiority in quality of content offerings. Even outside of particular channels specializing in certain types of content, the technology itself can enjoy n

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1071-5819/$ - see front matter & 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.ijhcs.2010.08.002

attributions relating to specialization and generalization (Leshner et al., 1998; Nass et al., 1996, 1994; Nass and Steuer, 1993). For example, Nass et al. (1996) demonstrated a relationship between the role assigned to TV sets (e.g., specialist TV set vs. generalist TV set) and viewers’ perception of the content provided by each TV set. A specialist television set is perceived as providing better content than a generalist television set. Identical news stories shown on both TV sets were rated as more newsworthy when viewed on the set labeled as being a News-only TV. Likewise, entertainment programming was considered more entertaining when viewed on a TV set labeled Entertainment-only TV. As a follow-up to this study, Leshner et al. (1998) explored how individuals perceived and evaluated the news story in each cable channel (e.g., specialist channels vs. generalist channels).

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They found that news stories on specialist channels (e.g., CNN and HNN) were rated more positively on news attributes than those on generalist channels (e.g., ABC and CBS). Therefore, the effect of specialization is not only felt at the level of TV sets but also at the level of TV channels. More generally, media specialization can be said to play a psychologically significant role at all levels of a given media source. This has particular relevance to computerbased media, especially online media, which feature a number of source layers (Sundar and Nass, 2001), such as the computer itself, internet browser, web portal, web site, interface agent, etc. Attributions of specialization could be made to each one of these sources in the online chain of communication. The media-equation literature, which demonstrates the mindless application of social rules and expectations to computers (Reeves and Nass, 1996), has attributed the tendency for treating computers as if they are human to the overuse of human social categories (e.g., gender, social status, and ethnicity) while interacting with computers (Langer, 1992; Nass and Moon, 2000). Specialization is one such social category, which is likely to be attributed to computers as much as they are to humans because, psychologically, the computer is treated as a source rather than as a medium of communication (Sundar and Nass, 2000). Likewise, other entities in the layered chain of communication in digital media are also likely to benefit from being labeled specialists rather than generalists in particular content domains. That is, we may attribute higher credibility to web sites that specialize in certain content domains (e.g., amazon.com for books and e-bay for auctions) and to web agents that do narrowly defined functions (e.g., Rea, the online real estate agent, Cassell, 2000) compared, respectively, to generalist web sites and web agents. As digital media continue to mature, specialized devices as well as portals and sites have grown in number. For example, a specialized device such as Kindle has overtaken a more generalist device such as PDA. Likewise, sites that feature specialized content (e.g., weather.com) or products (e.g., Edmunds.com) are more popular than their generalist counterparts. The appeal of specialization in digital media is assumed and deployed on a large scale, especially in e-commerce, but it has not been formally studied. The aforementioned studies with television boxes, while germane, cannot speak directly to specialization in computerbased online media—not just because the entities involved are different (computers, web sites, and web agents instead TV sets and cable channels), but because the interactive effects of multiple source layers (i.e., specific combinations of specialist computers and sites, specialist sites and agents) have not been investigated. The present study is an attempt to bridge this gap. Simply stated, the goals of our study are to (1) explore how media technology specialization (web agent, web site, and computer) is associated with individuals’ perceptions and attitudes in human–computer interaction (HCI), and (2)

examine the interactive effect of specialization in multiple layers of online communication. 2. Psychological bases for specialization effects of media technology The literature in social cognition offers two theoretical perspectives for understanding the psychological impact of specialization. Each of these is discussed in turn below, with an emphasis on applying them to media specialization. 2.1. Category-based perception Research in HCI has demonstrated that users perceive and evaluate computers based on social categories (Reeves and Nass, 1996). In addition, individuals show categorybased perception by relying on labels assigned to media (Leshner et al., 1998; Nass et al., 1996). Research in cognitive psychology has shown that individuals tend to show biased perception of a labeled social object based on the central attribute which is assigned to the label or the representation of the social category (Ashforth and Humphrey, 1997). For example, when a person is labeled (e.g., Maria is a feminist), other individuals perceive that person based on the central attribute of the social category that is associated with the label (Gelman and Heyman, 1999). According to the category-based perception model, to the extent social categories include well-learned schemas, they need less cognitive effort than individuated perception, invoking specific data or stereotypes that then guide our expectations (Fiske and Neuberg, 1990; Aronson, 1999). That is, individuals form their first impressions automatically based on the categories to which a social object is assigned. Once an object is categorized, individuals tend to interpret additional cues in order to support the categorization, and may disregard inconsistent information (Hamilton et al., 1990). In addition, labels have a significant effect on how individuals perceive social objects (Ashforth and Humphrey, 1997). According to Ashforth and Humphrey (1997), a label is a signifier of a given object, and typically activates a set of cognitions and related affect about the objects. To the degree that specific labels and cognitions are shared, labels enable individuals to understand and communicate about an object with minimum effort. Moreover, the labeling effect occurs regardless of the degree to which the central attribute of the social object is manifested in a specific labeled case. That is, when someone is labeled (e.g., Maria is a feminist), others perceive her based on the central attribute of a social category associated with the label, whether or not the person actually fits the labeled category (i.e., whether or not Maria is indeed a feminist) in reality. Therefore, when a person is typified categorically with a label (e.g., specialist), individual perceivers tend to think that the attribute (e.g., expertise) of the labeled category is central to the person. Stereotypes arise because

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the categorized label (e.g., specialist) activates the central attribute (e.g., expertise) associated with the representation of the category. Individuals tend to think that specialists have more expertise than generalists since the assigned label (specialist) activates the central attribute (expertise) associated with the representation of the category. Based on this logic, technologies labeled as specialists in a particular domain are likely to be perceived as superior in that domain compared to generalist technologies. Media technology specialization can be more extensively applied to online communication, specifically e-commerce, in which various media technologies are involved and a large amount of information is transacted. Since media technology specialization in e-commerce attributes expertise to the media technology (e.g., interface agent) by labeling it (e.g., Rea, the embodied conversational agent specializing in real estate, developed at MIT), online users perceive the specialized media technology as an expert in a content domain. That is, media technology specialization in online communication influences individuals’ perception and attitude by attributing the expertise (e.g., expertise in real estate) to the media technology (e.g., Rea). It also affects their evaluations, such that Rea is considered to possess more expertise in real estate information than another web agent, even though they may provide identical information. This perceived expertise might then carry over to trust and purchasing behavior in an e-commerce context. 2.2. Judgmental heuristics Research in psychology has demonstrated that individuals often use cognitive heuristics or mental shortcuts, when they form impressions and make judgments about other persons (Aronson, 1999; Zimbardo and Leippe, 1991). Individuals form attitudes and behaviors toward others following simple rules or salient cues (e.g., gender, race, and social status) associated with them (Aronson, 1999), especially when they have difficulty making a judgment or decision. The elaboration likelihood principle posits that individuals form and change their attitudes following either a peripheral route or a central route (Petty and Cacioppo, 1981). When they use a peripheral route, their attitude change is governed by so-called peripheral cues (e.g., source characteristics such as credibility, attractiveness, and expertise), rather than by central message content or arguments. As Zimbardo and Leippe (1991) demonstrated, individuals pay more attention to the source rather than the content of the message, even when the content contains information that is central and critical to forming the correct attitude. That is, the heuristic ‘route’ of attitude change and judgment involves applying a rule of thumb, usually based on a salient cue (e.g., source’s credibility, attractiveness, and expertise). Attitude change research has long argued that individuals tend to use judgmental heuristics in lieu of systematic processing (Aronson, 1999; Chaiken, 1987; Petty and Cacioppo, 1986; Zimbardo and

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Leippe, 1991), and are willing to let salient cues in the context affect their attitudes or attitudinal positions, without engaging in extensive message-relevant thinking (Petty and Cacioppo, 1981), regardless of the relevance of message content (Maddux and Rogers, 1980; Norman, 1976; Staats and Staats, 1958). Given this, individuals in online communication are more likely to evaluate a specialized media source (e.g., web site, web agent, etc.) as possessing more expertise than a generalist source, based on social cues (e.g., expertise) and the heuristics that they trigger (e.g., experts’ statements can be trusted), regardless of the relevance of the message content. For example, an online shopper looking for a digital camera can go either to bestbuy.com or walmart.com, among numerous other options. The fact that the former web site specializes in electronic goods may serve as a cue that either directly predisposes the shopper to trust bestbuy.com over walmart.com on all matters related to this purchase (as predicted by elaboration likelihood model) or triggers a heuristic (‘‘specialists are relative experts and therefore know more about less’’) which leads to greater trust of bestbuy.com over walmart.com (as would be predicted by the heuristic systematic model). Regardless, they make a quick and easy decision for evaluating information (pertaining to digital cameras in this case) based on a salient cue (pertaining to expertise in dealing with digital cameras) of the source (e.g., bestbuy.com and walmart.com) without extensively considering the content of the message (e.g., information about relative strengths and weaknesses of different kinds and brands of digital cameras), especially when they have difficulty in making a purchase decision. 3. Multiple sources in HCI Since individuals orient toward and interact with the computer as a source while they are working on a computer, source is a critical concept for understanding communication processes in online media (Sundar and Nass, 2001). Research in classic mass communication has considered source as the originator of all communications (Sundar and Nass, 2001). According to the psychology literature, individuals automatically assign responsibility for messages to those who deliver them and feel that the most proximate messenger is the source (Miller, 1976). That is, we treat the messenger (e.g., editor and spokesman) as the real source of messages. It follows then that in HCI, the computer itself is treated as a source. Sundar and Nass (2001) demonstrate that the media technology (such as computer and web agent) is a distinct source in the minds of users. They explain that source is whoever or whatever is perceived by the receiver to be the sender of the communication. That is, psychologically, source is what the receiver imagines the source to be (e.g., the visible gatekeeper-presenter of content such as the nytimes.com or the media technology that delivers the content). Considering that individuals interact with

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the computer as a source while they are working on a computer, the idea of a technological source is important from the perspective of HCI. That is, media technologies such as computer, web agent (e.g., interface agent) and web site can be the source toward which individuals orient and with which they interact. The technological source is psychologically perceived by the receiver as being autonomous, even though it is not ontologically independent. For example, the interface agent is often programmed to behave in certain systematic ways, but is treated by users as an autonomous source. Does this mean that the interface agent will replace the computer itself as the psychological source? Not necessarily. Sundar and Nass (2001) raise the possibility of multiple sources when they introduce the concept of ‘‘source layering’’ in online media. According to them, there could exist several psychologically relevant sources in the chain of communication—so-called visible sources, technological sources, and even receiver sources. Given this, cueing specialization at different levels (computer, web site, and web agent) could have combinatory effects on users’ perceptions and attitudes. The key application of multiple sources is in the area of e-commerce where a number of web portals and sites vie for user trust by touting their expertise in particular content domains (e.g., WebMD.com for health information), product categories (e.g., autobytel.com for automotive products), or services (e.g., monster.com for job searches). 4. Trust in e-commerce The emergent literature in e-commerce defines trust as a multidimensional construct involving dispositional trust, institutional trust, and interpersonal trust (Doney and Cannon, 1997; Lee and Turban, 2001; McKnight et al., 2002; Mcknight and Chervany, 2002; Tan and Sutherland, 2004). Dispositional trust (i.e., one’s tendency to trust) and institutional trust (i.e., one’s perceptions of the institutional environment such as the Internet) have been considered as factors influencing interpersonal trust, which is directly related to purchase intention in e-commerce (McKnight et al., 2002; McKnight and Chervany, 2002; Tan and Sutherland, 2004). Trust is also affected by numerous other factors (Cheung and Lee, 2001; Corritore et al., 2003; Doney and Cannon, 1997; Gefen, 2002; Lee and Turban, 2001; Mayer et al., 1995; McKnight and Chervany, 2002; Tan and Sutherland, 2004; Wang and Benbasat, 2005, 2007), including, most notably, perceived expertise of the e-commerce vendor (Muir and Moray, 1996). The credibility and expertise of the web site and web agent have been shown to influence trust in e-commerce (Corritore et al., 2003; Fogg et al., 2001a, 2001b; Smith et al., 2005; Steinbru¨ck et al., 2002; Wang and Benbasat, 2005, 2007). For example, Smith et al. (2005) demonstrated that the expertise of a peer recommender influenced the perceived trust of the recommender, leading to a reduction in consumers’ decision-making efforts.

Studies in e-commerce have addressed the notion of a technology (e.g., web site, intelligent agent) being the object of interpersonal trust (Wang and Benbasat, 2005, 2007; Wong and Sycara, 1999). HCI researchers have demonstrated that computer and web agent are social actors, even though philosophers argue that technologies cannot be the objects of trust (Solomon and Flores, 2001). For example, Reeves and Nass (1996) and Nass et al. (1996) show that individuals have more trust toward media such as television and computer when they carry certain desirable cues (such as specialization). That is, individuals perceive media technology (e.g., computer and web agent) as a source (Sundar and Nass, 2001). Media technology as trustee is highly associated with interpersonal trust in ecommerce. For example, Steinbru¨ck et al. (2002) explored the effect of social cues on customer trust in online vendors, showing that a picture as a social cue affects interpersonal trust (toward the vendor). They demonstrated that a web agent with social cues (e.g., photographs and video, speech) could increase consumer trust toward the online vendor, by simulating the social cues (e.g., eye contact and gestures) prevalent in a face-to-face situation. Consistent with this notion, Wang and Benbasat (2005) showed that online consumers tended to treat web agents as ‘‘social actors’’ and perceive human characteristics in them, leading to greater interpersonal trust. In addition, trust has been considered as one of the most critical factors in e-commerce success and strongly associated with purchase intention and behaviors (McKnight and Chervany, 2002; Tan and Sutherland, 2004). Trust is likely to accelerate the decision-making process on a web site. Users who show a high level of trust for the site (or the interface agent on it) are less likely to engage in detailed comparison-shopping and more likely to go through with the transaction unhesitatingly. 5. Rationale and hypotheses The literature reviewed thus far clearly indicates that (1) the role assignment of ‘‘specialists’’ (over ‘‘generalists’’) is associated with greater perceived expertise and credibility, (2) this assignment is not restricted to human professionals but can be applied to media technologies, such as computers, web sites, and web agents, producing the same kind of perceptual effects, and (3) trust in e-commerce is strongly associated with the perceived expertise of the vendor, which could be either a company or a technological element such as web site or interface agent. Therefore, we hypothesize as follows. H1. Individuals will show greater trust toward specialist media technology as compared to generalist media technology. Given that each media technology has several layers of sources (e.g., computer, web site, and agent), the degree of specialization attributed to the entity at each layer may

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interact with specialization in other layers. Therefore, the next hypothesis is as follows. H2. The degree of trust toward media technology is associated with the interactions of specialization at various layers of sourcing within media technologies. Clearly, specialization is a psychologically salient cue, and, as Chaiken and her colleagues have demonstrated, individuals tend to process information faster and with lesser effort if they are provided salient cues, whereas they take more time and effort to receive and analyze argumentation if salient cues are not provided (Chaiken, 1980, 1987; Chaiken et al., 1989; Chaiken and Maheswaran, 1994). Trust developed as a function of specialization is therefore likely to contribute to swift behavioral outcomes such as purchase behaviors, leading individuals to make faster purchase decisions when dealing with specialist media. Therefore, we hypothesize as follows. H3. Individuals will spend less time on a purchase decision with specialist media technology than with generalist media technology. 6. Method This study employed an experimental design with participants shopping online for wines and then answering an online questionnaire. They were exposed to one of eight conditions, depending on the specialization of media technology. The online questionnaire included multiple items about trust toward web agent, web site, and computer. 6.1. Design overview The study used a 2 (web agent: specialist web agent vs. generalist web agent)  2 (web site: specialist web site vs. generalist web site)  2 (computer: specialist computer vs. generalist computer) between-subjects experimental design. During the online shopping task, each participant was asked to check out of the site after purchasing eight bottles of wines, which resulted in an unobtrusive measure of ‘‘purchase decision time,’’ computed by recording the start time and the end time of the online shopping task for each participant. This, along with questionnaire measures of perceived trust, comprised our main dependent variables. The design also covaried pre-existing levels of institutional and dispositional trust among our participants.

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questionnaire. All participants were asked to sign an informed consent form prior to the start of the experimental session. They were then instructed to read the experimental instructions before proceeding to the shopping task. While the use of undergraduate students may undermine the external validity of the study, it was important for us to select a sample that would be less familiar with wines. If the shoppers are highly experienced with the product, they will be predisposed to engage in systematic processing; thereby minimizing—or even negating—the effect of heuristic cues relating to specialization. By using an unfamiliar topic, we follow a long-held tradition in the literature of dual-process models (Chaiken and Maheswaran, 1994). 6.3. Experimental treatment conditions The treatments for the three independent variables were introduced in layers, based on the sequence of their interaction with the subject. The first of the three was the computer. The subjects read instructions on the first screen of the experiment as follows: ‘‘You will be using this Wine computer (or this Computer) for your shopping today. The goal of today’s shopping session is to purchase eight bottles of fine wine. You should attempt to select wines of the highest quality, but stick to an overall budget of $300.00 (for all 8 wines combined)’’ in the condition of the computer (see Fig. 1). The specialist vs. generalist manipulation was implemented in a similar fashion for web site and web agent (see Fig. 2). Web site conditions included verbal cues such as ‘‘Welcome to Wine shop (E shop). Here is a wide selection of wines (products).’’ Web agent conditions also included verbal cues such as ‘‘You chose ‘Christian Moueix 2003 Merlot.’ Here are ‘Bordeaux’ Wine Agent (E Agent)’s recommendations’’ (see Fig. 3). Labels for specialist and generalist media technology were adapted from Nass et al. (1996) and Leshner et al.’s (1998) studies. In the conditions of specialist media technology, labels such as ‘‘wine computer,’’ ‘‘wine shop,’’ and ‘‘wine agent’’ were displayed on the screen of computer terminal during the task. In the conditions of generalist media technology, labels such as ‘‘computer,’’ ‘‘e shop,’’ and ‘‘e agent’’ were displayed on the screen of the computer terminal during the task. The manipulation check for media technology specialization was performed with two scaled items, adopted from Johnson and Grayson (2005) and Andaleeb and Anwar (1996). Two items, ‘‘the Wine Agent (Wine Shop, Wine You will be using this Wine Computer for your shopping today.

6.2. Participants The goal of today’s shopping session is to purchase eight bottles of fine wine.

124 undergraduate students, recruited from a variety of communication classes, participated in the experiment in exchange for extra credit. They were randomly assigned to one of the eight conditions of the experiment and were informed that they would complete a simple computer task, namely shopping online for wines, and fill out an online

You should attempt to select wines of the highest quality, but stick to an overall budget of $ 300.00 (for all 8 wines combined).

If you are instructed to start, please click HERE to start.

Fig. 1. Specialist computer: wine computer.

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Table 1 Items measuring trust toward media technology (wine computer). 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

Fig. 2. Specialist web site: wine shop.

I believe that the wine computer would act in my best interest If I needed help, the wine computer would do its best to help me The wine computer is interested in my well-being, not just its own The wine computer is truthful I would characterize the wine computer as honest The wine computer is sincere The wine computer is competent in providing shopping service The wine computer is proficient in providing shopping advice The wine computer is knowledgeable about wines The wine computer knows its products very well

Note: These 10 items were used to measure trust toward web site and trust toward web agent by replacing the words ‘‘wine computer’’ with ‘‘wine shop’’, and ‘‘wine agent’’, respectively. In the corresponding generalist conditions, the objects of trust were computer, e shop or e agent.

as ‘‘LegalAdvice.com is truthful in its dealings with me’’ taps into integrity while measures such as ‘‘LegalAdvice.com performs its role of giving legal advice very well’’ assesses perceived competence. The items were modified for this study to reflect the object of trust under assessment, e.g., ‘‘I believe that the wine agent would act in my best interest’’, ‘‘the wine shop was truthful,’’ and ‘‘the wine computer is proficient in providing shopping advice.’’ All measures were administered on a 7-point Likert-type scale (1 =completely disagree to 7= completely agree). See Table 1 for a complete listing of the trust measures.

Fig. 3. Specialist web agent: wine agent.

Computer) is an expert on wines’’ and ‘‘the Wine Agent (Wine Shop, Wine Computer) specializes in wines’’ were used to check the specialization manipulation of the specialist web agent (web site, computer). In addition, two items, ‘‘the E Agent (E Shop, Computer) is an expert on wines’’ and ‘‘the E Agent (E Shop, Computer) specializes in wines’’ were used to check the specialization manipulation of the generalist web agent (web site and computer). It was predicted that individuals would perceive greater expertise toward specialist media technology than generalist media technology.

6.4. Dependent measures 6.4.1. Trust Trust toward media technology (i.e., toward web agent, web site, and computer) was measured via 30 items in an online questionnaire, adapted from the McKnight et al.’s (2002) trusting beliefs scale. The scale was composed of three types of trust—benevolence, integrity, and competence. ‘‘I believe that LegalAdvice.com would act in my best interest’’ is an example of a benevolence item. Items such

6.4.2. Purchase decision time Purchase decision time was measured by subtracting the start time from the end time of the online shopping task for each participant, following Hostler et al. (2004)’s procedure for their study on e-commerce navigation. The start time was recorded when participants started their search of products for purchase by clicking ‘‘here’’ on the start page (shown in Fig. 1) and the end time was recorded when they finished the computer task by clicking ‘‘check out’’ on the shopping cart page. It must be noted that, regardless of condition, the start page took them to the wine-shopping area of the e-commerce site, i.e., participants using generalist technology did not have to wade through nonwine products before viewing the experimental portion of the study. The only difference between specialist and generalist conditions was the label used to indicate the source and the verbal cues associated with it. 6.4.3. Control variables Since previous research has demonstrated that dispositional trust and institutional trust influence interpersonal trust (Cheung and Lee, 2001; Tan and Sutherland, 2004; Hosmer, 1995; McKnight and Chervany, 2002; McKnight et al., 2002; Rousseau et al., 1998), we controlled for them in our experiment. Some individuals tend to have greater dispositional trust, a tendency to believe in the positive attributes of others in general, influencing their level of interpersonal trust. Different individuals may have had different kinds of experiences with the Internet, which

Y.J. Koh, S. Shyam Sundar / Int. J. Human-Computer Studies 68 (2010) 899–912 Table 2 Items measuring dispositional trust (Cronbach’s a =0.85). 1. 2. 3. 4. 5. 6. 7. 8.

People really care about the well-being of others I think people generally try to back up their words with their actions I believe that professional people do a good job at their work I trust people until they give me a reason not to trust them People are sincerely concerned about the problems of others People generally keep their promises Professionals are knowledgeable in their chosen field I generally trust new acquaintances until they prove that they should not be trusted

Table 3 Items measuring institutional trust (Cronbach’s a=0.94). 1. I feel good about purchasing things online 2. I feel that Internet vendors act a customers’ best interest 3. I feel good doing business online because Internet vendors fulfill their agreements 4. Internet vendors are competent at serving their customers 5. I feel assured that legal and technological structures adequately protect me from problems on the Internet 6. I am comfortable online shopping 7. If a customer needed help, Internet vendors would do their best to help 8. I feel confident relying on Internet vendors when I interact with them 9. The Internet is a safe environment to do business in transactions 10. Internet vendors do a good job at meeting customer needs

refers to institutional trust, affecting their interpersonal trust. Therefore, dispositional trust and institutional trust were measured, respectively, by the disposition-to-trust scale with 8 items and the institutional trust scale with 10 items, both adapted from McKnight et al.’s (2002) study (see Tables 2 and 3). 6.5. Procedure Upon arriving at the study lab, participants were instructed to randomly sit in front of computer terminals, each of which was assigned to one of the eight conditions in the experiment. The lab was equipped with twenty computer terminals so that at least 16 participants for two sets of eight conditions could participate in the experiment at the same time. The participants were told that they would be completing a simple task of online shopping followed by an online questionnaire. They were then given instructions about how to use the computer and the headphone during the online shopping task. They started their computer tasks by clicking ‘‘here’’ after reading the instruction of shopping online for wines. Once they were done with both parts of the experiment, they were debriefed about the purpose of the study, thanked for their participation, and then dismissed. 6.6. Data analysis This study employed a three-way full factorial MANCOVA with three independent variables (specialization of web

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agent, web site and computer), two covariates (dispositional and institutional trust), and three dependent variables (trust toward computer, trust toward web site and trust toward web agent), followed by 2  2  2 ANCOVAs with each dependent variable one at a time, to test the hypotheses. Significant interactions were followed up with post-hoc tests to detect statistically significant differences between cell means, using Student’s t, and interpreted by plotting them graphically. A 2  2  2 ANOVA was also conducted with the three independent variables (web agent, web site, and computer) and the other dependent variable, purchase decision time, with significant interactions examined via graphical plots and mean differences assessed by way of post-hoc analyses using Student’s t test. 7. Results 7.1. Manipulation checks If indeed our manipulation of specialization was successful, then subjects would attribute significantly greater expertise to the source in the specialist conditions compared to generalist conditions. Two items, ‘‘the wine agent/site/computer is an expert on wines’’ and ‘‘the wine agent/site/computer specializes in wines,’’ were used for the manipulation check of expertise resulting from media technology specialization (Pearson’s r of the two items for web agent, web site, and computer were 0.88, 0.72, and 0.70, respectively, all significant at po 0.01). Findings showed that there were significant differences in expertise of web site [t(122)=4.57, p o 0.001] and computer [t(122)=2.80, p o 0.01]. That is, individuals attributed more expertise to the specialist web site (M=5.03, SD=1.46) than to the generalist web site (M=3.87, SD=1.38) and to the specialist computer (M=4.36, SD=1.56) than to the generalist computer (M=3.51, SD=1.81). In addition, individuals gave a higher expertise rating to the specialist web agent (M=4.03, SD=1.80) than to the generalist web agent (M=3.53, SD=1.71), and this difference was marginally significant, one-tailed t(122)=1.58, p=0.05. 7.2. Validity and reliability of measures An exploratory factor analysis (EFA) was conducted with varimax rotation on the 30 trust items. The number of underlying factors was determined by the number of components with eigenvalues greater than or equal to one. The resulting factors were then examined for common, rather than specific, variance by applying the items-on-factor criterion (i.e., at least two items loading on a given factor). An item was said to load on a given factor if it had its highest loading on that factor, ideally 0.6 or higher, with secondary loadings on other factors being no greater than 0.4 (McCroskey and Young, 1979). The EFA yielded 5 factors. The 10 items measuring trust toward computer all had their highest loadings (Z 0.6) on the same

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factor with none of the other 20 items (relating to trust toward web site and trust toward agent) loading ( 0.6) or cross-loading (  0.4) on this factor. Eight of the 10 trust items for web site had their highest loadings on one factor with the remaining two loading on a separate factor. Six of the 10 trust items for agent loaded on one factor whereas the other four loaded on a separate factor. Overall, the majority of the items designed for a given trust index loaded together (convergent validity), but more importantly, the items that failed to load on the expected factor did not end up loading or cross-loading on the other two factors (discriminant validity), thereby lending construct validity (Straub et al., 2004) to the idea of differentiating indices based on the locus of trust. However, in the interest of maintaining comparability across the three trust indices, we decided to average the 10 items for each source, upon ensuring that they had good internal consistency: trust toward web agent (Cronbach’s a = 0.95), trust toward web site (Cronbach’s a =0.94), and trust toward computer (Cronbach’s a =0.93). Indices for institutional and dispositional trust were created similarly, and showed good reliability (Cronbach’s a= 0.94 and 0.85, respectively).

7.3. Hypotheses testing Results showed significant multivariate main effects for specialization of web agent (Wilks l= 0.93, F(3,112) = 2.73, p o 0.05, partial Z2 = 0.068) and specialization of computer (Wilks l= 0.90, F(3,112) = 3.96, p o0.01, partial Z2 =0.096). In addition, a significant multivariate three-way interaction effect for specialization of web agent, web site and computer was obtained [Wilks l =0.93, F(3,112) = 2.69, p o 0.05, partial Z2 = 0.067]. Table 4 provides the adjusted means and standard errors for the 8 experimental conditions, on each dependent variable and the covariates. The following results are reported in terms of ANCOVA outcomes for each of the three types of trust, in order. For trust toward web agent, a significant main effect was obtained (F(1,114) = 6.77, po 0.05), such that individuals show greater trust toward the specialist web agent (M= 4.28, SE = 0.15) than the generalist web agent (M= 3.71, SE = 0.16). Therefore, Hypothesis 1, which predicted that individuals would show greater trust toward a specialist media technology than a generalist media technology, was supported for web agent. In addition, results showed a marginally significant three-way interaction effect of the specialization of web agent, web site, and computer on trust toward web agent (F(1,114) = 3.44, p =0.066, see Fig. 4), such that trust for a specialist web agent was diminished if the agent appeared in a generalist site and a generalist computer (compared to a specialist site and/or computer), whereas trust for a generalist web agent was enhanced if it appeared in a specialist site and a specialist computer (rather than a generalist site and/or computer).

Regarding trust toward web site, results indicated another main effect of the specialization of web agent (F(1,114) = 4.91, po 0.05), such that individuals with the specialist web agent showed greater trust toward web site (M = 4.55, SE =0.13) than those with the generalist web agent (M =4.13, SE =0.13). Therefore, Hypothesis 1, which proposed that individuals would show greater trust toward a specialist media technology than a generalist media technology, was supported in terms of web agent specialization and trust toward web site. In addition, a significant three-way interaction effect was obtained for the specialization of web agent, web site, and computer on trust toward web site (F(1,114) = 8.18, po 0.01, see Fig. 5). Individuals showed less trust toward a specialist web site when they interacted with a generalist web agent via a generalist computer (compared to a specialist web agent and/or computer), whereas they showed greater trust toward a generalist web site when they interacted with a specialist web agent through a specialist computer (rather than a generalist agent and/or computer). Therefore, Hypothesis 2, which predicted that trust toward media technology would be associated with the interactions of specialization at various source layers, was supported for trust toward web site. For trust toward computer, results also revealed a main effect of the specialization of computer on trust toward computer (F(1,114) = 6.02, p o 0.05), such that individuals show greater trust toward the specialist computer (M = 4.28, SE =0.14) than the generalist computer (M = 3.80, SE = 0.14). Therefore, Hypothesis 1, which predicted that individuals would show greater trust toward a specialist media technology than a generalist media technology, was supported for trust toward computer. In addition, results showed a marginally significant three-way interaction of the specialization of web agent, web site, and computer on trust toward computer (F(1,114) =3.18, p= 0.077, see Fig. 6), such that trust toward computer is higher when at least two of the three source layers (computer, site, and agent) is specialized than when only one of them is specialized. Put another way, trust toward computer (both specialist and generalist computer) is lower when both web site and web agent are generalist than when only one of them is a generalist. For the dependent variable of purchase decision time, results indicated a significant two-way interaction of the specialization of web site and computer, (F(1,116) = 4.00, po 0.05, see Fig. 7 and Table 5), such that individuals spend more time on a purchase decision when both the web site and the computer are specialists or generalists than when either the computer or the agent is a specialist. Therefore, Hypothesis 3, which predicted that individuals would spend less time for a purchase decision when they were exposed to specialist media technology than generalist media technology, was only partially supported because even though participants took longest when both site and computer were generalists, the condition where both of them were specialists was not the fastest (lowest) on

Table 4 Means and their standard errors for dependent variables and covariates by specialization of media technologies (computer, web site, and web agent). Specialist computer

Generalist computer

Specialist web agent Trust toward computer M 4.41A SE 0.28 Trust toward web site M 4.27ABC SE 0.26 Trust toward web agent M 4.36A SE 0.31 Decision time M 8.92 SE 0.69 Institutional trust M 4.49 SE 0.29 Dispositional trust M 4.40 SE 0.21

Generalist web site

Specialist web site

Generalist web site

Generalist web agent

Specialist web agent

Generalist web agent

Specialist web agent

Generalist web agent

Specialist web agent

Generalist web agent

4.50A 0.28

4.36AB 0.28

3.86AB 0.29

4.07AB 0.28

3.60B 0.29

3.59B 0.29

3.94AB 0.28

4.60AB 0.26

4.70AB 0.26

3.70C 0.27

4.94A 0.26

4.03BC 0.27

4.28ABC 0.27

4.21ABC 0.27

4.15AB 0.31

4.49A 0.31

3.61AB 0.32

4.42A 0.31

3.33B 0.32

3.86AB 0.32

3.74AB 0.32

7.69 0.71

7.48 0.71

7.89 0.71

7.65 0.71

7.10 0.71

9.10 0.71

9.03 0.71

4.52 0.29

4.13 0.29

3.79 0.30

4.56 0.29

4.59 0.30

3.90 0.30

4.32 0.30

4.85 0.21

4.14 0.21

4.29 0.22

4.87 0.21

4.55 0.22

4.54 0.22

4.67 0.22

Note: This table reports adjusted (or least squared) means. Means that do not share a letter in their superscripts differ significantly (po0.05) according to post-hoc Student’s t tests. Standard error of the mean (estimated by dividing the standard deviation of the observations by the square-root of the sample size) is an indicator of the reliability of the mean estimate whereas standard deviation is an indicator of the dispersion among individual observations. Since the object of study is mean differences across conditions, rather than the amount of variation within any given condition, we report standard errors instead of standard deviations.

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Specialist web site

907

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Trust toward web agent

908

5

5

4

4

3

3 Specialist computer

Specialist computer

Generalist computer

Specialist web agent

Generalist computer

Generalist web agent Specialist web site Generalist web site

Trust toward web site

Fig. 4. Three-way interaction effect of the specialization of web agent, web site, and computer on trust towards web agent.

5

5

4

4

3

3 Specialist computer

Generalist computer

Specialist computer

Specialist web site

Generalist computer

Generalist web site Specialist web agent Generalist web agent

Trust toward computer

Fig. 5. Three-way interaction effect of the specialization of web agent, web site, and computer on trust towards web site.

5

5

4

4

3

3 Specialist web agent

Generalist web agent

Specialist web agent

Generalist web agent

Generalist computer

Specialist computer Specialist web site Generalist web site

Fig. 6. Three-way interaction effect of the specialization of web agent, web site, and computer on trust towards computer.

decision time. The fastest decision time, on average, belonged to the condition where the computer was a generalist and the site was a specialist. However, these results must be interpreted with the caveat that the post-

hoc test showed statistically significant differentiation between only two of the four means—the condition where both computer and site were generalists and the one with a generalist computer and specialist web site.

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Decision time

9

8

7 Specialist computer

Generalist computer Specialist web site Generalist web site

Fig. 7. Two-way interaction effect of the specialization of web site and computer on decision time. Table 5 Effects for specialization of media technologies on purchase-decision time. Purchase-decision time Specialist computer—specialist web site (cell 1) Specialist computer—generalist web site (cell 2) Generalist computer—specialist web site (cell 3) Generalist computer—generalist web site (cell 4)

8.30AB 0.50 7.68AB 0.50 7.37B 0.50 9.06A 0.50

Note: Adjusted means and standard errors are reported here. Means that do not share a letter in their superscripts differ significantly (po0.05) according to post-hoc Student’s t tests.

Table 6 is an ANOVA table that lists all the effects found in the analyses described above. In sum, results indicate that individuals show greater trust toward a specialist web agent than a generalist web agent, which appears to translate over to greater trust for the web site featuring the specialist agent. In addition, interaction effects reveal that trust toward each of the three sources is enhanced if the source is specialized and at least one of the other two layers is specialized or if the source is a generalist and both the other sources are specialized. Finally, for purchase decision time, results indicate that individuals spend more time for purchase decision when both the web site and the computer are generalists than when the computer is a generalist and the site is a specialist.

8. Discussion By attributing specialization to the various sources in online media, this study extends the effects of specialization in traditional mass communication to the domain of technology specialization in HCI. The idea of imbuing technological sources with the specialization cue itself was

909

successful, as evidenced by results of the manipulation check of expertise, verifying that study participants perceived specialist media technology (specialist web agent, web site, and computer) as possessing more expertise than generalist media technology (generalist web agent, web site, and computer). Just as the social-psychology literature has shown individuals to respect specialists and believe their suggestions or opinions more than those of generalists (Maddux and Rogers, 1980; Zimbardo and Leippe, 1991), we have demonstrated that individuals trust specialist media technology more than generalist media technology in online communication. Therefore, our study adds to the media equation literature in HCI, in that individuals exhibit mindless social responses toward media technologies by showing the same kinds of category-based perceptions and applying the same judgment heuristics as they do to human beings around them. For interface designers, it implies that technologies ought to convey specialization; this means technological tools stand to benefit by limiting their offerings rather than broadening them. This can be a particular challenge in the current industry climate of convergence. Our findings suggest that devices that specialize in a narrow set of functions are likely to be held in higher esteem by users compared to devices that try to combine a broad variety of functions and utilities. For example, we hold the addressbook and calendar functions of a Palm PDA in higher regard than the same functions on a cell-phone. This is because the PDA is a specialist in such functions whereas the newer cell-phones are becoming convergent devices that incorporate numerous functions such as calendar, addressbook and MP3 playing in addition to its central purposes of calling and texting. Likewise, portal sites that try to offer a one-stop comprehensive experience might suffer in credibility when compared to specialist sites such as Google.com (specializing in search) or Amazon.com (books). Marketing efforts surrounding most new online applications as well as digital media tend to tout their convergent abilities. Such claims of convergence might lead users to treat these technologies as generalists rather than specialists, thus undermining their perceived credibility for performing any given function with accuracy and stability. Findings from our study also strongly suggest that individuals interact with and orient to multiple source layers in online communication. While previous studies examined the relationship between specialization of one source (TV set or TV channel) and individuals’ perception and attitude, this study explored how multiple sources (web agent, web site, and computer) layered in HCI simultaneously influence individuals’ perception and attitudes. For example, the main effect for web site trust was affected by specialization of web agent, suggesting crossover effects of expertise in one source layer to another layer. Furthermore, interaction effects reveal that individuals show less trust toward a specialist web site when they interact with a generalist web agent via a generalist computer (compared to specialist web agent via a generalist

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Table 6 Main effects and interactions for specialization of media technologies (computer, web site, and web agent) on trust and decision time. DF

F ratio

Sig.

R2

7.31 1.18 0.54 0.48 0.15 0.10 3.87 37.01 0.47

1 1 1 1 1 1 1 1 1

6.02 0.97 0.45 0.39 0.13 0.08 3.18 30.49 0.39

po0.05 p=0.33 p=0.51 p=0.53 p=0.72 p=0.78 p=0.08 po0.001 p=0.53

0.32

Computer Web site Web agent Computer  web site Computer  web agent Web site  web agent Computer  web site  web agent Institutional trust Dispositional trust

0.04 1.43 5.32 0.01 0.19 0.52 8.87 36.28 0.46

1 1 1 1 1 1 1 1 1

0.03 1.32 4.91 0.01 0.17 0.48 8.18 33.48 0.42

p=0.86 p=0.25 po0.05 p=0.94 p=0.68 p=0.49 po0.01 po0.001 p=0.52

.32

Trust toward web agent

Computer Web site Web agent Computer  web site Computer  web agent Web site  web agent Computer  web site  web agent Institutional trust Dispositional trust

3.15 0.54 10.16 0.09 0.03 0.15 5.17 20.02 1.11

1 1 1 1 1 1 1 1 1

2.10 0.36 6.77 0.06 0.02 0.10 3.44 13.32 0.74

p=0.15 p=0.55 po0.05 p=0.81 p=0.88 p=0.75 p=0.07 po0.001 p=0.39

.22

Decision time

Computer Web site Web agent Computer  web site Computer  web agent Web site  web agent Computer  web site  web agent

422.29 11,477.50 13,601.62 14,6081.00 2381.75 7146.86 3776.25

1 1 1 1 1 1 1

0.01 0.31 0.37 3.98 0.07 0.20 0.10

p=0.92 p=0.58 p=0.54 po0.05 p=0.80 p=0.66 p=0.75

.01

Dependent variables

Factors

Trust toward computer

Computer Web site Web agent Computer  web site Computer  web agent Web site  web agent Computer  web site  web agent Institutional trust Dispositional trust

Trust toward web site

computer), whereas they show greater trust toward a generalist web site when they interact with a specialist web agent through a specialist computer (rather than a generalist agent through a specialist computer). Clearly, the specialization cue is psychologically salient at multiple source layers in HCI. However, this does not mean that specialization across the three source layers is functionally equal. Results indicate a significant main effect for the specialization of web agent [F(1,114) = 4.91, po 0.05] such that individuals with a specialist web agent show greater trust toward the web site than those with a generalist web agent. Curiously, trust toward web site was not affected by specialization of web site, but only by specialization of web agent. This result indicates the primacy of web agent over web site. It seems as though the agent is the locus of user interactions. When the agent is specialized, then that effect seems to overwhelm the effect of computer and site. This suggests the relatively greater visibility of the agent as the locus of HCI. Agents could be considered ‘‘visible sources’’ under the

Sum of squares

typology of online sources proposed by Sundar and Nass (2001) and the central target of social-psychological attributions to the computer made by users and documented in the media equation literature. This has clear practical implications for site designers and e-commerce vendors in that a specialist web agent can go a long way in overcoming the generalist nature of a site or the device used to access the site. That said, specialization of agent does not appear to have a behavioral consequence in terms of time taken to make a purchase decision. A specialist site on a generalist computer led to the shortest decision time, followed by a generalist site on a specialist computer. This could be an artifact of our study design wherein the participants were interacting with the site, rather than the agent, at the time of entering their purchase choices. Perhaps the specialization of the entity with which one is interacting is likely to show the best behavioral consequence of social category cues such as specialization. That is, users may not associate the agent very closely with the shopping-cart transaction part of their interaction on an e-commerce site, which can

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have its own advantages. These and other theoretical possibilities can be explored by future research, especially that which tracks actual user purchase of recommended products in an e-commerce setting. Given the general tendency for cognitive miserliness, the implication of our findings is that specialization at multiple source layers can impede the speed of decision-making. Therefore, in e-commerce contexts, it might be preferable to have one source (ideally the web site) clearly identified as a specialist in order to minimize time spent shopping and deciding. If, on the other hand, the critical need is for users to feel comforted by the shopping experience and have good attitudes about the interaction, then having a specialist web agent may be the way to go. In terms of theoretical positions outlined earlier, our findings imply the sustained use of heuristics, but in a systematic way. The fact that specialization at two of the three source layers cumulates to generate positive attitudes implies support for the additivity hypothesis of the heuristic systematic model (Chaiken, 1980; Chaiken et al., 1989). It is not uncommon for judgment-relevant heuristics to be used judiciously during systematic processing. Therefore, the specialization cues in the computer and the site appear to promote a conscious consideration of the perceived expertise conveyed by specialization. For the web agent though, it is not clear whether the specialization cue indeed conveys expertise, as evidenced by the marginally significant manipulation check. Perhaps, a specialist agent conveys warmth and hence generates trust affectively. It is after all the most human-like of the three sources studied in our experiment. What this implies is that specialization can carry different meanings when attached to different source layers in online media. A more complete understanding of the psychological meanings associated with specialization at each layer will enhance our ability to theorize about the combined effect of specialization cues in modern online media and help spark design innovations in the technologies involved. Acknowledgement This research was supported in part by KT and the Korea Science and Engineering Foundation under the WCU (World Class University) program funded by the Ministry of Education, Science and Technology, South Korea (Grant no. R31-2008-000-10062-0). References Andaleeb, S.S., Anwar, S.F., 1996. Factors influencing customer trust in salespersons in a developing country. Journal of International Marketing 4 (4), 35–52. Aronson, E., 1999. Readings About Social Animal eigth ed. Worth/W.H. Freeman, New York. Ashforth, B.E., Humphrey, R.H., 1997. The ubiquity and potency of labeling organizations. Organization Science 8 (1), 43–58. Cassell, J., 2000. Embodied conversational interface agents. Communications of the ACM 43 (4), 70–78.

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