Trustmarks, objective-source ratings, and implied investments in advertising: Investigating online...

Trustmarks, Objective-Source Ratings, and Implied Investments in Advertising: Investigating Online Trust and the Context-Specific Nature of Internet Signals

K. Damon Aiken Eastern Washington University

David M. Boush University of Oregon

The purpose of this study is to provide a preliminary investigation of the effectiveness of lnternet marketers' various attempts to develop consumer trust through Web signals. The work is an exploration of the context-specific nature of trust in e-commerce. An online experiment compares three potential signals of trust in an lnternet retail firm: (1) a third-party certification (i.e., a "trustmark"), (2) an objective-source rating (i.e., a review from Consumer Re- ports magazine), and (3) an implication of investment in advertising (i.e., a television advertisement to air during the Super Bowl). The trustmark had the greatest effect on perceived trustworthiness, influencing respondents' beliefs about security and privacy, general beliefs about firm trustworthiness, and willingness to provide personal information. The relationship between lnternet experience and trust was in the form of an inverted U.

Keywords: trust; signaling; Internet marketing

In the instant that it takes to read these words, millions of people are sending e-mail; listening to Web radio; checking stock prices online; and, in ever-increasing numbers, shopping on the Internet. Given consumers'

Journal of the Academy of Marketing Science. Volume 34, No. 3, pages 308-323. DOI: 10.1177/0092070304271004 Copyright �9 2006 by Academy of Marketing Science.

widespread acceptance of the Internet, combined with the multitude of technological advances in the past decade, e- commerce growth rates continue to climb at an astounding rate. From 1999 to 2000, retail spending on the Internet grew from $20.25 billion to $38.75 billion, and business- to-business e-commerce rose from $176.8 billion to more than $405 billion. More recently, retail e-commerce in the third quarter of 2003 was estimated to be $13.3 billion, an increase of 27 percent over the third quarter of 2002 (U.S. Census Bureau 2003). In an effort to attract new customers, Internet firms have spent correspondingly large amounts, and thus sales and marketing expenses have often exceeded revenues (Burke 2002).

The growing mass of retail dot-corn failures testifies to the difficulties online retailers face. Marketing practitio- ners, strategists, and researchers have realized that online retailing is distinctive and that it requires a great deal of new research. E-tailers and infomediaries are positioned between producers and the ever-growing legion of e- consumers (Parasuraman and Zinkhan 2002). Communi- cations and transactions now occur together in a single virtual medium, which has increased risks for online consumers and has placed a heavy communications burden on sellers whose Web site effectiveness is affected by a multitude of design characteristics (Geissler, Zinkhan, and Wat- son 2001). Internet consumers are placed in a unique inference-making position in which information asymmetry abounds. Such consumers must trust that Internet firms

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will not default on implied or explicit bonds. Furthermore, they must assume that merchandise is of good quality and that it will be delivered as promised. Perhaps more important, Internet consumers must trust that their personal information will be securely held and that their privacy will be respected.

In the struggle to signal trustworthiness, Internet firms often seek out and post certifications and references from objective third parties, in effect renting the reputation of another (Chu and Chu 1994). Thus, Internet consumers frequently ascribe notions of trustworthiness from outside-source signals. Such trust transference plays a new and important role in Internet relationship marketing (Doney and Cannon 1997; Milliman and Fugate 1988). This transference may be especially true in the case of Internet-based or "pure-play" Internet businesses for which there are no traditional bricks-and-mortar retail stores for consumers to visit. In traditional settings, consumer trust is affected by firms' investments in buildings, facilities, and personnel (Doney and Cannon 1997). Whereas Internet-enhanced businesses may benefit from such physical factors, Internet-based businesses cannot rely on credibility that is "bought" through structures, storefronts, or salespeople. Furthermore, to a limited extent, Intemet-based businesses cannot rely on common perceptions of size, reputation, and other such factors to convey reliability.

The primary purpose of this article is to examine the relative effectiveness of marketers' various attempts to signal Web-site trustworthiness. Furthermore, the study is an investigation of the complex, context-specific nature of Internet communications and e-consumer attitude development. An Intemet-based experiment compares three distinct signals: (1) a third-party certification (i.e., an Intemet "trustmark"), (2) an objective-source third-party rating (i.e., from Consumer Reports magazine), and (3) an implication of significant advertising investment (i.e., a television advertisement to air during the Super Bowl). The study also uses control measurements to determine how individuals' levels of Internet experience and proficiency are related to firm-specific trust development.

TRUST IN A COMPUTER-MEDIATED ENVIRONMENT

Trust is defined as a partner's willingness to rely on an exchange partner in the face of risk (Doney and Canon 1997; Moorman, Zaltman, and Deshpand6 1992; Schurr and Ozanne 1985). A small but growing subset of the business and marketing literature concentrates on how the concept of trust is different in a computer-mediated environment (CME; Handy 1995; Hine and Eve 1998; Jarvenpaa and Tractinsky 1999; McKnight and Chervany 2002). New definitions of trust in the CME reflect particular

concerns about risk, reliability, privacy, security, and control of information. To overcome perceptions of uncer- tainty, trust has been linked to the diffusion and acceptance of e-commerce in general (Grabner-Kraeuter 2002; Shankar, Urban, and Sultan 2002). Milne and Boza (1999) operationalized trust in terms of an affective privacy ele- ment as "the expectancy of a customer to rely upon data- base marketers to treat the consumer ' s personal information fairly" (p. 8).

Recent research reveals that concern for privacy is the most important consumer issue facing the Internet, ahead of ease of use, spam, security, and cost (Benassi 1999). Much of this concern for privacy may stem from fear of the unknown (Hoffman, Novak, and Peralta 1999). Research- ers note that privacy is a multidimensional concept that plays a criticalrole in consumers' fear of purchasing on the Internet (Hine and Eve 1998; Sheehan and Hoy 2000). Inasmuch as trust requires a cognitive and affective leap of faith (a movement beyond calculative prediction; see Wil- liamson 1993), trust on the Intemet implies, to some extent, an overcoming of a concern for privacy. Hine and Eve (1998) similarly view trust in the CME as concomitant with personal reserve and skepticism.

Issues of consumer control further substantiate the uniqueness of Intemet business relationships. Consumer control over personal information, over the actions of a Web vendor, and over the Internet site itself all relate to issues of trust. Control over the actions of a Web vendor affects consumers' perceptions of privacy and security of the online environment (Hoffman et al. 1999). Consumers often cite feelings of helplessness and fear while shopping on the Intemet (Hine and Eve 1998), and they often guard their personal information carefully. Hoffman and Novak (1998) noted that "virtually all web users have declined to provide personal information to web sites at some point, and close to half who have provided data have gone to the trouble of falsifying it" (p. 1).

SIGNALING IN A CME

Signaling theory has evolved from information economics and the widely accepted premise that parties to transactions have different amounts of information about the transactions (Bergen, Dutta, and Walker 1992; Mishra, Heide, and Cort 1998; Rao and Monroe 1996). This information asymmetry has implications for the terms of transactions as well as the relationships between parties (Bagwell and Riordan 1991; Boulding and Kirmani 1993; Ippolito 1990; Spence 1973). According to Kirmani and Rao (2000),

When one party lacks information that the other party has, the first party may make inferences from the information provided by the second party, and

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this inference information should play a role in the information the second party chooses to provide. (E 66)

Managers of a firm possess more information than outsiders about a business's viability, expected profits, risk levels, product quality, and so forth (Ippolito 1990; Levy and Lazarovich-Porat 1995). What managers choose to project to outsiders takes the form of an informational cue or signal.

Furthermore, signaling theory posits a rational consumer who expects a firm to honor the implicit commitment conveyed through a signal, largely because a firm's not honoring the commitment would be economically unwise (Bagwell and Riordan 1991; Boulding and Kirmani 1993). That is, firms that falsely proclaim a signal or do not keep the bond their signal projects will not sur- vive (because rational consumers will not continue to do business with them). In this sense, it is logical for high- quality firms to send signals and for consumers to make inferences based on these signals. Kirmani and Rao (2000) note four necessary conditions for successful signal transmission. First, the situation must sustain a context of prepurchase information scarcity. Consumers do not have complete information about fn'ms and the quality of products. A consumer segment's lack of information and its risk aversion make it an appropriate target for a signal. Second, the situation must enable postpurchase information clarity. After purchasing a product, consumers should be able to interpret quality unambiguously and, if necessary, exact retribution on any offending seller. Third, the situation must have payoff transparency in which firms and consumers have complete knowledge of the benefits of signaling. Fourth, the situation must contain bond vulnerability. In this case, a bond posted by a firm (in the form of a signal) must truly be at risk. In a sense, the bond is the firm's word that the signal is credible. Firms must stand to lose future revenues or other benefits by posting a false bond or by defaulting on their bond. Moreover, if the signal is to be transmitted successfully, a consumer must believe in the bond and the inherent risk of the situation.

In an attempt to gain a more complete understanding of signals as economic cues, Kirmani and Rao (2000) developed a classification scheme that separates signals into two categories and then defines four specific types of signals. First, default-independent signals are ones in which a monetary loss occurs independently of whether a firm defaults on its claim (i.e., up-front expenditures for which the loss is independent of the truthfulness of the signal). There are two types of default-independent signals: (1) sale-independent signals, in which the signal occurs regardless of whether anyone buys the product (e.g., advertising, investments in brand names, retailers' investments in advertising; see Kihlstrom and Riordan 1984;

Kirmani 1990; Kirmani and Wright 1989), and (2) sale- contingent signals that link signaling expenditures to the purchase of the product or service (e.g., coupons, slotting allowances, low introductory prices; see Chu 1992; Dawar and Sarvary 1997). Second, default-contingent signals are ones in which the monetary loss occurs only when the firm defaults on its claim. Again, there are two types: (1) revenue-risking signals that tie current and/or future revenues to a firm's bond (e.g., high prices, brand vulnerability; see Bagwell and Riordan 1991; Gerstner 1985; Rao, Qu, and Ruekert 1999) and (2) cost-risking signals that do not involve up-front monetary expenditures but credibly convey information in which false claims would involve direct costs to the firm (e.g., warranties, money-back guarantees; see Kelley 1988; Wiener 1985).

Much of the prior research on signaling notes the premise that consumers' interpretations and the processes involved in market signaling are context sensitive (Boulding and Kirmani 1993, Dawar and Sarvary 1997; Kirmani and Rao 2000). In the specific context of the Internet, consumers must rely on inferences made toward the host of signals put forth by both Intemet-based and Internet-enhanced firms. Internet marketing managers are faced with the daunting task of properly understanding their consumer base; choosing the right signals; and selecting the optimal site placement, design, and so on. Furthermore, the signaling process is complicated by the limited dimensionality of the online experience, the sud- den increase of relatively unknown Intemet-based firms, and the communications dynamics involved (i.e., emerg- ing perceptions of privacy, security, risk, and control of personal information). Moreover, promotional signals drastically change in a CME. Not only are there new types of interactive and customized messages, but the costs of such signals are also relatively inexpensive. The average cyber-shopper is likely aware of the low costs involved in creating a Web site and posting all types of messages. As a result, the rational Internet consumer may not make inferences of quality and/or firm commitment in the same manner as an offiine consumer (see Kirmani 1990, 1997; Kirmani and Wright 1989).

Trustmarks

A subset of outside-source certifications has been aptly labeled as trustmarks. Although previous research has grouped these marks under the broad term authenticators (Rust, Kannan, and Peng 2002), the notion of a trustmark connotes greater depth. Trustmarks are defined as any third-party mark, logo, picture, or symbol that is presented in an effort to dispel consumers' concerns about Internet security and privacy and, therefore, to increase firm- specific trust levels (Aiken, Osland, Liu, and Mackoy


2003). A trustmark is designed to communicate trustworthiness through behavioral insinuations of capability, rational suggestions of credibility, and emotional implications of benevolence and integrity. An Internet firm must usually license a third-party mark by compensating the party through both an up-front fee and monthly payments. The issuing firm investigates the company, its Internet security methods, and its specific e-commerce practices, and then it authorizes the licensor to post the mark on its Web site. Consumers are assured that certified security, privacy, and disclosure standards exist for the use and access of information given to the Internet firm (Russell and Lane 2002). Two examples of trustmarks are the TRUSTe and VeriSign logos that are featured on many Web sites. Although the Good Housekeeping seal of approval is a paid third-party certification, it is not a trustrnark because it does not address the Internet-specific concerns about privacy and security, nor does it warrant the posting firm.

Many trustmarks come from companies that specialize in Internet communications and related high-technology areas. Many of the firms that currently license trustmarks are nonprofit corporations. Consumers are likely to perceive such firms as experts that specialize in certifying secure Internet communications. In the context of evaluat- ing an unknown Internet-based firm, consumers are likely to weigh such third-party information heavily. As a viable signal, the mark should positively affect trust. Further- more, to the extent that people regard trustmarks as expert certifications, the marks should outperform many other types of signals. Trustmarks are distinguished from the other two signals we test here because they are specific to the Internet context, and therefore they may prove especially effective.

However, trustmarks may not be the best method to instill trust. First, Internet consumers are likely to have a high degree of unfamiliarity toward the Internet-based companies that issue trustmarks. Previous research has found that familiarity and reputation are primary anteced- ents of trust (Doney and Cannon 1997; McAllister 1995). Firms that issue trustmarks are relatively new and cannot easily capitalize on reputational factors. In addition, consumers may be aware that these firms are paid by the licensor, and thus they might infer that the issuing firms have no incentive to punish firms that cheat (and therefore they are not trustworthy). Moreover, payment decreases the overall credibility and perhaps the perceived objectivity of the message.

Objective-Source Ratings

In traditional media, objective,source ratings, such as those published in Consumer Reports, have been shown to facilitate consumer trust (Boush, Kim, Kahle, and Batra 1993). This transference process should operate similarly

in an Internet context. In essence, trust in the printed publication is transferred to trust in the Web-based firm. Even if consumers have no knowledge of the third party, they still may draw valuable insights and inferences from the posting of an objective-source rating. To the extent that consumers perceive credibility in the source and to the degree that they process the message as it relates to the Web vendor, their perceptions of vulnerability will decrease. More- over, because the objective-source rating has the lowest cost, consumers are likely to perceive it as the most objective and therefore the most credible. Finally, the objective- source rating can be viewed as an outside party's testament to the behavioral trustworthiness of the firm.

Alternatively, objective-source ratings are perhaps overused and often are not directly applicable to the purchase situation. For example, Consumer Reports ratings usually refer to product quality rather than to overall Web- site trustworthiness. Given consumers' concern about privacy and security issues, objective-source ratings may not signal trust effectively.

Implied Investments in Advertising

Previous work has determined that consumers are sensitive to significant investments in advertising and that such investments signal a firm's marketing confidence, effort, and commitment (Kirmani 1990; Kirmani and Wright t989). Research in the area of market valuation also suggests that investors respond to advertising and promotion. For example, the announcement of a new advertising campaign has been related to abnormally high stock prices (Conchar, Zinkhan, and Bodkin 2003). In the presence of information asymmetry between managers and investors, investors search for proxy indicators of future firm performance. Investment in advertising is a signal that a firm is confident in forecasting long-term profits (Conchar and Zinkhan 2002).

Internet consumers may similarly perceive the information asymmetry and thus interpret investment in advertising as a signal that the firm is concerned with the long term. Although these issues have not been tested in an Internet context, it appears that signal transmission, interpretation, and inference making function similarly in a CME. In this case, however, a firm's significant investment in advertising is difficult to convey over the Internet. For example, electronic transmissions of banner advertisements can be posted to a firm's Web site at relatively low costs. In the current study, we overcome this problem through the use of an implication of significant investment, that is, a typed statement and a hypertext link that announced the airing of a television advertisement during the upcoming Super Bowl. Researchers have posited that consumers are knowledgeable about the costs involved in advertising during such a large-scale event and that they will infer high levels of marketing commitment and effort

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on the part of the firm (Kirmani and Wright 1989). In turn, these inferences should relate to increased levels of behavioral and cognitive trust.

However, while making judgments about a firm's commitment and effort, consumers maintain persuasion knowledge and thus are aware of marketers' attempts to influence them through marketing communications (Friestad and Wright 1994). Consumers are likely to realize that heavy investments are related to a marketer's confidence, but they might also realize that marketers often try to influence and persuade them. Even in the uncharted communications context of the Internet, knowledge of such persuasion tactics can lead to a change in meaning that undermines signal credibility. Consumers are likely to revise their perceptions of firms that are thought to use such persuasion tactics.

HYPOTHESES

Internet vendors currently use outside-source ratings as signals to build trust, to reduce perceived risk, and to enhance beliefs about privacy and security. The potential effectiveness of the three signals we examine here likely depends on perceived objectivity, perceived area-specific expertise, and contextual appropriateness.

Effects of Different Signals on Perceived Trustworthiness

We expect that all three signals we use in this study elicit beliefs that the firm is trustworthy. Firms with high advertising expenses signal that they have much to lose by defaulting on their implied bonds/claims; thus, they should be trusted not to damage customer relationships (Kirmani 1990; Kirmani and Wright 1989). Firms whose products are positively evaluated by a familiar third party (Consumer Reports) may benefit by the reputation of the third party that rates them (Boush et al. 1993; Chu and Chu 1994). Such firms should be trusted to provide quality merchandise. However, we argue that trust is highly contextual, and recent research shows that online trust is dominated by concerns about privacy and security (Hine and Eve 1998; Sheehan and Hoy 2000).

Only one signal, the trustmark, is specific to the unique context of online shopping. The trustmark is designed ex- clusively to warrant that an online firm will respect the privacy and protect the security of online information. Furthermore, the trustmark carries with it an implied context-specific expertise in information technology. The trustmark should convey a feeling of comfort, that is, comfort under the assumption that a high level of technical certification has occurred. Consequently, we expect the following:

Hypothesis 1: Web sites that have a trustmark are perceived as more trustworthy than are Web sites that have either an implied investment in advertising or an objective-source rating.

Relationships Among Trust Components

Cognitive, affective, and behavioral aspects of trust have been studied and discussed frequently and across nu- merous research fields (see, e.g., Ganesan 1994; Lewis and Weigert 1985; McAllister 1995; Swan, Bowers, and Richardson 1999; Williamson 1993). In accordance with these offiine studies, we view Internet trust as an attitude that has cognitive, affective, and conative (behavioral intention) components. Cognitive and affective elements of trust contain dimensions of credibility (beliefs that the exchange partner can be relied on) and benevolence (beliefs about the exchange partner's motivation to seek joint gain; Doney and Cannon 1997). In the current study, we measure the effect of different signals on three components of trust: (1) beliefs related to the trustworthiness of the firm, (2) beliefs about the firm, and (3) willingness to provide personal information. The components corre- spond to cognitive, affective, and conative aspects of trust. For the cognitive component, we measure both general beliefs about firm reliability and more specific beliefs related to the firm's handling of privacy and security issues. The uniqueness of a trustmark is that it signals privacy and security. Therefore, we expect that the trustmark will not affect consumers' general beliefs toward the firm or willingness to provide personal information directly. Rather, we expect that the effect of the components of trust is mediated by specific beliefs about privacy and security. Stated more formally:

Hypothesis 2: Effects of the trustmark on (a) consumers' willingness to provide personal information and (b) overall trustworthiness of the fn-m are mediated by specific beliefs about privacy and security.

Effects of Using Multiple Signals

Our experimental design allows for a test of signals used in combination. The previous discussion enables us to infer general beliefs about the trustworthiness of the firm from all three types of signals, thus suggesting a positive relationship between the number of signals and perceived t rustworthiness . However, l imitat ions in information processing (Bettman 1979; Newell and Si- mon 1972) suggest a more complex relationship. As a result of mutual interference, the effect of the number of signals on firm-specific trust should level out and then decrease. Therefore, the theoretical relationship between the number of favorable signals and their positive effect has the shape of an inverted U. The inverted U-shaped rela-


tionship is similar to the relationship between stimulus complexity and communication effectiveness (Berlyne 1960). For example, in a recent study in an Internet context, Geissler et al. (2001) showed that optimal attention to a Web page is achieved when it surpasses a minimum level of complexity but is not too complex. In the current study, we use a maximum of three signals, although we do not know whether three signals are sufficient to produce interference. In recognizing this limitation in the range of the independent variable, we predict the following:

Hypothesis 3: There is a positive effect of the number of signals on general beliefs about firm trust.

As we mentioned previously, three signals may not be sufficient to interfere with one another in eliciting beliefs about firm trustworthiness; however, this is not the same for more specific beliefs for which some signals are more relevant than others. Signals in this study address different components of trust, so they may interfere with one another in communicating their respective messages. As we noted previously, the trustmark is a specific warrant of online informational privacy and security. Thus, we do not expect that additional signals of general firm trustworthiness will add anything other than noise to that signal. Therefore, we predict the following:

Hypothesis 4: Web sites that include a trustmark elicit more positive beliefs about privacy and security when they use the trustmark alone than when they use it in combination with either an implied investment in advertising or an objective-source rating.

Internet Signal "Undermines"

Because signals depend on inferences, credibility can be weakened by specific negative consumer deductions. Such signal "undermines" (Kirmani and Wright 1989) can change the overall interpretation of a signal in several ways. For example, consider that a large expenditure on advertising is a signal that the firm believes in the product and wants to convey its high quality. The no-pain undermine is associated with the belief that although expenditures may be high, they involve little risk; that is, the firm can afford to incur a loss even if it defaults on an implied bond. The desperation undermine relates to the amount of expenditure that seems excessive or is more than reason- ably warranted. The basic premise undermine occurs when a consumer receives information that casts doubt on the basic premise of the default attribution in the situation at hand. For example, if there is nothing special about the expenditure level, a consumer might consider that there is nothing special about the firm's product quality. Finally, the immunity undermine occurs when a consumer believes that the signaler's payoff is large even if the advertised

product's benefits are overstated. The immunity undermine could occur, for example, if the firm can succeed without repeat purchases.

The inferences that are most likely to undermine each of the three signals used herein are conjectures that we examine post hoc. For example, it seems intuitive that advertising in the Super Bowl might be viewed by consumers as an act of desperation. However, an implied investment in Super Bowl advertising should not be undermined because of inferences that it is not distinctive or that it involves little risk. Thus, theory suggests the following:

Hypothesis 5: As levels of undermines, which weaken signal credibility, increase, trust levels decrease.

Internet Experience and Online Trust

Internet consumers approach each new purchase situation with diverse levels of online experience. Hoffman and Novak (1998) and Hoffman et al. (1999) reported that negative perceptions of privacy and security increase as online computer proficiency increases. Hoffman et al. also found that the more experience a person has in the online environment, the more important his or her concerns are about control over personal information.

However, these studies examine concerns among people with higher levels of experience. The relationship between experience and trust across a broader range of experience is more complex. Completely inexperienced consumers have no basis for online trust. At the low end of the experience curve, consumers are likely to become more trusting as they gain the familiarity and confidence that occur with successful online activity. However, at some point, increased levels of Web experience and proficiency equate to greater knowledge of the intricacies of computer science, electronic data transfer, network communications, and so forth. Therefore, people at the high end of the experience curve have more knowledge of Interuet commerce, or they may simply have more procedural knowledge of how the system works. Thus, we posit that trust first increases with experience and then levels off and decreases for people with high levels of experience. We measured both trust and experience as continuous variables so that we could observe a curvil inear relationship. Stated more formally

Hypothesis 6: The relationship between Internet experience and trust is in the form of an inverted U.

METHOD

Participants were recruited from a nationally recog- nized musical program that is affdiated with a large north- western university. Management of the annual music


festival provided a list of 1,252 newsgroup e-mail sub- scribers throughout the United States. We sought this sub- ject group because we wanted to reach a sample of adult Internet users who differed in terms of age, gender, geo- graphic location, and online experience.

Experimental Design

The experiment was a between-subjects after-only design in which eight conditions represented all possible combinations of the presence of our three Web site trust signals (i.e., trustmarks, objective-source ratings, and implied investment in advertising). The eight experimental Web sites were as follows: one Web site with all three signals, three Web sites with combinations of two signals, three Web sites with one signal, and one Web site with no signal. We randomly assigned participants to view one of the eight possible Web sites, and all participants answered the same survey questions after exposure.

Procedures

We collected data from participants during a 1-week period approximately 2 weeks before the National Foot- ball League's Super Bowl. All participants were sent e- mail letters of invitation that linked them to the study's Web site. A follow-up e-mail was sent directly from music festival management. The entire process occurred over the Internet. Participants logged on, read through a procedural description and greeting, followed instructions, and then clicked through to the experimental stimuli and the various sections of the survey questionnaire. Presentations of the critical stimuli were masked, to some extent, in the context of a Web site described as "under construction" for a new online computer superstore. We chose this line of business for two reasons. First, we believed that the level of involve- ment was high for personal computers and related items because of the prices of the products and their importance as business and educational tools. Second, at the time of data collection, computers and electronics were the third most popular items purchased over the Internet, behind entertainment products and gifts and flowers (Business 2.0 2001).

The trustmark was provided by a popular nonprofit firm that licenses its online certification to firms that meet certain security and privacy standards. The mark was presented in three-color graphics with the words reviewed by and site privacy statement above and below the mark. The objective-source rating used a popular consumer publication. The rating was in quotations and noted the source directly below the message. The message stated, "PC-Supers tore .Com receives our highest rating r162162 Reports." The objective-source rating was a two-color quotation in a larger, distinctly different typeface. Finally, the signal of implied investment in

advertising was denoted as a statement with three-color hypertext graphic that stated, "Watch for our upcoming television ad, to be aired during half-time of Super Bowl XXXV (click here for a preview)." All three experimental stimuli were placed prominently in the center of the lower portion of the experimental home page.

A total of 26 firm-specific questions were presented in a new pop-up window so that participants could return to the Web site at any time. We formatted all 26 questions according to a 9-point, Likert-type scale (strongly disagree~strongly agree). Because of the hypothetical nature of the experimental Web site/firm, behavior-directed questions focused on intentions rather than actions. We derived the trust scale items mainly from the work of Har- rison and Rainer (1992); Doney and Cannon (1997); and Dwyer, Schurr, and Oh (1987), but we altered them to be Internet specific. Participants then filled out a 13-question experience and proficiency assessment. Participants were asked to estimate their number of years of experience with the Internet and to describe their usage-rate characteristics. To assess Internet proficiency, participants were also asked a set of five questions in a multiple-choice format. Whereas experience was a self-report of behavior, we measured proficiency through a knowledge-based quiz. Next, participants were asked a series of demographic questions. Finally, participants' e-mail addresses were collected for entry into a prize drawing before being linked to a message of thanks.

RESULTS

We received 299 usable surveys, which yielded a response rate of 23.9 percent. This relatively high response rate was likely due to the participants being part of an opt-in newsgroup that was closely affiliated with the music festival. Moreover, the response rate was likely boosted by the general ease and convenience of the online format, as well as the incentive of a small donation given to the festival for each response. All these factors have been linked to higher response rates in Internet-based surveys (Cook, Heath, and Thompson 2000; Sheehan and McMillan 1999). In general, the participants in the sample were wealthier (median income: $60,000), older (median age: 50), and better educated (college graduate: 86%) than either the U.S. population as a whole or U.S. Internet users (Graphic, Visualization, & Usability [GVU] Center 1998).

To accommodate a condition in which all three signals could appear without excessive clutter, we included little extra information. The control condition (i.e., the site with no Internet signals) was noticeably barren and apparently was deemed unrealistic by many participants. Taking advantage of the opportunity at the end of the survey, many participants in the control condition sent e-mails to the research team stating that they found many of the


questions difficult to answer because there was not enough information to evaluate the site. The combined effects of a mortality rate of more than 30 percent (from survey section to survey section) and nonresponse resulted in only 12 usable responses in the control condition. Consequently, we dropped the site from final analyses.

The Structure of Firm-Specific Trust

Initially, we ran a principal components analysis to evaluate the elemental nature of firm-specific trust. Pri- mary analyses yielded three factors regardless of the rotation method: varimax or oblimin. We aggregated and evaluated the variables contributing to the three factors according to correlation analyses. Because the three factors were highly correlated with one another (Pearson correlation coefficients ranged from .302 to .387), we chose the nonorthogonal oblimin rotation. Twelve variables yielded an optimal three-factor solution that explained 65.8 percent of the variance. The scale maintained adequate reliability with an overall Cronbach's alpha of .86. The factor structure is shown through the factor matrix displayed in Table 1.

We label the first component as cognitive trust because it stems from cognitive evaluations and beliefs about the reliability, honesty, trustworthiness, and the honorable nature of the experimental firm. Here, participants seem to use reasoning skills to evaluate the credibility of the fm-n. This general-beliefs component explains a large percent- age of the variance within the scale (46.6%; eigenvalue = 5.60).

The second component contains emotional elements, and thus we label it as affective trust. Within this component, two variables are positively valenced (relating to appreciation and admiration of the Internet-based firm and its business practices), and two are negatively valenced (relating to consumers' perceptions of privacy and security). This component accounts for 9.9 percent of the variance (eigenvalue = 1.19).

We label the third component as behavioral trust. This component is composed of variables related to Internet purchasing and consumption. The behaviors were stated hypothetically, such as "I'd provide this firm with my home address." The fourth variable that loaded on this component addressed psychographic information. The statement, "I would be willing to answer this firm's request for personal 'lifestyle' information," maintains high cross- loadings with both cognitive trust (-.371) and affective trust (.427). It is likely that many participants considered a firm's intentions and/or had highly developed emotional reactions to the request for personal information. This component accounts for 9.2 percent of the variance (eigenvalue = 1.11).

We conducted a confirmatory factor analysis to validate the emergent three-factor structure of firm-specific trust.

The validation sample consisted of 389 undergraduate stu- dents at a public research university. We used AMOS statistical software to model the data and calculate the fit statistics using the maximum likelihood method. First, we tested a single-factor model that related the scale variables to firm-specific trust. The resultant chi-square and root mean square error of approximation (RMSEA) statistics did not support an adequate fit ()~2 = 264.74, df = 54, p < .001, RMSEA =. 10). However, the Comparative Fit Index (CFI) indicated a relatively good fit of the data (CFI = .982). Second, we investigated another model using the three-factor structure previously identified through principal components analysis (i.e., the cognitive, affective, and behavioral bases). This model showed considerable improvement in the fit statistics. The chi-square statistic decreased to 111.23 (df= 51, p < .001), the CFI improved to .995, and the RMSEA also improved to .055, demonstrating a better fit of the data compared with the single-factor model.

Effects of Different Signals on Trust

The first hypothesis pertained to our expectation that the trustmark would elicit greater levels of trust than the other two signals. The mean ratings for affective, behavioral, and cognitive trust and for the three elements of trust combined are shown for each experimental condition in Table 2. All tests of hypotheses are significant atp < .05.

We can make the most direct comparisons between signal effects from the Web sites that used only one of the three signals. We ran specific analysis of variance tests and contrasts to evaluate the trustmark against both the objective-source rating ( Consumer Reports rating) and the implied investment in advertising (Super Bowl advertisement) in terms of cognitive, affective, behavioral, and average trust (see Table 3). Specific contrasts revealed that participants viewed the trustmark as more trustworthy than both the other two signals; however, there were no significant differences between the latter two signals. Thus, Hypothesis 1 was supported.

Relationships Among Trust Components

Hypothesis 2 addressed the expectation that the presence of a trustmark influences beliefs about privacy and security, which in turn influence beliefs about general firm trustworthiness and behavioral intentions. The factor structure of the trust scale led us to place perceptions of privacy and security in the category of affective trust and to characterize beliefs about firm trustworthiness as cognitive trust. We tested the order of effects of the trustmark on the three trust components by introducing the other components of trust as covariates. Specifically, Table 4 shows the effects of the trustmark on behavioral trust with cognitive and affective trust as covariates, on affective trust with


TABLE 1 Factor Matrix for Firm-Specific Trust

Component

Cognitive Trust Affective Trust Behavioral Trust

Eigenvalues Cumulative variance explained (%) Variable (communalities) Firm is reliable (.812) Firm is trustworthy (.829) Firm honors its promises (.660) Firm is honest (,736) Firm is not concerned with privacy (r) (.594) I appreciate the concern shown (.689) Firm maintains proper security (.547) I admire the firm and its business practices (.596) Provide phone number (.696) Give home address (.678) Supply personal lifestyle information (.557) Provide my e-mail address (.678)

5.60 1,19 1.11 46.6 56.5 65.8

.805 .154 .006

.781 .187 .010

.749 .112 .004

.742 .006 .210 -.004 .762 .002

.157 .756 .003

.103 .725 -.009

.256 .624 .003

.157 -.236 .846

.264 .166 .611 -.371 .427 .552

.226 .010 .551

NOTE: Numbers in italics indicate factor loadings above .30. (r) = items were reverse coded. Rotation: oblimin with Kaiser normalization, rcomponentl '

co, .~.a = .387; r~omvo.~.tl ' ~ompo.~.~s = .302; rcom~r~nt2, eom~w.at3 = .377.

TABLE 2 Trust Ratings by Site View

Site View Cognitive Trust Affective Trust Behavioral Trust Average Trust

Trustmark (TM) a M 5.36 5.26 4.77 5.14 n 36 36 35 35 SD 1.00 1.34 1.53 I. 12

Objective-source rating (OSR) b M 4.92 4.45 3.77 4.39 n 38 40 40 38 SD 1.22 1.00 1.76 1.17

Implied investment in advertising (IIA) r M 4.86 4.77 4.05 4.47 n 40 39 41 38 SD 0.50 0.95 1.40 0.78

TM and HA M 5.07 4.90 4.30 4.74 n 51 49 50 48 SD 0.99 1.28 1.88 1.23

IIA and OSR M 5.39 5.03 4.71 5.04 n 39 39 39 39 SD 1.00 0.75 1.55 .88

TM and OSR M 5.08 5.09 4.22 4.80 n 55 55 54 54 SD 0.73 0.97 1.26 0.79

All three signals M 5.35 5.19 4.69 5.11 n 22 22 21 21 SD 1.26 1.19 1.72 1.17

Total M 5.12 4.90 4.34 4.79 n 281 280 280 273 SD 0.96 1.11 1.60 1.04

NOTE: Scale is 1-9 (1 = strongly disagree, 9 = strongly agree). a. Third-party certification. b. Consumer Reports rating. c. Super Bowl advertisement.


TABLE 3 One-Signal Web Site Contrasts

Cognitive Trust Affective Trust Behavioral Trust Average Trust

t d~ t df t df t df

TM versus OSR 1.70 70.44 3.19 b 112 2.76 b 113 3.08 b 108

TM versus IIA 2.71 b 50.49 2.92 b 112 2.00 b 113 2.76 b 108

OSR versus IIA 0.27 48.62 -1.57 112 -0.80 113 -0.33 108

a. Equal variances are assumed for affective, behavioral, and average trust; they are not assumed for cognitive trust (based on Lavene's test for equality of variances). b. Two-tailed significance; p < .05.

cognitive and behavioral trust as covariates, and on cognitive trust with affective and behavioral trust as covariates. Table 4 describes the results both from Web sites that contained only one signal and from Web sites that combined the trustmark with one of the other signals. Note that we made the tests both with and without the inclusion of covariates; we centered the predictors (Aiken and West 1991).

For one-signal Web sites (the left-hand side of Table 4), introduction of cognitive and affective trust to the model removes the effect of the trustmark on behavioral trust. Beta coefficients suggest that cognitive and affective trust play an approximately equal role as covariates. Introduc- tion of affective and behavioral trust as covariates removes the effect of the trustmark on cognitive trust. However, the trustmark has a significant influence on affective trust even when cognitive and behavioral trust are introduced as covariates. For Web sites that contained one or two signals (the right-hand side of Table 4), the trustmark had a significant effect only on affective trust. This effect was not elim- inated with inclusion of the other two components of trust as covariates. Both cognitive and affective components were significant covariates, although cognitive trust had the greater effect.

The data support both Hypothesis 2a and Hypothesis 2b. However, a more complete description of the results is that the presence of a trustmark influences specific beliefs about privacy and security (affective trust), which influence more general beliefs about firm trustworthiness (cognitive trust). Both components influence a person's willingness to provide personal information (behavioral trust).

Multiple Web Site Signals

Hypothesis 3, which posited that more signals lead to more trustworthiness, was only partially supported. Whether two signals are better than one and three signals are better than two seems to depend on the type and strength of the signals. As we show in Table 2, the trustmark (appearing alone) had the highest mean rating of any Web site view. A contrast between the site view with three signals and the three site views with two signals was not significant for affective, behavioral, or cognitive trust.

Similarly, a contrast between the three site views with only one signal and the three site views with two signals was not significant for any of the three trust components. This leads us to conclude that more additional signals are not necessarily more effective in instilling trust.

However, contrasts that omitted the sites that contained a trustmark (objective-source ratings and implied investment in advertising versus each signal separately) showed statistical significance for affective, behavioral, and cognitive bases of trust. Along with the mean ratings in Table 2, the results indicate that the objective-source rating and implied investment in advertising were stronger together than they were separately, but the trustmark did not benefit by being paired with either of the other two signals.

Other signals did not seem to make the trustmark more effective, but did they actually interfere with it? Hypothe- sis 4 predicted that Web sites that include a trustmark elicit more positive beliefs about privacy and security when they use the trustmark alone than when they use it in combination with either of the other two signals. A planned contrast between the Web site with the trustmark alone did not elicit significantly more positive beliefs about privacy and security when the trustmark was used alone than when it was accompanied by other signals. Therefore, Hypothesis 4 was not supported.

Internet Signals and Undermines

As we stated previously, a reason for differences in trustworthiness across signals could be that signal credibility is undermined by specific consumer inferences. Our study measured each undermine by pairs of statements (evaluated by the same scaled-response categories) that we subsequently summed. Hypothesis 5 predicted that as levels of inferences that undermine signal credibility increase, trust decreases. In support of Hypothesis 5, there were significant, negative correlations between the level of undermines and beliefs about firm trustworthiness (r = -.41), beliefs about privacy and security (r =- .36) , willingness to provide information (r = -.39), and the average of the trust components (r = -.47).

We also examined the four undermines for each Web site view individually (see Table 5). Planned contrasts


TABLE 4 Regression Results: Trustmark Effect With Trust Components as Covariates

One-Signal Web Sites (N = 111) One- and Two-Signal Web Sites (N = 252)

Independent Standardized Standardized

Model Variables ~ T Model

Dependent variable: Behavioral trust 1 Trustmark a .248 2.67* 1 .059 .937 Adjusted R 2 = ,053 Adjusted R 2 = .001 2 Trustmark a .070 0.88 2 - .0t3 -.248

Affective trust .338 3.22* .286 4.45*

Cognitive trust .315 3.07" .398 6.31 *

Adjusted R 2 = .370 Adjusted R 2 = .369

Dependent variable: Affective trust 1 Trustmark a .311 3.42* 1 .183 2.94* Adjusted R 2 = .089 Adjusted R 2 = .030

2 Trustmark a .132 1.91" 2 .145 3.03" Cognitive trust .498 6.17" .450 7.74* Behavioral trust .261 3.22* .259 4.45*


Dependent variable: Cognitive trust 1 Trustmark a .230 2.47* 1 .049 .782 Adjusted R 2 = .044 Adjusted R 2 = .002

2 Trustmark a .003 .035 2 -.05 -1.05

Affective trust .527 6.17' .430 7.73* Behavioral trust .257 3.07* .350 6.30*


a. Presence of a trustmark. * Significant at p < .05.

compared one- and two-signal sites with respect to whether they contained each signal. The contrast between the trustmark and non-trustmark sites indicated significantly lower ratings on the desperation undermine (i.e., the trustmark suffered less than did other signals from that undermine). The contrast between Consumer Reports (objective-source ratings) and the other signals indicated a significantly higher rating on the basic premise undermine, which perhaps indicates that there is nothing special about these ratings. Overall, the mean ratings of undermines in Table 5 are slightly lower than the scale midpoint, which indicates that, in general, Web sites were not undermined to a large extent by these negative consumer inferences.

Internet Experience and Trust

Hypothesis 6 predicted that the relationship between Internet experience and trust is in the form of an inverted U. Experience was a self-reported measure, which included items such as number of years of Internet experience, frequency of Internet usage, number of online purchases, participation in chat rooms, and membership in a listserv. We aggregated survey items and grouped participants according to the quartiles of their experience scores.

The middle two quartiles were combined. We labeled the resultant three groups accordingly as "less experienced," "mid-experienced," and "highly experienced." A multivariate analysis of variance (MANOVA) tested the three experience groups using the three bases of trust as dependent variables. A Wilks's Lambda coefficient of .95 proved significant; however, further investigation of between-subjects effects showed that these findings were driven largely by differences in behavioral trust (willingness to provide information). The differences between experience groups in terms of both affective trust and cognitive trust were not significant. The linear and the quadratic trends across experience groups both were significant for the behavioral component of trust. Hypothesis 6 was supported for the behavioral component of trust.

Validation of the self-reported experience measure was provided by a five-question multiple-choice "quiz" that contained questions about Internet abbreviations and definitions. We grouped participants according to their aggre- gate scores on the quiz portion of the survey. A MANOVA tested for differences between group means using affective trust, behavioral trust, and cognitive trust as dependent variables, and it exposed significant differences (Wilks's lambda coefficient = .93). Further analysis of between- subjects effects revealed that these differences were driven


TABLE 5 Signal Undermine Measurements by Site View

Site View

Signal Undermine

No Pain Desperation Basic Premise Immunity M

Trustmark (TM) a M 4.33 n 36 SD 1.41

Objective-source rating (OSR) b M 4.60 n 40 SD 1.48

Implied investment in advertising (IIA) c

M 4.66 n 41 SD 1.60

TM and IIA

M 4.98 n 51 SD 2.05

IIA and OSR M 4,79 n 39 SD t .08

TM and OSR M 4.67 n 54 SD 1.45

All three signals M 5.05 n 22

SD 2.01 Total

M 4.72

N 283 SD 1,60

3.69 5.08 4.72 5.03 36 36 36 36

1.53 1.84 1.36 0.84

4.74 5.80 5.08 4.88 39 40 40 40

1.66 1.69 1.68 1.0

4.76 4.83 4.95 4.85 42 41 41 41

1.42 1.74 1.28 0.71

4.16 5.41 4.48 4.94 50 51 48 49

1.80 1.65 1.20 1.00

4.33 5.57 4.29 5.20 39 40 41 40

t .53 1.47 1.25 0.77

4.54 5.78 4.93 4.92 54 55 54 53

1.62 1.36 1.16 .64

4.18 5.23 4.14 4.93 22 22 22 22

1.59 1.82 1.61 .64

4.37 5.42 4.70 4.96 282 285 282 281

1.63 1.65 1.37 0.82

NOTE: Scale is 1-9 (1 = strongly disagree, 9 = strongly agree). a. Third-party certification. b. Consumer Reports rating. c. Super Bowl advertisement.

by significant differences in cognitive trust. The quadratic trend across proficiency groups was significant for the cognitive base of trust, mirroring the inverted U-shaped relationship between experience and behavioral trust. The relationship was a positive shift in trust from novices to intermediates and a negative shift from intermediates to experts.

DISCUSSION

This study is one of the first to investigate the complex processes surrounding Internet marketing communications and trust development. The three signals we used in the study show disparity relative to the different facets of Internet trust. Among the three signals, and across gender, age, and income groups, the third-party certification (i.e., the trustmark) was the most effective method for

developing trust. The data support the major premise of a tripartite view of trust. Whereas the results reveal clear dis- tinctions among these affective, behavioral, and cognitive bases, they also suggest that consumers evaluate signals differently with respect to the three elemental components of trust. Regardless of levels of experience, consumer concerns about privacy, security, and control seem to pervade the Internet context.

The study's key findings revolve around the notion that the processes of Internet signaling and interpretation are context specific. That the trustmark outperformed the other two signals in this particular context implies that consumers find more credibility in certification from a context-specific expert source. This is especially interest- ing because 85 percent of the sample reported never having seen the mark before, and a full 97.3 percent of participants said that they had never dealt with the issuing corporation. This evidence suggests that research should


place greater importance on exploring context-specific third-party certifications. Reputation, familiarity, and prior experience, especially in the Internet's relatively new consumption environment, do not derive trust. In addition, the trnstmark led to increased affective perceptions of benevolence and a reduction in consumer anxiety (i.e., concerns about privacy and security). Previous research has shown that signaling effects are more likely when consumers are faced with unfamiliar products and brands (Kirmani 1997). Although previous studies have linked perceived reputation to consumers' trust (Doney and Can- non 1997; Fombrun 1996), the proliferation of new Internet-based businesses cannot rely on such connec- tions. Printed recommendations and objective-source ratings inform consumers that a third party endorses and/or approves of the firm. In this case, that Consumer Reports magazine also prints recommendations on many other products may have damaged context-specific credibility to some extent. Finally, although significant investments in advertising signal high levels of marketing commitment and marketing effort (Kirmani and Wright 1989), commitment and effort do not appear to translate directly to a f irm's credibility, benevolence, and overall trustworthiness.

Whereas previous research has theorized three elemental components of trust at both the interpersonal (Lewis and Weigert 1985; Swan et al. 1999) and firm-specific (Smith and Barclay 1997) levels, the current research yields three such factors. The cognitive component involves consumer judgments of a firm's level of honesty and reliability. Consumers appear to consider the trustworthiness of the firm with respect to costs, benefits, risks, and rewards. The affective component in the current study is strongly related to beliefs about privacy and security. Items measuring this component include appreciation and admiration for the firm as well as negative emotional con- structs such as fear and concern for personal information. The behavioral component relates to consumers' willingness to provide various kinds of personal information.

The results further highlight the critical importance of information privacy and security in establishing trust online. Privacy and security dominate consumers' evalua- tion of a firm's trustworthiness online in different ways from the bricks-and-mortar retail world. Beliefs about privacy and security play a large part in the development of trust for the Internet-based firm, especially an unknown one. In such a context, the customer may want to know whether the site is secure. The results of the current study suggest that the trustmark influences beliefs about privacy and security, which in turn affect general beliefs about trustworthiness. Together, beliefs about firm trustworthiness and about privacy and security lead to willingness to provide personal information. Although the results relate to an unknown, Internet-based firm, they may be quite

different for other types of signals or for a well-estab- lished, Internet-enhanced firm (e.g., www.sears.com).

The relationship between the number of signals and the level of trust is complex. In this study, the trustmark did not benefit from the addition of either or both of the other signals; however, the implied investment in advertising and the objective-source ratings were more effective together than they were separately. The more effective trustmark dominated the effect of additivity. In other words, a firm's having more signals of trust is not as effective as having a "better" signal. There is no evidence that signals interfered with one another in the current work.

Another noteworthy finding is that the relationship between online experience and firm-specific trust is an inverted U. In the early stages, when a person's experience with the Internet increases (through repeated interactions and communications), behavioral trust increases (as noted offiine by Doney and Cannon 1997). At higher levels of experience, trust declines, presumably because knowledge of what can go wrong increases concerns about privacy and security, a premise that is in line with the work of Hoffman et al. (1999).

With regard to the context-specific nature of Internet marketing communications, society in the information age (Glazer 1991) has developed a new form of contextual trust. Contextual trust is likely affected by the communications media, the shopping environment, and the transaction-specific risks and rewards. This form of trust is characterized by the unique representations of e- consumers and online fnans as encoded, transmitted, and decoded through the electronics-driven CME. Online shoppers who desire the optimal buying experience strive to resolve a multitude of privacy-related concerns while navigating vast amounts of filtered information. Online firms face the daunting task of choosing the optimal type and number of signals and then sending them with strategic clarity. Thus, the field of Internet consumer behavior encompasses an ever-growing and evolving set of processing, inference making, and decision-making issues. The current work serves as an opening to this vast new area of research.

LIMITATIONS AND FUTURE RESEARCH

This study contains several limitations, many of which could be addressed in future research. First, in an effort to isolate the effect of the experimental signals, we kept the Web sites relatively simple and free from the myriad images and opportunities for interactivity that are common to commercial Web sites. Second, study participants did not shop the Web site; they simply examined and drew inferences from it. Subsequent studies might use commercial Web sites in testing these or similar market signals in a more realistic purchasing situation. Third, this study tested


only one type of online company that had a relatively limited product mix. Attributes that encourage customers to select one site over another depend on the shopping trip's specific purpose (Reibstein 2002). Therefore, shopping for travel, flowers, books, or music may be a distinctly different experience compared to shopping for consumer electronics. Fourth, this study tested only one execution of each type of signal, thus making the results vulnerable to minor differences in size or typeface. We should note, however, that we took great care to keep the superficial aspects of the stimuli similar in such respects. Fifth, it is possible that some study participants were aware that Con- sumer Reports does not sanction use of its ratings in advertisements, although none of the participants reported such concerns in the open-ended comments section of the survey. Regardless, further extension and replication of the current work is a sound means to strengthen the validity of the findings (Sawyer and Peter 1983).

In addition, future studies should consider sampling different demographic segments of the population and a broader range of participants. We used a university- and program-affiliated sample in the study, and thus through a sense of loyalty to the university, there might have been demand effects. We minimized the demand effects by using a between-subjects design in which there was no for- mal mention of the university. The sample was spread across the United States, but we estimate that approximately 70 percent of respondents came from the western United States. Furthermore, participants' age, income, and education levels were high. However, consumers who buy products over the Internet are also notably above national averages in education and income (GVU Center 1998).

The results of the current study and some of its limitations suggest topics for future studies, which could include tests of other categories of signals (Kirmani and Rao 2000) as well as other versions within the categories of signals we studied (i.e., other trustmarks, objective-source ratings, and implied investments in advertising). Future studies could more formally investigate consumer trust as a function of the number of signals projected. Such a relationship is likely to be more complex than that tested in the current work, which used a maximum of three signals. For example, there might be a threshold before any effects of trustworthiness can be observed. After trust is engaged, it may grow unevenly, perhaps at an increasing rate followed by a decreasing rate. Too many signals may be counterpro- ductive, producing a negative relationship between number of signals and trust. In addition, complexity has been described as an interaction between the individual and the stimulus (Zinkhan and Martin 1983). Therefore, inclusion of more individual difference measures in the analysis of Web site signal complexity would be of interest. Trust develops over time. Therefore, it would also be worth- while to explore the development of online trust longitudi- nally (see Aiken 1999). Finally, Jarvenpaa and Tractinsky

(1999) reported preliminary evidence that the bases of trust vary both in an Internet setting and across international boundaries. Therefore, a cross-cultural examination of online trust would be useful.

A relatively new and ever-expanding set of Internet- based signals can be classified according to Kirmani and Rao's (2000) offiine typology. That is, many Internet signals have not been studied in the CME context. Moreover, many of the projected Internet signals would be "new" to marketing research (e.g., Do Web site visit counters act as viable signals to e-consumers?). Although advertising and investments in reputation have been studied offiine as sale- independent, default-independent signals, researchers have yet to study the similarly categorized signals of trustmarks, banner advertisements, and privacy and security seals. From a broader perspective, the posting of trade association membership, foreign language translations of a site, and internal search engines could be perceived as sale-independent, default-independent signals. Sale- contingent, default-independent signals could also include Internet loyalty programs. Internet-based revenue-risking, default-contingent signals could include the increasing number of commercial postings of unsolicited objective-source reviews; consumer reviews; site-/firm- specific chat rooms; strategic alliance information; visit counters; and the posting of myriad pictures, symbols, and images. Finally, cost-risking, default-contingent Internet- based signals could include the posting of return policies, free trial periods, and information on warranties and guarantees. The wealth of Internet signals, the complexities of Internet-signaling processes, and the unique consumer context of the Internet provide a rich backdrop for future studies.

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ABOUT THE AUTHORS

K, Damon Aiken (kaiken @mail.ewu.edu) is an assistant professor at Eastern Washington University at Cheney, Washington. He received his PhD from the University of Oregon. His primary teaching and research interests lie in Internet marketing, consumer attitude formation, and trust development. He has also published in the area of sport marketing, investigating fan atti- tudes and values. His research has appeared in the Journal of Ad- vertising Research, the International Journal of Internet Marketing and Advertising, the Business Research Yearbook, and Sport Marketing Quarterly, among others.

David M. Boush (dmboush @lcbmail.uoregon.edu) is an associ- ate professor of marketing in the Lundquist College of Business at the University of Oregon in Eugene. He received his PhD from the University of Minnesota. His research interests center on the relationship between consumer behavior and marketing management decisions, especially those involving advertising, branding, and the Internet. His research has appeared in publications such as the Journal of Marketing Research, the Journal of Consumer Research, the Journal of Business Research, the Journal of Inter- national Business Studies, Psychology and Marketing, Market- ing Letters, and the Journal of Current Issues and Research in Marketing. He serves on the editorial board of the Journal of the Academy of Marketing Science.

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