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Journal of the Academy of Marketing Science

DOI: 10.1177/00920703042710042006; 34; 308Journal of the Academy of Marketing Science

K. Damon Aiken and David M. BoushTrust and the Context-Specific Nature of Internet Signals

Trustmarks, Objective-Source Ratings, and Implied Investments in Advertising: Investigating Onl

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10.1177/0092070304271004 ARTICLEJOURNAL OF THE ACADEMY OF MARKETING SCIENCE SUMMER 2006Aiken, Boush / ONLINE TRUST

Trustmarks, Objective-SourceRatings, and Implied Investments

in Advertising: Investigating OnlineTrust and the Context-SpecificNature of Internet Signals

K. Damon AikenEastern Washington University

David M. BoushUniversity of Oregon

Thepurpose of thisstudy is to provide a preliminary inves-

tigation of the effectiveness of Internet marketersvarious

attempts to develop consumer trust through Web signals.

Thework is an explorationof thecontext-specific natureof

trust in e-commerce. An onlineexperiment compares three

potential signals of trust in an Internet retail firm: (1) a

third-party certification (i.e., a trustmark), (2) an

objective-source rating (i.e., a review fromConsumer 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 be-

liefs about security andprivacy, general beliefsabout firm

trustworthiness, and willingness to provide personal in-

formation. The relationship between Internet 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 stockprices online; and, in ever-increasingnum-

bers, shopping on the Internet. Given consumers

widespread acceptance of the Internet, combined with the

multitude of technological advances in the past decade, e-

commerce growthratescontinue 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 custom-

ers, 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-com 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 andtransactionsnow occur together ina singlevir-

tual medium, which has increased risks for online con-

sumersandhasplaceda heavy communications burden on

sellers whose Web site effectiveness is affectedby a multi-

tudeof design characteristics (Geissler, Zinkhan, and Wat-

son2001). Internet consumersare placedin a uniqueinfer-

ence-making position in which information asymmetry

abounds. Such consumers must trust that Internet firms

Journal of the Academy of Marketing Science.

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

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will notdefault on implied or explicitbonds. Furthermore,

they must assume that merchandise is of good quality and

that it will be delivered as promised. Perhaps more impor-

tant, 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 ofanother (Chu and Chu 1994). Thus, Internet consumers

frequently ascribe notions of trustworthiness from

outside-sourcesignals.Suchtrust transference playsa 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, con-

sumer trust is affected by firms investments in buildings,

facilities, and personnel (Doney and Cannon 1997).

Whereas Internet-enhancedbusinesses may benefit from

such physical factors, Internet-based businesses cannotrely on credibility that is bought through structures,

storefronts, or salespeople. Furthermore, to a limited

extent, Internet-based businesses cannot rely on common

perceptions of size, reputation, and other such factors to

convey reliability.

Theprimary purpose of this article is toexamine therel-

ative effectiveness of marketersvarious 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 devel-

opment. An Internet-based experiment compares three

distinct signals: (1) a third-party certification (i.e., anInternet 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-MEDIATEDENVIRONMENT

Trustis defined as a partners willingness to rely on anexchange partner in the face of risk (Doney and Canon

1997; Moorman, Zaltman, and Deshpand 1992; Schurr

andOzanne1985).A small butgrowing subsetof thebusi-

ness andmarketingliteratureconcentrateson howthe con-

cept of trust is different in a computer-mediated environ-

ment (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 con-

trol of information. To overcome perceptions of uncer-

tainty, trusthasbeen linkedto thediffusion andacceptance

of e-commerce in general (Grabner-Kraeuter 2002;

Shankar,Urban,andSultan2002). MilneandBoza (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 consumers 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 forprivacy maystemfrom fear of the

unknown (Hoffman, Novak, and Peralta 1999). Research-

ers note that privacy is a multidimensional concept that

playsa criticalrole inconsumersfearof purchasing on the

Internet (Hine and Eve 1998; Sheehan and Hoy 2000).

Inasmuch as trust requires a cognitive andaffective leap of

faith (a movementbeyond calculative prediction; see Wil-

liamson 1993), trust on the Internet implies, to some

extent, an overcoming of a concern for privacy. Hine and

Eve(1998) similarlyviewtrustin theCME asconcomitantwith personal reserve and skepticism.

Issues of consumer control further substantiate the

uniqueness of Internet 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 (Hoffmanet al. 1999). Consumers

often cite feelings of helplessnessandfear while shopping

on the Internet (Hine and Eve 1998), and they often guard

their personal information carefully. Hoffman and Novak

(1998) noted that virtuallyall web users have declined toprovide 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 eco-

nomics 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 infor-

mationasymmetryhas implications for the terms of trans-actions 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 otherparty has, the first party may make inferences fromthe information provided by the second party, and

Aiken, Boush / ONLINE TRUST 309



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this inference information should play a role in theinformation thesecond party chooses to provide. (P.66)

Managers of a firm possess more information than outsid-

ers about a businesss viability, expected profits, risk lev-

els, product quality, and so forth (Ippolito 1990; Levy and

Lazarovich-Porat 1995). Whatmanagerschoose to project

to outsiders takes the form of an informational cue orsignal.

Furthermore, signaling theory posits a rational con-

sumer who expects a firm to honor the implicit commit-

ment conveyed through a signal, largely because a firms

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 basedon these signals.Kirmani andRao (2000)

note four necessary conditions for successful signal trans-mission. First, the situation must sustain a context of

prepurchase information scarcity. Consumers do not have

complete information about firms and the quality of prod-

ucts. A consumer segments lack of information and its

risk aversion make it an appropriate target for a signal.

Second, the situation must enable postpurchase informa-

tion clarity. After purchasing a product, consumers should

be able to interpret quality unambiguously and, if neces-

sary, 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 vul-

nerability. In this case, a bond postedby a firm (in the formof a signal) musttruly be atrisk. In a sense, the bond isthe

firms 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) devel-

oped a classification scheme that separates signals into

two categories and then defines four specific types of sig-

nals. First,default-independent signals areones 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 invest-

ments 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, lowintroductory prices; seeChu 1992;Dawar

and Sarvary 1997). Second, default-contingent signals are

ones in which themonetary loss occursonly when thefirm

defaults on its claim. Again, there are two types: (1)

revenue-risking signals that tie current and/or future reve-

nues to a firms bond (e.g., high prices, brand vulnerabil-ity; 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 theprior research on signalingnotes theprem-

ise 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 Internet-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 Internet-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 ofsuch 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 infer-

ences of quality and/or firm commitment in the same

manner as an offline consumer (see Kirmani 1990, 1997;

Kirmani and Wright 1989).

Trustmarks

A subsetof outside-source certifications hasbeen 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

310 JOURNAL OF THE ACADEMY OF MARKETING SCIENCE SUMMER 2006



5/17

2003). A trustmark is designed to communicate trustwor-

thiness through behavioral insinuations of capability,

rational suggestions of credibility, and emotional implica-

tions of benevolence and integrity. An Internet firm must

usually license a third-party mark by compensating the

party through both an up-front feeand 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 itsWeb 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

trustmark 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 trustmarksare nonprofit corporations. Consumers are likely to per-

ceive such firms as experts that specialize in certifying

secure Internet communications. In thecontext 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, con-

sumersmay be aware that these firms are paidby the licen-

sor, andthus they might infer that theissuing firmshaveno

incentive to punish firms that cheat (and therefore they are

not trustworthy).Moreover, payment decreasestheoverall

credibility and perhaps the perceived objectivity of themessage.

Objective-Source Ratings

In traditional media, objective-source ratings, such as

those published in Consumer Reports, havebeen shown to

facilitate consumer trust (Boush, Kim, Kahle, and Batra

1993). This transference process should operate similarly

inan Internet context. In essence, trust in theprinted publi-

cationis transferred to trust in theWeb-based firm.Even if

consumers have no knowledge of the third party, they still

may draw valuable insights and inferences from the post-

ing of an objective-source rating. To the extent that con-

sumers perceive credibility in the source and to the degree

that they process the message as it relates to the Web ven-

dor, their perceptions of vulnerabilitywilldecrease.More-

over, because the objective-source rating has the lowestcost, consumers are likely to perceive it as themost objec-

tiveand therefore themost credible.Finally, theobjective-

sourcerating canbe viewedas anoutside partys testament

to the behavioral trustworthiness of the firm.

Alternatively, objective-source ratings are perhaps

overused and often are not directly applicable to the pur-

chase situation. For example, Consumer Reports ratings

usually refer to product quality rather than to overall Web-

site trustworthiness. Given consumersconcern about pri-

vacy and security issues, objective-source ratings may not

signal trust effectively.

Implied Investments in Advertising

Previous work has determined that consumers are sen-

sitive to significant investments in advertising and that

such investments signal a firms marketing confidence,

effort, and commitment (Kirmani 1990; Kirmani and

Wright 1989). 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 infor-

mation asymmetry and thus interpret investment in adver-

tising 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, inter-

pretation, and inference making function similarly in a

CME. In this case, however, a firms significant invest-

ment in advertising is difficult to convey over the Internet.

For example, electronic transmissions of banner adver-

tisements can be posted to a firms Web site at relativelylow costs. In the current study, we overcome this problem

through the use of an implication of significant invest-

ment, 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 judgmentsabout a firms com-

mitment 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 real-

ize that heavy investments are related to a marketers con-fidence,but they mightalso realize that marketersoftentry

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

thatundermines signal credibility. Consumers are likely to

revise their perceptions of firms that are thought to use

such persuasion tactics.

HYPOTHESES

Internetvendors currentlyuseoutside-sourceratings 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 onPerceived 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, theyshould 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 thatrates them(Boushetal. 1993; Chu and Chu

1994). Such firms should be trusted to provide quality

merchandise. However, we argue that trust is highly con-

textual, andrecent research shows that online trust is dom-

inated by concerns about privacy and security (Hine and

Eve 1998; Sheehan and Hoy 2000).

Only onesignal, the trustmark, is specific to theunique

context of online shopping. The trustmark is designed ex-clusively to warrant that an onlinefirm will respect thepri-

vacy and protect the security of online information.

Furthermore, the trustmark carries with it an implied

context-specific expertise in information technology. The

trustmarkshouldconvey a feeling of comfort, that is,com-

fort under theassumption that a high levelof technicalcer-

tification has occurred. Consequently, we expect the

following:

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

Relationships Among Trust Components

Cognitive, affective, and behavioral aspects of trust

have been studied anddiscussed frequently and acrossnu-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 offline studies, we view Internet trust as an atti-

tude 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 (be-

liefs about the exchange partners motivation to seek joint

gain; Doney and Cannon 1997). In the current study, we

measure the effect of different signals on three compo-

nents of trust: (1) beliefs related to the trustworthiness of

the firm, (2) beliefs about the firm, and (3) willingness toprovide personal information. The components corre-

spond to cognitive, affective,and conative aspects of trust.

Forthe cognitive component, we measure both general be-

liefsabout firm reliability andmorespecificbeliefs related

to the firms handling of privacy and security issues. The

uniqueness of a trustmark is that it signals privacy and se-

curity. Therefore, we expect that the trustmark will not af-

fect consumers general beliefs toward the firm or

willingness to provide personal information directly.

Rather, we expect that theeffect of thecomponents of trust

is mediated by specific beliefs about privacy and security.

Stated more formally:

Hypothesis 2: Effects of the trustmark on (a) consumerswillingness to provide personal information and (b)overall trustworthiness of the firm are mediated byspecific 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 allthree types of signals, thus suggesting a posi-

tive relationship between the number of signals and per-ceived trustworthiness. However, limitations in

information processing (Bettman 1979; Newell and Si-

mon 1972) suggest a more complex relationship. As a re-

sult of mutual interference, the effect of the number of

signals on firm-specific trust should level out and then de-

crease. 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-




7/17

tionship is similar to the relationship between stimulus

complexity and communication effectiveness (Berlyne

1960). For example, in a recent study in an Internet con-

text,Geissler et al. (2001) showed that optimal attentionto

a Web page is achievedwhen it surpassesa 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 ofthe independent variable, we predict the following:

Hypothesis 3: There is a positive effect of the number ofsignals 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 an-

other in communicating their respective messages. As we

noted previously, the trustmark is a specific warrant of on-line informational privacy and security. Thus, we do not

expect that additional signals of general firm trustworthi-

ness will add anything other than noise to that signal.

Therefore, we predict the following:

Hypothesis 4: Web sites that include a trustmark elicitmore positive beliefs about privacy and securitywhen they use the trustmark alone than when theyuse it in combination with either an implied invest-ment 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 signalundermines(Kirmani andWright1989) 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 under-

mine is associated with the belief that although expendi-

tures 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 firms product quality. Finally,

the immunity undermineoccurswhen a consumer believes

that the signalers payoff is large even if the advertised

products benefits are overstated. The immunity under-

mine 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 ex-

amine post hoc. Forexample, it seems intuitive that adver-

tising 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 be-cause of inferences that it is not distinctive or that it in-

volves little risk. Thus, theory suggests the following:

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

Internet Experience and Online Trust

Internet consumers approach each new purchase situa-

tion with diverse levels of onlineexperience.Hoffman and

Novak(1998) andHoffmanet al.(1999) reported that neg-

ativeperceptions of privacy andsecurity increaseas online

computer proficiency increases.Hoffmanet al. alsofound

that the more experience a person has in the online envi-

ronment, themore important hisor her concerns areabout

control over personal information.

However, these studies examine concerns among peo-

ple with higher levels of experience. The relationship be-

tween experience and trust across a broader range of

experience is more complex. Completely inexperienced

consumers haveno 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 profi-ciency equate to greater knowledge of the intricacies of

computer science, electronic data transfer, network com-

munications, and so forth. Therefore, people at the high

end of the experience curve have more knowledge of

Internet commerce, or they may simply have more proce-

dural knowledge of how thesystem works. Thus, we posit

that trust firstincreases with experience andthen 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 curvilinear

relationship. Stated more formally

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

METHOD

Participants were recruited from a nationally recog-

nized musical program that is affiliated with a large north-

western university. Management of the annual music




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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-onlydesign 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 experimen-

tal 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 allparticipants 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 Leagues Super Bowl. All participants were sent e-

mail letters of invitation that linked them to the studys

Web site. A follow-up e-mailwassent directly from music

festivalmanagement. The entire process occurred over the

Internet.Participants logged on, readthrough a procedural

description and greeting, followed instructions, and then

clicked through to theexperimentalstimuliand thevarious

sections of the survey questionnaire. Presentations of the

critical stimuli were masked, to some extent, in thecontext

of a Web site described as under construction for a new

onlinecomputer superstore. We chose this line of business

fortwo reasons. First,we believed that thelevel 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

entertainmentproducts andgiftsand 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 pre-

sented in three-color graphics with the words reviewed byand site privacy statementabove 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-Superstore.Com receives our highest rating

Consumer Reports. The objective-source rat-

ing was a two-color quotation in a larger, distinctly differ-

ent 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 questionsaccording to a 9-point, Likert-type scale (strongly dis-

agree/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 characteris-

tics. 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, participantse-mail addresseswerecol-

lected for entry into a prize drawing beforebeing 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, aswell as theincentiveof a smalldonation 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, theparticipants in thesample

were wealthier (median income: $60,000), older (median

age: 50), andbettereducated (college graduate:86%) than

either theU.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. Thecontrol condition (i.e., the site with

no Internet signals) was noticeably barren and apparently

was deemed unrealistic by many participants. Taking

advantageof theopportunityat theend of thesurvey, many

participants in the control condition sent e-mails to the

research team stating that they found many of the




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questionsdifficult toanswerbecause therewasnot enough

information to evaluate the site. The combined effects of a

mortality rate of more than 30 percent (from survey sec-

tion tosurvey section) andnonresponse 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 rota-

tion method: varimax or oblimin. We aggregated andeval-

uated the variables contributing to the three factors

according to correlation analyses. Because the three fac-

tors were highly correlated with oneanother (Pearson cor-

relation 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 ade-

quate reliability with an overall Cronbachs alpha of .86.

The factor structure is shown through the factor matrixdisplayed 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

natureof theexperimental firm.Here, participantsseem to

use reasoning skills to evaluate the credibility of the firm.

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 compo-

nent, two variables are positively valenced (relating to

appreciation andadmirationof the Internet-basedfirm and

its business practices), and two are negatively valenced

(relating to consumers perceptions of privacy and secu-

rity). 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 Id provide this firm with my

home address. The fourth variable that loaded on this

component addressed psychographic information. The

statement, Iwouldbewilling toanswerthis firms request

for personal lifestyleinformation, maintainshighcross-loadings with both cognitive trust (.371) and affective

trust (.427). It is likely that many participants considered a

firms 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 conducteda confirmatory factor analysis to validate

the emergent three-factor structure of firm-specific trust.

The validation sample consistedof 389 undergraduate stu-

dents at a public research university. We used AMOS

statistical software to model the data and calculate the fit

statistics using themaximum likelihood method. First,we

testeda single-factor model that related thescale 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

online trust and the context-specific nature of internet signals

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