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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
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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
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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-
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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