Converting Web Site Visitors into Buyers: How Web Site Investment Increases Consumer Trusting Beliefs and Online Purchase Intentions

Abstract

The authors investigate the impact of Web site design investments on consumers' trusting beliefs and online pur-chase intentions. Such investments signal the component of trusting beliefs that is most strongly related to online purchase intentions: ability. These effects were strongest when consumers' goals were to search rather than to browse and when purchases involved risk.

Full text

133
Journal of Marketing
Vol. 70 (April 2006), 133–148
© 2006, American Marketing Association
ISSN: 0022-2429 (print), 1547-7185 (electronic)
Ann E. Schlosser, Tiffany Barnett White, & Susan M. Lloyd
Converting Web Site Visitors into
Buyers: How Web Site Investment
Increases Consumer Trusting Beliefs
and Online Purchase Intentions
The authors investigate the impact of Web site design investments on consumers’ trusting beliefs and online pur-
chase intentions. Such investments signal the component of trusting beliefs that is most strongly related to online
purchase intentions: ability. These effects were strongest when consumers’ goals were to search rather than to
browse and when purchases involved risk.
Ann E. Schlosser is Assistant Professor of Marketing, University of Wash-
ington Business School (e-mail: aschloss@u.washington.edu). Tiffany
Barnett White is Assistant Professor of Business Administration (Market-
ing Group), College of Business, University of Illinois (e-mail:
tbwhite@uiuc.edu). Susan M. Lloyd is Assistant Professor of Marketing
and Endowed Fellow, Kogod School of Business, American University
(e-mail: smlloyd@american.edu). The authors thank Ruth Bolton and the
two anonymous
JM
reviewers, as well as Jim Bettman, Katherine Lemon,
Michael Mazis, and Kent Monroe, for their helpful comments on previous
versions of this article. The authors also thank Sandra M. Dahne and S.
Rand Fishkin for their technical assistance. Funding for Study 1 was pro-
vided by the Center for Information Technology and the Global Economy
at the Kogod School of Business, American University.
“In important ways, using the Internet involves a leap
of faith. We type in our credit card numbers and
other personal information in order to make pur-
chases over the Internet and trust that this information will
not be used in unauthorized or fraudulent ways” (Bargh and
McKenna 2004, p. 585). Firms have responded to such con-
sumer concerns by investing in Web site security, which has
become a multibillion dollar industry (eMarketer 2005). Yet
even experienced online buyers view purchasing online as
risky (eMarketer.com 2005; Forrester 2005). Indeed, con-
sumers’ perceptions of the risks involved in providing per-
sonal information online often contrast the views of security
experts, causing these consumers to avoid online activities
that are actually safe (Dunn 2004). This avoidance has led
some experts to speculate that the immediate threat to e-
commerce is consumers’ perceptions (eMarketer 2005;
Hoffman, Novak, and Peralta 1999; Rust, Kannan, and Peng
2002). Thus, although it is necessary, investing in back-end
technology to protect consumers’ information and ensure
that e-commerce transactions run smoothly is not sufficient.
Firms, particularly those attempting to convert visitors to
buyers, still face the challenge of establishing consumers’
trust online. Thus, it is important to understand how compa-
nies can communicate their trustworthiness to consumers.
Marketing managers face the challenge of establishing
consumers’ trust in a variety of contexts, but doing so in
computer-mediated environments such as the Internet may
be particularly difficult (Naquin and Paulson 2003). A com-
mon approach is to post explicit statements that assure cus-
tomers that personal data will be discreetly used and pro-
tected (i.e., privacy and security statements). However,
evidence on the effectiveness of such statements is contra-
dictory. Whereas some research has shown that such state-
ments help instill consumer confidence in e-commerce sites
(Palmer, Bailey, and Faraj 2000), others suggest that they
are not necessarily the most important predictor of online
trust (Montoya-Weiss, Voss, and Grewal 2003; Sultan et al.
2002). Findings from a recent large-scale study suggest that
despite consumers’ claims that privacy policies are impor-
tant for establishing credibility, consumers refer instead to
“surface” elements, such as Web site design (Fogg et al.
2002).
In this article, we develop a conceptual framework for
understanding how marketing signals influence consumers’
trust in an e-commerce setting. We clarify important dis-
tinctions related to trust that have been largely overlooked
in the literature but are key to understanding how online
firms can best convert visitors to buyers. Specifically, we
argue that different Web site signals can influence different
beliefs about a firm’s trustworthiness, which in turn have
differential effects on online purchase intentions. Further-
more, these effects might vary according to the consumers’
purpose for visiting the site and the level of risk they per-
ceive in the purchase. Consistent with prior research (e.g.,
McKnight, Choudhury, and Kacmar 2002), we conceptual-
ize online purchase intentions in terms of customer acquisi-
tion—that is, consumers’ intentions to make an initial
online purchase from a firm, despite their online purchase
history with other firms. We begin with a review of the trust
literature. We then present and test our conceptual frame-
work and conclude with a discussion of the theoretical,
managerial, and public policy implications of these
findings.

134 / Journal of Marketing, April 2006
Trust
Trust has been defined as “a willingness to rely on an
exchange partner in whom one has confidence” (Moorman,
Zaltman, and Deshpandé 1992, p. 315); “a generalized
expectancy held by an individual that the word, promise,
oral or written statement of another individual or group can
be relied upon” (Rotter 1980, p. 1); and “a belief in a per-
son’s competence to perform a specific task under specific
circumstances” (Sitkin and Roth 1993, p. 373). Reflected in
these and other definitions of trust is a cognitive aspect (i.e.,
trusting beliefs) and a behavioral aspect (i.e., trusting inten-
tions) (Kim et al. 2004; Moorman, Zaltman, and Deshpandé
1992).
Trusting beliefs represent a “sentiment, or expectation
about an exchange partner’s trustworthiness” (Moorman,
Deshpandé, and Zaltman 1993, p. 315). Although various
trusting beliefs have been studied in the literature, the
majority can be conceptually clustered into three dimen-
sions: ability, benevolence, and integrity (McKnight,
Choudhury, and Kacmar 2002). “Ability beliefs” reflect
consumers’ confidence that the firm has the skills necessary
to perform the job (Mayer, Davis, and Schoorman 1995),
“benevolence beliefs” reflect confidence that the firm has a
positive orientation toward its consumers beyond an “ego-
centric profit motive” (Mayer, Davis, and Schoorman 1995,
p. 717), and “integrity beliefs” reflect confidence that the
firm adheres to a set of moral principles or professional
standards that guide its interactions with customers. These
trusting beliefs are related, yet distinct. For example, con-
sumers may believe that the firm cares about its customers
and thus intends to deliver a smooth, error-free transaction
(i.e., the firm is benevolent), but they may also believe that
the firm lacks the ability to do so. Likewise, although
integrity and benevolence beliefs are similar, the former
focuses on meeting objective standards of corporate citizen-
ship, and the latter focuses on customer welfare that goes
beyond normal business activity. For example, despite con-
sumers’ beliefs that the firm follows a professional code of
conduct (i.e., has integrity), they may still question the
firm’s genuine concern for its customers (i.e., its
benevolence).
Although ability, benevolence, and integrity beliefs are
acknowledged as conceptually distinct (e.g., Kumar, Scheer,
and Steenkamp 1995), they are often combined into a
global measure of trusting beliefs (e.g., Doney and Cannon
1997). Whereas combining these beliefs into a single varia-
ble is a parsimonious approach to studying trust, it can
make it difficult to identify what action should be taken to
build trust (Smith and Barclay 1997). Because a global
measure likely obscures the reason certain signals are more
effective than others in affecting online purchase intentions,
we treat each trusting belief separately.
Trusting intentions represent “a willingness to make
oneself vulnerable to another in the presence of risk” (Kim
et al. 2004, p. 105). What distinguishes trusting intentions
from other types of behavioral intentions is that they
involve risk (Moorman, Zaltman, and Deshpandé 1992). As
reflected in the opening quotation, purchasing online
involves risk, especially when a person lacks experience
with the online firm. Specifically, the consumer must be
willing to transfer resources (e.g., credit card and other per-
sonal information) to the online firm, the consequences of
which could be damaging. For example, among the real
and/or perceived risks are that the firm may overcharge, fail
to deliver the product, deliver an inferior product, or fail to
protect personal information. To the extent that consumers
are concerned about these and other risks of purchasing
online, online purchase intentions reflect trusting intentions.
The distinction between trusting beliefs and trusting
intentions has been acknowledged by some researchers
(e.g., Moorman, Zaltman, and Deshpandé 1992; Sirdesh-
mukh, Singh, and Sabol 2002) but ignored by others who
have studied only trusting beliefs, implicitly assuming that
these beliefs imply trust (e.g., Doney and Cannon 1997;
Ganeson 1994; Kumar, Scheer, and Steenkamp 1995;
Mayer, Davis, and Schoorman 1995; Morgan and Hunt
1994). For example, Morgan and Hunt (1994) argue that
trusting beliefs are sufficient for measuring trust because
such beliefs imply that trusting intentions will follow. In
contrast, Moorman, Zaltman, and Deshpandé (1992) argue
that trust is limited when trusting beliefs do not accompany
a corresponding trusting intention or when trusting inten-
tions occur without corresponding trusting beliefs (e.g.,
under conditions of coercion or limited alternatives). In
other words, these researchers argue that both trusting
beliefs and trusting intentions must be present for trust to
exist. Likewise, we argue that trusting beliefs are a neces-
sary but not sufficient condition for trust to exist, because
increasing trusting beliefs will not always have a corre-
sponding positive effect on trusting intentions.
Conceptual Framework
Drawing from research on trust (Mayer, Davis, and Schoor-
man 1995; Moorman, Deshpandé, and Zaltman 1993), con-
sumer goals (e.g., Hoffman and Novak 1996), and market-
ing signals (e.g., Kirmani and Wright 1989; Prabhu and
Stewart 2001), we develop a conceptual framework for
understanding how different signals influence ability,
benevolence, and integrity beliefs and thus influence online
purchase intentions (see Figure 1). Because our objective is
to understand how to increase consumers’ willingness to
buy online, we focus on those whose goal is most consistent
with buying online, namely, consumers who search for
product information (or searchers; Hoffman and Novak
1996; Moe 2003; Schlosser 2003). Indeed, searchers think
about and are persuaded more by product information
(Schlosser 2003) and have higher visitor-to-buyer conver-
sion rates than those who do not search (Moe 2003). We
begin by examining the relationship between searchers’
trusting beliefs and intentions.
Searchers’Trusting Beliefs and Online Purchase
Intentions
Searching reflects purposive, task-specific behaviors, such
as the planned acquisition of information during prepur-
chase deliberation (Hoffman and Novak 1996; Janiszewski
1998). Similar to those who read a text to find an answer to
a question (Rosenblatt 1978), searchers are likely motivated
to find the right answer efficiently. Such a fact-gathering,

Converting Web Site Visitors into Buyers / 135
Ability
Online purchase intentions
Signals
Trusting Beliefs
Moderators
Trusting Intentions
Benevolence Integrity
Searchers only
Browsers only
Strong privacy/security statement
(Study 2)
Web site investment
(Studies 1–4)
Perceived risk
(Study 4)
Goal:
search or browse
(Study 3)
FIGURE 1
Conceptual Framework of the Effect of Online Signals on Trusting Beliefs and Intentions
knowledge-seeking stance is typically outcome oriented,
concentrated, impersonal, and objective (Rosenblatt 1978).
Given this performance orientation, we expect that when
considering whether to purchase online, searchers will
focus on the trusting belief that is most relevant to perfor-
mance: ability (Mayer, Davis, and Schoorman 1995). If this
is the case, searchers’ ability beliefs should largely influ-
ence their online purchase intentions. In contrast, their
beliefs about the firm’s trustworthiness on non-
performance-related dimensions (i.e., benevolence and
integrity) should have relatively little effect on their online
purchase intentions.
H1: Searchers’ online purchase intentions depend on their
beliefs about the firm’s ability rather than their beliefs
about the firm’s benevolence or integrity.
Signaling Ability Through Web Site Investment
If trust in a firm’s abilities is critical to increasing online
purchase intentions, a fundamental question is, How can
firms use online cues to communicate that their abilities can
be trusted? To address this question, we draw from research
on marketing signals. Signals are the actions or announce-
ments that convey a firm’s abilities and intentions (Porter
1980). Marketers often use observable signals (e.g., price,
warranties, advertising expenditures) to communicate the
level of some unobservable quality (e.g., product quality;
Kirmani and Rao 2000). Signaling may be especially
important in an online purchasing context because of the
inherent asymmetry of relevant information between buyers
and sellers. Specifically, information about the quality of a
given online transaction is generally unobservable by con-
sumers before purchase.
1This assumes conditions of normality. Because a poorly
designed Web site (e.g., one with hyperlinks that do not work)
Investing in Web site design may be one observable sig-
nal that firms can use to communicate their abilities and
boost searchers’ online purchase intentions. Indeed, con-
sumers can distinguish between expensive and inexpensive
marketing tactics, such as ad production elements (Kirmani
and Wright 1989). Moreover, they make inferences about
companies (e.g., the company’s ability to make quality
products and its credibility) based on these perceived mar-
keting expenditures (for a review, see Kirmani and Rao
2000). The attribution literature provides insight into these
findings (Kirmani and Wright 1989). Specifically, when
interpreting others’ performance, people infer that investing
time and energy promotes success (Weiner 1986). Likewise,
consumers will likely infer that a firm that has invested in
Web site design can successfully handle online transactions.
As in prior research, we consider investment in broad
terms (Kirmani and Wright 1989); that is, investment
reflects expenditures of time, money, and effort to Web site
design. Importantly, it refers to investments in the front-end
(design) elements of a Web site (i.e., its observable charac-
teristics) and not back-end technologies, such as order ful-
fillment software, security encryption, and firewall capabil-
ities, which are typically unobservable before purchase. Yet
because ability is a stable, internal characteristic (Weiner
1972), people will likely generalize their trust in a firm’s
ability in one area (design) to other related areas (e.g., order
fulfillment). Thus, instead of being purely cosmetic, Web
site design likely communicates important performance
information. However, it likely communicates less about the
firm’s goodwill, ethics, values, or intentions to mislead than
it does about the firm’s ability.1Consequently, consumers

136 / Journal of Marketing, April 2006
likely violates consumers’ expectations about norms of conduct
for e-commerce firms, it will likely raise concerns about the firm’s
benevolence and integrity.
will likely use perceived Web site investment to infer a
firm’s ability more than its benevolence or integrity. If this
is the case and ability beliefs largely influence searchers’
online purchase intentions (H1), it follows that
H2: Searchers’ online purchase intentions will be higher at a
high-investment Web site than at a low-investment Web
site.
H3: Beliefs about the firm’s ability will mediate the relation-
ship between site investment and searchers’ online pur-
chase intentions.
The first two studies test these hypotheses, and the last
two studies examine two potential moderators (goal and
perceived risk). Furthermore, to test the generalizability of
these effects, we varied across studies the samples recruited
(students versus nonstudents), companies (a fictitious ver-
sus well-known firm), and products (home furnishings and
accessories versus cameras).
Study 1
Method
Sample and design. The sample consisted of 111
respondents who participated in exchange for $10 and were
recruited through an electronic and a printed newsletter dis-
tributed to university employees. The sample was 68%
female, with a mean age of 37.5 and a mean income of
$35,000 to $49,999. Respondents had a median education
of four years of college and used the Internet an average of
four to six times per week. We randomly assigned respon-
dents to a high- or low-investment site.
Web site investment manipulation. We manipulated Web
site investment through the presence of sophisticated Web
site technology and visual design elements. Specifically, the
high-investment site had a white background, sophisticated
fonts (images for the navigation bar; Garamond font), and
an enhanced zoom feature created with Design Within
Reach. This enabled users to zoom in on any part of an
image and to choose among three preselected zooms that
executed automatically with a single click. In contrast, the
low-investment site featured the default background color
and font (gray; Times New Roman) and a limited zoom fea-
ture, which, when clicked, simply provided a larger view of
the focal product. The content and layout of both sites were
identical.
To test the effectiveness of this manipulation, 43 under-
graduates viewed screen captures of the homepage (font
and background color were used to manipulate investment)
and a zoom page (technology was used to manipulate
investment). The order was counterbalanced, and partici-
pants viewed only those pages specific to the high- or low-
investment site. After viewing each page, participants
reported how much time, effort, and money they believed
the firm invested in each page on a scale from 1 (“very lit-
tle”) to 7 (“a great deal”). We averaged the responses to
these items (αs > .94) and analyzed them with a 2 (invest-
ment: high versus low) ×2 (page: zoom versus home) ×2
(order: viewed home or zoom page first) analysis of vari-
ance (ANOVA). In support of the investment manipulation,
participants perceived the high-investment site as requiring
greater investment than the low-investment site (Ms = 4.09
versus 3.18; F(1, 39) = 9.02, p< .01). Furthermore, the
effectiveness of the investment manipulation was unaffected
by the type or order of page viewed (Fs(1, 39) < 3.35, not
significant [n.s.]). Thus, it appears that font, background
color, and use of technology communicate investment. Par-
ticipants also reported how informative, entertaining, and
well organized they found the pages to be on the same
seven-point scale. As we expected, investment did not affect
these variables (Fs(1, 39) < 1.97, n.s.).
Procedure. Participants sat at a computer and received a
paper booklet. The first page instructed participants that
they would be visiting a site for a new firm called Urban-
Furniture (UF) and to limit their visit to the homepage and
living-room sections of the site. All participants were told to
imagine that they wanted to purchase contemporary furni-
ture and were considering UF. As in prior research
(Schlosser 2003), to instill a searching goal, participants
were asked to write down two questions they had for UF
about its products before visiting the site.
Participants then visited either the high- or the low-
investment site, which was preloaded on their computer.
Afterward, they completed the survey, which contained
three items that measured their online purchase intentions
(α= .91) and a modification of Mayer and Davis’s (1999)
scale of trustworthiness (for the measures used in this and
the other studies, see the Appendix). This scale measured
beliefs about UF’s ability (α= .90), benevolence (α= .88),
and integrity (α= .71). Then, to test the effectiveness of the
investment manipulation in the main experiment, partici-
pants completed the three-item Web site investment scale
(see the Appendix; α= .95).
At the end of the survey, participants reported their edu-
cation and income levels as well as how often they used the
Internet to purchase goods in the last six months on a scale
ranging from 1 (“not at all”) to 7 (“quite often”). We
included these measures to control for individual differ-
ences in Internet experience both directly (i.e., through self-
reported use of the Web) and indirectly (i.e., using demo-
graphic variables associated with Web use). To control for
variance due to mechanical error, we asked participants to
report the extent to which they encountered problems at the
UF site (e.g., error messages, server delays, crashing) on a
scale from 1 (“not at all”) to 7 (“quite often”).
Results
Manipulation checks. We analyzed the investment
manipulation with a one-way analysis of covariance
(ANCOVA), controlling for reported problems with the site,
prior Web purchase history, education, and income. In sup-
port of the manipulation, perceived Web site investment was
higher among those in the high-investment condition than
among those in the low-investment condition (Ms = 3.46
versus 1.97; F(1, 108) = 32.45, p< .0001).
Trusting beliefs and online purchase intentions. We
used hierarchical regression to test H1. We first modeled

Converting Web Site Visitors into Buyers / 137
online purchase intentions as a function of ability beliefs.
As we predicted, ability significantly influenced these
intentions (β= .27, t(108) = 2.88, p< .005; R2= .07,
F(1, 108) = 8.31, p< .01; see Table 1). Furthermore, the
addition of benevolence and integrity beliefs to the model
did not contribute significantly to explaining searchers’
online purchase intentions (ΔR2= .007, F(2, 106) < 1). We
replicated this pattern of results with a stepwise regression
analysis, which identifies the best subset of belief variables
that predict online purchase intentions. Thus, regardless of
the regression procedures we used, the results are consistent
with H1. Furthermore, given the inherent correlation
between trusting beliefs, we tested for multicollinearity by
examining the maximum variance inflation factor (VIF).
Multicollinearity is problematic for interpreting regression
analyses when the maximum VIF is greater than 10 (Neter,
Wasserman, and Kutner 1990). We found that multi-
collinearity was not an issue in the preceding analysis (the
maximum VIF = 3.10).
Web site investment, trusting beliefs, and online pur-
chase intentions. We analyzed ability beliefs with a one-
way ANCOVA. As we expected, ability beliefs were higher
for those who visited the high-investment site than for those
who visited the low-investment site (Ms = 2.41 versus 1.80;
F(1, 109) = 13.27, p< .005). In contrast, Web site invest-
ment did not affect benevolence or integrity beliefs
(Fs(1, 109) < 2.00, n.s.). Thus, Web site investment is more
effective in communicating the trustworthiness of a firm’s
ability than its benevolence and integrity.
In support of H2, online purchase intentions were higher
among those who visited the high-investment site than
among those who visited the low-investment site (Ms =
–.13 versus –.78; F(1, 109) = 4.06, p< .05). To test whether
ability beliefs mediated this effect (H3), we added ability
beliefs as a covariate to the ANCOVA. Consistent with the
requirements for mediation (Baron and Kenny 1986), abil-
ity was significant (F(1, 108) = 4.31, p< .05), and the
investment effect became nonsignificant (F(1, 108) = 1.48,
n.s.). We found further support for H3using the criteria that
Sobel (1982) endorsed for testing mediation (Goodman I
test statistic = 2.22; p< .05). Because investment did not
affect benevolence and integrity beliefs, these beliefs cannot
be considered mediators.
Conclusions
Study 1 provides support for H1–H3. Specifically,
searchers’ online purchase intentions were influenced by
their trust in the firm’s ability rather than their trust in its
benevolence and integrity. As a result, ability signals (i.e.,
Web site investment) influenced their online purchase inten-
tions. Furthermore, their trust in the firm’s ability mediated
this effect.
It is possible that searchers’ online purchase intentions
were influenced by their ability beliefs rather than by their
benevolence and integrity beliefs because there were no sig-
nals regarding the firm’s benevolence and integrity. In the
presence of such signals, ability beliefs may have less influ-
ence. Indeed, the impact of a given signal is weakened
when other, more relevant signals are present (Kirmani and
Wright 1989). If benevolence and integrity signals are more
relevant, the effect of investment on searchers’ online pur-
chase intentions should be weaker when such signals are
present. However, if ability is more relevant, investment
should affect searchers’ online purchase intentions regard-
less of whether benevolence and integrity signals are pre-
sent. We directly test this in Study 2 by manipulating the
presence and strength of a firm’s privacy and security state-
ment. Because benevolence represents the firm’s orientation
toward customers and integrity represents whether the firm
will do what it promises (Mayer, Davis, and Schoorman
1995), one method of signaling a firm’s benevolence and
integrity may be through formal statements of its intentions
to consumers, such as through privacy and security state-
ments. If so, such statements should affect consumers’
beliefs about a firm’s benevolence and integrity rather than
its ability. Yet if ability is a stronger driver of searchers’
TABLE 1
Hierarchical Regression Analysis of Trusting Beliefs on Online Purchase Intentions
Model 1 Model 2
Study Goal Variable ββR2F for R2ββΔΔR2F for ΔΔR2
1 Searchers Ability .27* .07 08.31* .19*.01 .38*
Benevolence .10*
Integrity .02*
2 Searchers Ability .39* .15 15.57* .39* .00 .08*
Benevolence .06*
Integrity –.06*
3 Searchers Ability .42* .18 15.70* .43* .03 1.50*
Benevolence .15*
Integrity –.20*
Browsers Ability .15*.02 01.62 .10*.10 4.28*
Benevolence .33*
Integrity –.01*
*
p
<.05.

138 / Journal of Marketing, April 2006
2Our analysis was limited to either strong or weak privacy and
security statements. Thus, for ease of exposition, we use the term
“privacy/security” to describe levels of privacy and security.
online purchase intentions (H1), investment should affect
searchers’ online purchase intentions regardless of whether
a privacy and security statement is present. That is, H2and
H3should be supported even when such a statement is
provided.
Study 2
Method
Sample and design. A total of 79 undergraduate students
participated in exchange for extra course credit. We ran-
domly assigned participants to one of six conditions in a 2
(investment) ×3 (privacy/security statement: strong or weak
versus absent) design.2We included the absent condition to
examine whether the presence of a strong statement is better
than having no statement. We included the weak condition
to examine whether merely having a privacy/security state-
ment might signal benevolence and integrity or whether the
contents of the statement influence such beliefs.
Privacy/security statement manipulation. We con-
structed the strong and weak privacy/security statements on
the basis of a content analysis of the privacy/security state-
ments gathered from more than 25 sites. For the strong
statement, explicit information was available about how UF
collects and uses customer information. There was also a
promise of confidentiality, a contact number, an opt-in fea-
ture, encryption information, and a 100% guarantee against
information theft. In contrast, the weak statement informed
the consumer that personal information would be collected
and made available to other vendors that “are offering prod-
ucts we feel are of interest to you.” There was no opportu-
nity to opt in or out of such correspondence. Consumers
were also informed that UF “tries to safely transmit your
account information.” No account protection or guarantee
was offered.
To pretest this manipulation, 37 undergraduates read
either the strong or the weak statement and rated its strength
on six items. Among the items were “I believe Urban-
Furniture is concerned about my privacy” and “I believe
that Urban-Furniture is concerned about the security of my
financial information.” Participants responded on a scale
ranging from 1 (“disagree strongly”) to 7 (“agree
strongly”), and we averaged the responses (α= .94). In sup-
port of the privacy/security manipulation, participants
agreed more that UF would preserve their privacy and secu-
rity when they read the strong privacy/security statement
than when they read the weak statement (Ms = 5.56 versus
2.61; F(1, 36) = 100.53, p< .01).
Procedure. The procedure and survey were the same as
in Study 1, with a few exceptions. To increase involvement,
participants were told to imagine that they accepted a job in
Manhattan after graduation and were searching for living-
room furniture for their apartment. After viewing the site,
participants in the strong and weak privacy/security condi-
tions read these statements before completing the survey. To
test whether reading such statements might artificially
increase participants’ risk perceptions of shopping online,
we added seven items to the end of the survey that mea-
sured such concerns (see the Appendix). We averaged
responses to provide a perceived risk score (α= .89).
Because the sample is homogeneous in terms of education
and income, we deleted these demographic questions. In
addition, because we measured online purchase experience
in Study 1 with a single item that captures only recent
online purchase experience, we added an item that mea-
sures general online purchase experience (i.e., how often
participants shop online) on a scale from 1 (“not at all”) to 7
(“quite often”). We averaged these items to capture online
purchase experience (r = .78).
Results
Manipulation checks. In support of the investment
manipulation, a 2 (investment) ×3 (privacy/security state-
ment) ANOVA yielded a significant investment effect
(F(1, 73) = 6.84, p= .01): Participants perceived the high-
investment site as requiring a greater investment than the
low-investment site (Ms = 3.36 versus 2.48). No other
effects were significant (Fs(1, 73) < 1).
To examine whether reading a privacy/security state-
ment might increase participants’ perceived risks of shop-
ping online, we compared risk perceptions using a 2 ×3
ANOVA. None of the effects were significant (the invest-
ment effect: F(1, 73) = 1.22, n.s.; the direct and interactive
effects of the privacy/security statement: Fs(2, 73) < 2.11,
n.s.). On average, participants perceived buying online as
risky (M = 5.71, which is significantly higher than the mid-
point of 4; higher numbers reflect greater perceived risk,
t(78) = 14.16, p< .01).
Trusting beliefs and online purchase intentions. As in
Study 1, we tested H1using hierarchical regression. As we
predicted, ability beliefs influenced searchers’ online pur-
chase intentions (β= .39, t(77) = 3.73, p< .005; R2= .15,
F(1, 77) = 15.57, p< .01, see Table 1). Furthermore, the
addition of benevolence and integrity beliefs to the model
did not significantly contribute to explaining online pur-
chase intentions (ΔR2= .002, F(2, 75) < 1). A stepwise
regression analysis yielded the same results. Multicollinear-
ity was not a problem (the maximum VIF = 2.58).
Web site investment and trusting beliefs. Here and else-
where, we analyzed the data with a 2 (investment) ×3
(privacy/security statement) ANCOVA, controlling for
online purchase experience and problems with the site.
Ability beliefs were higher for those who visited the high-
investment site than for those who visited the low-
investment site (Ms = 2.68 versus 2.13; F(1, 69) = 6.54, p<
.05), thus replicating the results of Study 1. No other effects
on ability beliefs were significant (Fs(1, 69) < 2.34, n.s.).
Furthermore, as we expected, Web site investment did not
affect benevolence or integrity beliefs (Fs(1, 68) < 2.29,
n.s.).
Whereas investment appears to communicate a firm’s
ability but not its benevolence and integrity, privacy/
security statements appear to communicate benevolence
and integrity but not ability: The privacy/security effect was
significant for benevolence beliefs (F(2, 69) = 3.30, p< .05)

Converting Web Site Visitors into Buyers / 139
and integrity beliefs (F(2, 69) = 4.03, p< .05) but not for
ability beliefs (F(2, 69) = 2.34, n.s.). Benevolence and
integrity beliefs were significantly higher among those who
read a strong statement than among those who read a weak
statement (benevolence: Ms = 3.04 versus 2.65; F(2, 68) =
2.89, p< .05 [one-tailed test]; integrity: Ms = 3.19 versus
2.66; F(2, 68) = 7.12, p< .05). These beliefs were also
higher for those who received a strong statement than for
those who received no statement (benevolence: Ms = 3.04
versus 2.44; F(2, 68) = 6.55, p< .05; integrity: Ms = 3.19
versus 2.76; F(2, 68) = 5.02, p< .05). However, the mere
presence of a privacy/security statement does not appear to
signal a firm’s benevolence and integrity; the difference
between a weak statement and no statement was not signifi-
cant at p< .05. It appears that both the presence and the
strength of the statement signal the firm’s benevolence and
integrity.
Web site investment and online purchase intentions. We
analyzed online purchase intentions with a 2 ×3 ANCOVA.
In support of H2, online purchase intentions were higher
among those who visited the high-investment site than
among those who visited the low-investment site (Ms =
–.16 versus –.79; F(1, 69) = 3.06, p< .05 [one-tailed test]).
A privacy/security statement did not moderate this effect
(F(2, 69) < 1). Thus, regardless of whether benevolence and
integrity signals (i.e., privacy/security statements) were pre-
sent, ability signals (i.e., Web site investment) significantly
influenced searchers’ online purchase intentions.
The only other significant effect was a privacy/security
effect (F(1, 69) = 4.95, p= .01). In support of the argument
that benevolence and integrity signals (e.g., a strong
privacy/security statement) should have little effect on
searchers’ online purchase intentions, online purchase
intentions did not differ between those who received the
strong privacy/security statement and those who received no
statement (Ms = –.36 versus .09; F(1, 46) = 1.10, n.s.).
However, online purchase intentions were lower among
those who received a weak privacy/security statement than
among those who received a strong statement (Ms = –1.15
versus –.36; F(1, 41) = 3.82, p< .05 [one-tailed test]) or no
statement (Ms = –1.15 versus .09; F(1, 49) = 8.58, p< .01).
This finding is consistent with existing research that nega-
tive cues regarding a firm’s character tend to be unexpected,
violating established norms about “business as usual”
(Garfinkel 1963). In such situations, people appear unwill-
ing to buy online from the firm.
To test whether ability beliefs mediate the investment
effect on online purchase intentions (H3), we added ability
beliefs as a covariate to the 2 ×3 ANCOVA. Consistent
with the requirements for mediation, ability was a signifi-
cant covariate (F(1, 68) = 8.47, p< .01), and the investment
effect became nonsignificant (F(1, 68) < 1). We replicated
this finding with the Sobel (1982) test (Goodman I test sta-
tistic = 1.93; p= .05). As in Study 1, benevolence and
integrity beliefs cannot be considered mediators, because
investment did not significantly affect these beliefs. How-
ever, it is possible that benevolence and integrity beliefs
mediate the privacy/security effect on online purchase
intentions. To test this, we added these beliefs as covariates
to the 2 ×3 ANCOVA. Inconsistent with the requirements
for mediation but in support of the prediction that searchers’
online purchase intentions are influenced by their ability
rather than by their benevolence and integrity beliefs (H1),
neither of these beliefs were significant (Fs(1, 67) < 2.02,
n.s.), and the privacy/security effect on online purchase
intentions remained significant (F(2, 67) = 5.75, p< .01).
Conclusions
Replicating the results of Study 1, we found that searchers’
ability beliefs, rather than their benevolence and integrity
beliefs, influenced their online purchase intentions. As a
result, ability signals (Web site investment) influenced their
online purchase intentions more than did signals of the
firm’s benevolence and integrity (the presence of a strong
privacy/security statement).
Because the objective of this research is to predict
online purchase intentions, thus far our focus has been on
people whose goals are most consistent with prepurchase
deliberation, namely, searchers. For those with this goal,
ability beliefs are a stronger driver of online purchase inten-
tions than are benevolence and integrity beliefs. However,
there may be goals that highlight the importance of a differ-
ent component of trust than ability beliefs. Specifically, for
those whose goal for visiting the site is more personal and
less outcome oriented (i.e., browsers), a different pattern of
results may emerge. Browsing is a moment-by-moment
activity rather than a search process for a specific piece of
information (Janiszewski 1998). As such, it is exploratory
(Moe 2003) and reflects recreational behavior (Hoffman
and Novak 1996). Similar to those who read a text for enter-
tainment (Rosenblatt 1978), browsers are likely focused on
what they are “living through” during their site visits. Thus,
whereas searchers tend to be more objective and outcome
oriented and thus are likely to disengage from having a per-
sonal experience with the site, browsers’ experiences are
likely more personal.
We argue that distinguishing between these goals has
important implications for the relative impact of each trust-
ing belief on online purchase intentions. Whereas searchers
focus on performance and thus base their online purchase
intentions on their ability beliefs, browsers likely have a
more personal experience with the site and thus will be
influenced by the most personal aspect of trust: benevo-
lence beliefs. Benevolence beliefs reflect consumers’ beliefs
that the firm cares about their welfare and well-being (e.g.,
“I trust that the firm is concerned about my wants and
needs, even if doing so results in profit reductions”),
whereas integrity beliefs reflect beliefs about the firm’s
moral standards, regardless of how it feels about the indi-
vidual (e.g., “I trust that the firm is guided by sound busi-
ness principles and standards”). Likewise, ability beliefs
reflect beliefs about the firm’s expertise, regardless of how
it feels about the individual (e.g., “I trust that the firm has
the necessary skills to be successful”). Thus, because
browsers’ experiences are highly personal, their online pur-
chase intentions should depend on the most personal
dimension of trust (benevolence) rather than on the less per-
sonal dimensions (i.e., ability and integrity).

140 / Journal of Marketing, April 2006
H4: Browsers’ online purchase intentions depend on their
beliefs about the firm’s benevolence rather than their
beliefs about the firm’s ability or integrity.
We also propose that the distinction between searching
and browsing has important implications for the influence
of Web site investment on online purchase intentions.
Whereas prior research has demonstrated the importance of
separating signals from interpretation, because not everyone
interprets a signal in the same manner (Prabhu and Stewart
2001), it may be equally important to separate signal inter-
pretation from response because even similarly interpreted
signals may lead to meaningfully different responses. In
particular, we argue that though both searchers and
browsers will likely interpret investment as signaling the
firm’s abilities, the impact of this belief on their online pur-
chase intentions will vary. Specifically, if searchers’ online
purchase intentions are affected by their ability beliefs, abil-
ity signals (or Web site investment) should influence their
online purchase intentions. However, if browsers’ online
purchase intentions are unaffected by their ability beliefs,
ability signals (even if interpreted as such) should have rel-
atively little influence on their online purchase intentions.
Consequently, we hypothesize the following:
H5: Web site investment influences searchers’ but not
browsers’ online purchase intentions.
Because the firm studied thus far was an unknown Internet-
only firm, an additional objective of Study 3 was to repli-
cate the results for searchers with a well-established firm
that sells a different set of products (electronics rather than
home furnishings and accessories).
Study 3
Method
Sample and design. A total of 152 undergraduate stu-
dents participated in exchange for extra course credit. We
randomly assigned participants to one of four conditions in
a 2 (investment) ×2 (goal: searching versus browsing)
experimental design.
Web site investment manipulation. In contrast to Studies
1 and 2, we manipulated investment using only technology.
Specifically, for the high-investment site, we used Macro-
media’s Shockwave technology to allow participants to
experience an online demonstration and to roll over the
product image to gather additional information about its
features. For the low-investment site, we conveyed the same
information through text and static graphics rather than
through the use of this technology.
Procedure. The procedure was similar to that used in
Study 2, with a few exceptions. Each participant was seated
at a computer terminal, which contained the instructions,
the site, and the survey. Participants were told that they
would be visiting a portion of Kodak’s site devoted to a spe-
cific model of digital camera. Those assigned to browse
were instructed to “have fun, looking at whatever you con-
sider interesting and/or entertaining.” Those assigned to
search were instructed that before doing so, they should
type two questions they have for Kodak about this digital
camera. To ensure that both groups would be attending to
information relevant to them, we did not specify what to
look for. These instructions are identical to those used in
prior research to instill a searching versus browsing goal
(Schlosser 2003). Participants then visited the site. To con-
trol for the amount of time browsers versus searchers spent
at the site and any possible impact of this on the dependent
variables, all participants viewed the site for five minutes,
which is comparable to the time imposed in prior research
(Schlosser 2003). We did not investigate privacy/security
statements in this study.
After visiting the site, participants completed the online
survey, which was identical to that used in Study 2, except
that the risk questions were replaced by the goal manipula-
tion check. Participants were asked the extent to which their
time at the site was spent looking for specific information (a
searching activity) or looking to be entertained (a browsing
activity) on a scale from 0 (“not at all”) to 5 (“a lot”). In
addition to the Web site investment items, participants rated
how informative they found the site to be on a scale from 1
(“very little”) to 7 (“a great deal”). We also made a slight
change to the trusting beliefs measure: We asked partici-
pants to focus on the trustworthiness of Kodak’s Internet
marketing department. We made this change to direct par-
ticipants’ attention toward the e-commerce side of the firm
rather than the firm in general or the brand. For example,
participants may trust the offline firm’s ability, benevolence,
and integrity, but they may not believe that its Internet mar-
keting managers share these same qualities.
Results
Manipulation checks. We analyzed the manipulation
check items with a 2 (investment) ×2 (goal) ANOVA. In
support of the goal manipulation, searchers reported spend-
ing more time searching (Ms = 2.52 versus 1.86;
F(1, 150) = 14.08, p< .01) and less time browsing (Ms =
1.31 versus 1.97; F(1, 150) = 13.80, p< .01) than did
browsers. In support of the investment manipulation, the
high-investment site was perceived as a greater investment
than the low-investment site (Ms = 4.51 versus 2.58;
F(1, 148) = 88.76, p< .01). This effect emerged for both
browsers (Ms = 4.15 versus 2.34; F(1, 74) = 20.19, p< .01)
and searchers (Ms = 4.86 versus 2.10; F(1, 74) = 85.14, p<
.01). No other effects were significant. The sites did not dif-
fer in perceived informativeness (F(1, 148) < 1).
Trusting beliefs and online purchase intentions. To test
whether ability beliefs explain searchers’ online purchase
intentions (H1) and whether benevolence beliefs explain
browsers’ online purchase intentions (H4), we conducted
separate hierarchical regression analyses for searchers and
browsers; we modeled ability beliefs as a function of online
purchase intentions (Model 1) before adding benevolence
and integrity beliefs (Model 2). As we predicted, ability
beliefs were significantly related to searchers’ online pur-
chase intentions (β= .42, t(74) = 3.96, p< .01; R2= .18,
F(1, 74) = 15.70, p< .01; see Table 2). The addition of
benevolence and integrity beliefs to the model did not sig-
nificantly contribute to searchers’ online purchase inten-
tions (ΔR2= .03, F(2, 72) = 1.50, n.s.). For browsers, ability

Converting Web Site Visitors into Buyers / 141
TABLE 2
Study 4: Hierarchical Regression Analysis of Trusting Beliefs on Online Purchase Intentions When Risk Is
High Versus Low
A: Results from a Hierarchical Regression Analysis
Model 1 Model 2
Social Risk Variable ββR2F for R2ββΔΔR2F for ΔΔR2
High Product attitudes .60** .53 7.91** .58** .02 1.80
Autotelic NFT –.09** –.14**
Instrumental NFT –.03** –.05**
Online buying .16** .06**
Site problems .10** .17**
Ability .29** .24**
Integrity .18**
Low Product attitudes .44** .27 2.40** .41** .02 1.12
Autotelic NFT –.12** –.10**
Instrumental NFT .02** .00**
Online buying .05** .14**
Site problems .09** .01**
Ability .08** .17**
Integrity –.17**
B: Results from a Stepwise Regression Analysis
High Product attitudes .64** .41 33.92** .56** .07 5.94**
Ability .27**
Low Product attitudes .48** .23 13.47**
*
p
< .10.
**
p
<.05.
Notes: NFT = Need for touch.
beliefs alone did not significantly explain their online pur-
chase intentions (R2= .02, F(1, 74) = 1.62, n.s.). However,
the addition of benevolence and integrity beliefs to the
model significantly contributed to browsers’ online pur-
chase intentions (ΔR2= .10, F(2, 72) = 4.28, p< .05). Con-
sistent with H4, browsers’ benevolence beliefs were signifi-
cantly related to their online purchase intentions (β= .33,
t(74) = 2.70, p< .01), whereas ability and integrity beliefs
were not (t(74) < 1). Multicollinearity was not a problem in
the analyses (maximum VIFs < 1.50).
Web site investment, trusting beliefs, and online pur-
chase intentions. We analyzed each belief with a 2 ×2
ANOVA. As we expected, ability beliefs were higher
among those who visited the high-investment site than
among those who visited the low-investment site (Ms =
3.51 versus 2.99; F(1, 148) = 21.74, p< .01). Investment
did not affect benevolence and integrity beliefs
(Fs(1, 148) < 1). Furthermore, people’s goals for visiting
the site neither directly affected nor interacted with invest-
ment to influence their ability, benevolence, or integrity
beliefs (Fs(1, 148) < 2.27, n.s.). Thus, investment appears to
signal a specific trusting belief (i.e., ability rather than
benevolence or integrity), replicating the results of Studies
1 and 2 with a different set of stimuli. Moreover, this find-
ing persisted despite differences in people’s goals.
In support of H5, searchers had higher online purchase
intentions after visiting the high-investment site than after
visiting the low-investment site (Ms = –.03 versus –.76;
F(1, 74) = 4.92, p< .05), whereas browsers’ online purchase
intentions were unaffected by investment (Ms = –.48 versus
–.08 at the high- and low-investment sites; F(1, 74) = 1.26,
n.s.). This investment ×goal interaction was significant
(F(1, 148) = 5.56, p< .05).
In addition to demonstrating that the site effect on
online purchase intentions varies across goals, we tested
whether the mediating effects of ability vary across goals
using the procedure that Baron and Kenny (1986) outline.
Specifically, we regressed online purchase intentions on
goal, site, goal ×ability, and site ×ability, and we found a
significant goal ×ability effect (t(146) = 1.82, p< .05 [one-
tailed test]). For searchers, mediation was supported: Abil-
ity was significant (F(1, 73) = 10.45, p< 01), and the
investment effect became nonsignificant (F(1, 73) < 1). A
Sobel (1982) test supports this finding (Goodman I test sta-
tistic = 2.74, p< .01). However, for browsers, mediation
was not supported: Ability had little effect on online pur-
chase intentions (F(1, 73) = 2.64, n.s.). Furthermore,
because investment did not affect benevolence and integrity
beliefs, they cannot be considered mediators.
Conclusions
In Study 3, we examined a boundary condition for the effect
of Web site investment on online purchase intentions: con-
sumers’ goals for visiting sites. We found that the effects of
Web site investment and ability on online purchase inten-
tions are specific to searchers and do not generalize to
browsers. For browsers, the most personal component of
trust (i.e., benevolence rather than ability) influences their

142 / Journal of Marketing, April 2006
online purchase intentions. Consequently, although
browsers recognized that Web site investment signals abil-
ity, it had relatively little influence on their online purchase
intentions.
A remaining question is whether the findings are really
a matter of trust. Recall that trusting intentions involve risk
(Moorman, Zaltman, and Deshpandé 1992), which causes
people to consult their trusting beliefs to determine whether
to perform the trusting behavior. If the findings are driven
by trust, Web site investment should influence consumers’
online purchase intentions under conditions of risk.
Although buying online is typically risky, certain situational
factors (e.g., buying a gift for a significant versus an
insignificant other) can make buying online more or less
risky. When there is relatively little risk, buying online
should involve relatively little trust. Consequently, people
are less likely to consult their trusting beliefs when deciding
how to act. Indeed, the degree of trust necessary to influ-
ence behavior has an approximate linear relationship to the
degree of risk involved (Corritore, Kracher, and Wieden-
beck 2003; Mayer, Davis, and Schoorman 1995). Thus,
when the purchasing scenario involves relatively little risk,
ability beliefs (and signals designed to influence such
beliefs) should have relatively little effect on searchers’
online purchase intentions.
H6: Ability beliefs affect searchers’ online purchase intentions
only when buying involves risk.
H7: Web site investment affects searchers’ online purchase
intentions when buying involves risk.
Another objective for Study 4 was to test the importance
of trust in determining online purchase intentions versus
online purchase experience and a desire to examine prod-
ucts physically before purchase. For people who are moti-
vated to touch products, barriers to touch can decrease con-
fidence in product evaluation, though conveying haptic
information through text and graphics can help (Peck and
Childers 2003b). If trust is critical in determining online
purchase intentions, ability beliefs should be related to
online purchase intentions even when we account for these
other factors.
Study 4
Method
Sample and design. A total of 98 undergraduate students
participated in exchange for extra course credit. We ran-
domly assigned them to one of four conditions in a 2
(investment: high- versus low-investment site) ×2 (risk:
high versus low) factorial design. They visited the UF site
used in Studies 1 and 2.
Risk manipulation. We manipulated risk by varying
social risk. Specifically, the high-risk scenario was as fol-
lows: “Imagine that you graduated and obtained your dream
job at a firm. You are invited to your boss’ house-warming
party, which takes place in a week.” The low-risk scenario
was as follows: “Imagine that you are invited to a former
roommate’s house-warming party, which takes place in a
month.” Participants in both scenarios were told that this
person likes modern furniture and accessories, such as those
3Moorman, Deshpandé, and Zaltman (1993) do not measure
benevolence beliefs; thus, we do not address them here.
offered at UF’s site, and that they intend to buy a home
accessory (e.g., a vase) as a house-warming gift for this
person.
To test the effectiveness of these scenarios, 41 under-
graduate students read either scenario and then rated how
nervous and concerned they would be about making this
purchase and how risky they considered this purchase on a
scale from 1 to 7 (higher numbers reflected greater risk).
We averaged these items to form a perceived risk score (α=
.76). As we expected, perceived risk was higher among
those who read the high-risk scenario than among those
who read the low-risk scenario (Ms = 4.22 versus 3.12;
F(1, 37) = 7.12, p= .01). Participants then looked at screen
captures of the high- or low-investment site and answered
the Web site investment items (α= .94; see the Appendix).
As we expected, perceived investment was higher for the
high-investment site than for the low-investment site (Ms =
5.12 versus 3.81; F(1, 37) = 14.78, p< .01). Moreover, risk
did not directly affect or moderate these investment percep-
tions (Fs(1, 37) < 1.39, n.s.).
Procedure. The procedure and survey were the same as
that used in Study 2, with a few exceptions. At the begin-
ning of the experiment, participants read either the high- or
the low-risk scenario before receiving the search instruc-
tions. Participants did not receive a privacy/security
statement.
Unlike Study 2, online purchase intentions were specific
to home accessories (the gift they were to buy). Participants
also reported their attitudes toward UF’s home accessories
to control for any individual differences in their liking of
UF’s offerings (see the Appendix). In addition, to test the
robustness of our effects related to ability versus integrity
beliefs, we replaced Mayer and Davis’s (1995) scale with
an adaptation of Moorman, Deshpandé, and Zaltman’s
(1993) ability and integrity items.3Three items measured
ability beliefs (α= .93), and two items measured integrity
beliefs (r = .31; see the Appendix). Finally, to test the
impact of trust on online purchase intentions relative to con-
sumers’ desire to touch products before purchase, we
administered Peck and Childers’s (2003a) need for touch
(NFT) scale, which measures the need for autotelic and
instrumental touch (see the Appendix).
Results
Trusting beliefs and online purchase intentions. To test
H6, we conducted a hierarchical regression analysis sepa-
rately for searchers in the high- and low-risk conditions; we
modeled ability beliefs and variables believed to influence
online purchase intentions (attitudes toward home acces-
sories, both types of NFT, online purchase experience, and
site problems) as a function of online purchase intentions
(Model 1) before adding integrity beliefs (Model 2). As we
predicted, for searchers in the high-risk condition, ability
beliefs were significantly related to online purchase inten-
tions (β= .29, t(48) = 2.39, p< .05; R2= .52, F(6, 43) =
7.91, p< .01; see Table 2). The only other significant varia-
ble was attitudes toward home accessories (β= .60, t(48) =

Converting Web Site Visitors into Buyers / 143
5.06, p< .01). The addition of integrity beliefs to the model
did not significantly contribute to explaining online pur-
chase intentions (ΔR2= .02, F(1, 42) < 1). For searchers in
the low-risk condition, the only significant variable from
Model 1 was attitudes toward home accessories (β= .44,
t(45) = 2.88, p< .01; R2= .27, F(6, 40) = 2.40, p< .05). The
addition of integrity beliefs did not significantly contribute
to explaining online purchase intentions (ΔR2= .02,
F(1, 39) = 1.12, n.s.). Consistent with H6, ability beliefs
influenced online purchase intentions only when risk was
high. When risk was low, neither ability nor integrity beliefs
could explain online purchase intentions. Moreover, ability
beliefs is an important variable in explaining searchers’
trusting intentions: In the high-risk condition, ability beliefs
explained more variance in their intentions to buy online
than did individual differences in NFT or online purchase
experience. A stepwise regression led to the same conclu-
sions. Furthermore, multicollinearity was not an issue: The
maximum VIFs were below 1.99.
Web site investment, trusting beliefs, and online pur-
chase intentions. For both beliefs, we conducted a 2 ×2
ANCOVA. As we expected, ability beliefs were higher
among those who visited the high-investment site than
among those who visited the low-investment site (Ms =
4.01 versus 2.84; F(1, 90) = 14.82, p< .01). Investment did
not affect integrity beliefs (F(1, 90) = 3.30, n.s.). Further-
more, risk had neither a direct nor a moderating effect on
either belief (Fs(1, 90) < 1). Thus, regardless of purchase
risk, investment communicates a firm’s ability.
For online purchase intentions for home accessories (the
gift), a 2 ×2 ANCOVA yielded a significant investment ×
risk interaction (F(1, 88) = 6.06, p< .05). Consistent with
H7, when risk was high, searchers’ online purchase inten-
tions were higher at the high-investment site than at the
low-investment site (Ms = .76 versus –.38; t(49) = 2.64, p<
.05), whereas when risk was low, investment had little effect
on their online purchase intentions (Ms = .46 versus .82 at
the high- and low-investment sites; t(47) < 1).
In addition to demonstrating that the site effect on
online purchase intentions varies across risk, we tested
whether the mediating effects of ability vary across levels of
risk by regressing online purchase intentions on risk, site,
risk ×ability, and site ×ability. As we expected, the risk ×
ability effect was significant (t(96) = 1.93, p= .057). In the
high-risk condition, mediation was supported: Ability was a
significant covariate (F(1, 43) = 8.67, p< 01), whereas the
previously significant site effect was reduced to nonsignifi-
cance (F(1, 43) < 1). The Sobel (1982) test provides further
support for this finding (Goodman I test statistic = 2.13, p<
.05). In the low-risk condition, however, mediation was not
supported: It did not affect online purchase intentions
(F(1, 40) < 1). As in Studies 1–3, integrity cannot be con-
sidered a mediator, because investment did not significantly
affect this variable.
Discussion
Collectively, these studies clarify the role of trust in predict-
ing online purchase intentions. Across four studies with dif-
ferent samples, companies, and products sold, we demon-
strate that Web site investment influences searchers’ inten-
tions to buy online by influencing one component of trust-
ing beliefs, ability (versus benevolence and integrity). Five
conclusions can be drawn from our findings: First, they pro-
vide empirical support for the position that trusting beliefs
should be considered separately from trusting intentions
(Moorman, Deshpandé, and Zaltman 1993; Moorman, Zalt-
man, and Deshpandé 1992). Although definitions of trust
typically contain both (e.g., Rotter 1980), most empirical
research on consumer trust implicitly assumes that measur-
ing trusting beliefs alone is sufficient (e.g., Morgan and
Hunt 1994). However, our research suggests that it is
important to measure trusting intentions because building
trusting beliefs does not necessarily lead to higher trusting
intentions. For example, although the presence (versus
absence) of a strong privacy/security statement increased
searchers’ benevolence and integrity beliefs in Study 2, it
did not lead to higher online purchase intentions. Likewise,
in Study 3, Web site investment increased browsers’ ability
beliefs but not their online purchase intentions.
Second, our findings are consistent with the view that
risk is a necessary (but not a sufficient) condition for trust
(Moorman, Deshpandé, and Zaltman 1993; Moorman, Zalt-
man, and Deshpandé 1992). Specifically, in Study 4, we
demonstrate that Web site investment and trusting beliefs
influence online purchase intentions only when risk is high,
or when the behavior requires trust. In contrast, when risk is
low (and, thus, the behavior does not require trust), online
purchase intentions were largely unaffected by Web site
investment and trusting beliefs.
Third, we demonstrate the importance of considering
the differential impact of trusting beliefs on trusting inten-
tions. As we demonstrated across four studies, trusting
beliefs do not have an equally strong influence on online
purchase intentions. For example, in Study 2, we find that
among searchers, gains in ability beliefs led to higher online
purchase intentions, whereas gains in benevolence and
integrity beliefs did not. Consequently, signals that were
effective in building benevolence and integrity beliefs (i.e.,
the presence of a strong privacy/security statement) were
less effective than ability signals (i.e., Web site investment)
in increasing online purchase intentions. Together, these
findings suggest that it is important for an online firm to
identify the most influential trusting belief in order to iden-
tify the signal that will most effectively increase trusting
intentions.
Fourth, our research contributes to the signaling litera-
ture. Prior research has demonstrated the importance of sep-
arating signals from interpretation because not all con-
sumers interpret signals in the same way (Prabhu and
Stewart 2001). Our research builds on this by demonstrating
that even when consumers interpret signals as a firm
intends, their responses may vary significantly. For exam-
ple, although searchers and browsers (Study 3) and those
perceiving high and low risk (Study 4) interpreted Web site
investment as a signal of ability, Web site investment signif-
icantly affected only the online purchase intentions of
searchers and for those perceiving high risk.
Fifth, these findings are also helpful in explaining what
appear to be contradictory findings regarding the effective-

144 / Journal of Marketing, April 2006
ness of privacy/security statements relative to Web site
design. Our research suggests that though privacy/security
statements can influence certain trusting beliefs (i.e., benev-
olence and integrity beliefs), they are less effective in
increasing searchers’ willingness to buy online. Conse-
quently, even when privacy/security statements improve
beliefs about a firm’s trustworthiness, they can still be rela-
tively less effective in converting visitors to buyers. These
findings also help explain why Web site design plays such
an important role in online purchase intentions. Instead of
serving a purely aesthetic function, it signals that a firm’s
ability can be trusted, which we found to be the most sig-
nificant driver of searchers’ online purchase intentions.
Managerial Implications
The prevailing wisdom has been that concerns about buying
online would diminish as consumers gain experience buy-
ing online (eMarketer 2005). Thus, marketing managers
may wonder whether they need to invest in establishing
trust as consumers gain online purchase experience. Con-
trary to this prevailing wisdom, however, recent reports
indicate that consumers at all levels of Internet experience
are increasingly cautious about buying online (eMarketer
2005; Forrester 2005). Compounding this problem is the
notion that the actual risks associated with being online
may even increase over time (Rust, Kannan, and Peng
2002). Consistent with the view that firms need to establish
trust regardless of their visitors’ online buying experience at
other sites, we found that Web site investment had a signifi-
cant effect on consumers’ online purchase intentions by
influencing their ability beliefs even when we controlled for
online purchase experience (Studies 1–4).
Likewise, marketing managers of established offline
firms may wonder whether they need to invest in Web site
design to establish trust online. Our findings suggest that
establishing trust is important for both an established offline
firm (Kodak) and an unknown pure play (UF). It may seem
surprising that consumers did not trust the ability of a
known firm to handle online transactions successfully, yet
this finding may reflect well-founded consumer skepticism
about whether firms have the same abilities online as they
do offline. Indeed, recently publicized examples of delivery
failures by known firms (e.g., Toys “R” Us during the 2003
Christmas season) likely contribute to such concerns. Part
of consumers’ caution may also stem from their awareness
that offline firms may outsource e-commerce functions,
thus making it difficult to predict the quality of the online
transaction on the basis of their offline experiences with the
firm.
To use Web site investment effectively, however, man-
agers should identify the reason most consumers visit their
site. Our results indicate that Web site investment is effec-
tive in boosting online purchase intentions when visitors are
searchers. Thus, if most visitors are searchers, Web site
investment is warranted, but if most visitors are browsers,
such investments may be ineffective. If the site attracts
both, it may be best to invest mainly in pages that are most
likely to attract searchers. For example, compared with
browsers, searchers tend to “drill down” beyond category-
level pages (e.g., a page featuring all the living-room furni-
ture that a store offers) to specific product pages (e.g., the
pages on the Bay Club chair; Moe 2003). Thus, investment
in such technology as Shockwave or Design Within Reach
may be most effective at the product level rather than the
category (or home page) level. Firms can identify the seg-
ment visiting their sites by using clickstream data
(searchers spend more time at product and search pages
than browsers do; Moe 2003), usability studies, or more tra-
ditional market research techniques, such as online surveys
or panel studies.
It appears that Web site investment is effective for pur-
chases that involve risk, particularly social risk. For exam-
ple, our findings suggest that sites selling gifts for important
social occasions (e.g., weddings, birthdays, formal business
functions) or holidays (e.g., Christmas, Valentine’s Day)
may benefit more from Web site investment than sites sell-
ing products with less risk (e.g., office supplies). For firms
selling products that vary in social risk, investing in Web
site design would likely be most effective at the product-
level pages for products that are most likely to carry high
social risk (e.g., jewelry).
Our focus has been on how to convert visitors into those
who are willing to buy online. However, the firm may have
other objectives, such as creating goodwill. Because Web
site investment did not significantly affect benevolence or
integrity beliefs in any of our studies, this tactic is likely to
be ineffective in meeting such objectives. The findings from
Study 3 suggest that privacy/security statements are better
than Web site investment for influencing these trusting
beliefs. It would be valuable to examine how other signals
(e.g., announcements of investments in social causes) might
communicate benevolence and integrity as well.
This research also has important public policy implica-
tions. Although signals can be used to communicate a firm’s
true abilities and intentions, they can also be used to mis-
lead (Prabhu and Stewart 2001). Unscrupulous retailers
may take advantage of consumers by offering high-
investment sites for inferior products or an inferior online
transaction or, worse, by stealing the consumer’s identity.
Indeed, according to the National Consumer League (2005),
the second most common Internet scam complaint was
about products that were never delivered or were misrepre-
sented at an e-commerce site. Yet many of the Federal Trade
Commission’s (FTC’s) initiatives focus on e-commerce
problems at the technological level by recommending better
encryption and firewall technology (e.g., FTC 2003).
Although such investments are important, our findings sug-
gest that e-commerce problems are a marketing issue as
well. Specifically, our research indicates that consumers
infer from investments in site design that the firm’s abilities
can be trusted. As a result, they are more willing to buy
from the firm. Although a firm with a well-designed site has
demonstrated an investment in site design, it does not fol-
low that the firm itself has these capabilities. Oftentimes,
site design and order fulfillment are outsourced and thus do
not reflect the firm’s online abilities. Furthermore, even if
the firm developed the site internally, it does not follow that
its abilities in this area will generalize to other areas, such
as order fulfillment. However, because consumers appear to
infer that a firm with a well-designed site can be trusted,

Converting Web Site Visitors into Buyers / 145
unscrupulous firms could use Web site investment to
deceive through implication.
As with regulating other e-commerce practices, there is
debate about whether government intervention is necessary
or whether industry self-regulation is sufficient (Rust, Kan-
nan, and Peng 2002). Currently, the FTC has taken a caveat
emptor stance toward such issues. Although the FTC pro-
vides standards for what to include in a privacy/security
statement (e.g., notifying consumers of a firm’s information
practices and ensuring the security of the information col-
lected) and advises consumers to consult these statements
before buying (www.ftc.gov), our results suggest that even
when consumers read these statements, their online pur-
chase intentions are largely unaffected by them unless they
are noticeably weak. Instead, consumers’ intentions to buy
online from a vendor are affected more by its Web site
investment. Thus, the FTC’s suggestions may not fully pro-
tect consumers from being victimized. Conversely, industry
self-regulation may be sufficient. Investing in Web site
design may not be profitable for most low-quality firms,
thus discouraging such firms from doing so. Publicly visi-
ble signals that require high up-front investments before any
sales transactions rely on repeat purchases and word-of-
mouth recommendations to recoup such sunk costs (Kir-
mani and Rao 2000). Thus, even if low-quality firms can
induce trial with a high-investment site, the firm’s true abil-
ities would be revealed. Consequently, future purchases
would be unlikely, making it difficult for low-quality firms
to recover Web site expenditures.
Limitations and Future Directions
As with any study, the findings should be considered in
light of their limitations. For example, instead of being part
of the site, the privacy/security statements used were
included in the survey booklets. This enabled us (1) to
ensure that those in the strong and weak privacy/security
conditions were exposed to the statement and (2) to control
the timing of when they read the statement relative to expe-
riencing the site. Although such gains in control come at the
expense of face validity, this likely provided a more conser-
vative test of our predictions. That is, by placing the state-
ments in the survey booklet and exposing participants to
them after their visit to the site (but immediately before
reporting their online purchase intentions), we likely
increased their salience. Yet despite this, investment had a
stronger effect on online purchase intentions than did the
presence of a strong privacy/security statement. Still, it
would be beneficial to examine what, if any, effect a
privacy/security statement would have if its exposure was
allowed to vary. Perhaps the first hurdle is to establish trust
in the firm’s ability. Only then might searchers consult such
statements.
Although we studied several products (i.e., furniture and
accessories, which are likely to be evaluated more on their
experiential qualities than on their search qualities, and dig-
ital cameras, which are likely to be evaluated more on their
search qualities than on their experiential qualities), all
were high-ticket items. It would be fruitful to examine
whether such results generalize to inexpensive items, such
as CDs or books. For such products, the risk of buying
online might be reduced, thus reducing the effect of invest-
ment on online purchase intentions. It would also be fruitful
to examine whether Web site investment has the greatest
influence on trusting intentions for sites that feature ser-
vices, such as banking or travel. When transactions are
more personal in nature, perhaps trust in the firm’s benevo-
lence would be as important as, if not more important than,
trust in its ability.
Note that participants did not have prior experience buy-
ing online from either UF or Kodak (which did not sell
directly from its site at the time of Study 3). It would be
worthwhile to examine how online purchase experience
with a firm might influence consumers’ interpretations of
the firm’s signals, as well as their trusting beliefs and inten-
tions. Furthermore, we examined only signals that can be
observed and experienced, but other types of signals are
also possible (Kirmani and Wright 1989). For example,
firms might signal their Web site investments through
announcements in press releases or on the site itself. How-
ever, because such announcements require relatively little
cost (and thus could be used by both high- and low-quality
firms), they may be less effective than site investment in
building trust. This would be a worthwhile future research
direction.
Conclusion
Despite these limitations, we believe that our framework
provides meaningful insights into how firms can most effec-
tively signal their trustworthiness to convert online visitors
to buyers. Furthermore, it contributes to the trust literature
by demonstrating the importance of treating trusting beliefs
as (1) a multidimensional construct and (2) a necessary but
not sufficient condition for trust. Similarly, it contributes to
the signaling literature by demonstrating that even if con-
sumers interpret signals as a firm intends, their responses
can vary significantly.

146 / Journal of Marketing, April 2006
APPENDIX
Measures and Items
Measures and Items Source
Online Purchase Intentions (Trusting Intentions) New scale
“Unlikely/likely” (–3 to +3)
“Impossible/possible” (–3 to +3)
“Improbable/probable” (–3 to +3)
Trusting Beliefs
Ability (scale used in Studies 1–3)
a, b Mayer and Davis (1999)
Urban-Furniture seems very capable of performing online transactions.
Urban-Furniture appears to be successful at the things it tries to do.
Urban-Furniture seems to have much knowledge about what needs to be done to fulfill
online transactions.
I feel very confident about Urban-Furniture’s online skills.
Urban-Furniture appears to have specialized capabilities that can increase its perfor-
mance with online transactions.
Urban-Furniture appears to be well qualified in the area of e-commerce.
Benevolence (scale used in Studies 1–3)
a, b Mayer and Davis (1999)
Urban-Furniture seems very concerned about my welfare.
My needs and desires appear to be important to Urban-Furniture.
It doesn’t seem that Urban-Furniture would knowingly do anything to hurt me.
Urban-Furniture seems to really look out for what is important to me.
Urban-Furniture appears to go out of its way to help me.
Integrity (scale used in Studies 1–3)
a, b Mayer and Davis (1999)
Urban-Furniture seems to have a strong sense of justice.
Urban-Furniture appears to try hard to be fair in dealings with others.
Urban-Furniture’s actions and behaviors are not very consistent. (reverse scored)
I like Urban-Furniture’s values.
Sound principles seem to guide Urban-Furniture’s behavior.
Ability (scale used in Study 4)
“Nonexpert/expert” (1 to 7)
“Untrained/trained” (1 to 7)
“Inexperienced/experienced” (1 to 7)
Integrity (scale used in Study 4)
“No integrity/integrity” (1 to 7)
Urban-Furniture does not have a great deal of integrity.c(reverse scored)
Web Site Investment New scale
The amount of time invested into developing this website seems to be: “very little/a great
deal.” (1 to 7)
The amount of effort devoted to developing this website seems to be: “very little/a great
deal.” (1 to 7)
The amount of money invested into developing this website seems to be: “very little/a
great deal.” (1 to 7)
Online Risk Perceptions
(scale used in Study 2)
cNew scale
Shopping online is risky.
Providing credit card information online is risky.
Providing personal information (i.e., social security number and mother’s maiden name)
online is risky.
Purchasing items online is risky.
Providing my e-mail address and phone number online is risky.
Registering online is risky.
It is riskier to shop online for a product than to shop offline for it.
Attitudes Toward Home Accessories
(scale used in Study 4)
New scale
“Bad/good” (–3 to +3)
“Unpleasant/pleasant” (–3 to +3)
“Dislike/like” (–3 to +3)
Moorman, Deshpandé,
and Zaltman (1993)
Moorman, Deshpandé,
and Zaltman (1993)

Converting Web Site Visitors into Buyers / 147
APPENDIX
Continued
Measures and Items Source
Bargh, John A. and Katelyn Y.A. McKenna (2004), “The Internet
and Social Life,” Annual Review of Psychology, 55 (February),
573–90.
Baron, Reuban M. and David A. Kenny (1986), “The Moderator-
Mediator Variable Distinction in Social Psychological
Research: Conceptual, Strategic, and Statistical Considera-
tions,” Journal of Personality and Social Psychology, 51
(December), 1173–82.
Corritore, Cynthia L., Beverly Kracher, and Susan Wiedenbeck
(2003), “Online Trust: Concepts, Evolving Themes, A Model,”
International Journal of Human-Computer Studies, 58 (June),
737–58.
Doney, Patricia M. and Joseph P. Cannon (1997), “An Examina-
tion of the Nature of Trust in Buyer–Seller Relationships,”
Journal of Marketing, 61 (April), 35–51.
Dunn, Julie (2004), “Survey Shows Online Security Perception
Gap Between Experts, Users,” Knight Ridder Tribune Business
News, (August 17), 1.
eMarketer (2005), “Privacy and Security: Fraud, Identity Theft,
Phishing, Viruses and Other Threats,” (accessed April 2, 2005),
[available at http://www.emarketer.com/Report.aspx?privacy_
mar05].
Fogg, B.J., Cathy Soohoo, David Danielson, Leslie Marable,
Julianne Stanford, and Ellen Tauber (2002), How Do Users
Evaluate the Credibility of Web Sites? A Study with over 2,500
Participants,” (accessed December 6, 2005), [available at http://
portal.acm.org/citation.cfm?doid=997078.997097?].
Forrester, Jonathan (2005), “Rebuilding Consumers’ Trust in the
Internet,” (accessed April 2, 2005), [available at http://www.
forrester.com/Research/Document/Excerpt/0,7211,35612,00.
html].
FTC (2003), “FTC Working to Protect Consumers and Businesses
from Information Security Breaches,” press release, (Novem-
ber 19), (accessed December 6, 2005), [available at http://
www.ftc.gov/opa/2003/11/cybersecurity.htm].
Ganeson, Shankar (1994), “Determinants of Long-Term Orienta-
tion in Buyer–Seller Relationships,” Journal of Marketing, 58
(April), 1–19.
Garfinkel, Harold (1963), “A Conception of, and Experiments
with, ‘Trust’ as a Condition of Stable Concerted Actions,” in
Motivation and Social Interaction, O.J. Harvey, ed. New York:
Ronald Press, 187–239.
Hoffman, Donna L. and Thomas P. Novak (1996), “Marketing in
Hypermedia Computer-Mediated Environments: Conceptual
Foundations,” Journal of Marketing, 60 (July), 50–68.
——— and Marcos A. Peralta (1999), “Information Privacy in the
Marketspace: Implications for the Commercial Uses of
Anonymity on the Web,” The Information Society, 15 (2),
129–39.
Janiszewski, Chris (1998), “The Influence of Display Characteris-
tics on Visual Exploratory Searching Behavior,” Journal of
Consumer Research, 25 (December), 290–312.
Kim, Peter H., Donald L. Ferrin, Cecily D. Cooper, and Kurt T.
Dirks (2004), “Removing the Shadow of Suspicion: The
Effects of Apology Versus Denial for Repairing Competence-
Versus Integrity-Based Trust Violations,” Journal of Applied
Psychology, 89 (1), 104–118.
Kirmani, Amna and Akshay R. Rao (2000), “No Pain, No Gain: A
Critical Review of the Literature on Signaling Unobservable
Product Quality,” Journal of Marketing, 64 (April), 66–80.
——— and Peter Wright (1989), “Money Talks: Perceived Adver-
tising Expense and Expected Product Quality,” Journal of Con-
sumer Research, 16 (December), 344–53.
Kumar, Nirmalya, Lisa K. Scheer, and Jan-Benedict E.M.
Steenkamp (1995), “The Effects of Supplier Fairness on Vul-
nerable Resellers,” Journal of Marketing Research, 32 (Febru-
ary), 54–65.
Mayer, Roger and James Davis (1999), “The Effect of the Perfor-
mance Appraisal System on Trust for Management: A Field
Quasi-Experiment,” Journal of Applied Psychology, 84 (Febru-
ary), 123–36.
———, ———, and F. David Schoorman (1995), “An Integrative
Model of Organizational Trust,” Academy of Management
Review, 20 (July), 709–734.
McKnight, D. Harrison, Vivek Choudhury, and Charles Kacmar
(2002), “Developing and Validating Trust Measures for e-
Commerce: An Integrative Typology,” Information Systems
Research, 3 (September), 334–59.
Moe, Wendy W. (2003), “Buying, Searching, or Browsing: Differ-
entiating Between Online Shoppers Using In-Store Naviga-
NFT
(scale used in Study 4)
c
Autotelic NFT
Touching products can be fun.
I like to touch products even if I have no intention of buying them.
When browsing in stores, I like to touch a lot of products.
When walking through stores, I can’t help touching all kinds of products.
When browsing in stores, it is important for me to handle all kinds of products.
I find myself touching all kinds of products in stores.
Instrumental NFT
I place more trust in products that can be touched before purchase.
I feel more comfortable purchasing a product after physically examining it.
I feel more confident making a purchase after touching a product.
If I can’t touch a product in the store, I am reluctant to purchase the product.
The only way to make sure a product is worth buying is to actually touch it.
There are many products that I would only buy if I could handle them before purchase.
aMeasured on a scale from 1 (“disagree strongly”) to 5 (“agree strongly”).
bIn Study 3, we replaced Urban-Furniture with Kodak.
cMeasured on a scale from 1 (“disagree strongly”) to 7 (“agree strongly”).
Peck and Childers
(2003a)
REFERENCES

148 / Journal of Marketing, April 2006
tional Clickstream,” Journal of Consumer Psychology, 13
(1–2), 29–39.
Montoya-Weiss, Mitzi M., Glenn B. Voss, and Dhruv Grewal
(2003), “Determinants of Online Channel Use and Overall Sat-
isfaction with a Relational, Multichannel Service Provider,”
Journal of the Academy of Marketing Science, 31 (Fall),
448–58.
Moorman, Christine, Rohit Deshpandé, and Gerald Zaltman
(1993), “Factors Affecting Trust in Market Research Relation-
ships,” Journal of Marketing, 57 (January), 81–101.
———, Gerald Zaltman, and Rohit Deshpandé (1992), “Relation-
ships Between Providers and Users of Market Research: The
Dynamics of Trust Within and Between Organizations,” Jour-
nal of Marketing Research, 29 (August), 314–28.
Morgan, Robert M. and Shelby D. Hunt (1994), “The
Commitment–Trust Theory of Relationship Marketing,” Jour-
nal of Marketing, 58 (July), 20–39.
Naquin, Charles E. and Gaylen D. Paulson (2003), “Online Bar-
gaining and Interpersonal Trust,” Journal of Applied Psychol-
ogy, 88 (1), 113–20.
National Consumer League (2005), Internet Fraud Statistics:
Internet Scams: Fraud Trends: January–June 2005, (accessed
December 6, 2005), [available at http://www.fraud.org/internet/
internet_scams_halfyear_2005.pdf].
Neter, John, William Wasserman, and Michael H. Kutner (1990),
Applied Linear Statistical Models. Boston: Richard D. Irwin.
Palmer, Jonathan W., Joseph P. Bailey, and Samer Faraj (2000),
“The Role of Intermediaries in the Development of Trust on the
WWW: The Use and Prominence of Trusted Third Parties and
Privacy Statements,” Journal of Computer-Mediated Commu-
nication, 5 (March), 1–25.
Peck, Joann and Terry L. Childers (2003a), “Individual Differ-
ences in Haptic Information Processing: The ‘Need for Touch’
Scale,” Journal of Consumer Research, 30 (December),
430–42.
——— and ——— (2003b), “To Have and To Hold: The Influ-
ence of Haptic Information on Product Judgments,” Journal of
Marketing, 67 (April), 35–48.
Porter, Michael E. (1980), Competitive Strategy: Techniques for
Analyzing Industries and Competitors. New York: The Free
Press.
Prabhu, Jaideep and David W. Stewart (2001), “Signaling Strate-
gies in Competitive Interaction: Building Reputations and Hid-
ing the Truth,” Journal of Marketing Research, 38 (February),
62–73.
Rosenblatt, Louise (1978), The Reader, the Text, the Poem: The
Transactional Theory of the Literary Work. Carbondale: South-
ern Illinois University Press.
Rotter, Julian B (1980), “Interpersonal Trust, Trustworthiness, and
Gullibility,” American Psychologist, 35 (January), 1–7.
Rust, Roland, P.K. Kannan, and Na Peng (2002), “The Customer
Economics of Internet Privacy,” Journal of the Academy of
Marketing Science, 30 (4), 455–64.
Schlosser, Ann E. (2003), “Experiencing Products in a Virtual
World: The Role of Goals and Imagery in Influencing Attitudes
Versus Intentions,” Journal of Consumer Research, 30 (3),
184–98.
Sirdeshmukh, Deepak, Jagdip Singh, and Barry Sabol (2002),
“Consumer Trust, Value, and Loyalty in Relational
Exchanges,” Journal of Marketing, 66 (January), 15–27.
Sitkin, Sim B. and Nancy L. Roth (1993), “Explaining the Limited
Effectiveness of Legalistic ‘Remedies’ for Trust/Distrust,”
Organization Science, 4 (August), 367–92.
Smith, J. Brock and Donald W. Barclay (1997), “The Effects of
Organizational Differences and Trust on the Effectiveness of
Selling Partner Relationships,” Journal of Marketing, 61 (Janu-
ary), 3–21.
Sobel, M.E. (1982), “Asymptotic Intervals for Indirect Effects in
Structural Equation Models,” in Sociological Methodology, S.
Leinhart, ed. San Francisco: Jossey-Bass, 290–312.
Sultan, Fareena, Glen L. Urban, Venkatesh Shankar, and Iakov Y.
Bart (2002), “Determinants and Role of Trust in E-Business: A
Large Scale Empirical Study,” Working Paper No. 4282, Sloan
School of Management, Massachusetts Institute of Technology.
Weiner, Bernard (1972), Theories of Motivation: From Mechanism
to Cognition. Chicago: Rand McNally.
——— (1986), An Attributional Theory of Motivation and Emo-
tion. New York: Springer-Verlag.