JoCTEC: Journal of Communication Technology (ISSN: 2694-3883)

Published by the Communication Technology Division

of the Association for Education in Journalism and Mass Communication
The Association for Education in Journalism and Mass Communication

Effects of Website Credibility and Brand

Trust on Responses to Online Behavioral
Peer review: This article
has been subject to a
Advertising
double-blind peer review
process
Xiaowen Xua, Tai-Yee Wub, and David J. Atkinc
open access a
Butler University, Indianapolis, Indiana, USA; bNational Yang Ming Chiao Tung
University, Hsinchu, Taiwan; cUniversity of Connecticut, Storrs, Connecticut, USA
JoCTEC is an open
Correspondence: xxu4@butler.edu
access journal meaning
that all content is freely
available without charge to
the user or their institution.
Users are allowed to read, Abstract
download, copy, distribute,
print, search, or link to the
full texts of the articles, or
Online behavioral advertising that tracks user data has witnessed a
use them for any other dramatic increase in popularity. Using Psychological Reactance Theory,
lawful purpose, without this study examines the effects of brand trust and website credibility on
asking prior permission
from the publisher or the responses to behavioral advertising via privacy concerns. A 2 (brand trust:
author. high vs. low) by 2 (website credibility: high vs. low) between-subjects
Open access is an experiment was conducted (N = 424). Results suggest that while brand trust
ongoing publication influences purchase intention—as mediated via affective reactance—
practice that differs from
the traditional manner
website credibility only exerts modest effects on the dependent variables.
academic journals are Implications for user perception factors and contextual factors—including
published and then ad effectiveness in the digital personalized marketing realm—are discussed.
received by the reading
public. In Open Access
publication model neither Keywords: online behavioral advertising, privacy, psychological reactance,
readers nor a reader’s media credibility, brand trust
institution are charged for
access to articles or other
resources. We ask that
users in turn give proper
citation of the original
publication or link to the
full texts of these articles
for any non-commercial
purposes A subscription to
the journal in which these
articles are published is
not required.

Xu, Wu, & Atkin. JoCTEC 2021 4(2), pp. 32-57 32

DOI: 10.51548/joctec-2021-009
`
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Introduction
Eighty-nine percent of digital marketers have invested in
personalization (Witcher, 2018) by tracking, collecting, and analyzing
user data on the Internet. Online behavioral advertising tracks user
metrics, such as browsing and transaction history, clicks made, time
spent, and overall interaction activities on a site, to create user
profiles for personalized advertising content (Interactive Advertising
Bureau, 2009). Despite their popularity (Benes, 2019) and
effectiveness in enhancing user attitudes and behaviors (e.g., Abdel-
Monem, 2021; Bleier & Eisenbeiss, 2015a; Tucker, 2014), behavioral
ads may trigger online privacy concerns given their reliance on
personal data surveillance (Chen et al., 2019; McDonald & Cranor,
2010). Behavioral advertising may threaten individual control of
personal information and arouse one’s motivation to restore personal
freedom, thus enhancing psychological reactance (Ham, 2017;
White et al., 2008). Privacy concern may thus lead to ad avoidance
(Jung, 2017; Turow et al., 2009) and reduce purchase intention for
online consumers (Gironda & Korgaonkar, 2018; Morimoto, 2020).
Sensing these user concerns, Facebook executives announced they
are “…building new tools to give people more control over their
information and addressed how privacy and personalized advertising
are not at odds” (Egan, 2020, Para 2).

These tensions governing behavioral advertising effectiveness

necessitate investigations of factors that may distinctively shape the
psychological and behavioral implications of behavioral advertising.
One such factor is trust, which influences online shopping “due to the
vast information asymmetries and customer uncertainty inherent to
the Internet” (Aguirre et al., 2015, p. 37). Consumers feel safe about
providing marketers with personal information in exchange for
benefits, based on the trust and expectation that personal
information will be responsibly managed and used by pertinent
parties (Ho & Chau, 2013; Okazaki et al., 2009). Trust in the marketer
may reduce one’s privacy concern and adverse feelings about being
targeted. Although privacy concern and reactance are highly relevant
reactions towards personalized advertising, it remains to be seen
whether they represent the underlying psychological mechanism
governing the influence of trust.

Many studies have explored trust as a dependent variable in the

context of personalized advertising and services on the Internet,
especially as a result of privacy concern (Ho & Chau, 2013; Miyazaki,
2008; Rifon et al., 2005; Stanaland et al., 2011; Varnali, 2019). Fewer
studies have treated existing trust, either of the brand (e.g., Bleier &
Eisenbeiss, 2015b) or of the publisher website (e.g., Aguirre et al.,
2015), as an independent variable. Furthermore, studies focusing on

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the variable of trust were largely conducted in the online shopping
context. In these situations, trust is directed towards the online
merchant/retailer where the brand/advertiser overlaps with the host
website (Chellappa & Sin, 2005; Jai et al., 2013; Miyazaki, 2008;
Pavlou, 2003; Stanaland et al., 2011) or trust is operationalized as a
global measure for multiple parties (Internet vendors, online
companies, the Internet, [e.g., Brinson & Eastin, 2016]). Yet in
practice, it is also common for a brand to post personalized ads on
an external website, typically using third-party cookies (Penn, 2012).
In fact, consumers may be more concerned about privacy risks from
third party-based cookies, as this mechanism implies unauthorized
collection and secondary use of personal information (Yazıcıoğlu,
2018). In sum, research examining the separate impacts of trust in
the brand and perceived trustworthiness of the publisher website on
affective and behavioral responses has been scant.

In an effort to bridge the behavioral advertising literature gap, the

contribution of the current study is twofold. Applying psychological
reactance theory, this study aims to explore the mediation roles of
privacy concern and reactance in explaining the influences of trust
on responses toward behavioral advertising. Moreover, brand trust
and publisher website credibility are treated simultaneously as two
separate factors, each reflecting users’ perceived risk associated
with the promotional use of their personal data, to help extend our
theoretical understanding of this ad form. Findings could highlight
practical questions of digital advertising, including how products can
be more effectively promoted in a different website in relation to
consumers’ pre-existing perceptions of brand and contextual factors.
Literature Review
Psychological Reactance
Psychological reactance describes an individual’s motivational state
aroused to restore a behavioral freedom that is perceived to be
threatened or eliminated (Brehm, 1966). The magnitude of threat to
behavioral freedom is crucial to the arousal of psychological
reactance (Brehm & Brehm, 1981). Dillard and Shen (2005) further
explicated the process of psychological reactance by not only
identifying cognitive and affective components of reactance, but also
validating a model that combines the antecedents of reactance (e.g.,
perceived threat of freedom) and the attitudinal and behavioral
responses for freedom restoration. Negative affects, including anger
(Dillard & Shen, 2005) and irritation (Edwards et al., 2002), are key
emotional indicators of psychological reactance. The current study
focuses on the emotional component of psychological reactance as
an important reaction to online behavioral advertising.

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Brand Trust
Consumers’ exchange of personal information is viewed as an
implied social contract in online shopping contexts, as suggested in
Social Contract Theory (Dunfee et al., 1999; Miyazaki, 2008).
Consumers assume a hypothetical social contract in a business
transaction (Fogel & Nehmad, 2009), where they feel safe to provide
a marketer with personal information in exchange for any benefits.
This security feeling is grounded on the trust and expectation that the
personal information will be responsibly managed and used by
pertinent parties (Ho & Chau, 2013; Okazaki et al., 2009). Past
research has demonstrated the importance of trust in the online
marketer in enhancing acceptance of general or personalized digital
and mobile advertising (Brinson & Eastin, 2016; Jafari et al., 2016).

Brand trust is consumers’ belief that a brand, product, or service is

dependable and competent (Herbst et al., 2012). For a new brand,
word-of-mouth and brand reputation are important factors for brand
trust (Ha, 2004). Due to the lack of face-to-face interaction and
enhanced perceived risks over the Internet, initial trust is especially
important for decision-making in online shopping contexts
(Aljukhadar et al., 2017; McKnight & Chervany, 2001; Wang et al.,
2004). Trust in advertisers positively influenced attitude (Jafari et al.,
2016) and watching intention (Cheung & To, 2017) toward mobile
advertising. Choi and Rifon (2002) reported a positive link between
advertiser credibility and purchase intention toward a web banner ad.
Although some studies addressed brand trust as a dependent
variable involving personalized marketing tactics (Ho & Chau, 2013;
Miyazaki, 2008; Rifon et al., 2005; Stanaland et al., 2011; Varnali,
2019), empirical investigations addressing implications of preexisting
brand trust on behavioral advertising have been scarce.

Given that brand trust can be influenced by privacy conceptions, it’s

useful to consider related concerns in the context of behavioral
advertising. Chen and Atkin (2020) defined privacy concern as
“individuals’ objective judgment of privacy risks” (p. 4), which could
be motivated by such issues as “suffering of identity theft, financial
loss, and relational conflicts in the past” (p. 4). Privacy has been
conceived as an interpersonal boundary that individuals regulate to
control the flow of information, especially online disclosures of an
intensely personal, private nature (e.g., Millham & Atkin, 2018).
Online behavioral advertising relies on collecting users’ personal
data to provide tailored services, putting individuals’ personal data at
high risk and increasing their privacy concerns (van Doorn &
Hoekstra, 2013).

Trust can attenuate perceived risk in disclosing personal information

during online transactions (McKnight & Chervany, 2001; Okazaki et
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al., 2009). Trust in one’s Internet partner is a crucial element that
individuals assess when balancing the costs and benefits involved in
information disclosure (Brinson et al., 2019). When managing one’s
personal information online, “once trust is established with a
particular corporate entity…users should willingly allow for
information collection in return for economic benefits” (Campbell &
Carlson, 2002, p. 593). Trust in a brand may make consumers
believe that their personal data are properly protected and used
when they are exposed to personalized ads, leading to reduced
privacy concern (Bleier & Eisenbeiss, 2015b; Schade et al., 2018).
Hence, greater trust in a brand is likely to induce less privacy
concern.

The topic of building trust and its consequences on the reactance

behavior remains understudied. Consumers tend to think that a
personalized ad from a trusted retailer is more beneficial and
personally relevant (Office of Fair Trade, 2010); this will thus reduce
psychological reactance toward the ad elicited by the exposure of
personal information (Utami & Agus, 2019). Individuals may also feel
intrusion, as an unsolicited behavioral ad limits their free choices for
alternatives, resulting in reactance. But a brand enjoying high trust
levels, with higher potential for quality products/services, could lower
such reactance and save consumers time when searching for
alternatives. Hence, reactance toward the ad may be lower when
trust in the brand is higher.

Even though a handful of studies have verified the benefits of brand

trust on consumer reactions towards personalized advertising, they
focused on other variables than purchase intention, such as click-
through rates (Bleier & Eisenbeiss, 2015b), acceptance of the ad
(Boerman et al., 2017), and website revisit intention (Sung, 2017).
The function related to purchase intention has only been discussed
in conceptual terms (e.g., Rony, 2018; Schumann et al., 2014),
rather than empirically. If users were willing to trust an online retailer
to handle personal information used in personalized or behavioral
advertising, they were more likely to disclose their information in the
transaction (Bol et al., 2018). Based on the above-mentioned
favorable influences of brand trust, it is logical to deduce that
consumer trust in the brand on a behavioral ad should also promote
greater purchase intention. More formally, we propose the following
hypotheses:

H1: Brand trust will be negatively related to privacy concern toward

a behavioral ad.

H2: Brand trust will be negatively related to affective reactance for

a behavioral ad.

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H3: Brand trust will be positively related to purchase intention for a
behavioral ad.
Website Credibility
Media credibility refers to a medium platform’s worthiness to be
believed and typically consists of accuracy, fairness, depth of
information, etc. (Johnson & Kaye, 2004). Based on source
credibility theory (Berlo et al., 1969; Hovland & Weiss, 1951), the
credibility of surrounding programming or editorial environments can
potentially influence advertising effectiveness. As a media source of
communication, website credibility can serve as a cue for consumer
inference-making about the content carried in the site, including the
ads (Colbert et al., 2014; Shamdasani et al., 2001). Credibility of the
host website could alleviate consumers’ privacy concern when they
interact with an ad on the website, with the belief that their personal
data will not be misused due to the favorable reputation of the
website (Phelan et al., 2016).

Furthermore, although only limited research in advertising has

focused on the relationship between website credibility and user
psychological reactance, implications can be drawn from studies on
compliance. Generally, a credible source is found to increase
behavioral compliance among receivers (Cialdini & Rhoads, 2001;
Meulenaer et al., 2018). In the same vein, information delivered by a
credible media platform is likely to convince the users to accept or
comply. As compliance and resistance are at opposite ends of a
spectrum, and reactance can be regarded as the affective side of
resistance (Knowles & Linn, 2004), the factors increasing an
individual’s compliance should otherwise decrease their reactance.

To date, studies focusing on the antecedents and consequences of

trust have been largely confined to the online shopping context. In
these situations, trust is directed towards the online
merchant/retailer, where the website is actually the brand/advertiser
(Chellappa & Sin, 2005; Jai et al., 2013; Miyazaki, 2008; Pavlou,
2003; Stanaland et al., 2011). These parties most likely rely on first-
party cookies to collect information, with control over their own
website (Hoofnagle et al., 2012). Yet in practice, ads may also be
posted on external websites with third-parties (e.g., ad networks,
data management platforms), which can track consumer browsing
histories and online activities on behalf of brands, and/or buy and
manage these data (Malthouse et al., 2018; Penn, 2012). In this
context, the credibility of the external website may matter in
consumers’ evaluations of the encountered ad.

Online advertisers may rely on the credibility of a particular website

to build more trust in their ads. Credibility of a media outlet is
positively related to consumers’ attitudinal and behavioral
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evaluations of the advertisement (Choi & Rifon, 2002). When a less
credible website presents a personalized ad, it elicits lower click-
through intentions than a generic ad. This effect disappears on a
more credible website (Aguirre et al., 2015), although the study did
not directly test the effect on purchase intention. Despite that the role
of a host website’s credibility in forming perceptions of corresponding
behavioral ads is not yet clear, research demonstrates that host
website credibility enhances purchase intention toward the
advertised brand (Bae et al., 2001; Choi & Rifon, 2002; Shamdasani
et al., 2001). Based on the theoretical dynamics outlined above, we
posit the following hypotheses:

H4: Website credibility will be negatively related to privacy concern

toward a behavioral ad.

H5: Website credibility will be negatively related to affective

reactance for a behavioral ad.

H6: Website credibility will be positively related to purchase intention

for a behavioral ad.
Privacy Concerns and Psychological Reactance in Online
Advertising
Threats to privacy will arouse one’s motivation to restore their
behavioral freedom regarding protection of personal information,
thus enhancing affective reactance toward the ad and the brand
(Ham, 2017). Empirical evidence has supported the relationship
between privacy concern and psychological reactance for
personalized ads, and specifically affective reactance. Chen et al.'s
survey (2019) demonstrated that privacy concern regarding online
personalized advertising was positively related to psychological
reactance towards it. Bleier and Eisenbeiss (2015b) found that for
both more and less trusted brands, there was a positive link between
privacy concern and psychological reactance towards a behavioral
ad. Hence, the following hypothesis is put forth:

H7: Privacy concern for a behavioral ad will be positively related to

affective reactance.

Negative affects elicited by the threat to freedom may influence

behavioral intention. Millham and Atkin (2018) found that the
emotional state of anger predicted the strongest effects on online
behaviors related to privacy (e.g., disclosing personal information on
commercial websites). Jung and Park (2018) investigated users’
responses to information privacy threats with a location-based
personalized service, revealing that anger not only provoked
retributive behaviors, such as complaining about service via word-of-
mouth messages, but also induced behavioral change (refusal to use

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the service thereafter). Psychological reactance toward personalized
ads reduces users’ click-through intentions (Bleier & Eisenbeiss,
2015b; White et al., 2008). Psychological reactance also mediated
the effect of personalization on attitude toward the ad, and attitude
toward the product (Bleier & Eisenbeiss, 2015a).

Nevertheless, none of these studies examined whether

psychological reactance towards a personalized ad has a similar
impact on purchase intention; some even adopted different
conceptualizations and operationalizations of reactance, such as
perception of highly distinctive personal knowledge (White et al.,
2008) or ad intrusiveness (Bleier & Eisenbeiss, 2015b). As a result
of psychological reactance to online behavioral advertising, users
may act to avoid advertised products that elicit negative thoughts and
feelings (Baek & Morimoto, 2012). Based on the above rationale, the
following hypothesis is proposed:

H8: Affective reactance will be negatively related to purchase

intention.

Despite discrete evidence supporting all of the aforementioned

hypotheses, research has yet to test possible mediation effects—as
derived from psychological reactance theory—in the context of
personalized or behavioral ads. Boerman et al. (2017) in their
systematic literature review argued that behavioral advertising
“seems to first trigger affective responses” (p. 8) such as privacy
concern and reactance, and consequently influence consumer
behavior. It remains unknown whether perceived brand trust and
website credibility for a behavioral ad brand will elicit privacy
concern, which further leads to psychological reactance and lowered
purchase intention. Hence, based on the theory and research
reviewed above, we further posit a serial mediation: low brand
trust/website credibility in online behavioral advertising may impose
privacy concerns, thus enhancing negative affect toward the ad and
dampening purchase intention. More formally:

H9: Brand trust will be negatively related to privacy concern and

affective reactance, consecutively; affective reactance will be
negatively related to purchase intention.

H10: Website credibility will be negatively related to privacy concern

and affective reactance, consecutively; affective reactance will be
negatively related to purchase intention.

Figure 1 illustrates the proposed hypotheses and research

questions.

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Figure 1. Research model.

Notes: H9 and H10 are not addressed in the model.

Methods
A 2 (brand trust: high vs. low) by 2 (website credibility: high vs. low)
between-subjects experimental design was used to test the
proposed hypotheses. Participants were randomly assigned to one
of the four scenarios.
Sample and Procedure
Given that young adults (aged 18-29) are one of the largest Internet
user segments, they represent an important target audience for
online advertising (Baek & Morimoto, 2012). A sample of
undergraduate students from a large northeastern university in the
U.S. was thus deemed appropriate; participants were recruited via a
multi-section introductory general education course to participate in
an online experiment. The original data included 445 cases. Some
21 cases were removed due to highly incomplete data (over 90% of
questions not answered). All told, 424 valid responses were finally
rendered (54.5% female). The average age of participants was 19.18
(SD = 1.17). Participants’ ethnic/racial composition encompassed
Caucasians (62.7%), Asians (16.3%), Hispanics (8.7%), African
Americans (7.3%), and other ethnic/racial groups (4.8%). There were
no significant differences in descriptive statistical profiles between
the deleted cases and the retained cases.

Participants were given background information about the advertised

brand, which manipulated brand trust. They were then exposed to a
fictitious behavioral ad scenario, either with a high-credible or low-
credible website. After exposure to the stimulus ad, participants
provided measurements for privacy concern, affective reactance,
and purchase intention, in that order.
Stimulus
To rule out influences from previous impressions with real-life
brands, this study used a fictitious bus company, GoTravel, as the

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brand. Trust in brand was manipulated by a text description of
positive or negative impressions, based on brand reputation and
word-of-mouth from friends and a family member (Alam & Yasin,
2010; Ha, 2004). This manipulation was given as background
information on a page prior to participants’ exposure to the
behavioral ad.

Moreover, Forbes was used as a high credible website, whereas

Buzzfeed was used as a low credible website (Mitchell et al., 2014;
Wu et al., 2016). Above the ad stimulus on the same page,
participants were told the scenario: they were planning a trip to New
York and searching for bus tickets online; the next day they saw the
ad post for the bus company GoTravel, when browsing Forbes (or
Buzzfeed).

The ad stimulus was designed and created by the researcher by

imitating a behavioral ad format (see Appendix). The ad contained
the company logo for GoTravel. Behavior-based information was
made salient by including a photo of the New York City (travel
destination) skyline in the night and copy saying “Leaving the town
soon? Book now @ GoTravel.”
Manipulation Check Questions
Brand trust

Participants were asked to evaluate the advertised brand with six

Likert-type questions adapted from Delgado-Ballester (2004) (e.g.,
“GoTravel will provide satisfying services;” 1 = strongly disagree to 7
= strongly agree). These items were then averaged to form a
composite variable of brand trust (α = .98, M = 3.79, SD = 1.61).
Website credibility

Participants were asked to evaluate credibility of the website in the

scenario on four semantic-differential (seven-point) items adapted
from MacKenzie and Lutz (1989) (e.g., “not trustworthy/
trustworthy”). These items were then averaged to form a composite
website credibility measure (α = .92, M = 4.37, SD = 1.36).
Measurements
Privacy concern

Privacy concern was measured by adapting four seven-point Likert-

type questions from Sheng et al. (2008) (e.g., “[When I see this ad]
it bothers me that the company/ website has too much information
about me;” 1 = strongly disagree to 7 = strongly agree; α = .91, M =
4.43, SD = 1.35).
Affective reactance

The measure was adopted from Gardner and Leshner (2016) to ask
the participants “how much the advertisement made you feel each of

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the following feelings” (irritated, angry, annoyed, and aggravated),
on a seven-point scale (1 = none of this feeling to 7 = a great deal of
this feeling; α = .94, M = 3.08, SD = 1.53).
Purchase intention

Participants also reported their intention to 1) “consider buying

tickets from GoTravel this time,” and 2) “consider GoTravel for my
future travelling” (1 = strongly disagree to 7 = strongly agree; r = .90,
M = 3.37, SD =1.49).

All scales (privacy concern and affective reactance) were subject to

a confirmatory factor analysis as an examination of measurement
validity, and demonstrated an adequate model fit, c2(15) = 22.13,
CMIN/DF = 1.48, p = .11, CFI = 1.00, NFI = .99, TLI = .99, RMSEA
= .03. All factor loadings were above .77. Descriptive statistics and
bivariate correlations for all variables are reported in Table 1.

Table 1. Bivariate correlations, means and standard deviations.

1 2 3 4 5

1. Privacy Concern --

2. Affective Reactance .37**

3. Intention -.11* -.21**

4. Website Credibility -.05 -.04 .05

5. Brand Trust .09 -.08 .27** .01

M 4.43 3.08 3.37 N/A N/A

SD 1.35 1.53 1.49 N/A N/A

Notes: *p < 0.05 level (2-tailed), **p < 0.01 level (2-tailed)
Results
Manipulation Check
Two 2-way ANOVAs were conducted as manipulation checks. The
factor of brand trust (but not website credibility) significantly
predicted perceived brand trust: F(1,420) = 338.81, p < .001. As
expected, the mean values for low brand trust conditions (Buzzfeed:
M = 2.82, SE = 0.11; Forbes: M = 2.76, SE = 0.12) were both lower
than those for the high brand trust conditions, respectively
(Buzzfeed: M = 4.90, SE = 0.13; Forbes: M = 5.00, SE = 0.12). The
factor of media credibility (but not brand trust) significantly predicted
perceived website credibility: F(1,420) = 123.97, p < .001. As
expected, the mean values for Buzzfeed conditions (low brand trust:
M = 3.77, SE = 0.11; high brand trust: M = 3.64, SE = 0.13) were

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both lower than those for the Forbes conditions, respectively (low
brand trust: M = 4.96, SE = 0.11; high brand trust: M = 5.06, SE =
0.12). No interaction between website credibility and brand trust was
found on these manipulation check measures.
Hypotheses Testing
Three hierarchical linear regressions were conducted (see Table 2).
In the model on privacy concern, brand trust and website credibility
were entered in the first step (both were dummy coded: high =1, low
= 0). In the model on affective reactance, brand trust and website
credibility were entered in the first step and privacy concern was
entered in a second step. In the model on purchase intention, all
predictors in the prior model were entered, as well as affective
reactance in a separate final step.

Table 2. Results of multiple regression analyses.

Privacy concern Affective reactance Purchase intention
2 2
Predictor β ΔR β ΔR β ΔR2
Block 1 .2% 1% 11%
Brand trust .07 -.09 .33***
Website credibility -.06 -.05 .06
Block 2 -- 14%*** 2%*
Brand trust -- -.12* .34***
Website credibility -- -.02 .05
Privacy concern -- .38*** -.12*
Block 3 -- -- 2%**
Brand trust -- -- .32***
Website credibility -- -- .05
Privacy concern -- -- -.06
Affective reactance -- -- -.16**
2
Total R .2% 14% 14%
Notes: *p < .05, **p < .01, ***p < .001.

H1 postulated that brand trust would be negatively related to privacy

concern for a behavioral ad. Contrary to that expectation, results
show that brand trust failed to emerge as a significant predictor of
privacy concern, β = .07, p = .20. Therefore, H1 was not supported.

H2 posited that brand trust would be negatively related to affective

reactance for a behavioral ad. Results reveal that in the final model,
brand trust was a negative predictor of affective reactance, β = -.12,
p = .02. H2 was therefore supported. Interestingly, the effect of brand
trust was nonsignificant in the first step, when privacy concern was
not accounted for.
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H3 asserted that brand trust would be positively related to purchase
intention for a behavioral ad. Brand trust was shown to positively
predict purchase intention, β = .32, p < .001. Hence, H3 was
supported.

H4 through H6 proposed that website credibility would be negatively

related to privacy concern and affective reactance, as well as
positively related to purchase intention for a behavioral ad. Results,
however, show that website credibility failed to emerge as a
significant predictor of privacy concern (β = -.06, p = .27), affective
reactance (β = -.02, p = .62), nor purchase intention (β = .05, p =
.33). Hence, H4 through H6 were not supported.

H7 proposed that privacy concern would be positively related to

affective reactance. Results suggest that privacy concern was a
positive predictor of affective reactance, β = .38, p < .001. Therefore,
H7 was supported. H8 posited that affective reactance would be
negatively related to purchase intention. Results show that affective
reactance was a negative predictor of purchase intention, β = -.16, p
= .003. Therefore, H8 was supported.

Figure 2. Results of hypothesized model.

Furthermore, Hayes’s (2017) Process Macro (version 3.1, Model 6

for serial mediation tests) was used to address the hypothesized
mediation effects in H9 and H10 (see Table 3). H9 proposed a serial
mediation effect from brand trust to purchase intention via privacy
concern and affective reactance. The direct effect of brand trust on
purchase intention was significant (b = -.90, SE = 0.14, p < .001).
Even though the mediation effect through privacy concern and
reactance consecutively was non-significant (b = -.01, SE = 0.01,
95% CI [-.03, .0003]), the indirect effect through only reactance was
significant (b = .04, SE = 0.02, 95% CI [.004, .08]). That is, brand
trust reduces affective reactance toward the behavioral ad and
further enhances purchase intention. Therefore, H9 was partially
supported.
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Finally, H10 predicted a serial mediation effect from website
credibility to purchase intention via privacy concern and reactance.
None of the indirect paths were significant. H10 was thus not
supported.

Table 3. Results of serial mediation tests from brand trust and website credibility.
Paths b (SE) p / CI

Direct effect

Brand trustàPrivacy concern .25 (.13) .06

Brand trustàAffective reactance -.33 (.14) .02

Brand trustàPurchase intention .90 (.14) < .001

Privacy concernàAffective reactance .44 (.06) < .001

Privacy concernàPurchase intention -.06 (.05) .25

Affective reactanceàPurchase intention -.14 (.05) .002

Indirect effect

Brand trustàPrivacy concernàPurchase intention -.02 (.02) [-.06, .01]

Brand trustàAffective reactanceàPurchase intention .04 (.02) [.004, .08]

Brand trustàPrivacy concernàAffective reactanceàPurchase -.01 (.01) [-.03, .0003]

intention

Direct effect

Website credibilityàPrivacy concern -.14 (.13) .28

Website credibilityàAffective reactance -.08 (.14) .57

Website credibilityàPurchase intention .11 (.14) .43

Privacy concernàAffective reactance .42 (.05) < .001

Privacy concernàPurchase intention -.02 (.06) .78

Affective reactanceàPurchase intention -.20 (.05) <.001

Indirect effect

Website credibilityàPrivacy concernàPurchase intention -.002 (.01) [-.02, .03]

Website credibilityàAffective reactanceàPurchase intention .02 (.03) [-.04, .08]

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 JoCTEC: Journal of Communication Technology
Website credibilityàPrivacy concernàAffective reactance .01 (.01) [-.01, .04]

àPurchase intention

Notes: b = unstandardized b coefficients, SE = standardized errors, p = probability value, and CI = bias-

corrected 95% confidence intervals.
Discussion
Despite the fast-growing applications of online behavioral
advertising, this emerging platform elicits considerable concern
about data security and information privacy. This study focused on
the impact of trust on affective and behavioral responses toward
behavioral advertising from the perspective of privacy concern.
Specifically, by applying and extending psychological reactance
theory, the present study was among the first to examine trust
separately for the brand and the hosting website. The impacts on
three main variables related to online behavioral advertising were of
particular interest: privacy concerns, reactance, and advertising
outcomes (i.e., purchase intention). Moreover, the current framework
went beyond measuring direct effects by examining the serial
mediation of brand trust to understand the relationship between
these variables. The study highlights the importance of risk concerns
and trust-related issues in determining consumer psychological and
behavioral responses to online advertising.

Experimental results indicate that brand trust influences affective

reactance and purchase intention, but not privacy concern. On the
one hand, this finding supports previous conceptual discussions
(Rony, 2018; Schumann et al., 2014) and empirical findings (Utami
& Agus, 2019) on personalized ad effects, suggesting that low-
reputation brand negatively impacts consumer perceptions,
prompting increased reactance and decreased likelihood to make a
purchase. In other words, behavioral ads from a trusted brand may
provide personally relevant and useful information to better serve
consumer needs (Office of Fair Trade, 2010), which could reduce
reactance toward the ad.

More interestingly, the effect of brand trust on affective reactance

only reached significance when privacy concern was also added as
a predictor in the model. This suggests that the positive impact of
brand trust is distinct from the negative impact of privacy concern on
the emotional reactions toward the ad. After controlling for privacy
concern, the facilitating function of brand trust on reactance was
clearly manifested. The results may inspire industry practitioners to
design general trust-building strategies, while considering privacy
red flags that may induce negative responses from the audience.

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JoCTEC: Journal of Communication Technology
On the other hand, the lack of significant influence of brand trust on
privacy concern contradicts past empirical results in location-based
personalized advertising (Schade et al., 2018). A possible
explanation could be that behavioral advertising is already disdained
by consumers, due to the intrusion and privacy risks that it poses.
Hence, distrust in a brand may not further aggravate privacy concern
toward behavioral ads from that brand. As Bergström (2015)
suggests, consumers usually lack a sense of trust in behavioral
advertising, which is unlikely to be offset by trust in a specific brand.
An alternative explanation could stem from artifacts in the study
design. In the absence of a pre-test, the ad stimuli may not have
been sufficiently robust to generate privacy concern, despite
triggering affective reactance. As research on this theoretical
question remains limited and may be subject to specific contexts,
future research should further explore the relationship between these
variables.

Unlike brand trust, website credibility does not exert main effects on
privacy concern, reactance, or purchase intentions. This finding was
inconsistent with previous empirical findings on generic online
display ads (Phelan et al., 2016; Um, 2017). As Aguirre et al. (2015)
reported, a less credible website negatively moderated the impact of
ad personalization on click-through intentions. Yet the current study
shows that such an influence may not be replicated in other
dependent variables. The results may also suggest distinctive effects
and mechanisms for first-party versus third-party behavioral
advertising. Here, the outside publishing website, as part of a third-
party-based behavioral advertising model, does not make a
difference in how respondents react to the ad and evaluate the
brand. The results may reflect that behavioral ads are more
commonly seen in online users’ daily web browsing experience. The
degree of website credibility does not make a substantial difference
in the attempts to collect user data, nor allow demonstrations of these
ads. Users may have also become savvier in attributing their
aversion to the brand or advertiser, rather than a behavioral ad
vehicle, thus diluting the transfer effect from perceptions of the
medium to those of the message.

The present study also contributes to psychological reactance

theory, substantiating and extending the conceptual understanding
of the psychological reactance concept, where, brand trust can
dampen whereas consumer privacy concern can enhance
psychological reactance. Psychological reactance toward a
behavioral ad can also directly affect purchase intention. The
mediation analysis also demonstrated that the effect of brand trust
on purchase intention was mediated through reactance, but not
privacy concern. This finding has shed light on the underlying

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JoCTEC: Journal of Communication Technology
mechanism of user reactions toward behavioral advertising. The
nonsignificant path through privacy concern suggests that
individuals’ reactance toward a behavioral ad may not depend on
their perceptions of privacy but operates instead through other
possible considerations. For example, in the presence of behavioral
ads, individuals may feel intrusion on freedom and less control
regarding accessibility to other brands (Bleier & Eisenbeiss, 2015a).
This could trigger reactance, even when it has nothing to do with
concerns about privacy. Compared to a low trust brand, a high trust
brand will reduce such reactance, with a more reliable offer rendering
less need for alternatives.

On balance, the current study has implications for both digital

advertising practitioners and regulators alike. Behavioral advertising
may be accepted or rejected for different reasons, depending on
contextual factors, which entail careful delineations of user
motivations and reactions. One may still exhibit reactance toward a
behavioral ad or hesitancy to patronize a brand, in which situational
brand trust would make a difference. Advertisers and brands need to
consider prior brand reputation and image when producing
behavioral ads. Our results suggest that companies would be wise
to 1) deliver behavioral advertising to individuals who have already
formed initial trust in their brand, and 2) enhance the legitimacy and
reliability of their products/services in both the content and design of
behavioral ads targeting new users. Considering the importance of
brand trust, it may also be beneficial for advertisers to engage in trust
building activities, including improving transparency in their data
tracking practices and giving consumers more control in managing
their personal data (Chellappa & Sin, 2005).

The stronger role played by brand trust relative to website credibility

(in response to behavioral advertising) may also offer insight to
marketing practitioners with third-party-based behavioral ads.
Although contextual factors of the hosting medium, such as matching
between website and ad content (Anagnostopoulos et al., 2007),
could differentially impact acceptance of the ad, its credibility may
not be as salient as the reputation of the brand per se. Instead of
turning to the vehicle where the ad will be shown, it is more important
to emphasize building a positive brand image and boosting
consumer confidence and comfort in patronizing the brand.

Finally, given the potential for increasingly obtrusive consumer

surveillance mechanisms, enhanced regulation and educational
programming is necessary to foster industry standards and enhance
consumer understanding of and trust in behavioral advertising. More
specific regulations are needed to facilitate the transparency and
consumer control of advertising/marketing practices of companies—

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JoCTEC: Journal of Communication Technology
and media—to disclose the underlying mechanisms in a more
straightforward and user-friendly fashion. In the face of proposals to
reign in the power of big tech, Facebook executives (Egan, 2020,
Para 3) seem keenly aware of the need to acknowledge these user
concerns, while pointing out the beneficial affordances supported by
such advertising: “These services—from search and social
networking, to video calls and private messaging—are all available
to people for free. And they're free because they're supported by
advertising. It's not a stretch to say that much of today's Internet has
been brought to us by ads.”

Those executives go on to trumpet advances made in online

advertising since the 1990s (e.g., reductions in SPAM, including
content blocked or overlaid with “flashing, annoying ads”). The fact
that such concerns delayed the rise of digital advertising—prompting
businesses to focus on TV and print advertising through the early
2010s—underscores the need to consider the kinds of user concerns
identified here. The fact that digital modalities now subsume the
lion’s share of advertising revenue stands testament to the enhanced
reputation, in the eyes of businesses and consumers alike, that
personalized (i.e., SPAM-free) online advertising now enjoys. As
these affordances help increase efficiency and render a less
intrusive platform, media literacy educators should foster a critical
understanding of online behavioral advertising amongst the public by
explaining how it is produced and disseminated. Information on
innovative forms facilitated by technological development will also
need to be updated. Facebook’s recent feature, which identifies the
actual party uploading user information for promotional purposes and
provides an opt-out option, represents a pioneering move along
these lines (Constine, 2019).
Limitations and Future Research
The present study has some limitations. First, this study did not use
a pre-test to capture the participants’ levels of privacy concern and
affective reactance before treatment for comparison. Second, the
manipulation based on word-of-mouth, rather than real interactions
or experiences with the brand, may be less effective in inducing
brand trust. Given the high valuation to which consumers assign to
their privacy (e.g., Millham & Atkin, 2016), subsequent work could
profitably extend this investigation to gauge one’s willingness to post
behavioral advertising-related information online. It may also be
beneficial to test and compare different sub-types of behavioral
advertising for these theoretical links; for example, generic
retargeting (i.e., an ad that showed a generic ad for the same product
type) versus dynamic retargeting (i.e., an ad that contained an image
of the specific product the consumer had previously browsed)
(Lambrecht & Tucker, 2013). Finally, later work could consider the

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JoCTEC: Journal of Communication Technology
role that these and related constructs (e.g., third-person effect) play
in determining public support for restrictions on such emerging forms
of advertising (e.g., Youn et al., 2000).
Conclusion
Although past research has examined various content, contextual,
and user factors influencing the effectiveness of online behavioral
ads, more insight is needed on the inter-relationships involving
consumer understanding and evaluations of the ad, the brand, and
the website. Study results found that brand trust significantly
determines affective reactance, which further affects purchase
intention. Results shed light on the importance of customer-brand
relationships in shaping acceptance/rejection of personalized
messages in an online context.

Xiaowen Xu (PhD, University of Connecticut) is an assistant professor in

the College of Communication at Butler University. She is interested in
how new media technology and mass media influence consumer, health
and environmental motivation, attitudes and behaviors.

Tai-Yee Wu is an assistant professor in the Institute of Communication

Studies at National Yang Ming Chiao Tung University. He is interested in
the use of new communication technologies and the effects of such use.
His current work largely focuses on the practices of user-generated
content on a variety of communication-related topics, including news
discussions (online news comments) and online marketing (consumer
product reviews).

David J. Atkin is a professor in the Department of Communication at

University of Connecticut. His research interests include the diffusion of
new media and program formats, media economics and
telecommunication policy. He is a coauthor and coeditor of three books,
including The Televiewing Audience, Communication Technology and
Social Change, and Communication Technology and Society: Audience
Adoption and Uses.

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JoCTEC: Journal of Communication Technology

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Appendix

High Brand Trust:

“GoTravel is a bus company. You have heard of it from your friends

several times before. This year one of your family members starts to
take the bus for business trips to Boston and New York. You heard
from him/her that the bus is new and comfy, and the schedule is
reliable. The drivers are very professional and friendly. They also offer
free, high-speed WiFi so he can always stay online while travelling on
the road.”

Low Brand Trust:

“GoTravel is a bus company. You have heard of it from your friends
several times before. This year one of your family members starts to
take the bus for business trips to Boston and New York. You heard
from him/her that the bus is obsolete, and arrives late several times.
The staff members are not friendly either. The WiFi on board is slow. It
constantly disconnects, and takes forever to load any social
networking site or text messages.”

High/Low Website Credibility:

“You are planning a trip to New York for spring break and searching on
Google for bus tickets at the beginning of the semester. The next day
when you browse The Forbes website (high)/ the Buzzfeed website
(low), you see the following ad post from the bus company GoTravel,
on the right-hand side of the screen. Please read this ad carefully and
then answer some questions later.”

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