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Consumer online Persuasion Knowledge
and perceptions of online targeting Master thesis, MSc in Marketing Management
Author: Egle Zilinskaite
Coach: Jason Roos
Co-reader: Gijs van Houwelingen
6th June, 2014
Keywords: targeting, persuasion, online advertising, Persuasion Knowledge Model.
Picture courtesy of grantmuller.com.au
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Table of Contents Executive Summary .............................................................................................................................. 2
1. Introduction.........................................................................................................................................3
2. Problem and contribution .................................................................................................................. 5
3. Theoretical background ..................................................................................................................... 5
3.1 Persuasion Knowledge......................................................................................................5
3.2 Targeting...........................................................................................................................7
4. Hypotheses ...................................................................................................................................... 11
5. Variables ......................................................................................................................................... 13
5.1 Key variables ................................................................................................................ 13
5.2 Other control variables ................................................................................................. 14
5.3 Persuasion Knowledge variable construct .................................................................... 14
6. Experiment design ......................................................................................................................... 176
6.1 Condition construct ...................................................................................................... 16
6.2 Experiment flow ........................................................................................................... 17
6.3 Pre-test .......................................................................................................................... 19
7. Data ................................................................................................................................................. 19
7.1 Sample............................................................................................................................19
7.2 Manipulation check .......................................................................................................20
7.3 Coding: Independent variable Persuasion Knowledge...................................................21
8. Research findings .......................................................................................................................... 232
8.1 Results of hypothesis testing ........................................................................................ 22
8.2 Exploratory analyses .................................................................................................... 26
9. Results and discussions ................................................................................................................... 29
10. Limitations and future research ..................................................................................................... 33
11. Conclusion ..................................................................................................................................... 35
12. References......................................................................................................................................36
13. Appendixes.....................................................................................................................................47
The author declares that the text and work presented in this Master thesis is original and that no sources other than
those mentioned in the text and its references have been used in creating the Master thesis. The copyright of the
Master thesis rests with the author. The author is responsible for its contents. Rotterdam School of Management,
Erasmus University is only responsible for the educational coaching and cannot be held liable for the content.
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“Almost overnight, the Internet’s gone from a technical wonder to a business must.”
– Bill Schrader, businessman
“There’s never been a better time to be in advertising, and there’s never been a worse time.”
– Aaron Reitkopf, North American CEO of digital agency Profero
Executive Summary
Online advertising is a vastly growing portion of company's advertising budget since it offers "reduced
cost of targeting" (Goldfarb, 2013, page 1). Online targeting enables advertisers to select specific
individuals for their communications instead of target groups, often based on demographics, associated
with traditional media.
Previous research on online targeting clustered around targeting systems and their optimization,
privacy concerns and field comparisons of targeting techniques. In comparison, this study focuses on
advertisement effectiveness from the consumers point of view, by comparing two targeting techniques:
demographic targeting, that employs users' online demographic data, and behavioral targeting, that
tracks users' online behavior to determine the best receiver for the advertisement.
Additionally, the study measured participants' persuasion knowledge (Friestad and Wright, 1994) of
online persuasion and assesses the impact it has on advertisement evaluation and purchase intent. In
contrast to other studies measuring persuasion knowledge by participants self reporting perceived
manipulation intent or skepticism, this study employed the practice of Boush et al. (1994) of
objectively evaluating the persuasion knowledge by asking the participants to specifically identify the
targeting stimuli used in the advertisements.
The results showed significant differences in evaluations of advertisements with advertisements
displaying demographic (gender, occupation) stimuli to be evaluated 12.3 % (0.86 point on a 7 point
scale) lower than advertisements with behavioral (location, product choice) stimuli. However, in
comparison to no targeting stimuli displayed in the advertisement, behavioral targeting was evaluated
only 2.4 % (0.17 point) higher and 9.6% (0.67 point) higher than both, behavioral and demographic,
targeting stimuli displayed (Appendix 2.2.2 B). Overall, demographic targeting showed a strong
negative effect (β = -.705) on advertisement liking, while behavioral targeting although had a positive,
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but a statistically insignificant effect. Both variables proved no statistically significant effect on to
purchase intent, which is mostly due to the lack of statistical power in the sample (estimated 1200
observations needed in comparison to the 120 in the study sample).
The objectively evaluated persuasion knowledge proved to explain 5.2% of advertisement evaluation
with a positive impact (β = .103). Most interestingly, persuasion knowledge showed to have modulus
relationship, with advertisement liking increasing away from 0 persuasion knowledge evaluation in both
positive and negative evaluations. This suggests that consumers who have inferences of being targeted
(even if they those are incorrect) to have more positive attitudes towards the advertisements. Overall,
persuasion knowledge, behavioral targeting and demographic targeting explain 12% of change in the
advertisement evaluation, while in turn advertisement evaluation and perceived benefit explain 50.7%
of purchase intent.
The study results have grate implications for all marketers employing online advertising, since the
study reveals that displaying demographic targeting to have strong adverse effects on advertisement
evaluation. While the study does not raise the question of targeting by delivering the message to only
the selected users, it raises the question of displaying the selection information to those users, since the
benefits of displayed targeting (presumably behavioral targeting that yields best advertisement
evaluations) were found to be statistically insignificant. Taking into account the cost of producing
multiple variations of the advertisement to display, the displayed targeting may prove to be
economically inefficient, favoring plainer and to the point advertisements. What is more, objectivelly
measured persuasion knowledge, although explaining just over 5% of variation in advertisement
evaluation, proved to have a positive effect for users with correct and incorrect evaluations of the
displayed targeting stimuli, signifying the inferences of targeting to associate with more attention and
processing of the advertisement.
Finally, privacy concerns showed to have no significant effect on advertisement evaluation or purchase
intent, in line with some previous research.
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1. Introduction
Internet changed the world, and also advertising. Being the "prominent feature of economic
life"(Bagwell, 2007, page 1705), advertising is shifting to online with online ad spending having
outgrown the print in the US in 2012 and continuously narrowing the gap to TV ad spending with a
yearly growth of over 10% in the US and Europe (eMarketer, 2012, 2014). This shift is predominantly
due to the "reduced costs of targeting" which became a dominant subject of literature on online
advertising (Goldfarb, 2013, page 1). By gathering users' data online, companies can address the online
advertisements to an exactly specified set of consumer (IEEE, 1997), thus reducing the costs associated
with advertising to non-target market - a prominent problem for offline media.
Consumers are not passive receivers of this improved communication, but actively learn about
persuasion and adjust their perceptions and reactions to online advertising. Hollis (2005) reported
response rates to banner ads to have declined significantly over the years, while users were found to
actively ignore them (Drèze and Hussherr, 2003). The number of studies about online privacy and its
influence on user behavior appeared (White et al. 2008; Tsai et al. 2011; Tucker, 2011 and 2012;
Goldfarb and Tucker, 2012a,b) with findings of consumers valuing their privacy in the exchange for
more personalized advertising.
There is a need to understand if consumers are able to distinguish the different techniques marketers are
using in online targeting and how this knowledge affects consumer perceptions of the online
advertising and the brands using them. With regard to offline media, research on consumer perceptions
and reactions to advertising applied Persuasion Knowledge Model (PKM) developed by Firestad and
Wright (1994) constructed around persuasion knowledge - a set of believes about the tactics advertisers
use to influence consumers. However, the model received little application in the context of online
advertising. With the online targeting getting more subtle and sophisticated (Goldfarb, 2013), what
knowledge do consumers have about this tactic used to persuade them?
This study will focus on consumer evaluations of online targeted advertising and the influence of
persuasion knowledge on that evaluation. Research will compare the effectiveness of demographic and
behavioral targeting techniques as well as evaluate consumers' persuasion knowledge with regard to
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online targeting and assess the importance of these factors in advertisement evaluation and purchase
intent.
2. Problem and contribution
Within the scope of this thesis I seek to find if consumers can distinguish different targeting stimuli and
if this capability affects consumer evaluations of the online advertisements and purchase intent.
Additionally, the two dimensions of targeting are compared: demographic and behavioral targeting, to
assess the potential effectiveness, but also the interaction with persuasion knowledge.
Research on targeting and online persuasion knowledge would firstly help advertisers in their choice
for targeting techniques as well as give more understanding on the consumers' persuasion knowledge in
the online environment as a potential friction. With regard to academic contribution, the study expands
the highly limited knowledge of persuasion knowledge effects online as well as in controlled laboratory
environment and compares the results with previous findings resulting in further research proposals.
3. Theoretical background
3.1 Persuasion Knowledge Model
Consumers develop persuasion knowledge (Friestad and Wright, 1994) - "dynamic knowledge
structures about persuasion", and exercise it "to identify and cope with other people's attempts to
influence them" (Wentzel et al. 2010, page 513). Persuasion knowledge is formed based on consumers'
beliefs (rather than a specific facts) about persuasion, for example, about the marketer's goals, used
tactics, how that tactics affect the consumer herself (grab attention, evoke emotions and raise interest)
as well as how to successfully cope with the persuasion attempt (Friestad and Wright, 1994; Kirmani
and Zhu, 2007). Consumers learn about persuasion over time and with exposure to the tactics used,
which they learn to recognize as such through "change of meaning" process (Friestad and Wright,
1994). This implies that if consumers do not perceive an action as a tactic, it will not activate
persuasion knowledge and will result in greater compliance. When persuasion knowledge is evoked,
consumers have greater suspicion and may perceive marketers as manipulative and deceptive (Wentzel,
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2010), which usually creates resistance to persuasion (Campbell and Kirmani, 2000, Carlson, Bearden
and Hardesty, 2007)
Persuasion Knowledge Model
(Friestad and Wright, 1994, page 2
Friestad and Wright emphasize the consumers'
capacity to learn about persuasion over time
and exposure (Williams et al. 2004), thereof,
every individual has a different set of beliefs
about the ways advertisers use certain
background information to try and influence
them.
Persuasion knowledge plays a role on both the
agent’s and target's side: it helps the agent to
craft a persuasive message and for the target to
assess it, along with employing the topic
knowledge (beliefs about the topic of the
message) as well as the agent knowledge
(beliefs about the goals, competencies and traits
of the persuasion agent) and target knowledge
(beliefs about the target audience) for the agent
(Friestad and Wright, 1994, page 3).
Persuasion knowledge is especially significant when targets do not have a prior knowledge of the topic
or the agent in order to "generate a valid product and agent attitude" (Friestad and Wright, 1994, page
10) to facilitate message comprehension. Boush et al. (1994) found higher levels of knowledge about
advertiser's tactics to be related to skepticism towards advertising. As a result of persuasion attempts,
individuals develop persuasion coping knowledge (Friestad and Wright, 1994) that guides them to
possible response options and goals as well as evaluates the best coping tactics based on situational
information and contains useful information of the interpreting and coping process. Persuasion coping
knowledge is presumably connected to people’s knowledge of the persuasion tactics, constructs and
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executions of influence attempts, because of the ease with which the roles of the agent and target get
switched in everyday life in individual relationships (e.g. influencing your friends to pick a movie you
prefer).
Studies on persuasion knowledge
There were several experiments researching persuasion knowledge in consumers. Campbell and
Kirmani (2000) conducted 2 studies comparing the use of persuasion knowledge in evaluating a sales
person (persuasion agent) while manipulating cognitive capacity and ulterior motive (flattery before or
after the purchase). Proportion of suspicious thoughts and evaluated attempt to persuade was used as a
proxy for persuasion knowledge. The researchers found the ease of accessibility of persuasion agent's
ulterior motives and the available cognitive resources to have a positive influence on the use of
persuasion knowledge (while less foreseeable motives and low cognitive capacity to decrease the use of
persuasion knowledge) and participants to evaluate the sales person as less sincere. Additionally, it was
found that priming the ulterior persuasion motives increased the use of persuasion knowledge of
cognitively busy participants.
Williams et al.(2004) manipulated persuasion knowledge by having participants read about the "mere
measurement effect" before being asked to participate for a charitable organization and thus inflicting
the "change of meaning" in the intention questions. The study confirms the moderating effect of
persuasion knowledge (measured by the self-reporting perceived persuasion intent) of seeing the
intention question as a persuasion tactic, once respondents are educated about it. Additionally,
Williams et al. (2004) found intent questions attributed to a self-interested source to result in negative
effects on willingness to participate in comparison to no identifiable source. Based on these two study,
the experiment had a clearly identifiable source, deliberately informed the participants of the use of
their personal data and briefly explained targeting, in order to ensure participants the relative ease of
access of persuasion knowledge.
With regard to content of the advertisements, Wentzel, Tomczak and Herrmann (2010) found
advertisements written in a narrative rather than factual form to be evaluated more favorably.
Furthermore, researchers varied manipulative intent and participants' cognitive resources and found
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that if the manipulative intent was salient or if the participant's available cognitive resources were low,
the advantage of narrative advertisement to disappear because of the activated persuasion knowledge.
As the mentioned studies by Campbell and Kirmani (2000) and Williams et al. (2004), the study
manipulates persuasion knowledge by presumably activating it in one of the conditions. However, with
exception to the experiment by Boush et al. (1994) in which the persuasion knowledge development in
adolescence over time was measured, no other experiment objectively measured the degree to which
persuasion knowledge is activated (with exceptions to non-objective participant self reports) as well as
the carry over effects of relatively little or extensive knowledge of persuaders' tactics.
Hibbert et al. (2007) conducted a more extensive study of effects of persuasion knowledge, also
inferred from manipulative intent and skepticism towards the advertisement, and the agent knowledge
(comprised of evaluations of affective and cognitive beliefs) in the context of guilt appeals and
charitable giving. The experiments results showed persuasion knowledge to directly (weaker, positive
effect) and indirectly (through guilt appeals, a stronger negative effect) explain 23% of the variation in
donation intent. Based on this finding, the following experiment seeks to evaluate the extent of
persuasion knowledge effects in the online environment.
Additional studies employing persuasion knowledge were conducted by Hardesty et al. (2007)
researching Price Tactic Persuasion Knowledge, and Artz and Tybout (1999) with experiments
manipulating agent credibility with expertise, bias and message format. Other studies on concentrated
on the type of expression the persuasion was executed, such as rhetorical questions (Ahluwalia and
Burnkrant 2004) analogies (Roehm and Sternthal 2001) and the development of persuasion knoweldge
in children (meta-analysis by Martin, 1997) and adolescence (Whright et al. 2005). However, they did
little to objectively evaluate or measure the effects of the activated persuasion knowledge.
3.2 Targeting
Agent’s persuasion technique and execution is often dependent on the consumer segment the agent
wants to reach, that is the target market - a set of buyers with common characteristics or needs that was
identified by the company with intention to serve (Kotler, Armstrong and Starr, 1991). Agents use the
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knowledge they have about the target market, their needs, wants, aspirations, interests, etc. in order to
persuade them, that is they target the specific segments within the market (Iyer et al. 2005).
In the online environment, this typically involves systems that "collect user data, apply web and data
mining techniques to this data and ultimately select the best-matching advertisement for the given user
or user group" (De Bock and Van den Poel, 2009, page 4). Targeting, and personalization at an
individual level, are based on the idea that advertisement effectiveness will be improved by distributing
it accurately, based on web user's behavior and characteristics (Gallagher and J. Parsons, 1997).
Thereof, the targeting depends on what user data is used to attribute the advertisement to a specific
user. Four levels of online targeting can be drawn: Behavioral, Demographic, Behavioral and
Demographic, and no targeting.
No targeting - is the advertisement that is displayed online to users not based on their
specific data. This type of advertising does not use any user information, but relies on the
media selection to address the appropriate audience. That is, the advertisement is
displayed on a specific media vehicle by matching the target audience with the inferred
audience profile of that media based on data provided by research bureaus (Gallagher and
J. Parsons, 1997).
Demographic targeting - an advertisement selected for a specific user based on the his or
her individual demographic data, such as gender, age, occupation, income, family
lifecycle stage (Gallagher and J. Parsons, 1997). Gender is treated as one of the most
significant demographic factors predicting intent to purchase (Jansen and Solomon,
2010), with experimental findings that men and women significantly differ in their
processing of information (Meyers-Levy 1988, 1989, Mayers-Levy and Sternthal 1998,
Carsky and Zuckerma, 1991) and advertising preferences (Bezjian-Avery et al. 1998,
Putrevu, 2002, Morrison and Shaffer, 2003). Online display advertising in the 90s mainly
employed user demographic targeting by placing advertisements to the desired
demographic audiences based on the website audience, similarly as in television.
Nowadays, demographic targeting can be very sophisticated by gathering user's online
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information, including information from various online profiles and account (Goldfarb,
2013).
Behavioral targeting - is the advertisement attributed according to the information about
the targeted user's web search and browsing behaviors (Yan et al. 2009), for example
historical visit patterns, search term usage or clickstream data (De Bock and Van den
Poel, 2009) to determine a good match. One of the forms of behavioral targeting is
"retargeting" which displays advertisement to a user based on a particular content they
saw or searched for (Goldfarb, 2013). Field study by Yan et al. (2009) found user
segmentation based on the responses to online advertisements to be significant in user
similarity (with ratio of within over between segments as high as 91).
Behavioral and Demographic targeting - is the employing the combination of both
demographic and behavioral data of a particular individual in order to display the best
matched advertisement (Ngai, 2003).
Studies on online targeting
Previous research found some differences in the effectiveness of different types of targeting. A survey
by American Advertising Federation (2006) found 52.4% of respondents to identify the behavioral
targeting as most effective, while 32.9% preferred demographic targeting. However, Hollis (2005)
noted that socio-demographic characteristics are often the determinants of the advertisers' target
groups. Jansen, Moore and Carman (2012), when testing for effectiveness of key phrases in sponsored
search advertisements, found gender-orientated key phrases to predict behaviors and performance in
consumers significantly, as suggested by Deaux's (1984) theory of social categories. However,
interestingly, they discovered gender neutral phrases to perform best overall, suggesting the theory of
individual differences (Motowidlo, Borman, and Schmit, 1997) predicting the unique cognitive,
contextual, affect and psychological needs to have an overriding effect on individual processing of
information.
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Further in online advertising research, Tucker (2011) studied the click-through rates of a nonprofit
organization’s Facebook advertisements that were personalized based on the data of user's background
(college attended) or interests (celebrity pages indicated as “liked”). Found advertisements targeted
based on interest to have marginally higher click-trough rates than background ones as well as
personalized advertisements incorporating a highly distinctive user information (those addressed as
facebook fans of a local singer with few thousands of followers as opposed to over 2 million fans of
Oprah Winfrey) to be relatively more effective in comparison to generic advertisements in the context
of high user control of their privacy. Similarly, in the off-line setting, research by Aaker et al. (2000)
demonstrated consumers to adopt favorable attitudes as a result of strong and favorable target market
effects based on distinctive traits (black versus white as non-distinctive; heterosexual versus
homosexual as non-distinctive) portrayed by the source.
However, the two studies by White et al (2008) examining the email personalization influence on click
through rates found messages with low levels of distinctiveness to outperform those of high
distinctiveness in the absence of justification for the usage of distinctive user data, while chick through
rates did not vary if the justification was present, essentially making an argument against
personalization and the use of distinct user information.
Other studies on online advertising and targeting (as discussed by Goldfarb 2013) cluster around the
topics of market effects (Chen et al. 2001; Iyer 2005; Gal-Or and Gal-Or 2005; Athey and Gans 2010;
Levin and Milgrom 2010; Bergemann and Bonatti 2011; Athey et al. 2011; Acquisti 2012; Goldfarb
and Tucker 2012a), targeting effectiveness with regard to sales (Chatterjee et al. 2002; Manchanda et
al. 2006; Lewis and Reiley 2012, 2014), targeting type (Lambrecht and Tucker 2011; Bart et al. 2012;
Tucker 2012), auction mechanisms (Liu and Chen 2006; Varian 2007; Edelman et al.2007; Agarwal et
al. 2009; Edelman and Schwarz 2010; Chen and He 2011) and privacy concern influence on
advertising effectiveness (Turow et al. 2009; Tsai et al. 2011; Goldfarb and Tucker 2011a,b; Jansen et
al. 2012; Malheiros et al. 2012).
While concentrating on performance or optimization of particular targeting (Yan et al. 2009; Jansen,
Moore and Carman, 2012) or of distinctive and non-distinctive information in the advertisement (White
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et al. 2008; Tucker, 2011) few studies concentrate on comparing different types of targeting in online
advertising and their effectiveness. An exception would be the field experiment by Goldfarb and
Tucker (2011b) exploring the effectiveness of banner ads with contextual targeting (advertisement that
matches the website content) and high visibility (obtrusiveness), that found these separate effects to
increase purchase intent, but to be ineffective when used in combination. Tucker's (2011) study gives
an indication for comparing demographic (user's background) and behavioral (selective favoring of a
specific celebrity) targeting and in the absence of other comparative studies, will be used to develop
hypothesis for behavioral and demographic targeting effectiveness.
Previous research suggests personalized or distinctively targeted advertisements to yield more
favorable consumer responses and attitudes. Furthermore, it implies that consumer should be aware of
the targeting techniques used to persuade them. However, with regard to persuasion knowledge,
previous research relied heavily on participant self reported skepticism or manipulation intent as
determinants of the persuasion knowledge activation. However, none attempted to objectively evaluate
the Persuasion Knowledge activated, with an exception to the study by Boush et al. (1994), as
discussed before.
In terms of online advertising, Goldfarb (2013) notes three general categories: search advertising,
classified advertising and display advertising. Display advertising includes banned ads, plain text ads,
video ads, media rich ads and ads on social media websites. It is the second revenue creator for online
media after search engines and offers a wide array of targeting possibilities (Goldfarb, 2013). This is
why this study will use still display advertisements for the manipulation of targeting.
4. Hypotheses
Based on the PKM and previous research on persuasion knowledge and online advertising, the model
was developed to test the influence of demographic and behavioral targeting as well as participant's
persuasion knowledge on advertisement liking and conversion to intention to purchase.
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The model builds on the targeted online advertising experimental research (excluding field research),
that measures the effect of targeting on advertisement effectiveness (Tucker, 2011, White et al. 2008, )
and purchase intentions (Goldfarb and Tucker, 2011b, Jansen et al. 2012).
H1 A: Targeting positively affects advertisement liking and purchase intention.
More specifically, based on online targeting research, it is expected to find behaviorally targeted
advertisements to be reported more favorably (American Advertising Federation, 2006).
H1 B: Advertisements with behavioral targeting are more effective than advertisements with
demographic targeting.
Additionally, based on research by Tucker (2011), it can be expected that the more cues of
personalization the advertisement shows, the more it will be liked, due to favorable target market
effects and personalization. However, the study does not focus on personalization, which requires
differentiating between the distinctive and non distinctive user information, and therefore would fail to
draw conclusive results on personalization effectiveness.
Differently to online advertisement targeting research, this experiment incorporates persuasion
knowledge to test for its effect in the online advertising context. Since persuasion knowledge varies at
an individual level, previous research manipulated it by increasing participants' suspicion of advertisers
manipulative intent (see Williams et al. 2004, Hibbert et al. 2007) or combining it with availability of
cognitive resources (see Wentzel et al. 2010, Campbell and Kirmani, 2000), essentially varying the
activation of the individual persuasion knowledge. Instead, this study seeks to account for an individual
level of persuasion knowledge activated (as measured by Boush et al.1994) and the effects of activated
persuasion knowledge on advertisement evaluation. Based on the PKM and the research of the effects
of activated persuasion knowledge (Williams et al. 2004, Hibbert et al. 2007, Wentzel et al. 2010, ), it
is expected that participants with low activation of persuasion knowledge will adopt more favorable
attitude towards advertisement and will have higher intensions of purchase, because they will be less
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likely to question the advertiser's claims than those with activated persuasion knowledge (Friestad and
Wright 1994, Boush et al. 1994).
H2: Persuasion Knowledge will have a negative effect on Advertisement Liking.
Based on the hypothesis and previous research on online advertising as well as persuasion knowledge,
a model was drawn to test the overall influence of the demographic as well as behavioral targeting and
persuasion knowledge on the relationship between the advertisement liking and purchase intent.
Additionally, a relationship between targeting and persuasion intent is inferred, based on the Persuasion
Knowledge Model, predicting that persuasion knowledge can be activated not only against the effects
of advertisement, but the targeting as an influence technique itself. Based on this hypothesis, a model
was established.
The model predicts a strong effect of advertisement liking on purchase intent moderated by persuasion
knowledge against the advertisement and demographic as well as behavioral targeting, which both in
turn are also affected by persuasion knowledge against targeting.
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5. Variables
5.1 Key variables
Purchase Intention - the dependent interval variable for this study is purchase intention, measured by
asking the participants "After seeing the advertisement, how likely would you be to apply for and use
the Campus+ card?" with a 7-point Likert scale for answers ranging from "Very Unlikely" to "Very
Likely".
Ad Liking - independent interval variable derived from 7-point Likert scale on a question "Do you like
this advertisement?" with answers ranging from "Dislike Extremely" to "Like Extremely".
Persuasion Knowledge - interval variable based on evaluating answers to the advertisement targeting
stimuli identification question: "Did the advertisement target you bases on: Gender, Occupation,
University degree, Location, Weekly meal budget, Product choice" with possible answers being
"Disagree", "Neither Agree nor Disagree", "Agree". Based on the condition randomly assigned to the
participant, his or her answers were evaluated as correct (+1), incorrect (-1) or absent (0), creating an
evaluation scale with a range of -6 to 6 (variable construct choice is discussed in the next section).
Demographic Targeting - dichotomous variable representing the presence (1) or absence (0) of
demographic targeting stimuli in the advertisement shown. The demographic targeting is present in
second and fourth conditions.
Behavioral Targeting - dichotomous variable, that marks the presence (1) or absence (0) of behavioral
targeting stimuli in the advertisement.
5.2 Other control variables
Suspicion - dichotomous variable derived from coding participants' answers to "Were you suspicious at
any point? If you were, give as much detail as possible" into 1 if suspicions was present and 0 if
suspicion was not present. This variable is set up to control for participants who infer the purpose and
manipulation of the study and may present demand effects.
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Privacy concerns - dichotomous variable coded based on participants' answers to "Did you have any
privacy concerns with regard to access of your data to target the advertisement?" by coding 1 for any
expression of concern and 0 for the absence of any privacy related concern.
Beneficial - interval variable derived from a 7-point Likert scale question "How beneficial is the card to
you?" with answers ranging from "Very Useless" to "Very Useful".
5.3 Persuasion Knowledge variable construct
In order to evaluate participants' Persuasion Knowledge, the variable was coded based on the number
of targeting stimuli each participant identified correctly (depending on the randomly assigned
condition).
Wright et al. (2005), described children's Persuasion Knowledge as implicit, automatically evoked and
primarily used “as a cognitive resource” (page 230), but difficult to orally report as an answer to a
direct question. Nevertheless, inferences about the advertisement persuasion tactic knowledge can be
inferred from accuracy and timeliness of the response (page 230). Authors suggest that capitalizing “on
the effects that an activated knowledge structure leaves on memory traces” is a valid measurement of
persuasion knowledge in children: “children who have implicit knowledge of an advertiser's specific
advertising tactic available in memory and access that in ad processing will thereby alter what they
remember about the advertisement's contents in predictable ways.” (page 230).
In their study, Boush et al. (1994) coded the “knowledge about Advertiser tactics” on a scale of -12 to
+ 12 based on logical associations between of four different tactics and 3 different effects in pair-wise
comparisons, by giving 1 point for correct identification, -1 for incorrect and 0 for “don’t know” (page
168). Hardesty, D., Bearden, W., Carlson, J. (2007) accounted participants who demonstrated
knowledge-related thoughts as Persuasion Knowledge as a reaction to an visual promotion with a
discount claim in their study on Price Tactics Persuasion Knowledge.
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Multiple authors presented scales to measure respondents' response to persuasion tactics. Participants'
skepticism towards a persuasion attempt is measured on a 7-point Likert scale asking respondents to
express their extent of agreements on multiple statements related to the advertisement, for example, the
advertisement is telling the truth. The scale was firstly created by Rossiter (1977) in the study on
children's opinions on TV advertising, but since was modified, among it, by Boush et al. (1994).
The scale by MacKenzie and Lutz (1989) measures credibility of the advertisement on a 7-point
discrepancy scale by incorporating questions determining unbelievable-believable, unbiased-biased and
unconvincing-convincing.
Finally, the scale developed by Campbell (1995) measures Inferences of Manipulative Intent (IMI) that
also employs a 7-point Likert scale when determining the extent of participants' agreement or
disagreement to statements about the manipulative intent of the advertiser.
Although the above described scales were used in numerous surveys and behavioral experiments
exploring persuasion knowledge and coping with persuasion attempts (Cotte et al. (2005), Wentzel et
al. (2010), Sagarin et al. (2002), Lafferty and Goldsmith, (1999), Burns and Lutz (2006), Riecken and
Samli (1981), Chan and McNeal (2003)), all three of them were incorporated only by Hibbert et al.
(2007) in the study on guilt evoking advertisements and intensions to donate, and the source credibility
(scale by MacKenzie and Lutz, 1989) was found to have no significant influence in arousing the feeling
of guilt.
This study relies on coding the Persuasion Knowledge variable, as opposed to determining it from
participants' self-reported impressions in order to impartially evaluate respondents' persuasion
knowledge with regard to behavioral and demographic targeting . Therefore, the study follows the
practice of Boush et al. (1994) and Hardesty et al. (2007) of evaluating which answers are correct and
present evidence of stimuli recognition and coding them based on the own developed point system.
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6. Experiment design
6.1 Condition construct
The goal of the study is to determine whether the audience finds behavioral or demographic targeting
more appealing and whether being able to identify the targeting stimuli influences the liking of the
advertisement and the purchase intention. To measure these factors, an online questionnaire,
introducing a cash-less card for food and drink expenses on campus was created.
The questionnaire comprises of 4 conditions (based on a 2 x 2 design between demographic and
behavioral targeting) that are randomly assigned to each participant (using "Randomize option" on
Qualtrics.com platform with the "Evenly Present Elements" switched on):
1) No targeting (Control): Picture of the card, link to the website and the slogan “Get 15% off food
& drinks on campus!”
2) Demographic targeting: A female or male student picture, link to the website, picture of the
card and a slogan “EUR student, Get 15% off on food & drinks on campus!”
3) Behavioral targeting: picture of a product of choice (sandwich or coffee), link to the website
and the card next to it with a slogan “Get your daily meal (cup) with 15% off! You are 40
meters away from the nearest cafe on campus”.
4) Demographic and Behavioral targeting: picture of a product of choice (sandwich or coffee),
female or male student picture, link to the website, picture of the card and a slogan “EUR
student, Get your daily meal (cup) treat with 15% off! You are 40 meters away from the nearest
cafe on campus”.
This way the study employs a between subject (between group) design, comparing how participants,
exposed to one of the 4 differently targeted advertisements, evaluate the advertisement and the
product.
The study simulates a market research questionnaire asking participants to evaluate pieces of marketing
communications from Marketing and Communications department of Erasmus University Rotterdam
(EUR). The questionnaire presents the students with “Campus+” – a cashless payment card for EUR
cafeterias, offering an instant 10% discount (15% for Coffee/Tea or Sandwiches depending on the
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individual choice) whenever the card is used as the payment method, to be launched in September
2014. The card system in the study is based on cash-less payment system “Eating@warwick”
(www2.warwick.ac.uk/services/retail/eating/) used at University of Warwick (similar cashless systems
are established at University of Reading and others) in order to create a realistic situation. However, the
non-existing brand is necessary to avoid participant’s prior perceptions as well as to minimize the
impact of topic knowledge and agent knowledge which all have high influence on persuasion episode
evaluations (Friestad and Wright, 1994).
Goldfarb (2013) stresses the overall lack of well-identified research on the advertising effectiveness
and the external validity issues for laboratory experiments, Lewis and Rao (2012) revealed field
experiments to often fail to demonstrate the conversion of online advertisements into actual sales, due
to the combination of low probability of a conversion from a single exposure to an ad and
unpredictability of individual-level sales, that can only be overcome with very large samples.
Nevertheless, a controlled laboratory setting was chosen to enable the measurement of persuasion
knowledge activated in participants at the different levels of targeting, which would be unreliable in a
field experiment were participants are exposed to multiple distractions around their natural
environment.
6.2 Experiment flow
At the very start, participants are requested to submit their university (student/staff) number and later
notified that the advertisements they see is based on their student record. Using university computers
with internet connection in the behavioral laboratory helped to create a realistic setting of Ethernet
connection with access to university student and staff data. Nevertheless, the usage of individual data
was repeatedly stated (" It <The advertisement> was assigned to you based on personal data available
from university records and online information"; "According to your data, this would be the
advertisement communicated to you via email or "facebook").
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All participants were presented with a single advertisement to be evaluated (Appendix 1). The
advertisement shown was determined by the randomly assigned condition (no targeting, demographic
targeting, behavioral targeting, demographic and behavioral targeting) and consequently was also
selected based on the gender of participant and the individual choice of the product for extra discount.
In order to manipulate the usage of participants’ data from university database, two online studies were
created. They both were identical apart from display advertisements that either features male or female
student. Depending on the gender of the participant, the research assistant would choose the appropriate
study link, thus ensuring the manipulation. Nevertheless, Qualtrics software would randomly assign the
experimental condition of the participant (1 out of 4 equally for males and females), leaving the
research assistants blind to the experimental condition. The product choice treatment was based on the
participant’s individual choice from discount for “coffee/tea” or “sandwiches” that was requested prior
to the display of the advertisement. Additionally, prior to exposure to the advertisement and the
conditional treatment, participants were asked to identify the usefulness of the card (Beneficial
variable, all variables as discussed in sections 5.1 and 5.2) based on the introductory information.
All advertisements feature the card design, the website url for more information and the background
picture of campus cafeteria in order to create credibility for the experiment cover.
After exposure to the advertisement, participants were asked to evaluate the advertisement (Ad Liking
variable), its effectiveness (to control for the meaning of Ad Liking) and the likelihood of using the
card (Purchase Intension variable). Participants then were informed that the advertisement is targeted
and their personal data was used, asked to look at it again before identifying up to 5 stimuli that the
advertisement is targeting them on (Persuasion Knowledge Open Text variable, discussed in section 7.3
Coding), in order to explore the persuasion knowledge unguided by the question construct for
exploratory analysis. After moving to a separate window, participants were asked to check the multiple
item scale to identify which stimuli were used in the advertisement presented to them (Persuasion
Knowledge variable) and if they had any privacy concerns (Privacy concerns variable). Participants
were asked if the targeting was inappropriate or ineffective and had a separate bar to record any
comments. Demographic data was collected and finally participants were asked about suspicion of the
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experiment (Suspicion variable) to determine any experimental demand effects (Orne, 1962), after
which they were debriefed.
6.3 Pre-test
The survey was pretested with 11 participants resulting in clarification of 4 questions and shortening of
the introductory text. Additionally, 8participants agreed to a participation in a study related interview
during which the opinions and perceptions of the conditional treatment were gathered. The unequal
distinctiveness of targeting stimuli was identified and resulted in the change of demographic targeting
employing the addressing to the reader by "EUR student" instead of "Student". After the change, the
differences in advertisements (in four conditions) were confirmed to reflect targeting data by the 8 pre-
test participants.
7. Data
7.1 Sample
The experiment was conducted in the Erasmus Behavioral Lab as the final part of a 30 min study by
prof. Nicole Mead between 29th
July and 13th
of August, 2013. The study consisted of a behavioral
study, a separate computer based questionnaire and the experiment discussed here. The first experiment
asked participants to read a piece of text expressively and without any expression before doing a
cognitive task of naming colors in which color names were written in an matching and then non-
matching manner. The second study did not manipulate any emotions, but asked participants to
evaluate hotel descriptions and criteria priorities. Tyler and Burns (2008), as well as research by Oaten
et al. (2008), found depleted cognitive resources to re-establish in 10 minute period between regulatory
tasks to a level of non-depleted and a 3 minute interval of purposeful relaxation to effectively replenish
the resources, therefore suggesting that the initial manipulation in the first experiment would have worn
out by the 3rd study taking place approximately 20 minutes later.
In total 125 participants finished the study and received the payment for participation.
The participants' age ranged between 18 and 38 (M = 24, SD = 3.42), 68 males and 55 females,
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nationals of 25 countries with majority of Dutch citizens (51.6%). 110 identified themselves as
Erasmus university students (53.6% of Bachelor students, 46.4% master students, 12 students
attending degrees in marketing or media studies), 6 - staff members and 4 participants identified as
"Other". 5 participants were mis-targeted and consequently removed from the experiment data for
manipulation validity purposes (discussed below). Furthermore, Suspicion variable and comments
section were inspected for indications of demand effects, but none of the participants were eliminated.
The final dataset had 120 participants' data.
The data was screened for outliers and the distribution of continuous variables was visually explored.
While Purchase Intension and Ad Liking variables resembled normal distribution, the Benefit and
Persuasion Knowledge variables do not (Appendix 2.1), however, due to the sample size, Central Limit
Theorem is assumed (explained further in Regression Analysis, page 19). Since there were no outliers
found and approximate normality is assumed, it was decided to leave the original data and use robust
tests (bootstrapping) as recommended by Fields (2013, page 196).
7.2 Manipulation check
Since the manipulation was to inflict an appropriate targeting to each participant, the back story of
experiment was designed based on the sample of participants available - Erasmus university students.
Based on the data collected, all participants who were not within this group, were identified, and if they
were exposed to the demographic targeting (which in that case would be inappropriate), were
eliminated for the final data. The appropriateness of targeting was determined by participant's
occupation question ("student", "staff", "other"), requirement to fill in Erasmus university student/staff
number, gender and the advertisement targeting suitability question ("Was the targeting inappropriate
or inefficient in any way (particularly to you)?"). As a result of occupation (confirmed by screening the
student/staff number), the data of 5 participants were removed because they were randomly assigned to
the condition with demographically targeted advertisements, which failed to target them appropriately.
Answers with regard to gender showed all participants to be assigned to the correct experiment version
(as explained in the "Experiment flow" section). Finally, the targeting suitability question which was
inspected for any identification of mis-targeting, but no participants were removed as a result.
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No additional manipulation check was used for several reasons. Firstly, the study does not employ
emotion infliction or latent variables, that demand manipulation checks for validity. Additionally, the
limitations of experiment conduction in the behavioral laboratory, in particular, time constriction and
participants' attention focus, permitted the employment of at least two manipulation and two confound
checks as well as two indicators of the dependent variables, as suggested by Sawyer et al. (1995). The
confidence in construct of the targeting conditions is drawn from the pre-test interviews with 8
participants who confirmed the conditional differences visible in the advertisements to reflect targeting
and as a result exact advertisements were used in the study. Pre-tested manipulation check is one of the
suggested methods by Kidd (1976) in order to avoid unwanted mere-measurement effects.
7.3 Coding
As an independent variable representing the intrinsic knowledge about persuasion, Persuasion
Knowledge variable had to be coded based on the participants' answers to the multiple choice question.
Independent Variable - Persuasion Knowledge
The Persuasion Knowledge variable was derived by asking participants to identify the factors based on
which the advertisement targeted them using multiple answer scale. Participants were asked if they
were targeted by Gender, Occupation, University degree (decoy choice), Location, Budget (decoy
choice) and Product Choice, and were able to choose an answer "Disagree", "Agree", "Neither Agree
nor Disagree". The answers were then coded into scores: 1 for correct identification, -1 for incorrect, 0
for "Neither Agree nor Disagree" option (as in the study by Boush et al. (1994)), the total score having
a range of -6 to 6.
Persuasion Knowledge Open Text variable
Persuasion Knowledge Open Text variable was measured and coded in order to account for participants
'generated thoughts regarding targeting.
This study relies on coding the Persuasion Knowledge variable, as opposed to determining it from
participants' self-reported impressions in order to impartially evaluate respondents' persuasion
knowledge. Therefore, the study follows the practice of Boush et al. (1994) and Hardesty et al. (2007)
23 | P a g e
of evaluating which answers are correct and present evidence of stimuli recognition and coding them
based on the own developed point system.
Persuasion Knowledge variable was coded based on the agreed score of two independent coders. Based
on the condition, and therefore the available stimuli, 1 point was assigned for identified gender, product
choice, location and occupation stimuli. 0 points were assigned for participants who failed to identify
the stimuli or identified it incorrectly. However, based on Persuasion Knowledge Model, persuasion
tactic and stimuli are not only decided by the Agent, but also, equally can be perceived as such by the
Target, therefore 2 additional stimuli that were recognized by participants were added to the
assessment. They were "Age", based on the picture of the student available in conditions with
demographic targeting (2 and 4) and "Background/Cafeteria", as recognition of the background picture
of the Erasmus University cafeteria used in all conditions. The additional stimuli were identified by 6
and 31 participants respectively. This shifter the range of Persuasion Knowledge variable's available
range from 0 - 4 to 0 - 6, nevertheless, the real range was 0 - 4 with only 4 participants having
identified 4 of the 6 stimuli in the open text question.
The exploratory results of Persuasion Knowledge Open Text variable are discussed in the section 8.2
Exploratory research.
8. Research findings
8.1 Results of hypothesis testing
8.1. 1 H1A: Targeting positively affects advertisement liking and purchase intention.
In order to test for consumers reactions to different types of advertising targeting, one-way Analysis of
Variance (ANOVA) was employed to compare the Ad Liking and Purchase Intention variable means
in differently targeted advertisements (Appendix 2.2.1).
The results show Ad Liking variable to be significantly different (F = 10.428, p = .002) when
demographic targeting is absent (M = 4.92) and present (M = 4.24). The test is also significant with
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Purchase Intention as a dependent variable (F = 8.448, p = .004), with average purchase intention at
4.54 in the presence of demographic targeting and 5.3 in the absence.
In terms of behavioral targeting, the mean differences of Advertisement Liking as well as Purchase
Intention are insignificant (p > .05).
Based on these results, the hypothesis has to be rejected. While demographic targeting creates
significant difference in Ad Liking as well as Purchase Intention, the behavioral targeting has no
significant effect on either.
8.1. 2. H1B: Advertisements with behavioral targeting are more effective than advertisements with
demographic targeting.
For the direct comparison of behavioral and demographic targeting effects, a dummy variable was
coded with demographic targeting present marked by 0 (as a base line) while the presence of behavioral
targeting was coded as 1, leaving all other values (of no targeting or demographic and behavioral
targeting) as missing and creating a sub-sample of 61 participants.
The independent samples t-test showed significant difference in Ad Liking when comparing
behavioral and demographic targeting (as a baseline), with p = .003, t = -3.152. Therefore,
advertisements with behavioral targeting on average were evaluated more positively (M = 5.0) than
advertisements with demographic targeting (M = 4.14) (Appendix 2.2.2).
Therefore, the hypothesis is not rejected, and the data are consistent with behavioral targeting yielding
more liking than demographic targeting. However, taking into account the rejection of previous
hypothesis, the positive difference is insignificant in comparison to advertisements with no targeting.
To better compare the targeting effects on Ad Liking and to take the non-normal distribution on the
variables into account (p < .05 for Kolmogorov-Smirnov test; p ≤ .01 for Shapiro-Wilk test for the
normal distribution of Ad Liking), a non parametric Kruskal-Wallis test was employed. After ranking
the data (Fields, 2013) the test showed significant differences between the targeting conditions (H(3) =
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9.27, p = .026). However, the follow up analysis of the differences (Pairwise Comparison), seeking to
identify the significant differences between conditions, revealed no significant differences after
adjustment for the number of tests. The greatest ranked mean difference was between demographic (M
= 48.52) and behavioral (M = 70.59) targeting, however, insignificant. An analogical test was
conducted with the Purchase Intention as the dependent variable with an insignificant result (p > .05).
Therefore, while there are differences in evaluation of advertisements with demographic and with
behavioral targeting, those differences were not found to be significant.
8.1.3 H2: Persuasion Knowledge has a negative effect on Ad Liking.
In line with Persuasion Knowledge Model, participants with activated persuasion knowledge are
expected to adopt a less favorable attitude towards the advertisement and less intent to purchase than
those, who did not have their persuasion knowledge activated. A means graph reveals a non-linear
relationship between the Persuasion Knowledge and Ad Liking variables (Appendix 2.2.3). The
regression analysis show Persuasion Knowledge to be significant (F = 6.511, p = 0.12, β = .103) and
explain 5.2% of variance in Ad Liking.
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Tests to further evaluate the strenght of relationship as well as to take into account the effects of
targeting were performed. Ad Liking was regressed on Persuasion Knowledge, Demographic targeting
as well as Behavioral targeting (Appendix 2.2.3.2). The full model was significant (F = 3.908, p = .005)
and independent variables explained 12 % of advertisement evaluation (R2 = .12). Within this model,
Persuasion Knowledge had a significant effect (t = 2.061, p = .042) and influenced Ad Liking with a
regression coefficient of .087, signaling a positive and significant yet small effect. Demographic
targeting was also significant (t = -2.344, p = .021) and had a strong negative influence over Ad Liking
with a coefficient -.705 while behavioral targeting failed to have significant influence as did the
interaction of behavioral and demographic targeting (p > .05).
Based on the test results, the hypothesis is rejected, since persuasion knowledge demonstrated
significant and positive effect on advertisement evaluation, although one of weak influence.
8.1.4 The model test, regression analysis
In order to test the test the model presented and establish the importance of each independent variable,
a hierarchical multiple regression test was used by employing SPSS Statistics (IBM statistical software
by IBM, version 22). Regression estimates were performed following the methods by Fields (2013). To
start with, the data was examined for outliers and Komorov-Smirnov and Shapiro-Wilk test for
normality of distribution was run with null hypothesis for normal distribution. None of the continuous
variables was approved to have normal distribution. However, the Central Limit Theory predicts the
sufficiently large samples (N ≥ 60) to be approximately normal, therefore this issue is ignored (study
sample size is 120).
The regression analysis of the theoretical model was tested based on steps (blocks of effects), entering
the main effect of the variables in to the first block (if possible, control variables first, to establish a
baseline) and further interaction effects in subsequent blocks according to the number of independent
variables in a single interaction effect.
The regression analysis output is in Appendix 2.2.4.
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Firstly, the 4 block model shows the R2
change between the 4 different regressions to change from R2
=
.29 in model 1 to R2 = .35 in model 4, but at each model after the 1st one, the ratio of improvement
change (F change) is insignificant (respectively: p2 = .201, p3 = .431, p4 = .31). ANOVA tests of the
models show all of the models to be significant in explaining the variance in Purchase Intent variable,
however, the F-rate falls from 11.742 in the 1st model to 4.802 in the 4th model.
The simple effects analysis (Coefficients table of the regression output) between the models shows only
Ad Liking variable to have a significant effect (t = 5.985, p = .000) with a coefficient in the 1st model β
= .611 (unstandardized). It is the only variable that has significance in one and all of the regression
models, with β = .624 in the 2nd model, β = .567 in the 3rd and β = .539. Notably, confidence intervals
for the Ad Liking coefficient are relatively tight, ranging from .408 to .813 in the 1st model and from
.305 to .773 in the 4th model. All other variables across the 4 models fail to have significance in
contribution to the model.
This analysis is enough to conclude the model established based on the literature research to be rejected
and invalid, indicating that the data sample did not provide results for expected relationships between
the measured variables.
8.2 Exploratory analyses
Firstly, in order to better understand the nature of persuasion knowledge in the online environment, the
coded Persuasion Knowledge Open Text variable was explored. As presented mentioned before,
Persuasion Knowledge Open Text variable was coded based on the participants' generated thoughts
with regard to targeting in the advertisements. The descriptive statistics (Appendix 2.2.5.1) of the
variable show 29.2% of participants' generated thoughts to identify none of the stimuli present in the
advertisement while 34.2% and 24.2% identified 1 or 2 stimuli representatively. 3 stimuli were
mentioned by only 10%, while 4 stimuli - by 2.5% of participants. Since the number of stimuli to
identify differs between conditions, the percentages were transformed to represent the average number
of stimuli identified by participants in each condition as a percentage expression of the total number of
stimuli within that condition.
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Within "no targeting" and the "behavioral and demographic targeting" conditions, the average
participant identified just over 30% of all the stimuli, while participants in "demographic targeting"
condition on average mentioned around 25% of stimuli. In comparison, participants exposed to
behaviorally targeted advertisements, mentioned 50% of the stimuli present in the advertisement. This
trend is confirmed by the core Persuasion Knowledge variable (Appendix 2.2.5.1 B) derived from the
multiple choice variable, with on average significantly more stimuli identified in the behavioral
targeting condition (M = 2.13 out of 6, section 7.3 Coding).
0%
10%
20%
30%
40%
50%
60%
no targeting demographic targeting
behavioural targeting
demograhic and behavioural
Mean as % expression of the range
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The distribution of Persuasion Knowledge variable resembles normal distribution, with notably
majority of participants (27.5%) to score 2 in identifying targeting stimuli from a multiple choice
question, while second greatest groups was those received a 0 score (15.8%). Overall, 29.2% of
participants received a negative score, while 55% - positive.
Further analyses were conducted seeking to uncover additional relationships and support the
interpretation of the main results. These analyses mainly employed the control variables used in the
study design. Firstly, the potential influence of Suspicion and Privacy Concerns variables was tested
with Analysis of Variance (ANOVA). There was no significant influence found on the Ad Liking or
Purchase intention variables by either of control variables (p > .05 in all tests). What is more, a fit in
the regression model also proved insignificant by both control variables (p > .05, Appendix 2.2.5.2 and
2.2.5.3). However, the Privacy Concern variable was not independent and randomly probable in the
four conditions of targeting (X2 (3)=10.228, p = .017), with behavioral targeting condition having the
highest Privacy concern rate (N = 15) (Appendix 2.2.5.2).
Additionally, simplified model with Persuasion Knowledge, targeting (condition variable) and Benefit
variables explaining the variance in Ad Liking was tested. The Analysis of Covariance (ANCOVA)
showed significant results (F = 4.181,p = .002) and the level of explanatory power of 15.5% (R2 =
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.115) (Appendix 2.2.5.4). In this analysis the Benefit variable had significant influence (F = 4.761, p =
.031) as did Persuasion Knowledge (F = 5.747, p = .018), but not the targeting variable representing the
condition participants were in (F = 2.533, p =.06). This analysis suggests that while persuasion
knowledge may not be significant at predicting Purchase Intention, it has significant influence on
advertisement evaluation and even overrides the effects of targeting. An additional regression analysis
revealed Benefit and Ad Liking to explain 50.7% of variance in Purchase Intention (Appendix 2.2.5.4
B). The significance of Benefit variables in both analyses indicates one of the previously unknown
components in the variation of advertisement evaluation and purchase intention.
9. Results and discussions
The study sought to analyze the performance differences, based on advertisement likability and
purchase intention, of advertisements with demographic and with behavioral targeting stimuli.
Additionally, the study accounted for effect of persuasion knowledge (based on PKM by Friestad and
Wright, 1994), which have been neglected in the previous online advertising research.
Testing the separate hypotheses it was found that although the targeting had significant influence on
advertisement liking, that influence failed to carry on to the purchase intention (with additional
analyses showing Ad Liking and Benefit variables to explain 50.7% of variance in Purchase Intention).
The results of each hypothesis will be discussed separately.
9.1 H1 A: Targeting positively affects advertisement liking and purchase intention.
Demographic targeting was found to have a significant effect on advertisement evaluation. However, it
had a negative effect on advertisement evaluation, while behavioral targeting increased the liking of the
advertisement insignificantly. The results are in contrast to the findings in favor for more distinct
targeting by Tucker (2011).
9.2 H1 B: Advertisements with behavioral targeting are more effective than advertisements with
demographic targeting.
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A survey by American Advertising Federation (2006) found 52.4% of respondents to identify the
behavioral targeting as most effective, while 32.9% preferred demographic targeting. Additionally,
Tucker's (2011) study inferred the behavioral targeting (base on interests) to outperform
advertisements with demographic targeting (based on background information). The direct comparison
of means of advertisement evaluation tests reveal significant differences, with advertisements targeted
behaviorally to be evaluated more positively (by 0.86 point on a 7 point scale). However, with regard
to results of the H1 A hypothesis testing, positive evaluation from behavioral targeting is not
significantly different from no targeting used. It is rather the demographic targeting that has a negative
effect on advertisement evaluations.
This result is unexpected and unidentified by previous studies, majorly, because they focused on
optimization (through personalization and distinctiveness of the advertisements or economic factors) ,
rather than consumer evaluations of the different targeting techniques. While demographic data is vital
in determining the appropriate targeting in off-line media, the results suggest consumers to dislike it
when used in online media, even in comparison to no targeting portrayed by the advertisement. This
result suggests the different expectations consumers have for online advertisements to the extent were
plainer and less clotted advertisements, displaying no targeting stimuli are favored more than those
employing sophisticated targeting techniques (differences between no targeting and behavioral
targeting to be insignificant).
9.3 H2: Persuasion Knowledge has a negative effect on Ad Liking.
The hypothesis built on the PKM predicting persuasion knowledge to raise suspicion of the advertiser's
tactics and therefore having a negative effect on advertisement evaluation (persuasion episode) was
rejected. The Persuasion Knowledge variable demonstrated a weak, but significant and positive
influence on advertisement evaluation (Ad Liking), meaning that on average, participants with higher
knowledge of the targeting tactics used liked the advertisement better than those who had lover
knowledge. Nevertheless, the relationship between the variables is non-linear, as illustrated by the
means plot, but significantly correlated.
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Even incorrectly identified stimuli (the negative range of the Persuasion Knowledge variable) on
average yielded more advertisement liking than no stimuli identified. Negative persuasion knowledge
evaluation could signify the participant's lack of attentiveness to the advertisement as they incorrectly
identify the targeting stimuli displayed. However, persuasion knowledge as a belief about inferred
advertising tactics includes the perceived tactics that were not specifically used as a tactic by the
advertiser (Friestad and Wright, 1994, page 14). Therefore, the incorrectly identified stimuli do not
signal the absence of evoked persuasion knowledge, but opposite: an active processing of the message
and inferred targeting tactic perceptions. Therefore, the overall relationship between the variables
shows participants with active persuasion knowledge (regardless of positive or negative, but not
evaluated by 0) to on average score the advertisement as more likable. Notably, the graph (page 25)
depicting advertisement evaluations based on measured persuasion knowledge reveals the somewhat
symmetrical reflection around the axis at the persuasion knowledge evaluation of 0. While some of this
effect can be due to conditional differences in persuasion knowledge evaluations (discussed in page
28), the overall effect needs further research to reach a sufficient explanation.
As mentioned before, this goes against the Persuasion Knowledge Model, predicting the negative effect
of persuasion knowledge, which was confirmed by Williams et al. (2004) in the intention question
experiment and Hibbert et al. (2007) study of guilt appeals (persuasion knowledge comprised of
manipulative intent and skepticism towards the advertisement). However, the later found manipulative
intent to have a divergent twofold impact: a negative effect on guilt (mediator) and a direct positive yet
weak effect on charitable giving. The study therefore supports the importance of research of overall
impact of the persuasion knowledge, since it has been underestimated (Hibbert et al. 2007).
However, the context of online advertising and especially the measurement of persuasion knowledge
have to be taken into account when comparing the results with other studies..
9.4 Model testing
The theoretical model predicting targeting and persuasion knowledge to moderate the link between the
advertisement evaluation and purchase intention was rejected, with only advertisement evaluation
having a significant effect. Although, based on evidence from previous analyses, both targeting
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variables and persuasion knowledge had significant effects on advertisements evaluation, the effects
failed to carry through onto purchase intention. The low level of explanatory power (R2) of the model,
although is often in the online advertising literature (Goldfarb and Tucker, 2011; Lewis and Reiley,
2014), suggests the existence of additional variables and more complex relationships. What is more,
taking into account the extensive number of variables that create purchase decisions, a significantly
larger sample size (i.e. 300 participants per condition) might be needed to overcome sample power
issues and prove the influence targeting or persuasion knowledge may have on purchase intention.
The result may question the purpose of advertisement targeting online, but the lack of influence the
advertisers have on consumer decisions and limited control over consumer perceptions makes even
small affects important.
Additional analyses of the sample showed privacy concerns to have no significant influence over
purchase intention or advertisement liking, which contradicts the results in Tucker's (2011) study of
targeting in social media ads, but is in line with the empirical findings of Beresford et al. (2010).
9.5 Results of exploratory analyses results
The exploratory analysis of Persuasion Knowledge Open Text variable supported by results of
descriptive analysis of Persuasion Knowledge variable (scored based on multiple choice question
results) revealed majority of participants to generate targeting related thoughts and identify the stimuli
that the advertisements use to target them as presented by scores of 70.8% of participants measured
with the Persuasion Knowledge Open Text variable and 55% of participants to correctly identify some
of the targeting stimuli as presented by the multiple choice Persuasion Knowledge total scores.
Notably, based on both variables, participants identified comparatively most stimuli in the behavioral
targeting condition. Persuasion Knowledge Model and the findings of previous studies suggest that
knowledge of the advertisers' tactic has an adverse effect of trusting the advertiser and willingness to
conform (Friestad and Wright, 1994; Campbell and Kirmani, 2000; Williams et al. 2004). However,
while behavioral targeting generated greatest participant persuasion knowledge scores as well as
highest average of privacy concerns, it also the highest advertisement evaluation. Therefore the results
suggest that in the behavioral targeting to perform better in comparison to demographic targeting even
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against the expected negative results of persuasion knowledge (which proved to have a weak but
positive effect) and privacy concerns (insignificant mean differences of Ad Liking). The insignificant
influence of privacy concerns goes against the findings of Tucker (2011), however, it may be a
reflection of the rapid change of consumer perceptions on this topic.
Although the model employing effects of persuasion knowledge and targeting (demographic and
behavioral) proved insignificant, additional exploratory tests revealed the initial product (Benefit
variable) and advertisement (Ad Liking) evaluation to explain 50.7% of variance in purchase intent. In
comparison, effects of persuasion knowledge were significant when testing the influence on
advertisement evaluation. However, persuasion knowledge and initial product evaluation explain
15.5% in variance of advertisement evaluation. Therefore, overall, while persuasion knowledge has
influence in the prediction of advertisement evaluation, the effects do not carry through in the
evaluation of purchase intent.
10. Limitations and future research
When interpreting and generalizing the study results a number of limitations ought to be recognized.
First of all, although the simulated back-story of the market research questionnaire made a viable
attempt to resemble real life condition, the controlled laboratory environment restricts the
generalization to effects in the field (where for example effects described by Elaboration Likelihood
Model would take place). Particularly, the targeting stimuli used in the study can be argued to be
exaggerated in comparison to the advertisements running online. However, the use of real market
advertisements restricts the comparison possibilities as well as has a potential issue of previous agent or
topic knowledge. The distinct stimuli were chosen purposefully, to better clarify the effect
demographic and behavioral targeting techniques may have.
Since persuasion knowledge was evaluated based on a recognition of particular stimuli used to portray
demographic and behavioral targeting techniques, this study is only comparable to the results of
knowledge of advertiser tactic over time in adolescence in the study by Boush et al. (1994) which
unfortunately reported only the change of that knowledge over time. Although, the employed
35 | P a g e
persuasion knowledge scoring cannot be directly compared with the results found in other studies, to
the knowledge of the author, it is the first study that gives an indication of effects of the objectively
identified persuasion knowledge on consumers' advertisement evaluation and purchase intent.
Additionally, the statistics of this variable gives a base line for comparison in the further research and
the development of scale to objectively measure consumers' persuasion knowledge of online
advertisers' tactics.
Additionally, the limitations associated with the experimental construct and the participant sample are
recognized, as a different stimuli construct, experiment flow or the set of participants could yield
different findings. It is important to note that the findings only apply to the display advertisements in
the online context and should be compared to results of other studies with different media or
advertisement type with great restraint.
Nevertheless, the discovered negative effect of demographic targeting is of high importance to the
advertising industry that frequently employs it. Since the activated persuasion knowledge was even
higher in the participants exposed to behaviorally targeted advertisement, the dislike of the
demographically targeted advertisements cannot be explained by heightened resistance to the targeting.
Therefore, further exploratory research on why consumers dislike the demographic targeting is in
needed.
What is more, the rejected theoretical model signifies the complexity of explaining consumer behavior.
Hibbert et al. (2007) questioned the capacity of a single forced exposure to advertisement to capture the
effects on behavior. In order to assess the influencing factors in transition between online advertising
and purchase intention, future research should focus on determining the measurements and varying
research designs to uncover the complex powers and their interactions.
The findings suggest the need of future research on persuasion knowledge effects in the online
environment since findings violate PKM predictions. Additionally, although the results of targeting
technique effectiveness conform to those of Tucker (2011), further research is needed to test targeting
effectiveness in the different online mediums and among other targeting techniques.
36 | P a g e
11. Conclusion
The research builds on persuasion knowledge impact and contributes to the Persuasion Knowledge
Model in the context of online display advertisements. The experiment comprised demographic and
behavioral targeting to test the effects on advertisement evaluation and the overall influence of
persuasion knowledge on advertisement evaluation and purchase intent in the online context. To the
knowledge of the author this study is the first attempt to objectively measure consumers' persuasion
knowledge and its influence online.
However, the results of exploratory analyses on evaluated persuasion knowledge reveal consumers to
actively learn and be able to distinguish the information used to target the advertisements to them
online. The overall impact of persuasion knowledge on advertisement evaluation proved to be positive,
regardless if the knowledge was scored positively or negatively (when comparing to knowledge score
of 0). Although persuasion knowledge had no notable influence on purchase intent, it had a weak but
significantly positive effect on advertisement evaluation. This result questions the effects of persuasion
knowledge in the online context as it is contradicting to the knowledge of persuasion knowledge from
previous findings, notably in the context of offline media.
With regard to targeting, behavioral targeting proved to have the highest identified stimuli rates but
also the highest advertisement liking and privacy concern rates. In comparison, the study found
demographic targeting to have a negative effect on advertisement evaluation when compared to
behavioral targeting or no targeting. With regard to the wide and growing use of targeting in online
display advertising, the results have important and pressing implications for advertisers. The
combination of results implies that an advertisement targeted based on behavioral data of consumers
will outperform demographically targeted advertisement or advertisement without targeting.
Finally, the study brings to light the lack of academic research on consumers' perceptions and
processing of online advertising. The experiment results question the degree of influence of persuasion
knowledge in the online advertising context and suggest it plays a lesser role in online than in the
offline advertising.
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13. Appendix
Appendix 1
Condition 1 – No targeting:
Condition 2 – Demographic targeting:
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Condition 3 – Behavioral targeting:
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Condition 4 – Demographic and behavioral targeting
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Appendix 2 - Statistics (SPSS output)
2.1 Sample, Benefit variable
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Scatter plots:
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Correlations between the variables:
Pearson correlation for continuous variables
Correlations
Ad Liking Purchase
Intention
Beneficial Persuasion
Knowledge
Ad Liking Pearson Correlation 1 ,524** ,200
* ,229
*
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Sig. (2-tailed) ,000 ,029 ,012
N 120 120 120 120
Purchase Intention
Pearson Correlation ,524** 1 ,577
** ,077
Sig. (2-tailed) ,000 ,000 ,404
N 120 120 120 120
Beneficial
Pearson Correlation ,200* ,577
** 1 -,065
Sig. (2-tailed) ,029 ,000 ,477
N 120 120 120 120
Persuasion Knowledge
Pearson Correlation ,229* ,077 -,065 1
Sig. (2-tailed) ,012 ,404 ,477
N 120 120 120 120
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Spearman correlation with dichotomous variables
Correlations
Ad Liking Purchase
Intension
Beneficial Persuasion
Knowledge
Targeting Demographic
_targeting
Behavioral_
targeting
Ad Liking
Correlation Coefficient 1,000 ,508** ,186
* ,273
** -,062 -,271
** ,066
Sig. (2-tailed) . ,000 ,042 ,003 ,501 ,003 ,477
N 120 120 120 120 120 120 120
Purchase
Intension
Correlation Coefficient ,508** 1,000 ,540
** ,097 -,097 -,242
** ,012
Sig. (2-tailed) ,000 . ,000 ,294 ,290 ,008 ,895
N 120 120 120 120 120 120 120
Beneficial
Correlation Coefficient ,186* ,540
** 1,000 -,002 ,099 -,090 ,156
Sig. (2-tailed) ,042 ,000 . ,981 ,284 ,328 ,089
N 120 120 120 120 120 120 120
Persuasion
Knowledge
Correlation Coefficient ,273** ,097 -,002 1,000 ,211
* -,139 ,306
**
Sig. (2-tailed) ,003 ,294 ,981 . ,021 ,129 ,001
N 120 120 120 120 120 120 120
Targeting
Correlation Coefficient -,062 -,097 ,099 ,211* 1,000 ,433
** ,894
**
Sig. (2-tailed) ,501 ,290 ,284 ,021 . ,000 ,000
N 120 120 120 120 120 120 120
Demographi
c_targeting
Correlation Coefficient -,271** -,242
** -,090 -,139 ,433
** 1,000 -,016
Sig. (2-tailed) ,003 ,008 ,328 ,129 ,000 . ,861
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N 120 120 120 120 120 120 120
Behavioral
_targeting
Correlation Coefficient ,066 ,012 ,156 ,306** ,894
** -,016 1,000
Sig. (2-tailed) ,477 ,895 ,089 ,001 ,000 ,861 .
N 120 120 120 120 120 120 120
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
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Appendix 2.2 Hypothesis testing
2.2.1 H1A: Targeting positively affects advertisement liking and purchase intention.
Demographic targeting:
Descriptives
Ad Liking
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Minimum Maximum Lower Bound Upper Bound
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,00 61 4,92 1,069 ,137 4,64 5,19 2 7
1,00 59 4,24 1,236 ,161 3,92 4,56 2 6
Total 120 4,58 1,199 ,109 4,37 4,80 2 7
ANOVA
Ad Liking
Sum of Squares df Mean Square F Sig.
Between Groups 13,899 1 13,899 10,428 ,002
Within Groups 157,268 118 1,333
Total 171,167 119
Descriptives
Purchase Intent
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Minimum Maximum Lower Bound Upper Bound
,00 61 5,30 1,229 ,157 4,98 5,61 2 7
1,00 59 4,54 1,590 ,207 4,13 4,96 1 7
Total 120 4,93 1,462 ,133 4,66 5,19 1 7
ANOVA
Purchase Intent
Sum of Squares df Mean Square F Sig.
Between Groups 16,992 1 16,992 8,448 ,004
Within Groups 237,333 118 2,011
Total 254,325 119
Behavioral targeting:
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Descriptives
Ad Liking
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Minimum Maximum Lower Bound Upper Bound
,00 58 4,48 1,246 ,164 4,16 4,81 2 7
1,00 62 4,68 1,156 ,147 4,38 4,97 2 7
Total 120 4,58 1,199 ,109 4,37 4,80 2 7
ANOVA
Ad Liking
Sum of Squares df Mean Square F Sig.
Between Groups 1,136 1 1,136 ,788 ,376
Within Groups 170,031 118 1,441
Total 171,167 119
Descriptives
Purchase Intent
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Minimum Maximum Lower Bound Upper Bound
,00 58 4,88 1,488 ,195 4,49 5,27 1 7
1,00 62 4,97 1,448 ,184 4,60 5,34 1 7
Total 120 4,93 1,462 ,133 4,66 5,19 1 7
ANOVA
Purchase Intent
Sum of Squares df Mean Square F Sig.
Between Groups ,234 1 ,234 ,109 ,742
Within Groups 254,091 118 2,153
Total 254,325 119
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2.2.2 H1B: Advertisements with behavioral targeting are more effective than advertisements with
demographic targeting.
Group Statistics
Behavioral_baseline
demographic
N Mean Std. Deviation Std. Error Mean
Ad Liking ,00 29 4,14 1,187 ,220
1,00 32 5,00 ,916 ,162
Independent Samples Test
Levene's Test for
Equality of Variances
t-test for Equality of Means
F Sig. t df Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
95% Confidence Interval
of the Difference
Lower Upper
Ad Liking
Equal variances
assumed
7,974 ,006 -3,192 59 ,002 -,862 ,270 -1,402 -,322
Equal variances
not assumed
-3,152 52,547 ,003 -,862 ,273 -1,411 -,313
62 | P a g e
2.2.2 B Additional Ad liking means comparison between conditions, ANOVA
ANOVA
Ad Liking
Sum of Squares df Mean Square F Sig.
Between Groups 14,914 3 4,971 3,691 ,014
Within Groups 156,253 116 1,347
Total 171,167 119
Descriptives
Ad Liking
N Mean Std.
Deviation
Std.
Error
95% Confidence Interval for Mean Minimum Maximum
Lower Bound Upper Bound
no targeting 29 4,83 1,227 ,228 4,36 5,29 2 7
demographic targeting 29 4,14 1,187 ,220 3,69 4,59 2 6
behavioural targeting 32 5,00 ,916 ,162 4,67 5,33 3 7
demograhic and behavioural 30 4,33 1,295 ,237 3,85 4,82 2 6
Total 120 4,58 1,199 ,109 4,37 4,80 2 7
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Kruskal-Wallis test
Ranks
Targeting N Mean Rank
Ad effectiveness no targeting 29 67.98
demographic targeting 29 48.52
behavioural targeting 32 70.59
demograhic and behavioural 30 54.08
Total 120
Test Statisticsa,b
Ad effectiveness
Chi-Square 9.268
df 3
Asymp. Sig. .026
Monte Carlo Sig. Sig. .017c
95% Confidence Interval Lower Bound .000
Upper Bound .040
a. Kruskal Wallis Test
b. Grouping Variable: Targeting
c. Based on 120 sampled tables with starting seed 1560921806.
64 | P a g e
65 | P a g e
2.2.3 H2: Persuasion Knowledge has a negative effect on Ad Liking.
Persuasion Knowledge
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 ,229a ,052 ,044 1,172
a. Predictors: (Constant), Persuasion Knowledge
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 8,951 1 8,951 6,511 ,012b
Residual 162,216 118 1,375
Total 171,167 119
a. Dependent Variable: Ad Liking
b. Predictors: (Constant), Persuasion Knowledge
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
t Sig. B Std. Error Beta
1 (Constant) 4,520 ,110 41,129 ,000
Persuasion Knowledge ,103 ,040 ,229 2,552 ,012
a. Dependent Variable: Ad Liking
66 | P a g e
2.2.3.1 Persuasion Knowledge between the 4 conditions
ANOVA
Persuasion Knowledge
Sum of Squares df Mean Square F Sig.
Between Groups 114,245 3 38,082 6,067 ,001
Within Groups 728,122 116 6,277
Total 842,367 119
67 | P a g e
2.2.3.2 Ad Liking regressed on Persuasion Knowledge, demographic targeting and behavioral targeting
Dependent variable: Ad Liking
Model 1
Variable B SE B β
Persuasion Knowledge 0,087 0,042 0,194*
Demographic targeting -0,705 0,301 -0,295*
Behavioral targeting -0,041 0,311 -0,017
Interaction between demographic and
behavioral targeting
0,168 0,424 0,061
constant 4,855 0,213
N = 120
F = 3.908, p = .005 R2= .118
*p < .05. **p < .01.
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2.2.4 Model estimates, regression analysis
Dependent variable: Purchase Intent
Model 1 Model 2 Model 3 Model 4
Variable B SE B β B SE B β B SE B β B SE B β
Persuasion Knowledge (PK)
-.031 .047 -.056 ,280 .234 .510 .526 .381 .956 .719 .425 1,308
Demographic targeting (DT)
-.359 .239 -.123 -.312 .334 -.107 -.391 .340 .,134 -.438 .343 -.15
Behavioral targeting (BT) 0.14 .241 .005 -.211 .368 -.072 -.084 .379 -.029 -.083 .379 -.028
Ad Liking (AdL) .611 .102 .501* .624 .105 .512* .567 .115 .465* .539 .118 .442*
Inte
ract
ion
s
BT x DT .243 .498 .072 .034 .516 .01 .085 .519 .025
PK x BT .165 .094 .211 -.348 .508 -.445 -.756 .646 -.966
PK x DT -.129 .109 -.143 -.236 .409 -.260 -.762 .658 -0,84
PK x AdL -.069 .042 -.629 -.106 .069 -.959 -.142 .078 -1.283
PK x AdL x BT -.017 .087 -.082 .103 .146 .489
PK x AdL x DT .075 .093 .481 .152 .119 .971
PK x BT x DT .384 .232 .297 1.249 .879 .966
PK x AdL x BT x DT -.188 .185 -.666
constant 2.315 .53
2.236 .552
2.548 .610
2.702 .628
N = 120
F
11.742**
6.737**
5.142**
4.802**
R2
.290
.327
.344
.350
F for change in R2 11.253** 1.52 .0925 1041
*p < .05. **p < .01.
55 | P a g e
2.2.5 Additional exploratory analyses
2.2.5.1 Persuasion Knowledge Open Text variable exploratory analysis
Persuasion_Knowledge_Open_Text
Frequency Percent Valid Percent Cumulative
Percent
Valid
,00 35 29,2 29,2 29,2
1,00 41 34,2 34,2 63,3
2,00 29 24,2 24,2 87,5
3,00 12 10,0 10,0 97,5
4,00 3 2,5 2,5 100,0
Total 120 100,0 100,0
Descriptives
Persuasion_Knowledge _Open_Text
N Mean Std.
Deviatio
n
Std.
Error
95% Confidence Interval for
Mean
Minimum Maximum
Lower Bound Upper Bound
no targeting 29 ,3103 ,47082 ,08743 ,1313 ,4894 ,00 1,00
demographic targeting 29 1,0000 ,88641 ,16460 ,6628 1,3372 ,00 3,00
behavioural targeting 32 1,5000 ,91581 ,16189 1,1698 1,8302 ,00 3,00
demograhic and behavioural 30 2,0333 1,03335 ,18866 1,6475 2,4192 ,00 4,00
Total 120 1,2250 1,05689 ,09648 1,0340 1,4160 ,00 4,00
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Mean Maximum Mean in % expression of the range
0,3103 1 31,03%
1 4 25,00%
1,5 3 50,00%
2,0333 6 33,89%
2.2.5.1 B Persuasion Knowledge explorative analysis between conditions
Descriptives
Persuasion Knowledge
N Mean Std.
Deviation
Std.
Error
95% Confidence Interval for Mean Minimum Maximum
Lower Bound Upper Bound
no targeting 29 -,31 2,989 ,555 -1,45 ,83 -6 6
demographic targeting 29 -,14 2,356 ,438 -1,03 ,76 -6 2
behavioural targeting 32 2,13 2,406 ,425 1,26 2,99 -4 6
demograhic and behavioural 30 ,63 2,220 ,405 -,20 1,46 -5 4
Total 120 ,62 2,661 ,243 ,14 1,10 -6 6
ANOVA
Persuasion Knowledge
Sum of Squares df Mean Square F Sig.
Between Groups 114,245 3 38,082 6,067 ,001
Within Groups 728,122 116 6,277
Total 842,367 119
57 | P a g e
Persuasion Knowledge
Frequency Percent Valid Percent Cumulative
Percent
Valid
-6 3 2,5 2,5 2,5
-5 4 3,3 3,3 5,8
-4 2 1,7 1,7 7,5
-3 7 5,8 5,8 13,3
-2 8 6,7 6,7 20,0
-1 11 9,2 9,2 29,2
0 19 15,8 15,8 45,0
1 11 9,2 9,2 54,2
2 33 27,5 27,5 81,7
3 6 5,0 5,0 86,7
4 11 9,2 9,2 95,8
5 1 ,8 ,8 96,7
6 4 3,3 3,3 100,0
Total 120 100,0 100,0
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2.2.5.2 Effects of Privacy Concerns
ANOVA
Ad effectiveness
Sum of Squares df Mean Square F Sig.
Between Groups ,037 1 ,037 ,025 ,874
Within Groups 171,130 118 1,450
Total 171,167 119
ANOVA
Product Evaluation
Sum of Squares df Mean Square F Sig.
Between Groups ,481 1 ,481 ,224 ,637
Within Groups 253,844 118 2,151
Total 254,325 119
Privacy concern levels in different conditions, Chi-squared test
Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N Percent
Targeting * Privacy
Concerns
120 100,0% 0 0,0% 120 100,0%
Targeting * Privacy Concerns Crosstabulation
Privacy Concerns Total
,00 1,00
Targeting
no targeting Count 23 6 29
Expected Count 21,5 7,5 29,0
demographic targeting Count 24 5 29
Expected Count 21,5 7,5 29,0
behavioural targeting Count 17 15 32
Expected Count 23,7 8,3 32,0
demograhic and behavioural Count 25 5 30
Expected Count 22,3 7,8 30,0
Total Count 89 31 120
Expected Count 89,0 31,0 120,0
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Chi-Square Tests
Value df Asymp. Sig. (2-
sided)
Pearson Chi-Square 10,228a 3 ,017
Likelihood Ratio 9,612 3 ,022
Linear-by-Linear
Association
,260 1 ,610
N of Valid Cases 120
a. 0 cells (0,0%) have expected count less than 5. The minimum
expected count is 7,49.
2.2.5.3 Effects of suspicion
ANOVA
Ad effectiveness
Sum of Squares df Mean Square F Sig.
Between Groups ,147 1 ,147 ,101 ,751
Within Groups 171,020 118 1,449
Total 171,167 119
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ANOVA
Product Evaluation
Sum of Squares df Mean Square F Sig.
Between Groups ,119 1 ,119 ,055 ,815
Within Groups 254,206 118 2,154
Total 254,325 119
Suspicion levels in different conditions, Chi-squared test
Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N Percent
Targeting * Suspicion 120 100,0% 0 0,0% 120 100,0%
Targeting * Suspicion Crosstabulation
Suspicion Total
,00 1,00
Targeting
no targeting Count 25 4 29
Expected Count 24,7 4,4 29,0
demographic targeting Count 25 4 29
Expected Count 24,7 4,4 29,0
behavioural targeting Count 25 7 32
Expected Count 27,2 4,8 32,0
demograhic and behavioural Count 27 3 30
Expected Count 25,5 4,5 30,0
Total Count 102 18 120
Expected Count 102,0 18,0 120,0
61 | P a g e
Chi-Square Tests
Value df Asymp. Sig. (2-
sided)
Pearson Chi-Square 1,841a 3 ,606
Likelihood Ratio 1,787 3 ,618
Linear-by-Linear
Association
,011 1 ,918
N of Valid Cases 120
a. 4 cells (50,0%) have expected count less than 5. The minimum
expected count is 4,35.
2.2.5.4 Benefit variable
Tests of Between-Subjects Effects
Dependent Variable: Ad Liking
Source Type III Sum of
Squares
df Mean Square F Sig.
Corrected Model 26,522a 5 5,304 4,181 ,002
Intercept 139,386 1 139,386 109,855 ,000
Persuasion Knowledge 7,292 1 7,292 5,747 ,018
Benefit 6,041 1 6,041 4,761 ,031
Targeting 9,643 3 3,214 2,533 ,060
Error 144,645 114 1,269
Total 2692,000 120
Corrected Total 171,167 119
a. R Squared = ,155 (Adjusted R Squared = ,118)
2.2.5.4 B Purchase Intention explained by Ad Liking and Benefit
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 ,712a ,507 ,499 1,035
a. Predictors: (Constant), Beneficial, Ad Liking
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ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 128,952 2 64,476 60,170 ,000b
Residual 125,373 117 1,072
Total 254,325 119
a. Dependent Variable: Purchase Intention
b. Predictors: (Constant), Beneficial, Ad Liking
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
t Sig. B Std. Error Beta
1 (Constant) ,244 ,443 ,552 ,582
Ad Liking ,518 ,081 ,425 6,419 ,000
Beneficial ,445 ,060 ,492 7,434 ,000
a. Dependent Variable: Purchase Intention