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0 | Page 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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Page 1: Consumer online Persuasion Knowledge and perceptions of … · 2015-12-04 · and reactions to advertising applied Persuasion Knowledge Model (PKM) developed by Firestad and Wright

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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)

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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

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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.

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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

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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.

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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

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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

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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.

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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

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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

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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