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MASTER THESIS MSc MARKETING MANAGEMENT - MEREL ZIMMERMAN
PAID SEARCH ADVERTISING:
INFLUENCING CLICK BEHAVIOR WITH AD CONTENT
THE EFFECT OF MESSAGE APPEAL AND THE MODERATING IMPACT OF
CONSUMERS’ SALES FUNNEL STAGE AND PRODUCT CATEGORY KNOWLEDGE
MASTER THESIS MSc MARKETING MANAGEMENT
AUTHOR: MEREL ZIMMERMAN
STUDENT NUMBER: 324951MZ
DATE OF SUBMISSION:
SEPTEMBER 13, 2012
COACH: MACIEJ SZYMANOWSKI
DEPARTMENT: MARKETING MANAGEMENT
CO-READER: RENE VAN DER EIJK
DEPARTMENT: ENTREPRENEURSHIP
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MASTER THESIS MSc MARKETING MANAGEMENT - MEREL ZIMMERMAN
© The copyright of the Master thesis rests with the author. The author is responsible for its contents.
RSM is only responsible for the educational coaching and cannot be held liable for the content.
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MASTER THESIS MSc MARKETING MANAGEMENT - MEREL ZIMMERMAN
ACKNOWLEDGEMENTS
I would like to express my special thanks to my coach,
Maciej Szymanowski, who’s feedback was very valuable
and who I could always approach for advice.
Especially his scientific input with regard to the
research design has been indispensable.
I also want to thank my co-reader, Rene van der Eijk,
for taking the time to follow my progress and providing me
with input for the finishing touches on the survey and report.
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MASTER THESIS MSc MARKETING MANAGEMENT - MEREL ZIMMERMAN
Abstract
Building upon Elaboration Likelihood Model (ELM) theory, this study suggests that ad content in paid
search advertising influences click behavior. In addition, the moderating role of consumers’ stage in
the sales funnel and their level of product category knowledge is examined. An online experiment
(final n=202) capturing the look-and-feel of a Google search result page was executed, in which
respondents had to make a choice between 2 paid search ads. The ads captured both central and
peripheral cues, by varying the levels of three ad attributes. The results of a binary logistic regression
indicate that people’s clicking behavior is influenced by the type of cue present in a paid search ad.
Furthermore, significant interaction effects between the type of cue, sales funnel stage and product
knowledge are found, indicating that the effectiveness of certain cues depend on consumers’ stage in
the sales funnel and their level of product category knowledge.
Executive Summary
Paid search advertising is the most important component of online advertising spending and a
growing body of research focuses on this area. A gap in research however exists, when it comes to
covering the impact of ad message content. To fill this gap, this research analyzes the relationship
between advertisement message characteristics and paid search advertising effectiveness. In
addition, it examines whether this relationship is dependent on people’s stage in the sales funnel
and/or level of product knowledge.
In order to explain people’s information processing behavior at different stages of the sales funnel
and at different levels of product knowledge, this study builds upon Elaboration Likelihood Model
(ELM) theory. This theory was proposed by Petty and Cacioppo (1986) and comprises an integrative
framework on the use of ad-executional cues to match specific levels of processing. Surprisingly, the
theory has been linked to both offline and online advertisements in numerous studies, but not yet to
paid search advertisements.
In line with ELM theory, three ad cues were selected to represent the manipulation of the paid
search ads’ message characteristics for this research; source expertise, argument quality and a twosided argument. The source expertise cue was developed for heuristic based persuasion at a low
information processing level (a peripheral cue). This cue captures the product approval of an expert
source. The argument quality cue was developed for message-based persuasion at a moderate
information processing level (a central cue). By manipulating the levels of two different product
features, this cue captures either weak or strong arguments about the product. The two-sided
argument cue was developed to anticipate or reduce the likelihood of negative cognitive responses
occurring at a high information processing level (a second central cue). This cue was realized by
derogating the product on an attribute of minor importance, thereby making the ad appear less
biased.
By means of online experimentation, respondents (n=202) were exposed to a search engine result
page (SERP) on which they had to make a choice between two paid search ads. These ads each
captured different attribute levels which corresponded to either capturing or not capturing one or
more of the three ad cues. Respondents’ position in the sales funnel was manipulated by asking them
to image a specific product situation beforehand, which placed them in one of three sales funnel
stages. Respondents’ product knowledge level was varied by using two different product categories,
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MASTER THESIS MSc MARKETING MANAGEMENT - MEREL ZIMMERMAN
one that was assumed to be very familiar to consumers (running shoes) and one that was assumed to
be less familiar to consumers (a DSLR camera).
The effectiveness of paid search advertising was measured by respondents’ clicking behavior. In
essence, a click on a paid search ad is effective in creating brand awareness by leading consumers to
an advertiser’s landing page. Because respondents were forced to click on one of two paid search
ads, this dependent variable was binary and hence a binary regression method was employed for
analyzing the data. The randomization of sixteen paid search ads with varying attribute levels,
allowed me to determine which ad cue was most effective in increasing the probability of clicking.
The results showed that ad message content indeed has an influence on people’s clicking behavior.
The argument quality cue showed to be most effective overall. When this cue was present in an ad,
the probability of clicking on this specific ad increased significantly. In addition, the effectiveness of
the three cues proved to be dependent on people’s stage in the sales funnel and level of product
knowledge; high-involved people (people with high product knowledge in later stages of the sales
funnel) were more sensitive to the expert source cue than low-involved people, the two-sided
argument cue was only effective for people with high product knowledge in the last stage of the sales
funnel, and people in an early stage of the sales funnel (with high product knowledge) were more
sensitive to the argument quality cue than high-involved people.
The functioning of the ad cues was not in line with ELM theory. Where the source expertise cue
(peripheral cue) was supposed to be effective for low-involved people, results showed that it rather
was effective for high-involved people. And where the argument quality cue (central cue) was
supposed to be effective only for high-involved people, results showed that it was the cue most
effective overall.
A second look at the results and the ads’ designs let to the justification of an alternative assumption,
where the source expertise cue functioned as a central cue (argument quality in the form of approval
from an expert source) and the argument quality cue as a peripheral cue, of which the varying levels
of a product feature allowed people to quickly draw inferences about the product without
scrutinizing the content of the ad.
Under the alternative assumption, ELM theory looks promising when it comes to understanding how
ads can be altered successfully to reach consumers with varying levels of involvement. But perhaps
an electronic version of ELM theory (eELM) is more in place, as this research contributes to a number
of other studies that find evidence for the combined influence of central and peripheral routes to
persuasion for high-involved people in varying online contexts.
This research’s main finding are a first step to theory on message content in paid search advertising.
They provide some first insights to paid search users. Firstly, message content can provide the user
with a competitive advantage. The key idea is that the content of one’s paid search ad can make a
valuable difference and that it is important for managers to find out which content is most effective
for their type of product. The use of central or peripheral cues can be a starting point, but testing
with other appeal, content or structure characteristics of a message can pay off. Secondly, a
consumer’s stage in the sales funnel and level of product knowledge influences the effectiveness of a
paid search ad. The paid search user needs to understand that not all consumers can be reached with
one type of message. Lastly, a concept like the sales funnel (in combination with product knowledge
or product complexity) can provide the paid search user with a valuable segmentation tool. The user
can decide whether s/he wants to reach the whole sales funnel or only a specific stage, and alter the
content of ads to (more) successfully to reach each stage.
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Table of Contents
1. Introduction ............................................................................................................................. 8
1.1 Research Problem.......................................................................................................................... 8
1.2 Research Method .......................................................................................................................... 9
1.3 Research Scope.............................................................................................................................. 9
2. Context: Paid Search Advertising ............................................................................................. 11
2.1 Background and Developments in Search Engine Marketing ..................................................... 11
2.2 Research Designs in Paid Search Studies..................................................................................... 12
3. Conceptual Framework and Theory ......................................................................................... 14
3.1 The Research Variables ............................................................................................................... 14
3.1.1 Advertisement Message Characteristics .............................................................................. 14
3.1.2 Paid Search Advertising Effectiveness .................................................................................. 14
3.1.3 Consumer’s Position in the Sales Funnel............................................................................... 14
3.1.4 Consumer’s Product Knowledge ........................................................................................... 15
3.2 Three Classes of Advertisement Message Characteristics .......................................................... 15
3.2.1 Message Structure ................................................................................................................ 16
3.2.2 Message Content .................................................................................................................. 16
3.2.3 Message Appeal ................................................................................................................... 16
3.3 The MAO Concept: Processing Motivation, Ability and Opportunity ......................................... 17
3.4 Information Processing Theory: The Elaboration Likelihood Model ........................................... 18
3.5 Hypotheses in line with ELM Theory ........................................................................................... 23
4. Data and Methods .................................................................................................................. 25
4.1 Variable Manipulations ............................................................................................................... 25
4.1.1 Message Characteristics ....................................................................................................... 25
4.1.2 Product Knowledge............................................................................................................... 25
4.1.3 Sales Funnel Stage ................................................................................................................ 25
4.1.4 Ad Development ................................................................................................................... 26
4.2 Empirical Study Design ................................................................................................................ 27
4.3 Data Analysis Method ................................................................................................................. 29
4.4 Dataset Adjustments ................................................................................................................... 29
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5. Results ................................................................................................................................... 30
5.1 Model Construction Procedure ................................................................................................... 30
5.2 Model Fit ..................................................................................................................................... 30
5.3 Model Interpretation................................................................................................................... 32
5.3.1 First ranking increases clicking ............................................................................................. 32
5.3.2 Central cue more effective than peripheral cue ................................................................... 32
5.3.3 Sales funnel stages do not react differently to ad cues ........................................................ 33
5.3.4 Level of product knowledge no influence on ad cue effectiveness ....................................... 33
5.3.5 Moderating effects of sales funnel stages and product knowledge levels ........................... 33
5.3.5.1 Highly involved people more sensitive to peripheral cue ............................................................ 33
5.3.5.2 Product knowledge level turns around effect central cue in the sales funnel............................. 34
5.3.5.3 Highly involved people sensitive to two-sided argument cue ..................................................... 35
5.4 Unexpected Ad Cue Functioning ................................................................................................. 36
6. Conclusions ............................................................................................................................ 38
6.1 Answer to the Research Question ............................................................................................... 38
6.1.1 Advertisement Message Characteristics .............................................................................. 38
6.1.2 The Alignment of ELM Theory .............................................................................................. 39
6.1.3 Ad Cue Effectiveness ............................................................................................................. 39
6.1.4 Discussion on Cue Design ..................................................................................................... 39
6.1.5 Moderating Effects ............................................................................................................... 40
6.2 Result Implications ...................................................................................................................... 41
6.2.1 Theoretical Relevance........................................................................................................... 41
6.2.2 Practical Relevance ............................................................................................................... 41
6.3 Study Limitations ......................................................................................................................... 42
6.4 Indications for Future Research .................................................................................................. 42
7. Appendices ............................................................................................................................ 44
7.1 Sales Funnel Introductions .......................................................................................................... 44
7.2 Google Paid Search Ads ............................................................................................................... 45
7.3 Ad Pairs on SERP .......................................................................................................................... 47
7.4 Search Engine Result Page........................................................................................................... 48
7.5 Distribution over Sales Funnel Stages and SERPs ........................................................................ 49
7.6 Sample Gender and Demographic Distribution .......................................................................... 50
7.7 Single Interaction Effects to Model 1 .......................................................................................... 51
7.8 Odds Ratios Model 1 ................................................................................................................... 52
8. References ............................................................................................................................. 53
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1. Introduction
1.1 Research Problem
With the advent of online marketing tools, managers and company owners have become eager to
apply these tools to reach potential customers online. Paid search advertising is one of these tools.
Advertisers can create search phrases that have a logical link to their product or service and decide
on the maximum amount they want to pay per click (PPC). When the advertiser’s PPC is competitive
enough, its advertisement will show on the search engine results page (SERP) when a user searches
on one of its phrases.
Paid search advertising is the most important component of online advertising, accounting for 49,7%
of global online advertising budgets ($76.4 billion) over 2011 (ZenithOptimedia, 2012). Paid search
advertising budgets are expected to grow by 52,1% over the next three years to a total global market
size of $57.8 billion in 2014 (id.). This amount of spending requires marketing managers to account
for the contribution of paid search advertising and therefore assess the tool’s effectiveness. In
addition, marketing managers should be interested in how they could raise the ROI in paid search
advertising.
With 86% market share of global advertiser spending over the second quarter of 2012, Google is the
global leader in the paid search advertising market (Covario, 2012). Google offers their customers an
analytical tool, that allows the user to assess which advertisements are most effective. Effectiveness
depends on the specific goal the advertiser has, which could be to lead the consumer to his web-site,
stimulate the consumer to request information or convince the consumer to make a purchase.
However, this analytical tool only tells the user which advertisements work best for certain key
phrases, they don’t tell the user why a certain advertisement is being clicked on more often.
Consequently, the user has limited knowledge on advertisement characteristics that could improve
the ROI in paid search advertising.
There is a growing body of research that focuses on the area of paid search advertising. Three
streams of research can be identified within this area, which differ in terms of the complexity of their
research models. One stream of research applies complex models that take into account the
interaction effects between different agents, in determining paid search advertising effectiveness. A
second stream of research attempts to unveil the complex determinants that underlie relationships
in the field of paid search advertising, in the form of moderating or mediating variables. A last stream
of research applies more simplified models which demonstrate a direct relationship between
variables that can be manipulated by the advertiser and performance metrics. Chapter 2 elaborates
on these streams and provides academic research examples. What is important to understand at this
point is that the papers in these streams focus on mechanisms at work around the content of the
advertisement itself. A gap in research exists when it comes to explaining why certain advertisements
themselves are more effective. To fill this gap, this research analyzes the relationship between
advertisement message characteristics and paid search advertising effectiveness. In addition, it
investigates a possible moderating impact of consumers’ product knowledge and position in the sales
funnel. More specifically, the research question for this study is:
Do advertisement message characteristics have an effect on paid search advertising effectiveness and
is this relationship dependent on consumers’ product knowledge and position in the sales funnel?
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This study builds upon Elaboration Likelihood Model (ELM) theory in order to explain people’s
behavior at different stages of the sales funnel. ELM theory was designed in order to better
understand the attitude changes that result from exposure to persuasive communications. The
theory can be applied to an advertising context, where the persuasive communication message is an
advertisement. Surprisingly, the theory has been linked to both offline and online advertisements in
numerous studies, but not yet to paid search advertisements.
The following sub questions will assist in addressing the research question:
 What are the different advertisement message characteristics according to advertising literature?
 How can these advertisement message characteristics be implemented in paid search
advertisement form?
 In what way can the ‘levels of processing’ framework of ELM theory be aligned to the conceptual
model of this actual research?
 Do different paid search advertisement message characteristics have a different effectiveness (Do
consumers click on a paid search advertisement with a certain message characteristic more)?
 Does ELM theory correctly predict the effectiveness of paid search advertisement message
characteristics for people in different stages of the sales funnel and with different levels of
product knowledge?
1.2 Research Method
An (online) survey pre-test examined whether the created search result advertisement messages
containing different characteristics are indeed perceived as containing these characteristics. The
main experiment was held amongst a different set of respondents.
By means of online experimentation, respondents were exposed to a search engine result page
(SERP) on which they had to make a choice between two paid search ads. The survey environment
came close to capturing the look-and-feel of an actual search result browser page. To ensure high
external validity, respondents were able to click on the advertisements. In order to manipulate
respondents’ position in the sales funnel, they were asked to image a specific product situation
beforehand, which placed them in one of three sales funnel stages. Respondents’ product knowledge
level was varied by using two different product categories, one that was assumed to be very familiar
to consumers and one that was assumed to be less familiar to consumers. To make sure that the
product knowledge level was indeed ‘expert’ or ‘novice’, survey questions were added to the main
survey that asked for the respondent’s level of product knowledge.
Data will be analyzed by means of regression analyses. Because the dependent variable of the
conceptual model tested is binary, the model will not be linear and hence the ordinary least squares
method cannot be applied. Therefore, the regression analyses will employ a binary response model
instead and adopt a Maximum Likelihood Estimation method (Park, 2009).
1.3 Research Scope
This study aims to investigate the impact of ad content on people’s clicking behavior. The dependent
variable is therefore limited to respondents’ clicking behavior. A click on a paid search ad is effective
in creating brand awareness by leading consumers to an advertiser’s landing page. This study does
not cover the extent to which ad content generates leads or drives sales, which are additional
measures of determining the effectiveness of paid search advertising.
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The research design of this study adopts re-created Google search engine result pages and ads that
cover products. The results and implications therefore only apply to (1) paid search as an online
marketing tool, (2) general search engines like Google, and (3) textual paid search ads for products
(not services).
The study is covering a sample of 202 respondents from a variety of gender, age, education and
income groups (see appendix 7.6).
The remainder of this paper is structure as follows. Chapter 2 provides background information on
paid search advertising as well as an overview of academic research performed in this area. Chapter
3 presents the conceptual framework of this research, elaborates on the variables within this
framework, and includes a literature review on the (expected) relationships between these variables.
Chapter 4 explains how the variables are manipulated for online experimentation and elaborates on
the empirical design of the study. Chapter 5 describes the research findings. Finally, chapter 6
interprets the results, identifies the study’s implications and limitations, and provides indications for
future research.
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2. Context: Paid Search Advertising
2.1 Background and Developments in Search Engine Marketing
As discussed briefly in the introduction, paid search advertising is an online marketing tool that
allows companies to have their text ad, including a link to a web page of the company, displayed on
the SERPs when the user of a search engine types in a specific phrase. The position of the ad is mainly
determined by the amount that the company bid for having a consumer click on its ad. Paid search
advertising, also known as sponsored advertising, or keyword advertising, is one type of search
engine marketing (SEM) technique. The other SEM technique is search engine optimization, a
structured approach used to increase the position of a company’s ad in the search engine’s organic
results listings for selected phrases (Chaffey et al, 2009). On a SERP, paid listings are displayed above
and/or to the right of the organic listings. As searchers prefer to click on the organic listings, it is
important to manage and find ways to increase the effectiveness of paid search advertising.
Paid search differs from traditional advertising, in a way that companies do not pay to have their ad
displayed. Companies only pay when their ad is clicked on by a searcher. This pay-for-performance
format substantially reduces the wastage incurred by advertisers compared to traditional pay-perexposure advertising formats (Animesh et al., 2010). Another reason why paid search leads to limited
wastage compared to other media, is because the tool is highly targeted. A company’s ad is only
triggered by a specific keyword, which enables the company to reach a more targeted audience.
Furthermore, because these ads are based on consumer’s own queries, they are considered far less
intrusive than online banner ads or pop-up ads (Ghose and Yang, 2009a).
In their historical overview of sponsored search auctions, Jansen and Mullen (2008) indicate that
from 1994 to 1998, web advertising consisted of pay-per-exposure banner advertisements. In 1998
Goto.com introduced the first paid search auction, where winning advertisers paid what they bid
(first-price auction). Goto.com was renamed Overture in 2001, and in 2002 they introduced a
second-price auction together with Google. In a second-price auction, winning bidders pay the next
highest bid instead of their own bid. Research found that a switch from a first- to a second-price
auction results in truth telling: advertisers’ bids for clicks approach their value for clicks more in a
second price auction (Yao and Mela, 2011). Later that same year, Google advanced the sponsored
search auction format, by adding quality-based bidding. No longer did the highest bid lead to the top
ranking, but also the advertisement’s quality was taken into account. Google developed the ‘quality
score’, because they believed that delivering relevance through the paid links was essential to their
user’s experience (Chaffey et al, 2009). Yahoo acquired Overture in 2003, and only introduced quality
based bidding to its auction in 2007.
The desktop search engine mostly used by consumers worldwide, with over 80% market share, is
Google, followed by Yahoo (7%), Microsoft’s Bing (4.6%), and Baidu (4.3%) (NetMarketshare.com,
2012a). The relative success of Baidu can be explained by its lead in the Chinese search market, a
market where Google’s search results are restricted/filtered by the Chinese government. Market
share for Google in the mobile/tablet market is over 90% (NetMarketshare.com, 2012b).
With respect to paid search advertising, Covario (2012) reports Google as a global leader with 86% of
the market share. BingHoo (representing the integration of Bing and Yahoo’s platform in 2010) and
Baidu have a market share of respectively 7% and 6% in the global paid search market.
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2.2 Research Designs in Paid Search Studies
Academic research performed in the area of paid search advertising can roughly be divided in three
streams. A first stream of research investigates complex models that include the role of different
agents that interact in the context of paid search engine advertising. These models allow for
manipulation of different effects, in order to determine advertiser, searcher/consumer, and/or
search engine behavior (the latter in the form of ranking search results) and corresponding results.
Below I will discuss one of these models from a recent article, in order to gain a better understanding
on this stream of research.
Yao and Mela (2011) developed a model that incorporates the role of three agents; the search
engine, the advertisers, and the searchers. Searchers can be understood as generating revenue for
the advertiser, and the advertiser’s bidding behavior as generating revenue for the search engine.
The model includes data on the bidding history of all active bidders, consumer information and
browser log files, and product files. This model allowed Yao and Mela to determine the implications
for search engine and advertiser’s profits as well as consumer welfare, when manipulating certain
search engine policies. They for example found that sort/filter options within search engines result in
an increase in consumer welfare, a loss in advertiser’s profits, and that positive consumer effects on
search engine profits outweigh negative advertising effects on search engine profits. In the same line,
they investigated the impact of auctioning keywords by market segment (in comparison to most
search engines that auction keywords across all market segments) and the impact of auction
mechanism designs (first-price and second-price auction).
Compared to the actual study, Yao and Mela adopted a different research design. The data
underpinning their analysis was drawn from a search engine for high-technology consumer products.
In contrast, this research’s experiment is conducted in a re-created Google search engine
environment, which is a more general and larger search engine. Apart from the different research
designs, these models (the one from Yao and Mela being an example) are not able to answer the
research question formulated in chapter 1.1. Where these models capture the interaction between
different agents whilst manipulating auction mechanism designs, this research manipulates the ad
itself and attempts to demonstrate a moderating effect in the form of consumer characteristics.
A second stream of research applies more simplified conceptual frameworks and attempts to find a
direct relationship between variables that can be manipulated by the advertiser and performance
metrics like click-through rates (CTR) and conversion rates. Academic research papers in this stream
up to date have mostly investigated the role of search engine rank and/or keyword characteristics on
these performance metrics. With regard to search engine rank, there is uniformity with respect to its
negative impact on CTR and conversion rates. Both these metrics decrease with ad position as one
goes down the search result page (Ghose and Yang, 2009a; Rutz et al, 2010). Ghose and Yang (2009a)
furthermore found that this relationship is increasing at a decreasing rate for both metrics and that
ads with more prominent positions on the search engine results page (which thus experience higher
click-through or conversion rates) are not necessarily the most profitable ones for the advertiser.
With regard to keyword characteristics, Ghose and Yang (2009a) found evidence that the presence of
retailer-specific information in the keyword is associated with an increase in CTR and conversion
rates, the presence of brand-specific information with a decrease in these metrics and the length of
the keyword with a decrease in CTR. In contrast, Rutz et al (2010) found that a branded keyword
performs better and that the number of words in a keyword has a positive effect on the CTR. There is
thus still some disagreement in this area, though heterogeneity of direct effects across keywords has
been proven. These two examples evaluate the effectiveness of paid search directly by attributing
direct revenues and pay-per-click costs to categories of keywords. A very recent piece of research has
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examined the indirect effect of paid search advertising, in the form of consumers returning directly
to the website at a later point in time (Rutz et al, 2011). The writers show a significant indirect effect
of paid search that clearly differs across keywords, where again branded keywords and more general
keywords (expensive because of higher levels of competition) are better at producing return visitors.
The actual research is not only interested in a direct relationship between the ad’s message
characteristics and paid search effectiveness, but also in a possible moderating effect of consumer’s
product knowledge and position in the sales funnel. Therefore, this second stream of research is also
not capable to answer the research question formulated in chapter 1.1.
A last stream of research is dedicated to finding a mediating variable that explains the success of
sponsored search or a moderating variable on which the success of sponsored search depends. These
studies attempt to unveil the complex determinants that underlie relationships in the field of paid
search advertising. This stream of research shows widely different research models of which I will
give a few examples next.
Animesh et al (2011) examined the interaction effects between a firm’s positioning strategy (quality
or price ad content), ad rank, and competitive intensity around a firm’s ad, in determining paid
search advertising effectiveness. They found that the relationship between on the one hand a firm’s
positioning strategy and ad rank and on the other hand the click-through rate is strongly moderated
by the firm’s ability to differentiate its ad from rivals’ ads. Another example is a spillover effect,
created when a consumer uses a generic keyword in a search activity. Rutz and Bucklin’s (2011)
results showed that this activity positively affects future branded search activity. Their research
demonstrates that the initial generic search results create awareness that a certain brand might be
able to meet the consumer’s need. It is this mediating variable that in turn causes the branded
search. A different type of spillover effect was investigated by Ghose and Yang (2009b), namely a
cross-category spillover effect, where a consumer searching for a product in one category eventually
purchases products from a different category as well. They found that the ability of brand- and
retailer-specific keywords to induce this spillover effect depended on the product category. As a last
example, Xu and Kim (2008) gave an explanation for the order or ranking effect in paid search. They
demonstrate that the underlying mechanism leading to this effect is the consumer’s time spent on
inspecting a vendor. Because of consumer’s declining motivation to process information, the higher
ranked vendors attract more consumer attention, which lead to a better impression of the vendor
and a higher probability of the vendor being accepted.
This actual research fits this last stream of research, because it attempts to demonstrate a
moderating effect in the direct relationship between advertisement message characteristics and paid
search advertising effectiveness. To my knowledge, there has only been made one recent attempt in
the literature in this direction. This attempt was made by Gauzente and Roy (2011), who also
investigated the impact of advertising message appeal on click behavior. They found that descriptive
message content is more clicked than commercial message content and that consumer’s priceconsciousness moderates this relationship (high price-conscious consumers are more influenced by
descriptive content). Their research builds on expectancy theory, where they believe that consumers
expect online search engines to gather unbiased/neutral results and that descriptive content is
therefore likely to be conform to consumer's neutrality expectations and commercial content to
appear biased. Furthermore, high price-conscious consumers would be more interested in
descriptive content, because this content would convey more information on product features. This
research fails to incorporate consumer’s position in the sales funnel and product knowledge, a
possible moderating variable that I believe to be better capable in segmenting online consumers into
meaningful target groups for the paid search advertiser.
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3. Conceptual Framework and Theory
3.1 The Research Variables
The conceptual framework that applies to this research is visualized in figure 1. The variables of this
framework will be defined and elaborated on next and the remainder of the chapter presents
findings from published studies that shed light on the relationships in this framework.
3.1.1 Advertisement Message Characteristics
Advertisement message characteristics are
the message’s appeal, structure and content,
which
impact
persuasion
of
the
advertisement message on the side of the
receiver. In executing an advertisement
message strategy, one can choose different
types of persuasive appeals, structures and
content, in order to seize the attention of the
receiver
and
communicate
in
an
understandable and believable manner
(Percy and Rossiter, 1980). Chapter 3.2
includes a literature review on advertising
message characteristics and chapter 3.3 and
Figure 1: Conceptual framework
3.4 build on information processing theory in
order to come to the advertisement characteristics that will be used in the actual experiment. These
characteristics will be implemented in paid search advertisement form. A paid search advertisement
consists of a headline and two lines of ad text. The headline has a 25 character limit and the two ad
lines a 35 character limit (Google, 2011). The advertisement form’s limitation with respect to length
and pure textual form provided a challenge to effectively implement advertisement message
characteristics. That is why the created advertisements were tested on their message characteristics
in a pre-test described later.
3.1.2 Paid Search Advertising Effectiveness
The effectiveness of paid search advertising can be judged from the different benefit perspectives of
the online marketing tool. Paid search advertising can be effective in creating brand awareness by
leading consumers to an advertiser’s landing page (a click), generating leads when the consumer
requests information or prices, and driving sales when the consumer makes an online purchase.
However, because the main focus of this research is on the effect of different advertisement
characteristics and the moderating influence of people’s stage in the sales funnel and product
knowledge, paid search advertising effectiveness will only be measured in terms of respondent’s click
on a certain paid search advertisement.
3.1.3 Consumer’s Position in the Sales Funnel
A common view of the sales funnel, also known as the buying funnel or buying cycle, is of a staged
process that a consumer takes in order to purchase a product or service (Ramos and Cota, 2008;
Seda, 2004, cited in Jansen and Schuster, 2011, p.2). Jansen and Schuster (2011) evaluated the
effectiveness of the sales funnel as a model for understanding consumer interaction with keyword
advertising campaigns on web search engines. They divided the sales funnel into four stages:
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MASTER THESIS MSc MARKETING MANAGEMENT - MEREL ZIMMERMAN
“The first stage is Awareness, when a customer realizes that there is a product that can solve his/her
problem or need. After a consumer realizes that a product can address a problem, the customer finds a
specific product line and become more knowledgeable about this type of product or service. This stage is
called Research. The third stage is Decision, when a consumer is deciding between different brands of a
specific product by forming choice set. The final stage of the funnel is Purchase. This stage is when a
consumer knows what specific product and brand they intend to purchase, and they are typically doing a
price, convenience to order, or similar comparison before executing the purchase.”
Findings from their analyses show statistically different consumer behavior in terms of search queries
among all stages of the sales funnel. Although they question the shape of the ‘funnel’, they do find
the stages of the funnel to be representative of actual consumer behavior within search engines. This
research paper will limit the sales funnel to 3 stages, namely the stages of awareness, research, and
decision. Chapter 4.1.3 will more precisely define these stages and indicate how they will be
manipulated in the experiment.
From the perspective of the consumer, the ‘buying’ funnel rests on information processing theory,
which captures consumer decision making as a process that can be divided into multiple stages (id.).
As consumers move through the funnel, they pass through different cognitive stages in deciding
whether and what product to purchase (id.). Dependent on the stage of the sales funnel they are in,
this means that consumers process information differently. Some advertisement characteristics
might therefore be more effective than others in enticing the consumer to click on it.
3.1.4 Consumer’s Product Knowledge
In addition to their position in the sales funnel, consumers’ product knowledge might also have an
influence on the effectiveness of different advertisement characteristics. Product knowledge is one
of the most important variables that affects information processing and represents the extent to
which a person has an organized structure of knowledge concerning a certain product category
(Petty and Cacioppo, 1983). Maheswaran and Sternthal (1990) indeed found that detailed processing
was stimulated by different advertisement characteristics, depending on the level of product
knowledge of the respondents. ‘Experts’ (high level of knowledge) were more likely to process a
message in detail when given only attribute information, and ‘novices’ (low level of knowledge) were
more likely to do so when given benefit information. In congruence with these authors, this research
will also apply two levels of product knowledge, one high and one low level.
Chapter 3.3 and 3.4 will build on information processing theory in explaining the (expected)
relationships between the discussed variables. But first, the following paragraph will give a
categorical overview of existing academic research in the field of advertising message characteristics.
This literature overview is necessary in order to receive a complete picture and a basic understanding
of the different types of message characteristics used in existing advertising research and provides a
basis for further consideration of the message characteristics to be used in this specific paper. In
order to ensure relevance to the research at hand, the literature is limited to advertising literature
and more specifically to print (textual) advertising messages for products.
3.2 Three Classes of Advertisement Message Characteristics
Persuasion theory provides us with insights into the field of advertising. Persuasion can be defined as
‘those situations involving conscious intent on the part of one person to influence another’
(Moriarty, 1986). Persuasion in advertising affects amongst others how we feel about products, their
price, or our self-image. When our beliefs, opinions, attitudes, or behaviors are in conflict with an
advertising message (a state of dissonance), we either change how we feel about things (affect) or
we change what we know (cognition).
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MASTER THESIS MSc MARKETING MANAGEMENT - MEREL ZIMMERMAN
Persuasive objectives could be summarized as creating new attitudes, reinforcing existing attitudes,
or changing old attitudes (id.). To reach these objectives an advertiser can choose different
advertisement message strategies. Percy and Rossiter (1980) classify advertisement message
characteristics in three major classes, namely message structure, message content, and message
appeal. Academic research on advertising message characteristics can well be categorized in these
same classes, which I will elaborate on next.
3.2.1 Message Structure
Message structure mainly concerns the order in which message points should be presented.
Research (Hovland, 1957, cited by Percy and Rossiter, 1980) for example found that it is more
effective to first communicate message-points that are most desirable to the receiver. A different
consideration is whether an explicit conclusion should be presented at the beginning (primacy effect)
or end (recency effect) of the message. Brunel and Nelson (2003) found a presentation order effect
in advertising, which is dependent on consumer’s gender; under low- involvement conditions,
females (males) prefer messages that were presented first (last).
3.2.2 Message Content
The characteristic of message content refers to the vocabulary used in the message, the linguistic
and grammatical structure of sentences, the writing style or the use of words. According to Anderson
and Jolson (1980), varying the levels of technical content influences the ad’s power to generate
interest and attention as well as the overall evaluation of the advertised product. A non-technical ad
was found to be better capable of gaining and holding the reader’s interest and attention, while a
technical ad had a higher overall evaluation. Examples of other research in this direction include
consideration of message content in the form of the number words, nouns, verbs and adjectives in
the advertisement’s headline (Rossiter, 1981) and number of words and brand mentions in copy
Holbrook and Lehmann (1980). Both papers focus on how these characteristics influence readership
scores in the form of the ad being noted and read. Rossiter found that the ad’s headline should
emphasize nouns and minimize verbs to ensure the ad being noted, but in order to be read the ad’s
headline should be kept to a minimum number of words with preferably nouns and adjectives.
3.2.3 Message Appeal
In constructing an advertisement message, one could persuasively appeal to the receiver’s moral
principles, emotions, or intellect (Percy and Rossiter, 1980). In appealing to moral principles
concentration lies more on the source rather than the message. An example would be a persuasive
message appealing to a credible spokesperson. Goldsmith et al (2000) found that endorser credibility
works only through its impact on attitude towards the ad, but also found that corporate credibility
influences consumers’ attitude toward the ad, attitude towards the brand, as well as their purchase
intention. Likewise, Yilmaz et al. (2011) find a relationship between source characteristics and
effectiveness of print advertising, though find this relationship to be very dependent on consumer’s
message processing motivation and product category knowledge. In a different setting, Grewal et al.
(1994) found that source credibility acts as a moderating variable between the effect of price in an
advertisement and consumer’s perceived performance risk.
In appealing to emotions one can think of any message that does not rely on source identification or
logical argumentation from the point of the receiver. All advertising in this category appeals to
feelings, values, or emotions, by associating strong affective cues with the product or brand. And
where emotional appeals stress the ‘reward’ of product use, advertising messages appealing to
intellect or logic, stress the attributes of a product more and require the receiver to deduce the
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MASTER THESIS MSc MARKETING MANAGEMENT - MEREL ZIMMERMAN
desired conclusion from a message. These two types of advertising appeals have been extensively
applied in academic research under the heading of rational versus emotional, hard-sell versus softsell, factual versus evaluative, or informational versus transformational. In studying existing
advertisements, it is found that rational appeals are used more for product advertisements, and that
service advertisements more often contain an emotional appeal (Albers-Miller and Stafford, 1999;
Cutler and Javalgi, 1993; Abernethy and Butler, 1992;). Rational advertising content is interpreted as
more credible, exerting a more positive effect on important beliefs, therefore favorably influencing
affect (Holbrook, 1978). However, the use of rational and emotional appeals has been found to vary
across cultures, indicating that there are cultural differences in the relative effectiveness of these
advertising appeals (Okazaki et al, 2010). Furthermore, the effectiveness of these appeals depends
on the receiver’s motivation, ability and opportunity (MAO) to process the advertising message,
where rational advertisement cues match a high level of processing and emotional (affect-based)
cues a low level of processing (Macinnis and Jaworski, 1990).
What becomes apparent from the literature discussed, is that many papers take into account (and
indeed find evidence for) a moderating role of a perceiver characteristic in the relationship between
message characteristics and advertising effectiveness. Examples that I have mentioned come in the
form of gender and cultural differences, processing motivation, ability and opportunity
(involvement), and product category knowledge or experience. In the early days of advertising
literature, Tamm (1958) indeed stressed that, with respect to advertising, ‘attention […] takes place
individually in a particular situation and is a result of more complicated procedures than generally
presumed in advertising theory’.
In deciding on the type of message characteristics to be applied to this research, it is therefore
necessary to first consider the possible moderating effect within the conceptual framework. This
effect will be addressed in the next paragraph.
3.3 The MAO Concept: Processing Motivation, Ability and Opportunity
The influence of the moderating variables in my research framework can best be understood from
the perspective of consumer’s motivation, ability and opportunity (MAO) to process an
advertisement message. The MAO concept has been defined in an advertising context by MacInnis et
al (1991); Motivation refers to consumers’ desire or readiness to process brand information in an ad,
ability to consumer’s skills or proficiencies in interpreting brand information in an ad, and
opportunity to the extent to which distractions or limited exposure time affect consumer’s attention
to brand information in an ad. ‘Brand information’ in an ad refers to any executional cue designed to
communicate the advertised message, which could be information about the brand name, brand
attributes, benefits, usage, users and/or usage situation, but could also be cognitive (attribute-based)
or affective (emotional) (id.). Motivation and ability can be directly related to consumer’s position in
the sales funnel and their level of category knowledge respectively.
The relationship between a consumer’s level of product knowledge and this consumer’s ability to
process an advertisement is quite straightforward. When an individual possesses a high amount of
prior knowledge with respect to a certain product category, this individual’s cognitive structure is
better developed with respect to this product category, which in turn leads to this individual being
better able to activate concepts from memory that can be used in interpreting new information in an
advertisement message (Okechuchu, 1992). Ability to process an advertisement message thus rises
with the level of product knowledge.
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MASTER THESIS MSc MARKETING MANAGEMENT - MEREL ZIMMERMAN
Motivation to process an ad message is linked to consumer’s stage in the sales funnel through
involvement. Where prior product knowledge relates to how well the cognitive structure is
developed with respect to a product category, motivation to process, in the form of involvement,
relates to the degree to which this cognitive structure is activated in a given situation (Okechuchu,
1992). As Petty et al. (1983) indicated, involvement is high when a person is about to purchase a
certain product, and involvement is low when a person is not considering buying this certain product
at the moment. As a consumer moves through the sales funnel towards the purchase stage, their
level of involvement would thus increase. Under high involvement conditions people appear to
activate their cognitive structure to a greater degree to evaluate the issue-relevant arguments
presented in the ad (id). Furthermore, as consumers move closer to the purchase decision stage of
the sales funnel, it seems likely that their risk of lacking sufficient information increases. Higher rates
of perceived risk bring about higher involvement and stronger motivation for information processing
(Okechuchu, 1992, cited by Liebermann and Flint-Goor, 1996). In addition, a further understanding of
how the stages of the sales funnel are different from one another (in a search engine context) gives
an explanation of the funnel’s link with consumer’s motivation to process. Chapter 3.1 already
described Jansen and Schuster’s (2011) research, in which they also classified search phrases into the
stages of the sales funnel. From the awareness to the purchase stage, search phrases become more
specific in containing product, brand and company name. In a first stage of brand awareness, search
queries are broadest, since consumers are searching for general knowledge on a product. It seems
unlikely that consumers are highly motivated to process an ad in detail in this stage, since they are
looking for general information only. Only in later stages, when consumers intent to find out more
about a specific product and the brands that offer that product, and when the mindset of the
consumer is perhaps more closure seeking in the form of making a purchase, will they be motivated
to scrutinize ad information content to compare products/brands on factors like price, convenience
and benefits. In conclusion, motivation to process an advertisement message thus rises with
consumer’s movement through the sales funnel.
The MAO concept has been linked to consumers’ information processing levels by a broad number of
research papers. A well-known model from Petty and Cacioppo (1983, cited by MacInnis and
Jaworski, 1989), the Elaboration Likelihood Model (ELM), links MAO to two different information
processing routes, namely a central and a peripheral route. Consumers with less processing MAO
engage in less effortful information processing and use peripheral cues to form attitudes, while when
processing MAO are each high, individuals take considerable effort to process information in a
central route, focusing on cues relevant to the true merits of the issue. Many papers have build from
and extended on this model. For example MacInnis and Jaworski’s framework (1989), which also
posits that MAO enhance the likelihood that processing resources will be devoted to the ad, but that
takes into account more detailed levels of higher and lower processing for which they present
relevant advertising cues.
The following paragraph elaborates on the work of these authors and provides evidence on the
effectiveness of certain ad cues for different levels of information processing. The discussion will lead
to the selection of ad cues for my research and hypotheses on the expected relationships between
these ad cues and the levels of the moderating variables of the conceptual framework presented.
3.4 Information Processing Theory: The Elaboration Likelihood Model
Petty and Cacioppo (1986) were one of the first to propose an integrative framework on the use of
ad-executional cues to match specific levels of processing. Their Elaboration Likelihood Model (ELM)
captures two distinct routes to persuasion, shown in abbreviated form in figure 2. This model can be
applied to an advertising context, where the persuasive communication message is an
advertisement.
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MASTER THESIS MSc MARKETING MANAGEMENT - MEREL ZIMMERMAN
Figure 2: Abbreviation of Petty and Cacioppo’s Elaboration Likelihood Model (1986)
The route that a consumer takes, depends on their prior level of involvement. Under ‘high
involvement’, consumer response to an advertisement is affected via the central route and under
‘low involvement’ via the peripheral route. A level of high involvement (in contrast to a level of low
involvement) is characterized by greater personal relevance or consequence of the product. An
example is a situation in which a consumer is about to purchase a new product. In this state of high
involvement consumers are more motivated to devote the cognitive effort required for the central
route. In the ‘central route’ to persuasion consumers actively seek and process product-relevant
information by considering the pros and cons of the product and attending to product-relevant
arguments. Attitude formation and change in this route are thus a result from a thoughtful
consideration of product-relevant arguments and attributes. In the ‘peripheral route’ to persuasion
consumers merely attend to positive or negative cues in the persuasion context. These cues allow
them to draw inferences about the product, without the need to scrutinize arguments. Attitude
formation and change in this route thus result from the presence of simple positive or negative cues.
In the studies that led to the justification of their model, Petty and Cacioppo found support for the
view that different advertisement cues are more or less effective, depending upon a consumer’s
involvement with the advertised product. The last column in table 1 on the next page lists advertising
cues that work best for the two levels of involvement. According to Petty et al. (1983), both central
and peripheral manipulation (in the form of ad cues) may be presented visually or verbally to be
effective.
Moving beyond the ‘central- versus peripheral-route’ processing paradigm, MacInnis and Jaworski
(1989, 1990) consider processing effects over an entire range of processing levels. They propose two
strategies for achieving communication objectives: a proactive strategy and a matching strategy. The
proactive strategy changes consumers’ level of information processing by use of certain ad cues and
the matching strategy fits communication objectives and ad cues to existing levels of information
processing. The latter strategy is of interest to this research, as it aims to match ad cues to
respondents’ level of information processing in the form of their position in the sales funnel and
stage of brand awareness. The authors’ matching framework (1990) links five levels of processing to
communication objectives and to ad cues that match these processing levels. The framework is
valuable to this research, as it captures relevant findings of academic research up to the year 1990 on
the effectiveness of certain ad cues for different levels of processing. Table 1 captures the findings
which I will elaborate on next.
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MASTER THESIS MSc MARKETING MANAGEMENT - MEREL ZIMMERMAN
Table 1: MacInnis and Jaworski’s (1990) framework linking processing antecedents, processing levels, communication objectives, and ad-executional cues, extended with Petty and Cacioppo’s
(1986) Elaboration Likelihood Model.
MAO
Level
Low:
Motivation=Low
Ability=Irrelevant
Relatively low:
Motivation=
Low/Moderate
Ability=Irrelevant
Level of
processing
Pre-attention
Communication
Objectives
None
Divided attention Brand name recognition, Brand
name recall (ad memory),
globalized positive attitude or
emotional response associated
with the ad or the brand as a
whole.
Low-moderate:
Focal attention
Motivation=
Moderate OR high
Ability=
Irrelevant OR low
Moderate:
Motivation=
Moderate/High
Ability=
Moderate/High
Comprehension
High:
Motivation=High
Ability=High
Elaboration
Awareness of brand name and
memory for comprehension of
ad's main point. Consumers are
likely to derive inferences
regarding brand benefits, quality,
or attributes which influence
beliefs, which in turn influence
brand attitudes.
Memory for specific ad cues and
copy points, enduring memory for
ad elements, ad affect, beliefs,
brand attitudes via message-based
persuasion
Matching Cues
(Sources cited in MacInnis & Jaworski (1990) – see references)
None
Affect-based persuasion through salient cues:
1
Likable music
2
Attractive pictures
3
Attractive sources
4
Likable sources
5
Celebrity sources
6
Singing and dancing
Heuristic based persuasion though cues that indicate product
benefits or attributes:
7
▲ Credible/expert sources
8
Long message
9
Large number of message arguments
10
Draw a conclusion
11
Non-technical information
Message-based persuasion:
Convincing persuasive arguments
Relevant cues/source to the advertised message rather
than affective qualities
Complementary cues (consistent with the brand message)
▲ Strong/compelling arguments
Enduring memory for presented
Anticipate or reduce the likelihood of negative cognitive
and un-presented information and responses:
12
feeling responses, strongly held
Refutational appeals
12
beliefs and brand attitudes (relate ▲ Two-sided arguments
13
directly to purchase intentions),
Don't draw conclusions
14
brand attitudes via cognitive
Directed imagery (to specific brand usage outcomes)
15
response, self-brand emotional
Similarity source / target audience
associations, enduring attitudes.
▲ Cues applied in this research
20
Elaboration Likelihood Model
(Sources cited in Petty and Cacioppo, 1986)
Low involvement peripheral cues:
▲ Source expertise/credibility (Hovland
and Weiss, 1951; Rhine and Severance,
1970; Petty, Cacioppo & Goldman, 1981)
Source likability (Chaiken, 1980; Petty,
Cacioppo & Schumann, 1983)
Well-known source/endorser (Petty,
Cacioppo & Schumann, 1983)
Number of message arguments presented regardless of quality (Petty and Cacioppo,
1984a)
Pleasant music (Gorn, 1982; Park and
Young, 1986)
High involvement central cues:
▲ Product-relevant attributes (Gorn, 1982)
▲ Argument quality – strong arguments
that generate predominantly favorable
thoughts (Petty, Cacioppo & Goldman,
1981; Petty, Cacioppo & Schumann, 1983)
Number of strong message arguments
presented (Petty and Cacioppo, 1984a)
MASTER THESIS MSc MARKETING MANAGEMENT - MEREL ZIMMERMAN
At a ‘pre-attention’ level of information processing consumers have very little motivation to process
ad information. Because consumers are unlikely to attend or react to the ad’s cues at this level,
advertisers are unlikely to achieve any communication objectives. This stage is irrelevant to this
research, as consumers using a search engine are assumed to have at least some motivation to
process an ad in order to determine which of the ads to click. The first level of processing applicable
to this research is therefore better captured with the level of ‘divided attention’. At this level, the
motivation to process ad content is still relatively low, but sufficient to categorize salient cues
embedded in the ad. Persuading the consumer at this stage is based on triggering emotional
responses by using salient cues. The salient cues presented in table 1 trigger these emotional
responses, which might in turn be transferred to an attitude towards the ad or the advertised
brand/product. The emotional associations linked to salient cues as music or celebrity sources do not
automatically become associated to the brand. This requires a certain learning process, where
repeated exposure will eventually lead to the consumer linking the elicited ‘mood’ to the brand.
The difference with the next level of information processing, is that the inferences formed from the
ad cues are directly associated with the brand. This ‘focal attention’ stage is activated either when
processing motivation is moderate or when processing motivation is high but consumers lack the
ability, or knowledge structures, to process the ad. At this stage, consumers make an attempt to
understand the main point of the ad and use heuristic cues to derive inferences regarding brand
benefits, quality, or attributes. These inferences are thus still based on superficial ad analysis, but
enable advertisers to ‘effectively communicate basic brand meaning and establish globally favorable
attitudes by using salient stimuli that heuristically indicate product benefits or attributes’ (id.). Brand
quality is inferred from cues as expert sources and number of message arguments, and
comprehension of the main (indented) theme of the ad can be stimulated by providing a conclusion.
A headline could also qualify for this conclusion. Because the level of processing is only low to
moderate in this stage, technical information which is difficult to process should be avoided.
The increased ability of consumers to process an ad in the ‘comprehension’ stage means that
cognitive operations become more complex. Where the former processing levels only allowed
advertisers to transfer brand meaning by use of salient cues, this level lets consumers integrate both
salient and non-salient cues in forming brand impressions. Non-salient cues are the specific ad copy
points that consumers attend to, in the form of the message or arguments provided. As consumers
search for specific information related to the brand in this stage (and ignore information unrelated to
the brand), the relevancy of the cues presented in the ad becomes important. In addition, as
consumers spend more processing resources in evaluating specific ad copy points, only strong and
compelling arguments that are regarded by the target audience as persuasive lead to favorable
brand attitudes.
In the final ‘elaboration’ stage, motivation and ability to process ad information are both high.
Sufficient cognitive capacity leads to consumers both interpreting new information and relating this
to prior knowledge. The consumer is therefore able to come up with counterarguments, to imagine
the use of the advertised product/service (and how this use could solve consumption problems), or
to relate presented information to oneself. Advertisers have less control over this self-generated
elaboration, which requires a somewhat different approach when it comes to effective ad cues. The
content of self-generated elaboration should be made positive, which could be achieved with ad cues
that ‘anticipate or reduce the likelihood of negative cognitive responses’ (id.). Refutational appeals
present consumers with both sides of an issue and offer arguments to refute the negative brand
associations, thereby anticipating negative cognitive responses. Two-sided arguments reduce the
number of cognitive responses and make an ad appear less biased. Both these strategies therefore
make ads more persuasive. In addition, where drawing a conclusion in the ‘focal attention’ stage
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MASTER THESIS MSc MARKETING MANAGEMENT - MEREL ZIMMERMAN
stimulated comprehension of the main theme of the ad, it has a negative resistance effect in this
stage. Consumers rather draw their own conclusions from presented information, therefore
conclusion drawing should be avoided. At last, similarity between the source and the target
consumer allows for better identification with the product/service on the side of the consumer. This
last stage has a strong relationship to purchase intentions, as consumers have considered brand
attributes and self-usage of the advertised product/service.
In summary, as the level of processing increases, consumers spend more time on analyzing the
information in an ad, attend more to non-salient cues, and are better able to organize information in
a coherent association framework. Appropriate advertising cues therefore differ between and
depend on the level(s) of processing. Marked in table 1 are three ad cues that were selected to
represent the manipulation of the ads’ message characteristics for this research; source expertise,
argument quality and a two-sided argument.
While the ELM and extensions to this model have proven to be viable in an offline advertising
context, this actual research takes place in an online advertising context. As Martin et al. (2011)
noted, ‘it is important to address [the moderating role of] involvement toward the online channel
because of the relative novelty and expansion of the Internet as a shopping channel, as well as its
peculiarities that make online purchases very different from those made through traditional
channels’. Fortunately, the central and peripheral routes to information processing have also been
tested in an online context in more recent studies.
Table 2: Research testing the ELM in an online context
Study
Wang et
al. (2009)
Context
Banner ad
TV game
console
Martín et
al. (2011)
Website
SanJosé
et al.
(2009)
Lin et al.
(2011)
Website
Travel
agency
Online
reviews
Kim &
Benbasat
(2009)
B2C ecommerce
website
Park &
Kim
(2008)
Online
consumer
reviews
Lee
(2009)
Online
review
Phones
Online
shopping
Pentina
(2010)
Qi et al.
(2010)
Online ad
Signature
pen
Findings
Substantive (instead of cosmetic) variation and informational (instead of emotional) appeals
generate better advertising effectiveness for high-involvement consumers. Impacts of cosmetic
variation and emotional appeal did not vary significantly with level of involvement. Appealoriented (variation) advertising strategies are more effective for (non-)goal-directed consumers.
Greater effect of cognitive ‘service quality’ signal on satisfaction and cognitive ‘service quality’
and ‘warranty’ signal on trust for high-involvement consumers. No differential effect of
experiential signals (peripheral cues) on satisfaction for different level of involvement.
When cognitive motivation levels are low (high), the amusing (serious) format is favored. When
affective motivation levels are low (high) the serious (amusing) format is favored. Peripheral cue
(web page presentation format) relevant in high-involvement context (exposure to Web pages).
Consumers with a high need for cognition take the central route in attitude change (quality of
online reviews) and consumers with a low need for cognition take the peripheral route (quantity
of online reviews) in forming attitude.
When customers purchase a high-price product (high-involvement), they form trusting beliefs by
scrutinizing argument content rather than by depending on heuristic cues (argument source).
The effect of a third party’s arguments over a store’s arguments (argument source as peripheral
cue) on trusting beliefs was not significantly larger under low price than under high price.
Experts have higher purchase intention and better cognitive with attribute-centric reviews,
while novices have higher purchase intention and better cognitive fit with benefit-centric
reviews. The type (number) of online consumer review(s) has a stronger effect on the purchase
intention of consumers with high (low) expertise.
Consumers under high-involvement conditions take the central route (review quality) in attitude
change and low-involvement consumers adopt the peripheral route (review quantity) in forming
attitude.
Under low involvement purchase conditions, the avatar's (salesperson) physical characteristics
affect buying intentions. Under high involvement conditions, the avatar's characteristics do not
affect buyer cognitive effort (sales arguments alone determine purchase intentions).
Involved consumers’ responses (attitude towards the ad and brand, purchase intention and
source credibility) to two-sided online ads are more favorable than that of one-sided online ad.
Two-sided ad is no more persuasive for uninvolved consumers except for those who recognize
the two-sided nature of the communication (which only influences source credibility).
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MASTER THESIS MSc MARKETING MANAGEMENT - MEREL ZIMMERMAN
Table 2 captures a compilation of these studies and summarizes their main findings. The collection of
these studies once again show the importance of this research in testing the effects of message
characteristics in a paid search advertising context, as the ELM has not been tested in this specific
online context yet. As shown in table 2, the ELM has been tested in the context of online shopping,
online (banner) ads, website characteristics and online reviews. One insight from these studies is that
the ELM proves to be applicable also in an online context. However, the studies marked in red point
to an additional insight that might appear in some situations. Where high-involved consumers in
these studies indeed responded more favorably to central cues, there was no difference in response
to peripheral cues between high- and low-involved consumers. Where the ELM would expect highinvolved consumers to only respond more favorably to central cues, these studies show a combined
influence of central and peripheral routes in high-involvement contexts. Martin et al. (2011)
describes this finding as fitting the electronic version of the ELM, or the eELM. In the hypotheses that
follow, it will be assumed that the ELM (and not the eELM) will be applicable to this research (which
does characterize an online context).
3.5 Hypotheses in line with ELM Theory
Building upon ELM theory discussed, I can speculate about the impact of advertisement message
characteristics in the context of paid search advertisements. More specifically, I can hypothesize the
impact of the ad cues selected for this research (marked in table 1). According to ELM theory, the
effectiveness of the ad cues will depend on consumers’ prior level of involvement: consumers with a
high (low) level of involvement towards the advertised product will attend to central (peripheral)
cues.
In a state of high involvement, consumers are more motivated to devote the cognitive effort required
for the central route. As consumers move to the purchase decision stage of the sales funnel, their risk
of lacking sufficient information increases, their search becomes more specific, and their mindset is
more closure seeking in the form of making a purchase. These factors lead to the consumer
becoming more motivated (and therefore more involved) to scrutinize ad information content and
thus attend to the central cues of the ad. In a beginning stage of the sales funnel, where involvement
is low, peripheral cues allow consumers to draw inferences about the product, without the need to
scrutinize arguments. Therefore:
H1: In an early stage of the sales funnel, people will be more sensitive to the part of the ad that captures a
peripheral cue (source expertise) compared to people in later stages of the sales funnel.
Consumers’ level of involvement is not only characterized by their motivation to process an ad, but
also by their ability to process an ad. ELM theory postulates that “if a person is going to carefully
scrutinize the arguments in a persuasive message and thereby follow the central route to persuasion,
the person must have the ability to evaluate the arguments” (Petty and Cacioppo, 1986). Prior
product knowledge is a factor affecting the ability to process an ad. When a consumer possesses a
high amount of prior knowledge with respect to a certain product category, this consumer’s cognitive
structure is better developed with respect to this product category. This in turn leads to this
consumer being better able to activate concepts from memory that can be used in interpreting new
information in an advertisement message. “Even if a person is highly motivated to scrutinize a
message, if ability is lacking the person may be forced to rely on simple cues such as source
credibility in order to evaluate the message” (id.). Therefore:
H2a: People with low product knowledge will be more sensitive to the part of the ad that captures a peripheral
cue (source expertise) compared to people with high product knowledge (regardless of their stage in the sales
funnel).
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MASTER THESIS MSc MARKETING MANAGEMENT - MEREL ZIMMERMAN
H2b: There is no interaction effect between people’s stage in the sales funnel and a central cue (argument
quality).
H2c: People with high product knowledge in later stages of the sales funnel will be more sensitive to the part of
the ad that captures a central cue (argument quality) compared to people in an early stage of the sales funnel
(regardless of their level of product knowledge).
Petty and Cacioppo (1986) explain that “the more issue-relevant knowledge people have [and the
more they are motivated to use this knowledge], the more they tend to be able to counterargue
communications opposing their initial positions”. MacInnis and Jaworski (1990) confirm that
consumers with high motivation and ability to process an ad are able to come up with
counterarguments. A two-sided argument strategy that derogates the brand on an attribute of minor
importance, reduces counter-argumentation likelihood and makes the ad appear less biased and
more credible (id.). Therefore:
H3: People with high product knowledge in a late stage of the sales funnel will be more sensitive to the part of
the ad that captures a two-sided argument cue compared to people in an early or intermediate stage of the
sales funnel (regardless of their level of product knowledge).
Table 3 below summarizes the expected influence of the moderating variables. I thus expect that the
effectiveness of advertisement message characteristics is dependent on consumer’s product
knowledge and their position in the sales funnel. This means that the different ad cues created might
all show an effect but only in the interaction with the moderating variables, not directly. However,
the hypotheses predict the sensitivity to the source expertise cue to be high for most of the levels of
the moderating variable. Therefore:
H4: There is a relationship between advertisement message characteristics and paid search advertising
effectiveness; overall, the probability of clicking will increase when a peripheral cue (source expertise) is
present in an ad.
Table 3: Summary of hypotheses on the moderating influence of sales funnel stage and product knowledge
Higher information
processing level ↘
Sales Funnel Stage
Brand Awareness
 Higher motivation to process
Brand Research
Brand Decision
 Higher ability to process
Low product knowledge
Route to persuasion: Peripheral
High product knowledge
Route to persuasion: Peripheral
Effective cue: Source expertise (H1, H2a)
Effective cue: Source expertise (H1)
Route to persuasion: Peripheral
Route to persuasion: Central
Effective cue: Source expertise (H2a)
Effective cue: Argument quality (H2c)
Route to persuasion: Peripheral
Route to persuasion: Central
Effective cue: Source expertise (H2a)
Effective cue: Argument quality (H2c) +
Two-sided argument (H3)
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MASTER THESIS MSc MARKETING MANAGEMENT - MEREL ZIMMERMAN
4. Data and Methods
4.1 Variable Manipulations
The method for testing my research model required three variables to be manipulated: the message
characteristics of the independent variable and product knowledge and the sales funnel stage of the
moderating variable. In the following paragraphs these manipulations are described.
4.1.1 Message Characteristics
Three ad cues were selected to represent the manipulation of the ads’ message characteristics;
source expertise, argument quality and a two-sided argument. These three cues were selected on
the basis of two criteria. First of all, they had to be executional in an Adwords context. This context
inhibits the use of any pictorial or non-static cues, as the ad is limited to text. Secondly, the
manipulation of the cues’ levels had to match clearly distinct levels of information processing, as the
objective of this study is to test whether certain cue levels indeed match certain levels of the
moderating variable.
 Source expertise cue: This cue was developed for heuristic based persuasion at a low information
processing level (a peripheral cue). One level of this cue captures the product approval of an
expert source. This was realized in line with Petty and Cacioppo’s (1983) study, in which they
verbally translated the expert source with an ad line reading: “Professional athletes agree: …”. A
second (low) level of the cue captures the product approval of a non-expert source.
 Argument quality cue: This cue was developed for message-based persuasion at a moderate
information processing level (a central cue). By manipulating the levels of two different product
features, this cue captures either weak or strong arguments about the product.
 Two-sided argument cue: This cue was developed to anticipate or reduce the likelihood of
negative cognitive responses occurring at a high information processing level (a second central
cue). A two-sided argument is realized by derogating the product on an attribute of minor
importance. This cue should appeal to consumers at a high information processing level, by
making the ad appear less biased.
4.1.2 Product Knowledge
Two products were selected to be advertised, a Digital Single-Lens Reflex (DSLR) Camera and running
shoes. With the selection of these products I intended to manipulate consumer’s product knowledge.
The expectation is that the average consumer has low product knowledge on DSLR cameras and
higher product knowledge on running shoes. This expectation will be tested in the main survey by
asking respondents questions to determine their level of product knowledge. This measurement
procedure is described later in this chapter. In addition to their differentiating level of product
knowledge in the minds of consumers, the products selected were also of a category for which
buyers ordinarily conduct some pre-purchase (online) information search.
4.1.3 Sales Funnel Stage
Respondents were placed in a specific stage of the sales funnel by means of instructions in the main
survey. For a given stage of the sales funnel, a respondent is asked to image a specific situation. The
situation descriptions for the three sales funnel stages for both products can be found in Appendix
7.1. The three sales funnel stages are adopted from Jansen and Schuster’s (2011) methodology and
can be described as follows:
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MASTER THESIS MSc MARKETING MANAGEMENT - MEREL ZIMMERMAN
- Stage 1: Awareness – In an awareness stage the consumer has the knowledge that the product
exists and there is a need for it. The consumer is searching for general knowledge, which could
possibly lead to a purchase.
- Stage 2: Research – In a research stage the consumer has decided on the type of product they
want, but not yet on a brand or store to purchase from. Their search becomes more focused.
- Stage 3: Decision – In a decision stage the consumer is comparison shopping to consider the
alternatives. Technical specifications are often included in their search.
4.1.4 Ad Development
Paid search ads were developed for the two different products, capturing the three cues described in
4.1. These three cues were captured by means of varying the levels of three ad attributes, outlined in
table 4 below.
-
The source expertise cue is present in ads which capture level 2 of the source attribute.
The argument quality cue is present in ads which capture level 2 of product feature 1.
The two-sided argument cue is present in ads which capture both level 2 of product feature 1
and level 1 of product feature 1 (derogation of brand on attribute of minor importance).
Table 4: Attribute levels captured by the paid search ads for DSLR Camera and Running Shoes
DSLR CAMERA
Ad attribute
Source
Product feature 1
Product feature 2
Level 1
approved by local photography club
(low expertise)
five x zoom (good)
comes in black (bad)
Level 2
approved by prof. photographers
(high expertise)
ten x superzoom (excellent)
comes in five colors (good)
Level 1
approved by local athletics club
(low expertise)
good stability (good)
no extra laces (bad)
Level 2
approved by professional athletes
(high expertise)
max. stability (excellent)
extra pair of laces (good)
RUNNING SHOES
Ad attribute
Source
Product feature 1
Product feature 2
The combination of the attribute levels led to the creation of 16 ads (8*2 brands) for both products
(Appendix 7.2). A pre-survey (n=32) confirmed that professional photographers/athletes were seen
as significantly more expert product endorsers compared to a local photography/athletics club1. In
addition, product feature 2 was perceived as an attribute of minor importance for both products by
the respondents2, which was an important requirement for the two-sided argument cue.
1
The level of expertise was tested on a 5 point scale (Low expertise level – High expertise level):
Professional photographers (µ= 3.91) vs. Local photography club (µ= 3.06)  (t = -3.369 ; p = 0,002)
Professional athletes (µ= 3.81) vs. Local athletics club (µ= 3.09)  (t = -2.777 ; p = 0,009)
2
The level of product feature importance was tested on a 5 point scale (Not important – Very Important):
DSLR zoom range (µ= 4.16) vs. DSLR camera color (µ= 2.50)  (t = 5.346 ; p = 0,000)
Running shoe stability (µ= 4.47) vs. Additional laces (µ= 2.31)  (t = 9.990 ; p = 0,000)
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MASTER THESIS MSc MARKETING MANAGEMENT - MEREL ZIMMERMAN
Furthermore, manipulating the product feature levels led to the intended formation of weaker and
stronger arguments for the product (as perceived by the respondents on a 7-point scale). This can be
seen in figure 3 where panel a shows a rise in argument strength for different levels of DSLR features
and panel b shows a rise in argument strength for different levels of running shoe features. Table 5
indicates that the arguments capturing a high level of product feature 1 are indeed significantly
different in strength from the arguments capturing a low level of product feature 1.
4,78
5
4
6
5,16
3,97
3,44
3
2
Argument strength
Argument strength
6
5
4,62
5,06
5,28
4,06
4
3
2
1
1
0
0
five x zoom + five x zoom +
ten x
ten x
comes in black comes in five superzoom + superzoom +
colors
comes in black comes in five
colors
good stability good stability max. stability max. stability
+ no extra + extra pair + no extra + extra pair
laces
of laces
laces
of laces
Figure 3a: Results pre-survey argument strength DSLR Camera
Figure 3b: Results pre-survey argument strength running shoes
Table 5: Significantly different product performance arguments in terms of argument strength
Product
DSLR Camera
DSLR Camera
DSLR Camera
DSLR Camera
Running Shoes
Running Shoes
Running Shoes
Argument 1
five x zoom + comes in black
five x zoom + comes in black
five x zoom + comes in five colors
five x zoom + comes in five colors
good stability + no extra laces
good stability + no extra laces
good stability + extra pair of laces
Argument 2
ten x superzoom + comes in black
ten x superzoom + comes in five colors
ten x superzoom + comes in black
ten x superzoom + comes in five colors
max. stability + no extra laces
max. stability + extra pair of laces
max. stability + extra pair of laces
Statistics
t= -4.946 / p= 0.000
t= -4.300 / p= 0.000
t= -2.290 / p= 0.029
t= -5.350 / p= 0.000
t= -5.402 / p= 0.000
t= -2.931 / p= 0.006
t= -3.144 / p= 0.004
4.2 Empirical Study Design
The retained method for testing the conceptual model is online experimentation. Different versions
of a nine-page online questionnaire were randomly assigned to respondents. In this questionnaire,
respondents were first asked to image a specific situation, which placed them in one of the three
sales funnel stages (Appendix 7.1). A corresponding Search Engine Result Page (SERP) was presented
next. Respondents were asked to click on the link that is most relevant to them. The remaining pages
were used to collect respondents’ product category knowledge and socio-demographic information.
Each of the SERP versions contained a combination of 2 different paid search ads (appendix 7.3).
Appendix 7.4 shows a screenshot of one of the SERP versions for both products. For high external
validity (a realistic SERP), the SERPs were presented fully to the respondents, but the organic ads on
the left were blurred to have them focus on the paid search ads.
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MASTER THESIS MSc MARKETING MANAGEMENT - MEREL ZIMMERMAN
The combination of paid search ads shown was dependent on the sales funnel stage respondents
were placed in. For each sales funnel stage, seven SERP versions contained one benchmark ad on top
combined with one of the seven remaining ad versions of a second brand (Appendix 7.2). The
difference between the sales funnel stages was the version of the benchmark ad. I needed to ensure
that the combinations would not lead to the benchmark ad (not) being preferred in all cases. This
required me to select the 4th position ad (in terms of expected preference) of 8 ads for each sales
funnel stage: In line with my hypotheses, respondents in the awareness stage of the sales funnel
were expected to prefer ads with the source expertise cue in them. The benchmark ad in this stage
thus was the ‘worst’ version of 4 ads that captured the source expertise cue, namely the ad which
also captured a low level of product feature 1 and 2. Respondents in the research stage of the sales
funnel were expected to prefer ads with an argument quality cue. The benchmark ad for this sales
funnel stage thus captured a high level of product feature 1 and low levels of source expertise and
product feature 2. Respondents in the decision stage of the sales funnel were expected to prefer
both a quality and two-sided argument. The benchmark ad in this case thus captured a high level for
both product feature 1 and 2, as well as a low level of source expertise.
Next, product knowledge was measured. Product knowledge can be measured in different ways.
According to Brucks (1985), two measures of knowledge are directly linked to behavior. The first
measures an individual's perception of how much s/he knows (subjective knowledge). The second
measures the amount, type, or organization of what an individual actually has stored in memory
(objective knowledge). A third measure, which is less directly linked to behavior, measures the
amount of purchasing or usage experience with the product (experience-based knowledge). This last
measure is indirect because information processing theory holds that experience affects behavior
only when experience results in differences in memory (id.).
Two of these measures were applied to this research in line with Brucks’ (1985) methodology.
Subjective knowledge was measured by asking respondents to use a 7-point scale (one of the least
knowledgeable : one of the most knowledgeable) to respond to the following statement: “Rate your
knowledge of DSLR cameras / running shoes, as compared to the average consumer”. In addition,
respondents were asked to indicate their familiarity with DSLR cameras / running shoes on a 7-point
scale (not at all familiar : extremely familiar). Experience-based knowledge was measured by having
respondents indicate whether they have ever bought a DSLR camera / running shoes.
All respondents were placed in two of the six levels of the moderating variable randomly. Therefore,
the first part of the questionnaire was about DSLR cameras (low expected product knowledge) for
one randomly selected stage of the sales funnel and the second part of the questionnaire was about
running shoes (high expected product knowledge) for one randomly selected stage of the sales
funnel. The final distribution of respondents amongst the sales funnel stages and product knowledge
levels is set out in appendix 7.5.
Respondents to this questionnaire were gathered in three ways. Firstly, personalized emails were
send, where participation in the study was proposed and a link to the online questionnaire was
provided. Some of the addressees were asked to forward the email to acquaintances. Secondly, the
link to the online survey was placed in social media groups and events related to the Erasmus
University. Lastly, flyers with a QR code and hyperlink to the online survey were distributed door-todoor in Vianen (Utrecht).
In total 202 respondents fully completed the questionnaire (n= 202). The final sample contains
slightly more men (59,9%) than women (40,1%). Furthermore, the respondents show a wide
distribution amongst the different age, education and income levels (appendix 7.6).
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MASTER THESIS MSc MARKETING MANAGEMENT - MEREL ZIMMERMAN
4.3 Data Analysis Method
A regression method was employed for analyzing the data. A basic regression analysis requires the
dependent variable to be continuous. The dependent variable in this actual conceptual model is not
continuous but binary, since it takes on only two values. The two values represent the choice
between 2 alternative ads: respondents either click (1) or do not click (0) on an ad. A basic linear
regression was not appropriate, because the fitted value of the dependent variable is not restricted
to lie between zero and one in such an analysis. The appropriate method for analyzing the data in
this case was a binary logit regression. This regression model is designed to handle the specific
requirements of a binary dependent variable.
The analysis was conducted in the statistical software package EViews7. In this specific software
package, the regression model correctly accounts not only for the choice made, but also for the
alternative choice option(s) presented to the respondent. This was an important prerequisite, as the
empirical design of this study captures a choice between two ads with varying attribute levels.
4.4 Dataset Adjustments
In order to test the reliability of the two items measuring respondent’s subjective product knowledge
the Cronbach’s alpha test has been applied. High correlation between the items results in a high
Cronbach’s alpha, making the two items measuring subjective product knowledge a better indicator
of this knowledge. A coefficient alpha of 0.93 for subjective knowledge of DSLR cameras and 0.92 for
subjective knowledge of running shoes was found. These values indicate that both items reliably
measure the same construct and they were therefore summed to form one scale.
As expected, respondent’s subjective knowledge level for DSLR cameras is significantly lower
compared to their subjective knowledge level for running shoes3. Furthermore, significantly more
respondents purchased running shoes (n=145) than a DSLR Camera (n=68) (χ2=27.836 ; p=0.000).
These findings confirmed initial expectations on product knowledge and led to the decision to
categorize respondent’s product knowledge (low/high) based on the type of product (DSLR Camera =
low product knowledge and running shoes = high product knowledge).
Respondent’s exposure to a specific SERP version was re-coded into three variables. These three
variables capture the attribute levels of the ads presented to the respondent. The three attributes
and their respective level descriptions were already presented in table 3 (4.1.4). A high level (level 2)
of a specific attribute was coded ‘1’ and a low level (level 1) was coded ‘0’. Table 6 below shows how
this led to the creation of four lines of data per respondent in the dataset; two lines for each product
type (DSLR Camera/Running shoes), of which one line for the ad the respondent clicked on and
another line for the ad they did not click on.
Table 6: Part of dataset showing how each respondent covers four data lines
Respondent #
1
1
1
1
…..
Product
0
0
1
1
…..
DV
1
0
1
0
…..
Source
1
0
1
0
…..
3
Product Feature 1
0
0
1
1
…..
Product Feature 2
0
1
0
1
…..
…..
…..
…..
…..
…..
…..
Subjective knowledge DSLR (mean= 3.42) vs. subjective knowledge running shoes (mean= 3.80)  (t = -2.427 ;
p = 0,016)
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MASTER THESIS MSc MARKETING MANAGEMENT - MEREL ZIMMERMAN
5. Results
This chapter will discuss the results of the binary logit regression analyses. An overview of the
variables, their descriptions and their levels is presented in table 8. The variable coding applied in this
table will also be applied as from here.
5.1 Model Construction Procedure
Table 7 summarizes the coefficient estimates of both a direct-effects model (Model 1) and models
including direct and interaction effects (Model 2 and 3).
In a first step, regression model 1 was run with only main effect variables. The models capture the
probability of clicking on a specific ad, since each of the respondents are forced to click on one of two
ads. This choice depends only on differences between the ads. Therefore, the main effect variables
are limited to the three attributes captured by the ads and their ranking position.
In a second step, all possible interactions between the ad attribute levels, sales funnel stages and
product knowledge levels were included to model 1 one at a time. A list of all interactions tested can
be found in appendix 7.7.
In a third step the main effects were modeled with only the significant interaction effects from step
two. Model 2 includes interactions between (1) ad attributes and (2) single ad attributes, sales funnel
stages and/or product knowledge levels. In addition to model 2, model 3 includes interactions
between multiple ad attributes, sales funnel stages and product knowledge levels. Both model 2 and
3 are reported, since model 3 takes two significant effects away from model 2.
5.2 Model Fit
The LR statistic tests the joint null hypothesis that all slope coefficients except the constant are zero.
This statistic can therefore be used to test the overall significance of a model. The probability (pvalue) of the LR statistic indicates that all models are all significant overall.
McFadden R-squared is the likelihood ratio index and an analog to the R2 reported in linear
regression models. It has the property that it always lies between zero and one. As an analog to the
R2, its interpretation is not the same. Although its value can be interpreted as an approximate
variance in the outcome accounted for by the predictor variables, this value tends to be smaller than
R2 and values of .2 to .4 are considered highly satisfactory. The McFadden R-squared values reported
in table 7 indicate that a fair amount of the variance in the probability of clicking on a specific ad is
explained by the included predictor variables. Model 3 captures the highest McFadden R-squared of
.08. The increase in the McFadden R-squared from Model 1 to 3 indicates that the models with
interaction terms included are better predicting the outcome.
An alternative way of determining the fit of the models is by performing a Pearson χ2-type test of
goodness-of-fit. The Hosmer-Lemeshow goodness-of-fit test compares the fitted expected values to
actual values by group. If these differences are ‘large’ (significant), the model is rejected as providing
an insufficient fit to the data. In this case, the differences in all four models are not significant, which
allows me to conclude that the models fit the data.
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MASTER THESIS MSc MARKETING MANAGEMENT - MEREL ZIMMERMAN
Table 7: Main and interaction effects on dependent variable “probability of clicking”
Model 1
Parameter
constant
source
pf1
pf2
ranking
source*pf1
source*knowledge
pf1*knowledge
source*stage_1*knowledge ▲
source*stage_2*knowledge
source*stage_3*knowledge
pf1*stage_1*knowledge ▲
pf1*stage_2*knowledge
pf1*stage_3*knowledge
pf2*stage_1*knowledge ▲
pf2*stage_2*knowledge
pf2*stage_3*knowledge
(pf1*pf2)*stage_1*knowledge ▲
(pf1*pf2)*stage_2*knowledge
(pf1*pf2)*stage_3*knowledge
(source*pf1)*stage_1 ▲
(source*pf1)*stage_2
(source*pf1)*stage_3
(source*pf1*pf2)*stage_1 ▲
(source*pf1*pf2)*stage_2
(source*pf1*pf2)*stage_3
LR statistic
Probability (LR statistic)
McFadden R-squared
H-L statistic
Probability (H-L statistic)
Note 1:
Note 2:
-0,758
0,694
0,584
0,151
0,107
Model 2
*
*
*
24,908
0,000
0,022
6,216
0,718
* Significant at 1%
** Significant at 5%
▲ Base level dummy variable
-0,981
0,547
1,069
0,047
0,506
-0,556
-0,009
0,517
.
1,753
1,485
.
-1,980
-0,676
.
1,511
-0,863
Model 3
*
***
*
-1,036
0,498
0,953
0,157
0,657
0,061
-0,107
0,421
.
1,901
1,689
.
-1,936
-0,075
.
1,348
-0,635
.
0,602
-0,824
.
-0,082
-0,787
.
-1,311
-0,261
**
*
*
*
*
***
84,875
0,000
0,076
36,468
0,160
*
*
**
*
*
*
**
***
92,624
0,000
0,083
31,226
0,556
*** Significant at 10%
Table 8: Variable coding and descriptions
Variable Name
source
pf1
pf2
stage_1
stage_2
stage_3
knowledge
ranking
Description
ad attribute 1 - source expertise
ad attribute 2 - performance product feature 1
ad attribute 3 - performance product feature 2
Sales Funnel Stage 1: Awareness
Sales Funnel Stage 2: Research
Sales Funnel Stage 3: Decision
Respondent's level of product knowledge
Ranking of the ad on the SERP
31
Level = 0
low expertise
good
bad
low
ranked second
Level = 1
high expertise
excellent
good
x
x
x
high
ranked first
MASTER THESIS MSc MARKETING MANAGEMENT - MEREL ZIMMERMAN
5.3 Model Interpretation
In comparison to linear regression models, interpretation of the coefficient values in table 7 is
complicated by the fact that estimated coefficients from a binary model cannot be interpreted as the
marginal effect on the dependent variable. Instead, the coefficients in table 7 provide a measure of
the relative changes in the probability of clicking on a specific ad: the ‘log odds’.
When taking the exponential of the log odds, they can be converted to odds ratios, which are better
interpretable. The odds ratios for model 1 indicate the factor increase in the odds of clicking on an
ad, for every unit increase in a specific predictor variable (holding all other predictor variables
constant). Appendix 7.8 captures model 1 from table 7 with the odds ratios. The interpretations in
7.3.2 will be based on these odds ratios.
The interpretation of the odds ratios does not hold for model 2 and 3, where interaction terms
between predictor variables are included. Therefore, the interpretations in model 2 and 3 will be
based solely on the direction of the effect (increase or decrease in probability of clicking).
At this point, it is important to keep in mind that the variables source, pf1, and pf2 represent not
only the three ad attributes, but also the three ad cues (as described in chapter 4.1.4):
-
The source expertise (peripheral) cue is captured when the level of source is ‘high’;
The argument quality (central) cue is captured when the level of pf1 is ‘high’ (or ‘excellent’);
The two-sided argument (central) cue is captured when the level of pf1 is ‘high’ and the level
of pf2 is ‘low’ or ‘bad’.
5.3.1 First ranking increases clicking
The main effects are limited to differences between ads, since these differences determine the
choice between ads and thus the probability of clicking on a specific ad. Besides the levels of the
three ad attributes, ad ranking is also a difference between the two ad options presented to a
respondent. Models 2 and 3 show that there is a significant ranking effect present; when ads are
ranked first, the probability of clicking increases. By including this main effect in the model, the effect
of ad ranking is controlled for in the other main and interaction effects described hereafter.
5.3.2 Central cue more effective than peripheral cue
In the direct relationship between advertisement message characteristics and paid search advertising
effectiveness, it was expected that the source expertise cue would be the only cue effective in
increasing the probability of clicking. However, model 1 shows that both the main effect of source
and the main effect of pf1 are significant. When source expertise in an ad is high (instead of low), the
odds of clicking on this ad are 2 times higher (holding all other predictor variables constant). When
the level of product feature 1 in an ad is ‘excellent’ (instead of ‘good’), the odds of clicking on this ad
are 1.8 times higher (holding all other predictor variables constant).
More surprisingly, the main effect of source weakens in model 2 and 3, when adding the interaction
effects. In model 2 the main effect of pf1 on the probability of clicking is stronger than the main
effect of source, and in model 3 there is no main effect of source at all. Model 3 shows that when an
ad captures an ‘excellent’ level of pf1, the probability of clicking increases. This finding does not
support H4.
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MASTER THESIS MSc MARKETING MANAGEMENT - MEREL ZIMMERMAN
5.3.3 Sales funnel stages do not react differently to ad cues
No interaction effect is found between one of the ad attributes and stages of the sales funnel. This
means that people in different stages of the sales funnel do not react differently towards a certain ad
attribute.
This finding does not support H1. It was expected that people in an early stage of the sales funnel
would show an increase in the probability of clicking on an ad ones this ad captured a high level of
source expertise (compared to people in later stages of the sales funnel and regardless of their level
of product knowledge).
This finding does support H2b. It was expected that people in later stages of the sales funnel would
show an increase in the probability of clicking on an ad ones this ad captured a high level of pf1, but
only when their product knowledge is high. A low product knowledge level should force even highly
motivated people in later stages of the sales funnel to rely on a peripheral instead of a central cue.
The effect of a central cue (a high level of pf1) should thus only be present in the interaction
between pf1, stage and knowledge and not in the interaction between pf1 and stage directly.
5.3.4 Level of product knowledge no influence on ad cue effectiveness
The interaction between source and knowledge was added to the full model, because it showed a
significant effect when it was added to the main effects by itself. In the full model, the significance of
this effect is lost. This means that people with low product knowledge do not react differently
towards the source expertise cue compared to people with high product knowledge.
This finding does not support H2a. People with low product knowledge were expected to be more
sensitive to the source expertise cue, because their lack of ability should force them to rely on
peripheral cues.
5.3.5 Moderating effects of sales funnel stages and product knowledge levels
Both model 2 and 3 show three significant interaction effects between single ad attributes, sales
funnel stages and product knowledge levels. This paragraph will first describe the direction of these
effects and next link them to the formulated hypotheses on these effects.
5.3.5.1 Highly involved people more sensitive to peripheral cue
The first interaction effect is between source, stage and knowledge. Figure 4 graphically depicts this
effect: The left panel compares the effect of source and knowledge between stage_2 and stage_1,
and the right panel compares the same effect between stage_3 and stage_1.
Both panels show that differences between sales funnel stages in the probability of clicking are
affected by high product knowledge and source expertise. People in later stages of the sales funnel
with high product knowledge are more sensitive to an expert source cue in comparison to people in
an early stage of the sales funnel with high product knowledge. When product knowledge is low,
there is no difference in the effect of the source expertise cue between an early stage and later
stages of the sales funnel.
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Sales Funnel Stage 2 (Stage 1 as base)
Sales Funnel Stage 3 (Stage 1 as base)
.9
Probability of clicking
Probability of clicking
.9
.8
.7
.8
.7
.6
prod_know low
prod_know high
.6
.5
.5
.4
0
Source expertise (0=low 1=high)
.4
1
0
prod_know low
prod_know high
Source expertise (0=low 1=high)
source expertise (0=low 1=high)
1
source expertise (0=low 1=high)
prod_know low
prod_know high
Figure 4: Probability of clicking (model 3) as a function of sales funnel stage, product knowledge, and source expertise.
5.3.5.2 Product knowledge level turns around effect central cue in the sales funnel
The
1 second interaction effect is between pf1, stage_2 and knowledge. Figure 5 graphically depicts
this effect: The graph compares the effect of pf1 and knowledge between stage_2 and stage_1. In
this case, the same effect was not present between stage_3 and stage_1.
The graph shows that differences between an intermediate and early stage of the sales funnel in the
probability of clicking are affected by product knowledge and the level of pf1. People in an
intermediate stage of the sales funnel with low product knowledge are more sensitive to an excellent
level of pf1 in comparison to people in an early stage of the sales funnel with low product
knowledge. When product knowledge is high this effect is turned around: people in an intermediate
stage of the sales funnel are then less sensitive to an excellent level of pf1.
Sales Funnel Stage 2 (Stage 1 as base)
.75
Probability of clicking
.70
.65
.60
prod_know low
high
prod_know low
prod_know
high
prod_know
.55
.50
.45
.40
.35
0
Product feature 1 (0=low 1=high)
1
product feature 1 (0=low 1=high)
1
Figure 5: Probability of clicking (model 3) as a function of sales
funnel stage, product knowledge, and product feature 1.
source expertise (0=low 1=high)
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5.3.5.3 Highly involved people sensitive to two-sided argument cue
The third interaction effect is between pf2, stage and knowledge. In model 2 this effect is present
between both stage_2 and stage_1 and stage_3 and stage_1. In model 3 however, this effect is not
present between stage_3 and stage_1. Figure 6 graphically depicts the effect within model 2 and
figure 7 the same effect within model 3.
Sales Funnel Stage 2 (Stage 1 as base)
Sales Funnel Stage 3 (Stage 1 as base)
.9
Probability of clicking
Probability of clicking
.9
.8
.7
.8
.7
.6
prod_know low
prod_know high
.6
.5
.5
.4
0
Product feature 2 (0=low 1=high)
.4
1
0
prod_know low
prod_know high
Product feature 2 (0=low 1=high)
product feature 2 (0=low 1=high)
1
product feature 2 (0=low 1=high)
prod_know low
prod_know high
Figure 6: Probability of clicking (model 2) as a function of sales funnel stage, product knowledge, and product feature 2.
The graphs in figure 6 and 7 show that differences between sales funnel stages in the probability of
clicking are affected by high product knowledge and the level of pf2.
1
The first panel in figure 6 and the graph in figure 7 shows that people in an intermediate stage of the
sales funnel with high product knowledge are more sensitive to a high level of pf2 in comparison to
people in an early stage of the sales funnel with high product knowledge. The second panel in figure
6 shows that people in a later stage of the sales funnel with high product knowledge are less
sensitive to a high level of pf2 in comparison to people in an early stage of the sales funnel with high
product knowledge. Stated differently, people in a late stage of the sales funnel with high product
knowledge are more likely to click when the level of pf2 is low. When product knowledge is low,
there is no difference in the effect of the level of pf2 between an early and later stages of the sales
funnel.
Sales Funnel Stage 2 (Stage 1 as base)
Probability of clicking
.9
.8
.7
prod_know low
high
prod_know low
prod_know
high
prod_know
.6
.5
.4
0
Product feature 2 (0=low 1=high)
1
product feature 2 (0=low 1=high)
1
Figure 7: Probability of clicking (model 3) as a function of sales funnel stage, product knowledge, and product feature 2.
source expertise (0=low 1=high)
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The remainder of this paragraph will discuss to what extent the above described interaction effects
are in line with the stated hypotheses H2c and H3.
It was expected that people with high product knowledge in later stages of the sales funnel would be
more sensitive to a central cue (pf1) compared to people in an early stage of the sales funnel. Figure
4 and 5 show that the findings do not support H2c. People with high product knowledge in an
intermediate stage of the sales funnel are less sensitive to a high level of pf1 compared to people in
an early stage of the sales funnel. Surprisingly, figure 4 shows that people in later stages of the sales
funnel with high product knowledge are rather more sensitive to a peripheral cue (source). Figure 5
shows that people in an intermediate stage of the sales funnel with low product knowledge are more
sensitive to a high level of pf1 compared to people in a beginning stage. According to the theory this
however should not have occurred, since only people with high product knowledge should have the
ability to evaluate the arguments of a central cue.
Finally, it was expected that people with high product knowledge in a late stage of the sales funnel
would be more sensitive to the part of the ad that captures a two-sided argument cue compared to
people in earlier stages of the sales funnel. The two-sided argument cue was supposed to be
captured by a high level of pf1 and a low level of pf2. A low level of pf2 (an attribute of minor
importance) should make the ad appear less biased and therefore more credible to people who are
highly involved. In appreciating the credibility of the ad, these people should show a higher
probability of clicking on an ad ones it captures a low level of pf2. The interaction between pf1*pf2,
stage, and knowledge was not present. Support for H3 could however be found in the single
interaction of pf2 with stage and knowledge. The left panel in figure 6 shows that (at high product
knowledge) an intermediate stage in the sales funnel is more sensitive to a high level of pf2 than an
early stage. In contrast, the right panel of this figure shows that (at high product knowledge) a late
stage of the sales funnel is less likely to click when the level of pf2 is high in comparison to people in
the early stage or more likely to click when the level of pf2 is low. This finding supports H3.
5.4 Unexpected Ad Cue Functioning
Thus far it was assumed that the ad attribute source would function as a peripheral cue and the ad
attribute pf1 as a central cue. Under this assumption, H2c and H4 could not be supported. The above
discussed results however question this assumption. There are indications in the results to believe
that pf1 functioned as a peripheral cue and source as a central cue in this specific research setting.
The direct effect of pf1 discussed in 7.3.2 is a first indication. According to the theory, people with a
low level of involvement towards the advertised product will attend to peripheral cues. A low level of
involvement is characterized by either a low motivation or a low ability to process an ad. In this
research, people in an early stage of the sales funnel (low motivation) or people in a later stage of
the sales funnel with low product knowledge (low ability) have a low level of involvement. This
description of low involvement applies to 67% of a total of 2*202 respondents (they are placed in
two conditions). Assuming that the theory is correct, a direct effect of a peripheral cue in the data
should thus be present, as the largest part of the respondents should have reacted to this peripheral
cue. The only ad attribute showing a direct effect in the probability of clicking (model 3) is pf1; when
the level of pf1 in an ad is high, the probability of clicking is higher. This observation, combined with
the knowledge derived from theory, leads me to believe that pf1 functioned as a peripheral cue,
rather than a central cue in this research.
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On a more intuitive level, this idea also seems to make sense. A peripheral cue should allow people
with low involvement to draw inferences about the product advertised without scrutinizing the
content of a message. When scanning the ads in appendix 7.2, the varying levels of pf1, captured
directly in the beginning of the second ad line, could form a simple positive or negative cue from
which respondents could quickly draw their conclusions. In contrast, the source cue captured in the
first line of the ads, requires respondents to read the content of the message more carefully. In
addition, an approval from an expert source (high level source) could have functioned as an
argument quality (central) cue, increasing the strength of the argument for buying the product (or at
least clicking on the ad).
Assuming this alternative scenario to be valid, support can actually be found for H2c and H4. Figure 4
than shows that people with high product knowledge in later stages of the sales funnel are indeed
more sensitive to the part of the ad that captures a central cue (argument quality in the form of
approval from an expert source). In addition, figure 5 than shows that people with high product
knowledge in a later stage of the sales funnel are indeed less sensitive to the part of the ad that
captures a peripheral cue (a high level of pf1). In support of H4, the direct effect of pf1 (as a
peripheral cue) on the probability of clicking confirms that the probability of clicking will increase
when this peripheral cue is present in an ad.
As a summary to this chapter, table 9 outlines which of the hypotheses (formulated in chapter 3.5)
were (not) rejected under both scenarios and describes the findings for each of these hypotheses.
Table 9: Hypotheses outcome for both the original and alternative scenario
H1
H2a
H2b
H2c
H3
H4
Scenario 1:
Scenario 2 (alternative scenario):
Source = peripheral cue / pf1 = central cue
Source = central cue / pf1 = peripheral cue
People in an early stage of the sales funnel do not react differently towards a peripheral cue compared to
people in later stages of the sales funnel
People with low product knowledge do not react differently towards a peripheral cue compared to
people with high product knowledge
There is no interaction effect between people’s stage in the sales funnel and a central cue
People with high product knowledge in later stages People with high product knowledge in later stages
of the sales funnel are not more sensitive to pf1 as of the sales funnel are more sensitive to source as
a central cue compared to people in an early stage a central cue compared to people in an early stage
of the sales funnel with high product knowledge
of the sales funnel with high product knowledge
People in a late stage of the sales funnel with high product knowledge are more likely to click when the
level of pf2 is low (when a two-sided argument is captured by the ad)
The probability of clicking will not increase when The probability of clicking will increase when pf1 as
source as a peripheral cue is present in an ad
a peripheral cue is present in an ad
Hypothesis rejected
Hypothesis not rejected
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6. Conclusions
The purpose of this study was to bring together four different elements in a research setting: An
online marketing tool (Adwords), a marketing communications message (a paid search ad), the sales
funnel, and product category knowledge. The intent was to evaluate the effectiveness of paid search
advertising messages on multiple stages of the sales funnel and different levels of product
knowledge. Therefore, the following research question was formulated:
Do advertisement message characteristics have an effect on paid search advertising effectiveness and
is this relationship dependent on consumers’ product knowledge and position in the sales funnel?
In addition, several sub research questions were formulated, aiming to assist in addressing the
research question:
 What are the different advertisement message characteristics according to advertising literature?
 How can these advertisement message characteristics be implemented in paid search
advertisement form?
 In what way can the ‘levels of processing’ framework of ELM theory be aligned to the conceptual
model of this actual research?
 Do different paid search advertisement message characteristics have a different effectiveness (Do
consumers click on a paid search advertisement with a certain message characteristic more)?
 Does ELM theory correctly predict the effectiveness of paid search advertisement message
characteristics for people in different stages of the sales funnel and with different levels of
product knowledge?
In answering the research question, paragraph 6.1 will cover the sub research questions and
interpret the results from chapter 5. Paragraph 6.2, 6.3 and 6.4 will next address the study’s
implications and limitations and provide indications for future research.
6.1 Answer to the Research Question
6.1.1 Advertisement Message Characteristics
According to advertising literature, there are three classes of advertisement message characteristics,
namely message structure, message content and message appeal. Message structure concerns the
order in which message points should be presented. Message content refers to the vocabulary,
writing style and words used in the message or the linguistic and grammatical structure of sentences.
Message appeal is about constructing the ad’s message to appeal to the receiver’s moral principles,
emotions, or intellect.
The last class, message appeal, was chosen to be implemented in paid search advertisement form. In
line with Elaboration Likelihood Model (ELM) theory (Petty and Cacioppo, 1986), three ad cues were
selected to appeal to the receiver depending on its stage in the sales funnel and level of product
knowledge. These ad cues are source expertise, argument quality and a two-sided argument. The
cues were implemented in paid search ads by means of varying the levels of three ad attributes, as
described in chapter 4.1.4.
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6.1.2 The Alignment of ELM Theory
The Elaboration Likelihood Model captures distinct levels of processing of which the determinants
could be aligned to the moderating variables of this specific study. Both people’s motivation and
ability to process an advertisement message determine the route to persuasion in the ELM. When
people are motivated and have the ability to process they have a high level of involvement and will
follow a central route to processing. When people are not motivated or do not have the ability to
process they have a low level of involvement and will follow a peripheral route to processing.
Motivation and ability was linked to consumer’s position in the sales funnel and their level of
category knowledge respectively in chapter 3.3. Ability to process an advertisement message rises
with the level of product knowledge and motivation to process an advertisement message rises with
consumer’s movement through the sales funnel.
6.1.3 Ad Cue Effectiveness
The results from chapter 5 showed that the different ad cues have a different effectiveness. Overall,
consumers click on a paid search advertisement more when this advertisement contains an
argument quality cue. This finding did not confirm initial expectations. The argument quality cue was
designed as a central cue. A central cue is persuasive for consumers with a high level of involvement,
who actively seek and process product-relevant information by considering the pros and cons of the
product and attending to product-relevant arguments and attributes. This cue was present in an ad
when the level of product feature 1 (one of the ad attributes) was ‘excellent’. For a DSLR Camera, a
part of the ad read: ‘ten x superzoom’ and for running shoes, a part of the ad read ‘max. stability’.
According to ELM theory, people will only attend to or read these arguments when they have a high
level of involvement. However, a dominant part of the respondent sample was placed in a level of
low involvement (early stage of the sales funnel or low product knowledge). A peripheral cue was
therefore expected to be most effective overall, as this cue is persuasive for consumers with a low
level of involvement.
The source expertise cue was designed as a peripheral cue. This cue was present in a DSLR ad reading
‘approved by prof. photographers’ and in a running shoes ad reading ‘approved by professional
athletes’. These lines should have served as ‘heuristic’ cues, allowing the consumer with a low level
of involvement to derive inferences regarding brand benefits, quality, or attributes (based on
superficial ad analysis). As this source expertise cue did not show a direct effect, and the argument
quality cue did, a discussion was raised in chapter 5.4 about whether the central and peripheral cues
were effectively designed.
6.1.4 Discussion on Cue Design
Assuming that the manipulation of sales funnel stages and product knowledge levels was effective
and that the combination of these variables indeed correspond to different levels of involvement
(chapter 3.3), a peripheral cue should show a direct effect in the probability of clicking on an ad.
As the argument quality cue is the only cue showing a direct effect, this cue could have served as a
peripheral cue instead of a central cue. On a more intuitive level, this idea also seems to make sense.
A peripheral cue should allow people with low involvement to draw inferences about the product
advertised without scrutinizing the content of a message. When scanning the ads in appendix 7.2,
the varying levels of product feature 1, captured directly in the beginning of the second ad line, could
form a simple positive or negative cue from which respondents could quickly draw their conclusions.
In contrast, the source expertise cue captured in the first line of the ads, requires respondents to
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read the content of the message more carefully. Furthermore, an approval from an expert source
could have functioned as an argument quality (central) cue, increasing the strength of the argument
for buying the product (or at least clicking on the ad).
In the next paragraph, which will conclude on the results of the moderating effects, I will therefore
come back to this issue and conclude on the effects from both scenarios (1. Argument quality as
central cue and source expertise as peripheral cue, 2. Source expertise as central cue and argument
quality as peripheral cue).
6.1.5 Moderating Effects
The results from chapter 5 showed significant interaction effects between the ad cues, sales funnel
stages and product knowledge levels. This proves that the effectiveness of paid search ad cues is
indeed dependent on consumer’s product knowledge and their position in the sales funnel.
Support could be found for the effectiveness of a two-sided argument cue on highly involved people.
Derogating the brand on an attribute of minor importance (a two-sided argument cue) is namely only
effective for people in a late stage of the sales funnel with high product knowledge; they are more
likely to click when the applicable attribute level is low.
The direction of the effects of the argument quality and source expertise cue did not confirm initial
expectations, when assuming that these ad cues functioned as intended (argument quality cue as
central cue and source expertise cue as peripheral cue).
People with a high (low) level of involvement were expected to be more sensitive to a central
(peripheral) cue. A high level of involvement is characterized by both a person’s motivation (sales
funnel stage) and ability (product knowledge) to process an ad. If motivation is high but ability is
lacking, the person should be forced to rely on peripheral cues (source expertise) in order to evaluate
the message. The results however showed that the source expertise cue is most effective for people
in later stages of the sales funnel with high product knowledge. In addition, the argument quality cue
showed to be more effective for people in a beginning stage of the sales funnel with high product
knowledge. At last, interaction effects were only found between all three variables. No interaction
effects were found between the ad cues and sales funnel stages or between the ad cues and product
knowledge levels. This means that people in different stages of the sales funnel do not react
differently towards the ad cues and that people with low product knowledge do not react differently
towards the ad cues compared to people with high product knowledge. It should have been the case
that people in an early stage of the sales funnel (lacking motivation) or with low product knowledge
(lacking ability) are more sensitive to the source expertise cue.
When assuming that the source expertise cue functioned as a central cue and the argument quality
cue as peripheral cue instead, support could actually be found for the expected direction of the
interaction between the ad cues, sales funnel stages and product knowledge levels. People with high
product knowledge in later stages of the sales funnel are then indeed more sensitive to the part of
the ad that captures a central cue (argument quality in the form of approval from an expert source).
In addition, people with high product knowledge in an early stage of the sales funnel are then indeed
more sensitive to the part of the ad that captures a peripheral cue (an excellent performance level of
product feature 1).
The applicability of the Elaboration Likelihood Model in a paid search advertising context remains
only partially proven in the latter scenario; support is found for the cues’ effects amongst people
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with a high level of knowledge (high ability), but no support could be found for the effectiveness of a
peripheral cue for people with a low level of product knowledge (low ability).
In a final conclusion to the research question, this study found that (1) advertisement message
characteristics (in the form of ad cues) have an effect on paid search advertising effectiveness and
that (2) this relationship is dependent on consumers’ product knowledge and position in the sales
funnel. When assuming that the source expertise cue functioned as a central cue and the argument
quality cue as peripheral cue, support could be found for the (partial) applicability of the Elaboration
Likelihood Model in a paid search advertising context. The implications of these findings will be
addressed next.
6.2 Result Implications
6.2.1 Theoretical Relevance
This study is relevant to both (online) advertising theory and information processing theory for
making a first attempt at closing two different gaps in research. At first, this study investigates the
effect of ad message content in paid search advertising, where other studies mainly investigate
mechanisms at work around the content of the ad itself. Secondly, this study makes a first step in
linking ELM theory to paid search advertising. In an online context, ELM theory had only been linked
to banner ads, websites and reviews.
In addition, the theoretical relevance of this study rests in its contribution to a growing body of
literature that finds evidence for an electronic version of the ELM (the eELM). At the end of chapter
3.4 I covered a number of studies that tested the ELM in an online context. What became apparent
from some of these studies is that the ELM only partly explained consumers’ behavior in an online
context. High-involved consumers indeed responded more favorably to central cues, but there was
no difference in response to peripheral cues between high- and low-involved consumers. This actual
study appears to contribute to these findings, as a peripheral cue was found to be most effective
overall, but not more effective for low-involved people than for high-involved people, while a central
cue was indeed more effective for high-involved people than for low-involved people (in scenario 2).
At last, this study further strengthens the evidence found with regard to search engine rank. As
addressed in chapter 2.2, there is uniformity with respect to the negative impact of an ad’s ranking
position on CTR and conversion rates. This study also finds a ranking effect, where the probability of
clicking increases when ads are ranked first.
6.2.2 Practical Relevance
The ranking effect found in numerous studies and also replicated in this study provides an example of
the functionality of research. Replication leads to solid theories on which managers can build. With
regard to the impact of ranking, managers can learn that their paid search ad’s ranking indeed has an
impact, and that it is worth the investment of raising an ad in the ranking position.
Even though this research’s main finding are only a first step to theory on message content in paid
search advertising, they do provide some first insights to paid search users. Firstly, message content
can provide the user with a competitive advantage. ELM theory looks promising when it comes to
understanding how ads can be altered successfully to reach consumers with varying levels of
involvement. The probability of clicking increases when an ad captures a peripheral cue, as this type
of cue is most effective overall. But if the user is more interested in gaining the attention (and click)
of a user who is already further down the sales funnel, a central cue might be more effective. The key
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idea is that the content of one’s paid search ad can make a valuable difference and that it is
important for managers to find out which content is most effective for their type of product. The use
of central or peripheral cues can be a starting point, but testing with other appeal, content or
structure characteristics of a message can pay off.
Secondly, a consumer’s stage in the sales funnel and level of product knowledge influences the
effectiveness of a paid search ad. Not all consumers interested in a company’s product(s) are the
same. Some are in the beginning stage of a sales funnel, barely aware of their need for a certain
product, while others are further down the sales funnel, fully aware of their need and ready to
purchase. The complexity of a company’s products might also differ in the minds of consumers,
where some have more product knowledge than others. The paid search user needs to understand
that not all these consumers can be reached with one type of message.
Lastly, a concept like the sales funnel (in combination with product knowledge or product
complexity) can provide the paid search user with a valuable segmentation tool. The user can decide
whether s/he wants to reach the whole sales funnel or only a specific stage, and alter the content of
ads to (more) successfully reach each stage.
6.3 Study Limitations
One main limitation to this study lies in the research design stage of variable manipulations. From the
research findings it appeared as though the manipulation of the independent variable was not
successful; the functionality of the ad cues did not work out as expected. The source expertise cue
seemed to have functioned as a central cue and the argument quality cue as a peripheral cue, while
this should have been vice versa. Even though the cues were designed and tested with care, the paid
search ad’s limitation with respect to text length proved to be a difficult factor for effectively
implementing the ad cues. A plausible and useful explanation to this finding was discussed in chapter
5.4, but it nevertheless affected the reliability of the research findings.
The design of the online survey could also be considered a possible limitation. The survey captured a
recreated Google search engine result page (appendix 7.4), from which respondents could make a
choice between two paid search ads. The organic ads on the left were blurred (and not clickable) to
have the respondents focus on the paid search ads. Even though the survey environment came close
to capturing the look-and-feel of an actual search result browser page, this alteration affected the
external validity of the research.
Further affecting the external validity is the fact that the respondents were placed in an
unanticipated situation, where they had to image a specific product need situation for a given
product, and the fact that they had limited control over the search process (they could not enter a
search query or search multiple times). However, unlike many other paid search experiments,
respondents did not complete the survey in a computer lab, but from the comfort of their own PC’s
or laptops at home.
6.4 Indications for Future Research
The research implications and limitations addressed in the last two paragraphs indicate directions for
future research. Where this research made a first step in linking message content and ELM theory to
paid search, additional research is required that further investigates the impact of message content
on paid search advertising effectiveness and the usability of ad cues drawn from ELM theory. This
research focused on message appeal (in the form of central and peripheral cues) as advertisement
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message characteristic, but future research could also explore other characteristics like message
content or structure, discussed in chapter 3.2.
Because the ad cues of this research did not function as they were designed for, further research is
necessary on how ELM ad cues could be effectively implemented and distinguished in paid search
ads. These ads have a strict limitation with regard to text length and it might be possible that ELM is
simply more suitable for large text ads and visuals, where central cues have more room to be
distinguished from peripheral cues.
A last avenue in future research lies in further investigating the signs of an ‘eELM’ theory (the
electronic version of the ELM). High-involved consumers show a combined influence of central and
peripheral routes to persuasion in varying online contexts (including this research’s paid search
context). This might be caused by the (surfing) speed of the Internet and the distractions of rich
visuals on websites. Especially in paid search advertising, the searcher is rushed in finding the most
suitable ad to click on, to be directed to a webpage of interest. Very different than an offline
advertisement, on which the reader is supposed to find all necessary information. Future research
could more carefully explore this possible ELM boundary.
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7. Appendices
7.1 Sales Funnel Introductions
Stage 1 – Awareness
You have an upcoming holiday and you realize that you still need to replace your old photo camera
with a new one. You have been thinking about buying a more advanced photo camera this time. You
are aware of the fact that a Digital Single-Lens Reflex (DSLR) Camera (NL = spiegelreflexcamera) could
fulfill your need for a more advanced photo camera. You decide to make use of the online search
engine Google to search for information.
Over the last few months you have become quite active in outdoor running. You realize that it would
be better for your performance and body health to replace your old flat sole fitness shoes with new
ones. You are aware of the fact that running shoes could fulfill your need for better performance and
body health during running. You decide to make use of the online search engine Google to search for
information.
Stage 2 – Research
You have an upcoming holiday for which you need a new photo camera. You have decided to buy a
Digital Single-Lens Reflex (DSLR) photo camera (NL = spiegelreflexcamera) for this upcoming holiday.
You haven’t decided on a brand or (online) store to purchase from yet. You decide to make use of the
online search engine Google to support you in this decision.
Over the last few months you have become quite active in outdoor running. You have decided to buy
running shoes to support your increased outdoor running activities. You haven’t decided on a brand
or (online) store to purchase from yet. You decide to make use of the online search engine Google to
support you in this decision.
Stage 3 – Decision
You have decided to buy a Digital Single-Lens Reflex (DSLR) photo camera (NL = spiegelreflexcamera)
for your upcoming holiday. You are aware of the fact that two brands, Canon and Nikon, are leaders
in this product category. You decide to consider the alternatives these brands have to offer and
choose to make use of the online search engine Google to support you in your quest.
You have decided to buy running shoes to support your increased outdoor running activities. You are
aware of the fact that two brands, Adidas and Nike, are leaders in this product category. You decide
to consider the alternatives these brands have to offer and choose to make use of the online search
engine Google to support you in your quest.
44
MASTER THESIS MSc MARKETING MANAGEMENT - MEREL ZIMMERMAN
7.2 Google Paid Search Ads
Ad attribute levels
Ad #
1
2
3
4
5
6
7
8
Source expertise
low
low
low
low
high
high
high
high
Attribute levels
Product feature 1
low
high
low
high
low
high
low
high
Product feature 2
low
low
high
high
low
low
high
high
Ads DSLR Camera
Ad 1
DSLR Camera EOS 60D
DSLR Camera D3200
www.canon.com/…eos60d…/…
Approved by local photography club,
five x zoom, comes in black
www.nikon.com/…d3200…/…
Approved by local photography club,
five x zoom, comes in black
DSLR Camera EOS 60D
DSLR Camera D3200
www.canon.com/…eos60d…/…
Approved by local photography club,
ten x superzoom, comes in black
www.nikon.com/…d3200…/…
Approved by local photography club,
ten x superzoom, comes in black
DSLR Camera EOS 60D
DSLR Camera D3200
www.canon.com/…eos60d…/…
Approved by local photography club,
five x zoom, comes in five colors
www.nikon.com/…d3200…/…
Approved by local photography club,
five x zoom, comes in five colors
DSLR Camera EOS 60D
DSLR Camera D3200
www.canon.com/…eos60d…/…
Approved by local photography club,
ten x superzoom, comes in five colors
www.nikon.com/…d3200…/…
Approved by local photography club,
ten x superzoom, comes in five colors
DSLR Camera EOS 60D
DSLR Camera D3200
www.canon.com/…eos60d…/…
Approved by prof. photographers,
five x zoom, comes in black
www.nikon.com/…d3200…/…
Approved by prof. photographers,
five x zoom, comes in black
Ad 2
Ad 3
Ad 4
Ad 5
45
MASTER THESIS MSc MARKETING MANAGEMENT - MEREL ZIMMERMAN
Ad 6
DSLR Camera EOS 60D
DSLR Camera D3200
www.canon.com/…eos60d…/…
Approved by prof. photographers,
ten x superzoom, comes in black
www.nikon.com/…d3200…/…
Approved by prof. photographers,
ten x superzoom, comes in black
DSLR Camera EOS 60D
DSLR Camera D3200
www.canon.com/…eos60d…/…
Approved by prof. photographers,
five x zoom, comes in five colors
www.nikon.com/…d3200…/…
Approved by prof. photographers,
five x zoom, comes in five colors
DSLR Camera EOS 60D
DSLR Camera D3200
www.canon.com/…eos60d…/…
Approved by prof. photographers,
ten x superzoom, comes in five colors
www.nikon.com/…d3200…/…
Approved by prof. photographers,
ten x superzoom, comes in five colors
Ad 7
Ad 8
Ads Running Shoes
Ad 1
Running shoe ClimaCool
Running shoe FreeRun+
www.adidas.com/…climacool…/…
Approved by local athletics club,
good stability, no extra laces
www.nike.com/…freerunplus…/…
Approved by local athletics club,
good stability, no extra laces
Running shoe ClimaCool
Running shoe FreeRun+
www.adidas.com/…climacool…/…
Approved by local athletics club,
max. stability, no extra laces
www.nike.com/…freerunplus…/…
Approved by local athletics club,
max. stability, no extra laces
Running shoe ClimaCool
Running shoe FreeRun+
www.adidas.com/…climacool…/…
Approved by local athletics club,
good stability, extra pair of laces
www.nike.com/…freerunplus…/…
Approved by local athletics club,
good stability, extra pair of laces
Running shoe ClimaCool
Running shoe FreeRun+
www.adidas.com/…climacool…/…
Approved by local athletics club,
max. stability, extra pair of laces
www.nike.com/…freerunplus…/…
Approved by local athletics club,
max. stability, extra pair of laces
Ad 2
Ad 3
Ad 4
46
MASTER THESIS MSc MARKETING MANAGEMENT - MEREL ZIMMERMAN
Ad 5
Running shoe ClimaCool
Running shoe FreeRun+
www.adidas.com/…climacool…/…
Approved by professional athletes,
good stability, no extra laces
www.nike.com/…freerunplus…/…
Approved by professional athletes,
good stability, no extra laces
Running shoe ClimaCool
Running shoe FreeRun+
www.adidas.com/…climacool…/…
Approved by professional athletes,
max. stability, no extra laces
www.nike.com/…freerunplus…/…
Approved by professional athletes,
max. stability, no extra laces
Running shoe ClimaCool
Running shoe FreeRun+
www.adidas.com/…climacool…/…
Approved by professional athletes,
good stability, extra pair of laces
www.nike.com/…freerunplus…/…
Approved by professional athletes,
good stability, extra pair of laces
Running shoe ClimaCool
Running shoe FreeRun+
www.adidas.com/…climacool…/…
Approved by professional athletes,
max. stability, extra pair of laces
www.nike.com/…freerunplus…/…
Approved by professional athletes,
max. stability, extra pair of laces
Ad 6
Ad 7
Ad 8
7.3 Ad Pairs on SERP
Sales
Funnel
Ad #
1
2
3
4
5
6
7
8
Stage_1
Stage_2
Stage_3
5
SERP 1
SERP 2
SERP 3
SERP 4
X
SERP 5
SERP 6
SERP 7
2
SERP 8
X
SERP 9
SERP 10
SERP 11
SERP 12
SERP 13
SERP 14
4
SERP 15
SERP 16
SERP 17
X
SERP 18
SERP 19
SERP 20
SERP 21
47
MASTER THESIS MSc MARKETING MANAGEMENT - MEREL ZIMMERMAN
7.4 Search Engine Result Page
DSLR Camera
Running Shoes
48
MASTER THESIS MSc MARKETING MANAGEMENT - MEREL ZIMMERMAN
7.5 Distribution over Sales Funnel Stages and SERPs
Variable Name
Level
Frequency
Percent
Stage_1 (DSLR Camera)
Stage_2 (DSLR Camera)
Stage_3 (DSLR Camera)
Total
1
2
3
69
66
67
202
34,2
32,7
33,2
100
Stage_1 (Running Shoes)
Stage_2 (Running Shoes)
Stage_3 (Running Shoes)
Total
1
2
3
68
69
65
202
33,7
34,2
32,2
100
Stage_1 (Total)
Stage_2 (Total)
Stage_3 (Total)
Total
1
2
3
137
135
132
404
33,9
33,4
32,7
100
Distribution SERP Versions DSLR Camera
ad 2
6
x
13
10
14
9
5
9
ad 4
11
7
13
x
5
11
9
11
ad 5
12
3
9
7
x
17
7
14
Distribution SERP Versions Running Shoes
ad 2
ad 1
10
ad 2
x
ad 3
9
ad 4
11
ad 5
10
ad 6
10
ad 7
9
ad 8
10
ad 4
8
10
9
x
9
10
10
9
ad 5
10
11
9
10
x
10
9
9
ad 1
ad 2
ad 3
ad 4
ad 5
ad 6
ad 7
ad 8
49
MASTER THESIS MSc MARKETING MANAGEMENT - MEREL ZIMMERMAN
7.6 Sample Gender and Demographic Distribution
Gender
81
Male
121
Female
Age
18
14
≤ 21 years old
13
22 - 25 years old
25
71
26 - 30 years old
31 - 40 years old
27
41 - 50 years old
34
51 - 60 years old
> 60 years old
Education
< High School
322
10
10
79
High School: MAVO
16
High School: HAVO
High School: VWO
College: MBO
54
College: HBO
26
College: WO
Master's Degree
PhD Title
Income
13
18
< €500,-
30
€500,- - €1.000,-
17
35
€1.000,- - €2.000,-
€2.000,- - €3.000,43
€3.000,- - €4.000,-
36
€4.000,- - €5.000,> €5.000,50
MASTER THESIS MSc MARKETING MANAGEMENT - MEREL ZIMMERMAN
7.7 Single Interaction Effects to Model 1
1= Interactions with sales funnel stages added to main effects per 1 of 3
2= Interactions with sales funnel stages added to main effects per 2 of 3 – stage 1/2
3= Interactions with sales funnel stages added to main effects per 2 of 3 – stage 1/3
4= Interactions with sales funnel stages added to main effects per 2 of 3 – stage 2/3
Interactions
source*pf1*pf2
source*pf1
source*pf2
pf1*pf2
source*stage_1
source*stage_2
source*stage_3
pf1*stage_1
pf1*stage_2
pf1*stage_3
pf2*stage_1
pf2*stage_2
pf2*stage_3
source*knowledge
pf1*knowledge
pf2*knowledge
source*stage_1*knowledge
source*stage_2*knowledge
source*stage_3*knowledge
pf1*stage_1*knowledge
pf1*stage_2*knowledge
pf1*stage_3*knowledge
pf2*stage_1*knowledge
pf2*stage_2*knowledge
pf2*stage_3*knowledge
(pf1*pf2)*stage_1
(pf1*pf2)*stage_2
(pf1*pf2)*stage_3
(pf1*pf2)*knowledge
(pf1*pf2)*stage_1*knowledge
(pf1*pf2)*stage_2*knowledge
(pf1*pf2)*stage_3*knowledge
(source*pf1)*stage_1
(source*pf1)*stage_2
(source*pf1)*stage_3
(source*pf1)*knowledge
(source*pf1)*stage_1*knowledge
(source*pf1)*stage_2*knowledge
(source*pf1)*stage_3*knowledge
1
2
3
4
-0,890
**
-0,890
**
-0,890
**
-0,890
**
0,573
-0,445
*
**
0,573
-0,445
*
**
0,573
-0,445
*
**
0,573
-0,445
*
**
1,228
*
1,247
*
1,350
0,765
*
**
-0,597
**
-0,560
**
-0,712
-0,503
*
**
1,235
-0,603
*
**
1,316
*
-0,725
-0,612
-0.584
**
1,119
-0,471
*
***
**
-0,699
**
-0,685
**
***
-0,678
***
-0,776
**
51
MASTER THESIS MSc MARKETING MANAGEMENT - MEREL ZIMMERMAN
Interactions
(source*pf1 *pf2)*stage_1
(source*pf1 *pf2)*stage_2
(source*pf1 *pf2)*stage_3
(source*pf1 *pf2)*knowledge
(source*pf1 *pf2)*stage_1*knowledge
(source*pf1 *pf2)*stage_2*knowledge
(source*pf1 *pf2)*stage_3*knowledge
1
-1,04
2
**
7.8 Odds Ratios Model 1
Model 1
Parameter
constant
source
pf1
pf2
ranking
Odds ratio
0,469
2,002
1,793
1,163
1,113
52
3
-0,993
4
**
-1,150
**
MASTER THESIS MSc MARKETING MANAGEMENT - MEREL ZIMMERMAN
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55