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1 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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Page 1: MasterThesis Merel Zimmerman

MASTER THESIS MSc MARKETING MANAGEMENT - MEREL ZIMMERMAN

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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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© 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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A C K N O W L E D G E M E N T S

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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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 two-sided 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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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-per-exposure 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 price-consciousness 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 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:

Figure 1: Conceptual framework

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“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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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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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 soft-sell, 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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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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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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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

Level of processing

Communication Objectives

Matching Cues (Sources cited in MacInnis & Jaworski (1990) – see references)

Elaboration Likelihood Model (Sources cited in Petty and Cacioppo, 1986)

Low: Motivation=Low Ability=Irrelevant

Pre-attention None None 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)

Relatively low: Motivation= Low/Moderate Ability=Irrelevant

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.

Affect-based persuasion through salient cues: - Likable music

1

- Attractive pictures2

- Attractive sources3

- Likable sources4

- Celebrity sources5

- Singing and dancing6

Low-moderate: Motivation= Moderate OR high Ability= Irrelevant OR low

Focal attention 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.

Heuristic based persuasion though cues that indicate product benefits or attributes: - ▲ Credible/expert sources

7

- Long message8

- Large number of message arguments9

- Draw a conclusion10

- Non-technical information

11

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)

Moderate: Motivation= Moderate/High Ability= Moderate/High

Comprehension Memory for specific ad cues and copy points, enduring memory for ad elements, ad affect, beliefs, brand attitudes via message-based persuasion

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

High: Motivation=High Ability=High

Elaboration Enduring memory for presented and un-presented information and feeling responses, strongly held beliefs and brand attitudes (relate directly to purchase intentions), brand attitudes via cognitive response, self-brand emotional associations, enduring attitudes.

Anticipate or reduce the likelihood of negative cognitive responses: - Refutational appeals

12

- ▲ Two-sided arguments12

- Don't draw conclusions

13

- Directed imagery (to specific brand usage outcomes)14

- Similarity source / target audience

15

▲ Cues applied in this research

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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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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 Context Findings

Wang et al. (2009)

Banner ad TV game console

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. Appeal-oriented (variation) advertising strategies are more effective for (non-)goal-directed consumers.

Martín et al. (2011)

Website 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.

SanJosé et al. (2009)

Website Travel agency

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).

Lin et al. (2011)

Online reviews

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.

Kim & Benbasat (2009)

B2C e-commerce website

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.

Park & Kim (2008)

Online consumer reviews

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.

Lee (2009)

Online review Phones

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.

Pentina (2010)

Online shopping

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).

Qi et al. (2010)

Online ad Signature pen

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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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 high-involved 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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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 ↘

Higher ability to process

Sales Funnel Stage Low product knowledge High product knowledge

Brand Awareness Route to persuasion: Peripheral Route to persuasion: Peripheral

H

igher m

otivatio

n to

pro

cess

Effective cue: Source expertise (H1, H2a)

Effective cue: Source expertise (H1)

Brand Research Route to persuasion: Peripheral Route to persuasion: Central

Effective cue: Source expertise (H2a)

Effective cue: Argument quality (H2c)

Brand Decision 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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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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- 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 Level 1 Level 2

Source approved by local photography club (low expertise)

approved by prof. photographers (high expertise)

Product feature 1 five x zoom (good) ten x superzoom (excellent) Product feature 2 comes in black (bad) comes in five colors (good)

RUNNING SHOES

Ad attribute Level 1 Level 2

Source approved by local athletics club (low expertise)

approved by professional athletes (high expertise)

Product feature 1 good stability (good) max. stability (excellent) Product feature 2 no extra laces (bad) extra pair of laces (good)

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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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.

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 Argument 1 Argument 2 Statistics

DSLR Camera five x zoom + comes in black ten x superzoom + comes in black t= -4.946 / p= 0.000 DSLR Camera five x zoom + comes in black ten x superzoom + comes in five colors t= -4.300 / p= 0.000 DSLR Camera five x zoom + comes in five colors ten x superzoom + comes in black t= -2.290 / p= 0.029 DSLR Camera five x zoom + comes in five colors ten x superzoom + comes in five colors t= -5.350 / p= 0.000 Running Shoes good stability + no extra laces max. stability + no extra laces t= -5.402 / p= 0.000 Running Shoes good stability + no extra laces max. stability + extra pair of laces t= -2.931 / p= 0.006 Running Shoes good stability + extra pair of laces max. stability + extra pair of laces 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.

3,44 3,97

4,78 5,16

0

1

2

3

4

5

6

five x zoom + comes in black

five x zoom + comes in five

colors

ten x superzoom +

comes in black

ten x superzoom + comes in five

colors

4,06 4,62

5,06 5,28

0

1

2

3

4

5

6

good stability + no extra

laces

good stability + extra pair

of laces

max. stability + no extra

laces

max. stability + extra pair

of laces

Arg

um

en

t st

ren

gth

Arg

um

en

t st

ren

gth

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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-to-door 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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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 # Product DV Source Product Feature 1 Product Feature 2 …..

1 0 1 1 0 0 …..

1 0 0 0 0 1 …..

1 1 1 1 1 0 …..

1 1 0 0 1 1 …..

….. ….. ….. ….. ….. ….. …..

3 Subjective knowledge DSLR (mean= 3.42) vs. subjective knowledge running shoes (mean= 3.80) (t = -2.427 ;

p = 0,016)

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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 (p-value) 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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Table 7: Main and interaction effects on dependent variable “probability of clicking”

Model 1 Model 2 Model 3

Parameter

constant -0,758 * -0,981 * -1,036 *

source 0,694 * 0,547 *** 0,498 pf1 0,584 * 1,069 * 0,953 *

pf2 0,151

0,047

0,157 ranking 0,107

0,506 ** 0,657 **

source*pf1

-0,556

0,061

source*knowledge

-0,009

-0,107

pf1*knowledge

0,517

0,421

source*stage_1*knowledge ▲

.

.

source*stage_2*knowledge

1,753 * 1,901 *

source*stage_3*knowledge

1,485 * 1,689 *

pf1*stage_1*knowledge ▲

.

.

pf1*stage_2*knowledge

-1,980 * -1,936 *

pf1*stage_3*knowledge

-0,676

-0,075

pf2*stage_1*knowledge ▲

.

.

pf2*stage_2*knowledge

1,511 * 1,348 **

pf2*stage_3*knowledge

-0,863 *** -0,635

(pf1*pf2)*stage_1*knowledge ▲

.

(pf1*pf2)*stage_2*knowledge

0,602

(pf1*pf2)*stage_3*knowledge

-0,824

(source*pf1)*stage_1 ▲

.

(source*pf1)*stage_2

-0,082

(source*pf1)*stage_3

-0,787

(source*pf1*pf2)*stage_1 ▲

.

(source*pf1*pf2)*stage_2

-1,311 ***

(source*pf1*pf2)*stage_3

-0,261

LR statistic 24,908

84,875

92,624 Probability (LR statistic) 0,000

0,000

0,000

McFadden R-squared 0,022

0,076

0,083 H-L statistic 6,216

36,468

31,226

Probability (H-L statistic) 0,718

0,160

0,556

Note 1: * Significant at 1% ** Significant at 5% *** Significant at 10%

Note 2: ▲ Base level dummy variable Table 8: Variable coding and descriptions

Variable Name Description Level = 0 Level = 1

source ad attribute 1 - source expertise low expertise high expertise

pf1 ad attribute 2 - performance product feature 1 good excellent

pf2 ad attribute 3 - performance product feature 2 bad good

stage_1 Sales Funnel Stage 1: Awareness x

stage_2 Sales Funnel Stage 2: Research x

stage_3 Sales Funnel Stage 3: Decision x

knowledge Respondent's level of product knowledge low high

ranking Ranking of the ad on the SERP ranked second ranked first

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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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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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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 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.

Figure 5: Probability of clicking (model 3) as a function of sales funnel stage, product knowledge, and product feature 1.

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Sales Funnel Stage 2 (Stage 1 as base) Sales Funnel Stage 3 (Stage 1 as base)

Sales Funnel Stage 2 (Stage 1 as base)

Product feature 1 (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.

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. 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.

Figure 7: Probability of clicking (model 3) as a function of sales funnel stage, product knowledge, and product feature 2.

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Sales Funnel Stage 2 (Stage 1 as base) Sales Funnel Stage 3 (Stage 1 as base)

Product feature 2 (0=low 1=high) Product feature 2 (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

Scenario 1: Source = peripheral cue / pf1 = central cue

Scenario 2 (alternative scenario): Source = central cue / pf1 = peripheral cue

H1 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

H2a People with low product knowledge do not react differently towards a peripheral cue compared to people with high product knowledge

H2b There is no interaction effect between people’s stage in the sales funnel and a central cue

H2c People with high product knowledge in later stages of the sales funnel are not more sensitive to pf1 as a central cue compared to people in an early stage of the sales funnel with high product knowledge

People with high product knowledge in later stages of the sales funnel are more sensitive to source as a central cue compared to people in an early stage of the sales funnel with high product knowledge

H3 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)

H4 The probability of clicking will not increase when source as a peripheral cue is present in an ad

The probability of clicking will increase when pf1 as 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.

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7.2 Google Paid Search Ads

Ad attribute levels

Attribute levels

Ad # Source expertise Product feature 1 Product feature 2

1 low low low

2 low high low

3 low low high

4 low high high

5 high low low

6 high high low

7 high low high

8 high high high

Ads DSLR Camera

Ad 1

DSLR Camera EOS 60D

www.canon.com/…eos60d…/…

Approved by local photography club,

five x zoom, comes in black

DSLR Camera D3200

www.nikon.com/…d3200…/…

Approved by local photography club,

five x zoom, comes in black

Ad 2

DSLR Camera EOS 60D

www.canon.com/…eos60d…/…

Approved by local photography club,

ten x superzoom, comes in black

DSLR Camera D3200

www.nikon.com/…d3200…/…

Approved by local photography club,

ten x superzoom, comes in black

Ad 3

DSLR Camera EOS 60D

www.canon.com/…eos60d…/…

Approved by local photography club, five x zoom, comes in five colors

DSLR Camera D3200

www.nikon.com/…d3200…/…

Approved by local photography club,

five x zoom, comes in five colors

Ad 4

DSLR Camera EOS 60D

www.canon.com/…eos60d…/…

Approved by local photography club,

ten x superzoom, comes in five colors

DSLR Camera D3200

www.nikon.com/…d3200…/…

Approved by local photography club,

ten x superzoom, comes in five colors

Ad 5

DSLR Camera EOS 60D

www.canon.com/…eos60d…/…

Approved by prof. photographers,

five x zoom, comes in black

DSLR Camera D3200

www.nikon.com/…d3200…/…

Approved by prof. photographers,

five x zoom, comes in black

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Ad 6

DSLR Camera EOS 60D

www.canon.com/…eos60d…/…

Approved by prof. photographers,

ten x superzoom, comes in black

DSLR Camera D3200

www.nikon.com/…d3200…/…

Approved by prof. photographers,

ten x superzoom, comes in black

Ad 7

DSLR Camera EOS 60D

www.canon.com/…eos60d…/…

Approved by prof. photographers, five x zoom, comes in five colors

DSLR Camera D3200

www.nikon.com/…d3200…/…

Approved by prof. photographers,

five x zoom, comes in five colors

Ad 8

DSLR Camera EOS 60D

www.canon.com/…eos60d…/…

Approved by prof. photographers,

ten x superzoom, comes in five colors

DSLR Camera D3200

www.nikon.com/…d3200…/…

Approved by prof. photographers,

ten x superzoom, comes in five colors

Ads Running Shoes

Ad 1

Running shoe ClimaCool

www.adidas.com/…climacool…/…

Approved by local athletics club,

good stability, no extra laces

Running shoe FreeRun+

www.nike.com/…freerunplus…/…

Approved by local athletics club,

good stability, no extra laces

Ad 2

Running shoe ClimaCool

www.adidas.com/…climacool…/…

Approved by local athletics club,

max. stability, no extra laces

Running shoe FreeRun+

www.nike.com/…freerunplus…/…

Approved by local athletics club,

max. stability, no extra laces

Ad 3

Running shoe ClimaCool

www.adidas.com/…climacool…/…

Approved by local athletics club,

good stability, extra pair of laces

Running shoe FreeRun+

www.nike.com/…freerunplus…/…

Approved by local athletics club,

good stability, extra pair of laces

Ad 4

Running shoe ClimaCool

www.adidas.com/…climacool…/…

Approved by local athletics club,

max. stability, extra pair of laces

Running shoe FreeRun+

www.nike.com/…freerunplus…/…

Approved by local athletics club,

max. stability, extra pair of laces

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Ad 5

Running shoe ClimaCool

www.adidas.com/…climacool…/…

Approved by professional athletes,

good stability, no extra laces

Running shoe FreeRun+

www.nike.com/…freerunplus…/…

Approved by professional athletes,

good stability, no extra laces

Ad 6

Running shoe ClimaCool

www.adidas.com/…climacool…/…

Approved by professional athletes,

max. stability, no extra laces

Running shoe FreeRun+

www.nike.com/…freerunplus…/…

Approved by professional athletes,

max. stability, no extra laces

Ad 7

Running shoe ClimaCool

www.adidas.com/…climacool…/…

Approved by professional athletes,

good stability, extra pair of laces

Running shoe FreeRun+

www.nike.com/…freerunplus…/…

Approved by professional athletes,

good stability, extra pair of laces

Ad 8

Running shoe ClimaCool

www.adidas.com/…climacool…/…

Approved by professional athletes,

max. stability, extra pair of laces

Running shoe FreeRun+

www.nike.com/…freerunplus…/…

Approved by professional athletes,

max. stability, extra pair of laces

7.3 Ad Pairs on SERP

Sales Funnel

Stage_1 Stage_2 Stage_3

Ad # 5 2 4 1 SERP 1 SERP 8 SERP 15 2 SERP 2 X SERP 16 3 SERP 3 SERP 9 SERP 17 4 SERP 4 SERP 10 X 5 X SERP 11 SERP 18 6 SERP 5 SERP 12 SERP 19 7 SERP 6 SERP 13 SERP 20 8 SERP 7 SERP 14 SERP 21

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7.4 Search Engine Result Page

DSLR Camera

Running Shoes

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7.5 Distribution over Sales Funnel Stages and SERPs

Variable Name Level Frequency Percent

Stage_1 (DSLR Camera) 1 69 34,2

Stage_2 (DSLR Camera) 2 66 32,7

Stage_3 (DSLR Camera) 3 67 33,2

Total 202 100

Stage_1 (Running Shoes) 1 68 33,7

Stage_2 (Running Shoes) 2 69 34,2

Stage_3 (Running Shoes) 3 65 32,2

Total 202 100

Stage_1 (Total) 1 137 33,9

Stage_2 (Total) 2 135 33,4

Stage_3 (Total) 3 132 32,7

Total 404 100

Distribution SERP Versions DSLR Camera

ad 2 ad 4 ad 5

ad 1 6 11 12

ad 2 x 7 3

ad 3 13 13 9

ad 4 10 x 7

ad 5 14 5 x

ad 6 9 11 17

ad 7 5 9 7

ad 8 9 11 14

Distribution SERP Versions Running Shoes

ad 2 ad 4 ad 5

ad 1 10 8 10

ad 2 x 10 11

ad 3 9 9 9

ad 4 11 x 10

ad 5 10 9 x

ad 6 10 10 10

ad 7 9 10 9

ad 8 10 9 9

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121

81

Male

Female

13

71

34

27

25

18 14 ≤ 21 years old

22 - 25 years old

26 - 30 years old

31 - 40 years old

41 - 50 years old

51 - 60 years old

> 60 years old

2 2

10 10

16

54

26

79

3 < High School

High School: MAVO

High School: HAVO

High School: VWO

College: MBO

College: HBO

College: WO

Master's Degree

PhD Title

30

35

36 43

17

13 18 < €500,-

€500,- - €1.000,-

€1.000,- - €2.000,-

€2.000,- - €3.000,-

€3.000,- - €4.000,-

€4.000,- - €5.000,-

> €5.000,-

7.6 Sample Gender and Demographic Distribution

Gender

Age

Education

Income

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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 1 2 3 4

source*pf1*pf2

source*pf1 -0,890 ** -0,890 ** -0,890 ** -0,890 **

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 0,573 * 0,573 * 0,573 * 0,573 *

pf1*knowledge -0,445 ** -0,445 ** -0,445 ** -0,445 **

pf2*knowledge

source*stage_1*knowledge

source*stage_2*knowledge 1,228 * 1,247 * 1,350 *

source*stage_3*knowledge 0,765 **

pf1*stage_1*knowledge

pf1*stage_2*knowledge -0,597 ** -0,560 ** -0,712 *

pf1*stage_3*knowledge -0,503 **

pf2*stage_1*knowledge

pf2*stage_2*knowledge 1,235 * 1,316 * 1,119 *

pf2*stage_3*knowledge -0,603 ** -0.584 ** -0,471 ***

(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 -0,725 ** -0,699 ** -0,685 **

(source*pf1)*stage_1

(source*pf1)*stage_2

(source*pf1)*stage_3 -0,612 *** -0,678 *** -0,776 **

(source*pf1)*knowledge

(source*pf1)*stage_1*knowledge

(source*pf1)*stage_2*knowledge

(source*pf1)*stage_3*knowledge

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Interactions 1 2 3 4

(source*pf1 *pf2)*stage_1

(source*pf1 *pf2)*stage_2

(source*pf1 *pf2)*stage_3 -1,04 ** -0,993 ** -1,150 **

(source*pf1 *pf2)*knowledge

(source*pf1 *pf2)*stage_1*knowledge

(source*pf1 *pf2)*stage_2*knowledge

(source*pf1 *pf2)*stage_3*knowledge

7.8 Odds Ratios Model 1

Model 1

Parameter Odds ratio constant 0,469

source 2,002

pf1 1,793

pf2 1,163

ranking 1,113

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