Message content in keyword campaigns, click behavior, and price-consciousness: A study of millennial consumers

Journal of Retailing and Consumer Services 19 (2012) 78–87

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Journal of Retailing and Consumer Services journal homepage: www.elsevier.com/locate/jretconser

Message content in keyword campaigns, click behavior, and price-consciousness: A study of millennial consumers Claire Gauzente a,b,n, Yves Roy c a

LEMNA—University of Nantes, France ESC Rennes School of Business, France c CEREGE—IAE University of Poitiers, France b

a r t i c l e i n f o

a b s t r a c t

Available online 1 November 2011

Building upon the expectancy theory, this study suggests that message content in keyword advertising influences click behavior. The moderating role of price consciousness is also examined. An online experiment using an ex-post filter sampling method is implemented (final n ¼ 165). The results of binary logistic regressions indicate that descriptive message content is more clicked than commercial. Priceconsciousness appears to moderate the relationship, with high price-conscious consumers being more influenced by descriptive content than less price-conscious consumers. The obtained results are meaningful and significant for the millennial consumers under study. & 2011 Elsevier Ltd. All rights reserved.

Keywords: Search engines Keyword advertising Message content Expectancy theory Price-consciousness

1. Introduction Approximately 70% of web searchers use a search engine on a daily basis (Jansen, 2010). Major search engines receive millions of queries per day and present billions of results in response to these queries (Sullivan, 2006). Because of this, Search Engine Marketing has become the major form of online advertising for e-tailers (see www.iab.net). Search advertisements are printed on search engine result pages (SERP) and are presented in textual form in response to a consumer search query. Such advertising is also referred to as sponsored advertising, paid results, or keyword advertising. Industry figures demonstrate the importance of sponsored results both for the search engines’ business model and for advertisers. Parallel to this, academic knowledge is growing at a rapid pace. Of particular interest are the psychological foundations of how and why keyword advertising works. In this context, it is necessary to understand the individual factors that influence sponsored results click behavior. This research focuses on the exploration of potential moderators influencing the relationship between message content and sponsored results click behavior. Sponsored results are composed of different elements, one of which is the description of what surfers are supposed to find if they click on the link. This description is textual and limited to a certain number of characters. Consumers will decide whether or not to click on the link

n

Corresponding author at: LEMNA—University of Nantes, France. E-mail addresses: [email protected] (C. Gauzente), [email protected] (Y. Roy). 0969-6989/$ - see front matter & 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.jretconser.2011.09.003

using this text as the principal motivation. How it is written and the chosen content are therefore essential for the consumers’ decision-making process. The article is organized as follows. In the next section, we review studies and research dedicated to search engines and keyword advertising. Secondly, we examine the literature devoted to the role of advertising as a text, and the role of price consciousness on consumer behavior. We derive two research hypotheses concerning the main and the interaction effects. The retained research method is described and empirical results are exposed. Lastly, we discuss the theoretical and the managerial implications of this research.

2. Search engines and keyword advertising In their historical overview of sponsored search, Jansen and Mullen (2008) indicate that online advertising began with banner ads in 1994. Four years later, the first sponsored search auction was launched by GoTo.com (renamed Overture in 2001, and acquired by Yahoo! in 2003). Among the different types of online commercials that are available to advertisers (banner ads, institutional, products, brand, and event Web sites, company or brand blogs, and micro-blogging), search engine optimization and keyword advertising are the most common. This advertising format is based on the consumer’s search request, a phase which traditional consumer behavior models recognize as crucial. Thus, Search Engine Marketing (SEM) relies on the basic idea according to which consumers using search engines to gather pre-purchase information and exposed to results that point to merchants’ web sites, will be more likely to go and visit these web sites and eventually to purchase from them (Chaffey et al., 2008;

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Smith and Chaffey, 2001). Even if exposition to sponsored results does not necessarily lead to a click (and a fortiori to a purchase); it increases notoriety, in the same way as banners do (Dre ze and Hussherr, 2003). SEM aims to increase both traffic on targeted websites and consumer awareness of websites through enhanced visibility. In order to achieve these objectives, both organic results and sponsored results are available to advertisers. Organic results are generated through the search engine’s basic searching and indexing activities. They are sometimes also described as editorial results. Sponsored results, on the other hand, appear because an advertiser has a paid agreement with the search engine relating to a number of previously defined keywords. A pay per click (PPC) agreement is set up with the search engine consisting of a previously established amount being due whenever a consumer clicks on a sponsored result. Sponsored results generally appear next to other search results: either at the beginning or on the right-hand-side or sometimes at the end of the list. Even if consumers may be unwilling to click on ads, given the general increase in ad skepticism (Obermiller and Spangenberg, 2000; Saaksjarvi and Pol, 2007), observations show that consumers are more likely to recall the name of a company which had previously appeared in a web search listing than a banner ad (Dobrow, 2004). Therefore, there are some marketing benefits from appearing on search engine result listings whatever the type of result (organic or sponsored). Keyword advertising is an increasingly investigated research area. The early studies conducted by Jansen et al. (2007), Jansen and Molina (2006) and Jansen and Mullen (2008) have been followed by modeling efforts in the marketing field. In a short period of time, a large amount of information has been acquired, although there are still some research gaps. Initial information was gathered from empirical studies pertaining to the use of browsers. Amongst these some contain findings about the use of sponsored results. Both qualitative (Marable, 2003) and quantitative approaches (Fallows, 2005; Hotchkiss et al., 2004; Interactive Advertising Bureau, 2003) indicate that surfers are, at best, unconcerned about sponsored results (Interactive Advertising Bureau, op. cit.). At that time, most of them were unfamiliar with these results, resulting in a naive use of search engine result pages (SERP) as consumers perceive them to be unbiased or neutral. However, some researchers (Brooks, 2005a, 2005b; Greenspan, 2004) suggested that consumers are increasingly reluctant to use them as they become more aware of the nature of sponsored results. Jansen and Molina (2006) showed however that once consumers click on the sponsored results and obtain relevant information, reluctance disappears. Moreover, they discovered that users of sponsored results come to view them as relevant as organic results. Jansen and Spink (2009) showed that blending organic and sponsored results, rather than highlighting sponsored search (which is recommended by the FTC), does necessarily increase or decrease the click rate on sponsored results. Such findings demonstrate that SERP users are more concerned with relevance and actual content than with the organic or sponsored nature of the obtained results. In the Marketing field, there have been numerous research studies over the last few years. The 2010 Informs Marketing Science Congress dedicated not less than 4 sessions to search engines. Most of the published studies can be placed in one of the three following categories (see Table 1):

 A first stream of research is focused on market and auction mechanisms with a modeling approach. The objective of the proposed models is to optimize the advertiser’s bidding strategy or search engine revenue strategy. These studies range from





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purely econometric modeling to models tested with panel data (Animesh et al., 2010; Chen and He, 2006; Katona and Sarvary, 2010; Viswanathan et al., 2007; Yao and Mela, 2011). The second stream of research is dedicated to the study of interaction effects. Based on real data, usually large data sets, these studies unveil the complex determinants of click trough rates (CTR), conversion rates and advertising revenues (Animesh et al., 2011; Ghose and Yang, 2009; Rutz and Bucklin, 2011; Yang and Ghose, 2010). The last stream of research examines performance and metrics. Proposed models attempt to inform bidding strategies based on either the performance of individual keyword (Rutz and Bucklin, 2007) or the opportunity to increase one of the following metrics: cost effectiveness, CTR, conversion or revenues (Ghose and Yang, 2009; Jansen and Schuster, 2011).

Most of these empirical studies use large data sets obtained from panels: having behavioral data at hand is a real advantage. The limitation, however, is that the attitudinal and psychological dimensions of behavior are left unexplored.

3. Advertising content Tixier (1992) stressed that the communicativeness of written texts, and particularly advertisements, is often neglected. A few years later, Liebermann and Flint-Goor (1996) again emphasized how important it is to adapt claims to products. However, while the advertising message itself is recognized as one of the three fundamental persuasion variables in many theories of persuasion (cognitive-response model of persuasion, dual-process models of persuasion, and resource-matching theory) and in integrative frameworks (Meyers-Levy and Malaviya, 1999), few academic studies have examined the effects of the message’s characteristics. Anderson and Jolson (1980, p. 57) emphasized that advertisers ‘‘usually present the message in a manner highly compatible in both linguistic structure and semantic content, consistent with that commonly experienced and expected by the intended receiver’’. Content is salient here. In the context of durable consumer goods, the researchers examine how technical wording influences consumers’ product and advertising judgment. They show that technical content does not necessarily increase ad believability but that it does increase the purchase intention of those consumers with prior product experience. This highlights the importance of content in effective communication. Researchers such as Keller et al. (2003) examined the impact of message framing. While message framing is not explicitly defined in this study, a good example would be a glass of water considered as either half-full or half-empty (McCusker and Carnevale, 1995), demonstrating that message framing is a matter of word choice (McKay-Nesbitt et al., 2011). The message is presented in a positive or negative light and influences the way in which the product’s benefits are highlighted. Content is also reflected in the use of appeals which fall into two general categories: soft-sell versus hard-sell (Okazaki et al., 2010). Other researchers use the emotional–rational distinction to qualify appeals (Roehm and Roehm, 2007). Rational, or hard-sell, appeals exhibit content ‘‘that provide details, facts and figures’’ whereas emotional, or soft-sell, appeals link consumer decisions to supposed psychographic needs (Liebermann and Flint-Goor, 1996). Message content refers to the words that are chosen to convey the message and the features that are highlighted for the receiver. By varying the content of messages, advertisers can influence one dimension or another in receivers’ mind and create an appropriate reaction.

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Table 1 A selected bibliography on Search Engine Marketing (SEM) research. Main topic

Authors

Main results

Data and method

Market equilibrium, auction process and bidding strategies

Chen and He (2006)

Modeling approach to market equilibrium for firms selling online differentiated products and consumers searching for product variety, with search engine as an intermediary. A firm’s relative ad expenditures serve as a strong signal of quality in online sponsored search markets. Propose a model for optimizing advertisers’ revenue that incorporates advertiser budgets, allocation options, query frequency forecasts, and pricing and ranking schemes. Model extensions are proposed. Under high uncertainty (which corresponds to sponsored search markets), adverse selection can occur as low quality firms can mimic the advertising strategy of high quality firms. Identification of bidding patterns under different bidding situations. Models are created in order to optimize bidding strategies in case of one sponsored result or of multiple sponsored results. Proposition of a dynamic model incorporating lagged effects. Demonstrate that (1) advertisers have a dynamic bidding behavior and (2) only 10% of the consumers do 90% of the clicks.

Modeling

Viswanathan et al. (2007) Abrams et al. (2008)

Animesh et al. (2010)

Katona and Sarvary (2010)

Yao and Mela (2011)

Mediation and interaction effects

Jerath et al. (2011)

Unveil a counter intuitive result showing that high quality firms may bid lower than lower quality ones, thus obtaining second or third positions and yet still attract consumers’ clicks.

Xu and Kim (2008)

Click on sponsored search results is impacted by an order effect. However this order effect is mediated by consumer’s attention and affects the probability of the online vendor being accepted. Examines spillover effects between product categories. Spillovers exist between the initial search and the final purchase behavior. Consumers initially searching for a product in one category end up purchasing the product and also products from a different category. However, this cross-category effect does not necessarily happen symmetrically. There is a positive interdependence between organic and paid results: total click through rate, conversion rate, and vendor’s revenue are higher in the presence of both organic and paid results than in the absence of paid results.

Ghose and Yang (2010)

Yang and Ghose (2010)

Animesh et al. (2011)

Rutz and Bucklin (2011)

Performance and metrics

Rutz and Bucklin (2007) Ghose and Yang (2009)

Jansen and Schuster (2011)

Underline the importance of ‘‘unique selling proposition’’ and creativity on click behavior. The effect on click through rate of a firm’s positioning strategy and rank is moderated by its ability to differentiate from adjacent competitors. There exists a spillover effect between branded and generic search. This effect is asymmetric. Generic search activities affect positively brand search via increased awareness (the reverse is not true). Propose a model to assess individual keyword performance given the sparseness of conversions. Focus on the monetary value of a click given keyword characteristics, position of ad, landing page quality, and purchase behavior. The monetary value of a click is not uniform across all positions and decreases as one goes down the SERP. Landing page quality increases conversion rate and decreases cost per click Hypothesize that bidding strategies based on the ‘‘buying funnel’’ categorization of queries (awareness, research, decision, purchase) might be more effective in terms of critical keyword advertising metrics. Results show that it is not. Queries that could be interpreted as an indication of ‘‘awareness stage’’ cost less and generate more sales revenues compared to ‘‘purchase’’ queries.

Building upon the expectancy theory (Porter and Lawler, 1968; Vroom, 1964), we can speculate about the impact of message content in the context of online searches. The expectancy theory was initially designed in order to better understand the motivation at work, however this theory has largely been used in the field of marketing (see for instance: Tsiros et al., 2004) for the comprehension of customer satisfaction. The theory also nurtures hypotheses that are still used to understand consumer behavior, such as the Theory of Planned Behavior (Ajzen, 1991), or the use of technologies (for instance: Luo et al., 2011). The expectancy theory deals with the mental processes at work when an individual makes a decision. Expectancy is defined as the strength of an

Lab experiment Modeling, linear programming, simulation Sponsored search auction data Modeling

Search engine proprietary data Dynamic structural modeling Panel data from a popular Korean search engine 52 students Tobit regression Panel dataþ counterfactual experiments

Panel data from a US nationwide retailer Hierarchical Bayesian modelingþ counterfactual analysisþ field experiment Field experiment

Panel data from a major lodging chain Dynamic linear modeling Panel data Panel data Hierarchical Bayesian model

Panel data

individual’s belief that a particular outcome is attainable. This belief, coupled with two others: valence and instrumentality, determines the level of motivation to engage in a decision. In this research, we focus on the expectancy dimension of the theory. In the context of search engine use, individuals form expectations about the tool’s properties and the nature and the quality of the obtained results. While they also form expectations concerning different aspects of the researched product, such as brand, price, and product features, they use the search engine in order to gather preliminary, large-scope, neutral information about products. This reflects what Yahoo!, a few years ago, called the ROBO process: ‘‘Research Online, Buy Offline’’. If consumers have

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a specific brand in mind, they will either gather information: directly at the store, on the merchant’s website, or engage in navigational queries (also called branded queries). Because navigational queries are just one part of the numerous types of queries on search engines (Yahoo! Academy, 2010), there is a need to control this aspect via appropriate scenario design. Pre-purchase online product information using search engines has been continuously observed in several empirical studies (BIGresearch, 2006; Jansen, 2010). Such research has become a reflex for most consumers, more than 80% according to Jansen (2010). Several empirical studies have also shown that surfers consider search results to be unbiased (Hotchkiss et al., 2004), so consumers’ expectation when using search engines to gather online product information is that obtained results are unbiased and therefore neutral. Based on this, we hypothesize that descriptive content will be preferred over commercial content in messages. Descriptive message content is characterized by factual, descriptive information about products or services on the other hand; commercial content is defined as offer-focused with an emphasis on consumer advantages. Consequently descriptive content is likely to conform to consumers’ neutrality expectations, whereas commercial content is more likely to appear biased to consumers. We thus posit:

 H1: Descriptive content messages generate more clicks on

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Fig. 1. Proposed research model.

Sponsored results adopting a descriptive content are such results compared to commercial ones that are more focused on tempting and attracting consumers based on an attractive offer. Hence, we expect that price-consciousness moderates the relationship between message content and click behavior. Our moderating hypothesis is as follows:

 H2: Price-consciousness moderates the relationship between sponsored results’ content and click behavior. More specifically: J H2a: High price-conscious consumers will click more on sponsored results with descriptive content than those with commercial content. J H2b: Low price-conscious consumers will click equally on descriptive and commercial sponsored results.

sponsored ads than commercial content messages.

4. The role of price-consciousness Based on persuasion theories, several potential moderators can be investigated. Because we contrast descriptive versus commercial content, we suspect that price-consciousness can be a powerful moderator in the relationship. We expand the rationale for this hypothesis. The concept of price-consciousness was initially defined by Monroe and Petroshius (1981, p. 44), who describe the high priceconscious consumer as ‘‘unwilling to pay a higher price for a product, and if the price is greater than what is acceptable to pay, the buyer may refrain from buying.’’ This definition suggests that price is integrated as a criterion in consumer’s decision making and at the same time implies that consumers construct an idea of what is acceptable and within what limits. Price-consciousness not only has negative implications for the buying decision (buy/ do not buy); it also has implications for information processing. High price-conscious consumers generally face lower search costs because they focus on price information (Kukar-Kinney et al., 2007; Lichtenstein et al., 1993). Parallel to this however, they process price-related information more thoroughly (Lichtenstein et al., 1988). Kukar-Kinney et al. (2007, p. 214) contend that high price-conscious consumers ‘‘are not looking for cues that would help them reduce the search’’. A key factor that is important to this discussion is the notion of price-related information. Although information that explicitly mentions price can be clearly identified as ‘‘price-related’’, the question is raised for other product features, as price evaluation has to be made taking into account other offer characteristics. Because price-conscious consumers engage more easily in information searching, they may also be expected to spend more time on evaluating associated product features. In addition, as they process information in a rational, economic mode, they balance the price-offer with other offer aspects. Hence, it is expected that consumers high in priceconsciousness will be more interested in descriptive content messages than consumers low in price-consciousness, precisely because sponsored results with a descriptive content convey more information, rendering rational decision making easier.

The proposed research model is composed of one endogenous variable and three exogenous variables (Fig. 1). The endogenous variable is the click behavior (click on a sponsored result versus an organic result). As for the three exogenous variables, the sponsored results content can be either descriptive or commercial. An additional variable is integrated in the form of a covariable as attitude toward sponsored results has proven to be highly predictive in previous studies (Gauzente, 2010). Lastly our moderating variable, price-consciousness, is integrated into the model, in interaction with the message content.

5. Empirical study design The retained method is detailed here with the study design, population and sampling, stimuli and experiment outline, and exogenous variable measurement. 5.1. Study design and procedure The retained method for testing our research model is online experimentation. We use a between-subjects two factorial design. Two versions of a five-page html questionnaire were randomly assigned to participants. In this questionnaire, an online search situation is described and the corresponding Search Engine Results Page (SERP) is presented next. Participants are asked to click on the link that is most relevant to them. The following pages are used to collect socio-demographic and Internet familiarity information and the two exogenous variables (Price-consciousness and Attitude towards sponsored results). With regard to the description of the search situation, the chosen product is a digital camera. According to Nelson (1974) product classification, digital cameras are highly researched products because they can be fully assessed before a purchase is made. Klein (1998) and Shim et al. (2001) suggest using search products when studying online consumer behavior. A pre-test (n ¼33) indicates that digital cameras are considered as products that can be evaluated before purchase (30 respondents out of 33 agree with this).

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5.2. Population and sampling Several studies have pinpointed some form of generational effect in online consumer behavior (Horrigan, 2008). However mixed results are observed. van Deursen and van Dijk (2009) state that younger people develop some formal Internet skills better than older people, while Jones and Fox (2009) describe the ‘‘millennial’’ generation as less mature concerning online behavior than the ‘‘generation X’’ (see also Hargittai, 2010). We believe that the ‘‘millennial’’ generation is, at least in developed countries, more homogenous in education, skills, and behavior than past generations. A predictive model applied to the whole population of a country could then easily be biased by confounded factors and spurious correlations. So our research model is estimated using only the ‘‘millennial’’ generation, defined as people aged between 18 and 30 years (inclusive). Initial participants to this survey were contacted via email (emails were gathered from a previous online study) and

participation in this new study about ‘‘online behavior’’ was proposed. Participants were asked to forward the email to their friends. The total sample therefore comprises people that are out of the millennial boundaries (n¼376) because of this viral effect. So we applied an ex-post filter to the whole sample in order to gather only the millennial generation (final n¼165).

5.3. Stimuli Two sponsored links that could have been found on a Google SERP in response to a generic query (‘‘digital camera’’) were created. They share the same characteristics, except one:

 the same link anchor, equal to the request content,  the name pointing to a unique well known online retailer (Pixmania), and

Fig. 2. Screenshots of the two clickable SERP images.

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 two different short contents formatted as usual on this search engine, manipulated in order to reflect our main hypothesis (one ‘‘descriptive’’ content and one ‘‘commercial’’ content). The descriptive content message focuses on facts and product characteristics, is presented as a juxtaposition of keywords (‘‘7-megas pixels, 2.5 inch LCD screeny’’, a reasonable standard at that time). The commercial content message urges to action by presenting product advantages and commercial offer (‘‘Sales on digital cameras! Catch up our crazy offers!’’). Message contents were assessed by marketing and communication experts (n¼5). In addition, a qualitative manipulation check, using a sample of 18 marketing students, was also conducted. They were asked to qualify their overall impression concerning the content of the manipulated messages. Respondents unanimously describe commercial message content as ‘‘attractive, dynamic, interesting, commercial, price-oriented, nice offer, promotional’’ and descriptive message content as ‘‘brief characteristics, product explanation, purely informative, descriptive, neutral, concise, and precise’’. Each manipulated sponsored link was then inserted in a modified Google response page, which contained only one sponsored result in order to avoid any position bias. To prevent any unwanted click behavior, the HTML text pages were transformed into GIF images associated with a HTML image map. The image maps were built with two active zones and two corresponding URLs (Uniform Resource Locator) pointing to the measurement system, one for the sponsored link zone and the other for the other zone corresponding to organic links. The result is shown in its original language in Fig. 2. 5.4. Online experiment outline The email invited respondents to participate in an internet survey about online behavior with a link to the URL’s questionnaire. The measurement process was the following: (1) The respondent was presented the scenario of an online consumer who wishes to buy a digital camera. (2) The next web page was one of the two manipulated SERP, chosen randomly by the system. (3) A click on one of the active zones of the image map was recorded by the measurement system, for that session giving

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both the binary dependent variable (click on the sponsored link or on one of the organic links, hereafter named click-type) and the binary manipulation factor (SERP with a descriptive or commercial sponsored link). (4) The following pages were a simple online questionnaire measuring the moderating effect, the co-variable and a few socio-demographics and control variables, such as the age of the respondent. 5.5. Measurement of exogenous variables Price consciousness is measured through a three-items Likerttype scale (Lichtenstein et al., 1988) with anchors ranging from 1 ‘totally disagree’ to 5 ‘totally agree’ (see scale in Appendix 1). The distributions of the three items of the Price consciousness scale show slight skewness (absolute values inferior to 0.2) and a significant negative kurtosis (around 1.0). The unstandardized Cronbach’s Alpha is modest (a ¼0.62), but no item deletion is suggested and as shown by Maydeu-Olivares et al. (2007); this indicator can be significantly downward biased in such a case of small sample size and high kurtosis. Attitude toward sponsored results is measured through a fouritems Likert-type scale (Gauzente, 2010) with anchors ranging from 1 ‘totally disagree’ to 5 ‘totally agree’. The four items are well distributed. The unstandardized Cronbach’s alpha is satisfactory (a ¼0.87) and suggests no item deletion. This variable is included as a co-variable. Although structural equation modeling methods exist that can cope with binary data, the emphasis here is put on parsimony and predictive power in order to improve its relevance to professionals (Breiman, 2001). Thus, we use the summated scales for model estimations (see measurement scales in Appendix 1). 5.6. Final sample characteristics The final sample contains slightly fewer men (39%) than women (61%). However when testing for gender differences in terms of click behavior, no significant bias is observed (w2 ¼2.3, p410%). The median of ages is very close to the mean (24.8 versus 25.2) and is evenly distributed. Also the sample contains only 46 (28%) students or unemployed youngsters, versus 119 (72%) working people. This is rather unusual in experimental studies, often conducted on

Table 2 Direct effects’ model. Analysis of maximum likelihood estimates Parameter Intercept Content Attitude twd SL Price conscious

Descriptive

DF

Estimate

Standard error

Wald w2

Pr 4 w2

Exp (Est)

1 1 1 1

 4.3662 0.5316 0.9099 0.0621

1.1176 0.2121 0.2664 0.2211

15.2636 6.2841 11.6676 0.0790

o0.0001 0.0122 0.0006 0.7786

0.013 1.702 2.484 1.064

Table 3 Direct and interaction effects’ model. Analysis of maximum likelihood estimates Parameter Intercept Content AttitudeTsl Price conscious Price Consc. X Content

Descriptive

Descriptive

DF

Estimate

Standard error

Wald w2

Pr 4 w2

Exp (Est)

1 1 1 1 1

 4.1239  1.0461 0.8631  0.0109 0.5383

1.1331 0.7071 0.2751 0.2340 0.2341

13.2465 2.1890 9.8451 0.0022 5.2876

0.0003 0.1390 0.0017 0.9627 0.0215

0.016 0.351 2.371 0.989 1.713

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Fig. 3. Additive effects of content and attitude.

Fig. 4. Interaction between content and price consciousness.

undergraduate students. This result is due to the enrollment method, which leads working status proportions of the sample to be much closer to those of the actual sub-population of interest. 6. Results Due to the nature of the dependent variable, two binary logistic regressions1 are conducted to test our research model. 1 Logistic procedure of SAS/Stats 9.22 package, with standard parameterization, i.e. descriptive ¼1 versus commercial ¼  1.

Table 2 summarizes the parameter estimates of a direct-effects model and tests our first hypothesis. The model is significant and exhibits a Wald w2 of 17.7 with 3 degrees of freedom (po0.1). The predictive power is satisfactory (Nagelkerke r2 ¼19%), and the first hypothesis is clearly supported. The Content estimate is significant (p o0.05) and the exp(b) of 1.7 shows that replacing the commercial content of a sponsored result by a descriptive content almost doubles the odds of clicking on this result. As expected, there is no significant direct effect of Price consciousness. Table 3 summarizes the parameter estimates of the full model including both direct and moderating effects.

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Table 4 Odds-ratios. Wald confidence interval for odds ratios Label

Estimate 95% confidence Limits

Content commercial versus descriptive at Price conscious ¼3.0323 AttitudeTsl Price conscious at content¼ commercial Price conscious at content¼ descriptive

0.310

0.124

0.772

2.371 0.577 1.695

1.383 0.291 0.922

4.065 1.148 3.113

The model is still significant (Wald w2 ¼20.7, p o0.01) and its predictive power is higher (Nagelkerke r2 ¼23.7%). The difference in likelihood ratios between the two models (d ¼5.5) is significant at the 5% level, demonstrating a significant improvement. In the presence of a significant interaction (b¼0.54, po0.05), direct effect estimates cannot be discussed separately, being only adjustment parameters. Therefore two graphs are generated, showing probabilities predicted by the full model. For a Price-consciousness equal to the sample mean, the ratio of the relative propensities to click on a sponsored result with a descriptive content versus a commercial content is significantly greater than one. We also observe in Fig. 3 a very strong influence of the variable ‘‘Attitude toward sponsored results’’, which is in line with the previous studies (Gauzente, 2010). For an Attitude equal to the sample mean, the interaction shown in Fig. 4 predicts that, as the Price consciousness increases the probability to click on a sponsored link increases if its content is descriptive, while it decreases in the case of commercial content. This fully supports our second hypothesis. In addition, at the sample mean of Price consciousness, the probability of clicking on the sponsored link is almost doubled by a descriptive content versus a commercial one, which is fully consistent with our first hypothesis and similar to the results of the reduced model. This can be seen, although in the opposite way, in Table 4, where the odds ratio of commercial versus descriptive, at the sample mean of Price consciousness, is significantly less than 1. For Attitude towards sponsored links and Price consciousness, the odds ratios are given for an increase of 1 of the value of each scale.

7. Discussion Literature on the role of message content in advertising is relatively scarce. In a context in which new and concise formats for advertising emerge, with strict constraints on the number of characters, there is a need to evaluate how advertisers can communicate effectively. Just as SMS-advertising messages must not contain more than 160 characters, keyword advertising is constrained to a limit of 25 characters for the title and 70 characters for the body of the ad.2 The execution of the advertising message becomes crucial. This research assesses the effect of two different types of message content. The first type of message content is descriptive and provides facts and product characteristic information and the second type is commercial. Conforming to expectations, the impact of message content is ascertained and the superiority of messages with descriptive content appears clearly. This is an important outcome as even in a context where a consumer is searching for pre-purchase information; commercial content is probably seen as more aggressive. When confronted with advertising, consumers are 2

Although this standard may change from one search engine to another.

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now expecting more subtle persuasive attempts. Hence while the role of expectations is comforted, an evolution is probably at work. From a theoretical standpoint, these results provide high credit to current researches on consumers’ resistance to advertising, with seminal studies and models such as the Prior knowledge model – PKM proposed by Friestad and Wright (1994) – and developments on skepticism (Obermiller and Spangenberg, 1998). From a managerial standpoint, this suggests that internet advertisers and e-tailers should pay careful attention to their creative strategy and advertising execution. Animesh et al. (2010) already demonstrated that creative strategy is of foremost importance for click through rate (CTR). Descriptive content messages are more suited to reflect a firm’s unique selling proposition (USP) and positioning than merely commercial content focusing on priceoffer. So results obtained in this study are in complete accordance with this previous study and provide complimentary and accurate perspectives to existing knowledge. A second significant finding pertains to the moderating role of price-consciousness. Low price-conscious consumers tend to click more easily on commercial content messages, while high priceconscious consumers obviously prefer descriptive contents. The gap between descriptive sponsored ads and commercial sponsored ads increases significantly when consumers are highly price-conscious. Interestingly, this gap is less important in the case of low priceconsciousness. The hypothesis positing that high price-conscious consumers will prefer sponsored results with descriptive contents is fully supported. We observe that low price-conscious individuals are less influenced by sponsored results content. Price-conscious individuals tend to spend more time on gathering and processing pricerelated information and this includes not only explicit price-information but also other product information. This suggests that priceconscious consumers tend to have a wide view of price evaluation, including non-price features. The attention paid to price is enlarged to complimentary elements. Hence price-conscious people may be even more attentive to each specific product characteristic. From a theoretical viewpoint, this suggests that the price consciousness construct involves much more than strict price information.

8. Limitations and future research avenues Building an experiment has many advantages: a precise control of the stimulus of interest, here the sponsored link message content, the ability to measure perceptions at the same time as a behavior, etc. Obtaining an equivalent precision with real data would be extremely costly. An experiment has also drawbacks: along with the necessary simplification of the presented SERP (only one sponsored link), the respondents were put in an unanticipated situation (having to find information about a specific product), with a limited control (no second or third SERP lookup, no query refinement). However, other possible biases were reduced as much as possible by the sampling method. Unlike what can be found in a great number of experiments in the field of marketing or information systems, the respondents were not gathered into a computer lab and rewarded with a financial or course credit compensation. No incitation system was used in this study and should a participant feel uncomfortable with the situation, he or she could simply quit the experiment by simply closing his or her browser window. So it is a reasonable assumption that the actual respondents felt, when they completed the forms, concerned or motivated by the digital camera buying process. Combined with an a posteriori filtering based on age,3 this led to a sample 3 An a priori filter, known to participants, would have had uncontrollable side effects.

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size of n¼ 165 limiting the complexity of the models that can be estimated with reasonable confidence and limited bias (Sik-Yum et al., 2010). The gender disequilibrium in the sample could have led to a misspecification of the model but this is not the case: a conservative reweighing scheme4 shows no real difference in the estimated coefficients. However, the limited predictive power of exogenous variables is unsatisfying: on the target sub-population, the ‘‘millennial’’ generation, the Nagelkerke r2 of nearly 24% is almost correct but a generalization, applicable to the entire population of search engines’ users is needed. The ex-post filtering scheme allows the re-estimation of the model using the whole data set (n ¼376). The results are consistent with those presented here (same sign and order of magnitude of the coefficients), but the predictive power drops to 10%, which is less meaningful in terms of managerial implications. All in all, the present study sets an encouraging basis for further investigation of the impact of message content.

Acknowledgements The authors wish to thank Mathieu Montebianco for his help in data collection.

Appendix 1. Measurement scales Each item is measured on a 5 points Likert scale ranging from ‘‘totally disagree’’ to ‘‘totally agree’’. Price consciousness scale (Lichtenstein et al., 1988)

 I usually buy—when they are on sale.  I buy the lowest priced—that suits my needs.  When it comes to choosing a—for me, I rely heavily on price. Attitude towards sponsored results (Gauzente, 2010)

   

I think it is normal to get sponsored results on SERP. I find sponsored results useful. I am happy to have them. Sponsored results are a good thing.

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