Product involvement and search behaviour: searching in the good old offline environment or 21 century style in the online universe

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Product involvement and search behaviour:

searching in the good old offline environment or

21 st

century style in the online universe

University of Groningen Faculty of Economics and Business

Master thesis for MSc Marketing

Supervisor: Ms. L. Lobschat Second supervisor: Ms. D.A. Naydenova

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Abstract

The aim of this study is to investigate the influence of product involvement on consumer search behaviour. To do so, a conceptual model was developed which proposes that exposure to media (single/multiple media) moderates the effect of product involvement (high/low) on consumer search behaviour (online/offline). An online experiment was conducted to study these effects. The results indicate that searching for a high involvement product relative to low involvement product has a significant effect on offline search behaviour, and that searching for a high involvement product relative to low involvement product has more influence on online search behaviour, however this result is not significant. No moderating effects were found regarding the relationship between product involvement and search behaviour. The reasons for and implications of these findings will also be discussed.

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1. Introduction

In the process of search behaviour, choosing a source or channel for information is an important step which influences the outcome of the search process to a great extent. Back in the days where consumers used to go to a store or read magazines for product information, the rise of the internet and development of the omnichannel landscape has changed consumers’ search behaviour. Searching across multiple channels has become a well-entrenched behaviour among many consumers. 54 per cent uses two or more search channels before they make a purchase (Oracle, 2011) and according to the McKinsey iConsumer research (McKinsey&Company, 2012), 60 per cent of all consumers research products they want to buy online. Searching via a computer is the most common search tool accounting for 50 per cent of the search process. Most of the consumers start their research on a store’s website (23%), followed by Google (19%) and the manufacturer’s site (14%). This shows that consumers mostly use online search channels. However, a staggering 75 per cent of all consumers visit a store to see and experience the product before they buy it (Oracle, 2011). All in all, it can be said that consumers engage themselves in different search behaviours.

The fact that consumers behave differently when searching, depends on their level of product involvement, motivation and search objectives, consequently finding different kinds of information, which contribute to their hunt for information. This study’s aim is to identify which level of product involvement influences the types of search behaviour consumers engage themselves in, depending on the media (single or multiple media) to which consumers are exposed. While much research has been done in the field of search behaviour, most of the conducted research (e.g. Huang et al., 2009) focuses on the kind of product (search and experience products), effects of multichannel marketing (Laroche et al., 2013) and effects of discounts (Bayer & Ke, 2013) influencing search behaviour. This study shifts its focus toward product involvement in order to provide managers a thorough understanding where consumers search for product information.

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increased. Where consumers used to go to a store to search for product information, online channels have widened the options for retail shoppers when searching for information. The online universe has made it possible to search to a great extent, making the internet an alternative for search purposes that would otherwise involve a long trip. Easy accessibility of online channels saves consumers time, money and effort (Chocarro et al., 2013). But the question is, for what types of products do consumers conduct online or offline search? It is proposed that higher product involvement leads to an increased amount of time and effort spent on searching. Therefore, it is assumed that high involvement leads to both increased online and offline search behaviour. From a managerial point of view, it is of interest to understand in what kind of search behaviour consumers engage themselves, so managers know which kind of channel should be emphasized in their communications.

From the body of research focusing on consumers’ search behaviour and the channels consumers used, the leading theories are based on pioneer research conducted in the 80s and 90s. The focus of these researches was on traditional search behaviour, such as consumers engaging themselves in reading magazines and newspapers, asking individuals (experts, salespeople and/or friends), and visiting stores (Furse et al., 1984, Beatty & Smith, 1987, Hauser et al., 1993, Urbany et al., 1996). However, new possibilities for consumer search arose as the result of the internet. Providing interactive information has become possible in a way that was unknown in traditional media (Kleinmuntz & Schkade, 1993). This has reduced the costs, time and effort for gathering information. The abovementioned studies suffer from the limitation that they focus only on one type of behaviour, either offline or online. The current study investigates both types of search behaviour.

When consumers start their search process for a specific offering, it is very likely they are being exposed to information provided by media, which may change consumers’ choices and behaviours (De Paolo & Scoppa, 2014). Consumers are exposed to a plethora of advertisements delivered through different online and offline media, such as banner ads, sponsored search advertising, TV commercials and the like. This research focuses on exposure to TV commercials (within media exposure) and a combination of a TV commercial and a banner ad (cross media exposure). It is argued that the exposure to a single medium (TV commercials) or multiple media (TV commercial and banner ad) is a potential moderator to product involvement that influences search behaviour.

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suitable for communicating emotional contents, TV ads are expected to be particularly effective in informing consumers on low involvement offerings (Wills et al., 1991, Leong et al., 1998). On the other hand, consumers that search for information on high involvement offerings, usually put much effort in their search process. Therefore, TV ads can be seen as valuable additional sources of information (Voorveld et al., 2012). A consumer which is highly involved with an offering might value exposure to multiple media, which provides different kinds of information, what could potentially lead to both increased online and offline search behaviour. It was shown that the effects of different media reinforce each other (Edell & Keller, 1989, Naik & Raman 2003). So from a managerial point of view, it is of interest to understand which kind of media (within or cross) influences the search process for low and high involvement offerings, so managers know which kinds of media outlets they should emphasize in their communications.

Differences in involvement levels for product categories have a variety of effects on search behaviour. These are e.g. amount of time and effort spent on the search process (Zaichowsky, 1985). According to Petty et al. (1983), people who feel that a product is important or relevant to them, (high involvement) will be likelier to put more time and effort into information search and processing. Therefore, it is likely they engage themselves in the more traditional offline search behaviour which goes along with the option of expert advice by sales personnel, touch-and-feel shopping and shorter delivery times (Kollmann et al., 2012). If consumers feel an offering is not very relevant to them, they may want to search in online channels. Online search behaviour is characterised by its convenience, relatively low costs and internet’s ease of use. Low involvement goods tend to be of less monetary value and/or of high purchase frequency, and thus need not to be researched extensively (Chocarro et al., 2013).

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The contributions to the literature are as follows: first, from a managerial perspective, the aim of this study is to provide more insight into which level of involvement influences which type of search behaviour. In this empirical work, consumers’ search behaviour for TVs and DVDs is examined, whereas in other studies about search behaviour, search behaviour about automobiles is mostly researched (e.g. Punj & Staelin, 1983, Moorthy et al., 1997, van Rijnsoever et al., 2009). Automobiles are considered as major durables, which need to be tested and mostly, the entire household is involved in the search process (Punj & Staelin, 1983), which is presumably not the case for e.g. a DVD. Second, whereas most of the current studies focus on online search behaviour (e.g. Laroche et al. 2013, Joo et al., 2014) this study also includes offline search behaviour. Third, using the methodology of this study shows how single or multiple media influences search online or offline search behaviour. Results can be used to ground managerial decisions on what type of search behaviour consumers engage themselves in when searching for a low or high involvement offering. With this information and knowledge in mind, marketing managers can adapt their marketing strategy and anticipate on how consumers search for information. For example by introducing online vendors to help them answering questions about products consumers are looking for, or in cases where consumers want to spend little time and effort on the search process, to make searching easy and fast so the consumers find all information in a few mouse clicks. The purpose of this study is to give marketers empirical ground for their decisions on how to expose products through different kinds of media.

The outline of this study is organized as follows. In chapter two, the literature on product involvement, search behaviour and exposure to media is discussed. In the third chapter, the data collection method and study design are discussed. The following chapters discuss the results, findings, implications and limitations of this study.

2. Literature review

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2.1 Search behaviour

In today’s world, consumers are overloaded with advertisements and an extended supply of products in all product categories. Consumers have to engage themselves in a great deal of searching when they have an offering in their mind. They have to process a lot of information and frequently are not perfectly informed about product information such as product attributes or point of sales. Therefore, consumers have to spend time, effort and money to gather information (Bayer & Ke, 2013) needed in order to make a purchase decision. The following section gives a definition of search behaviour and discusses two types, online and offline search behaviour.

Search behaviour, which is part of a consumer’s decision-making process, is defined as the process by which a consumer carefully examines his or her environment for useful and appropriate data in order to make reasonable decisions (Solomon et al., 2006). Because search behaviour takes place on a rational basis, it is assumed that consumers gather information selectively to maximize and optimize their individual ratios of benefit to effort (Solomon et al., 2006). The cost or effort of search behaviour is the cognitive effect; the time spent on searching. It is assumed that when consumers are motivated to search (in case of a high product involvement), they spend more time and effort on their hunt for information.

Searching for information on the web is one of the most common performed tasks by consumers. The rise of the internet has shaped a new environment where consumers have access to a plenitude of information (Klein & Ford, 2003). The internet has put together all kinds of information at just a few mouse clicks away. Hence, online search behaviour is characterised by its convenience, relatively low costs and internet’s ease of use (Rose & Samouel, 2009). Consumers engage in online search behaviour when their goal is to find information concerning an offering in anticipation of purchasing, as well as to gather general information about an offering (Shim et al., 2001). When consumers engage in online searching, they heavily make use of search engines: in 2012, Google accounted for 5.1 billion quests per day (Laroche et al., 2013). Lastly, according to the online foraging theory, consumers try to maximize the information value while minimizing the amount of sources (e.g. energy, time) they use to obtain it (Dennis & Taylor, 2006).

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delivery times (Kollmann et al., 2012). If consumers are not convenience oriented, they may want to search in offline channels. The lower the customers’ convenience orientation, the higher will be their propensity to search via offline channels. Additionally, consumers tend to avoid or reduce risk. The higher the consumers’ risk aversion, the higher will be their propensity to seek information through offline channels. The possibility to touch and try out offerings before buying them is unique to offline channels (Kollman et al., 2012).

2.2 Product involvement

The current literature discusses many types of involvement to explain consumers’ attitudes and behaviour intentions towards advertised products and brands. Researches have indicated many types of involvement, such as involvement in purchase situation/advertisements/service/interest and product (e.g. Day et al., 1995 & Kim et al., 2009). This study focuses on the latter form of involvement, which is reflected as the degree of perceived personal relevance. That is consumers’ level of involvement with an action, situation or object, determined by the degree to which the consumers perceive the concept to be personally relevant. In this study, it is suggested that concepts are personally relevant to the extent that the consumer perceives it to be self-related or in some way helpful in accomplishing a personal goal or fulfilling needs (Celci & Olson, 1988).

To the degree that product characteristics and attributes are associated with personal goals and needs, consumers will experience strong feelings of personal relevance or involvement with the product (Celsi & Olson, 1988). This is also known as high-involvement, meaning that more relevant products draw consumers’ attention and generate more motivated processing. It is suggested that in high-involvement scenarios, consumers will engage with the arguments and claims of the message about the product/service (Atkinson & Rosenthal, 2014).

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In case of high product involvement, consumers spend time and energy to make a decision before purchasing the offering (Richins & Bloch, 1986). According to Petty et al. (1983), people who feel that a product is important or relevant to them, (high involvement) will be likelier to put more effort into information search and processing. They will also search information via the best medium available (Okazaki & Hirose, 2009) and search for as much information as possible. The Internet is an appropriate medium for consumers searching for information about a high involvement product. Websites typically display many information and rational appeals. Interacting with a website requires cognitive effort, which is only performed by highly involved consumers and therefore they are willing to interact with a website (Liu & Shrum, 2009). Besides, when being highly involved with a certain offering, consumers could also search for more thorough and detailed information and actual experiences through the means of reading online consumer reviews, forums or blogs.

In contrast to high involvement products, consumers do not want to spend great amounts of time and energy to make decisions before purchasing when searching for low involvement offerings. When product involvement is low, consumers are less likely to induce elaborations in the decision making process (Buchholz & Smith, 1991). It is believed that for low product involvement, consumers do not have a high desire to seek information (Rose & Samouel, 2009). The lower the product involvement, the higher consumers will perceive the costs of searching. These costs associated with searching are time, effort, inconvenience and money, including traveling to stores and dealers (Hoyer et al. 2013). Searching online is characterised by among others ease of use, convenience, and perceived financial benefit (Rose & Samouel, 2009). Efficiencies in online searching reduce consumers’ search costs (Bakos, 1997). In this study it is assumed that for low involvement products, consumers do not engage in extensive searching and therefore limit their search activities to the Internet because of its convenience, ease of use and timesaving characteristics. Considering the previous arguments and findings, the following hypothesis is tested:

H1: Searching for a high involvement product has a greater positive effect relative to searching for a low involvement product on consumers’ online search behaviour.

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offerings (e.g. experience goods) simply need to be experienced through e.g. tasting, touching, smelling or trying it (Kempf & Smith, 1998). This can only be done in offline settings, such as in brick-and-mortar stores. Time and risk plays an important role in engaging in offline search behaviour. When engaging in offline search behaviour, consumers generally have less time pressure and will thoroughly process information. Consumers that are motivated to search and do not feel time pressure, search and process information more systematically (Suri & Monroe, 2003). Besides time, high risk also plays a role in offline search. Consumers are usually motivated to search for information to reduce or avoid risk. As a result, consumers can collect additional information by e.g. reading news articles or engage in comparative shopping (Hoyer et al., 2013).

As low product involvement products do not motivate people to spend great amounts of time in searching for information, it is assumed that consumers will try to engage in search behaviour that is convenient and timesaving. As mentioned before, in cases of high product involvement, consumers are motivated to search for information than people with lower motivation in case of low product involvement. In cases of low involvement, consumers invest little time and effort in searching for information (Wu & Lin, 2012). Therefore, the Internet is an appropriate medium when looking for information. It is easy and quick to use, so it is expected the consumers with low product involvement do not engage in offline search behaviour. Considering the previous arguments and findings, the following hypothesis is tested:

H2: Searching for a high involvement product has a greater positive effect relative to searching for a low involvement product on consumers’ offline search behaviour.

2.3 Media exposure

The early part of the 21st_{century has experienced an enormous increase in the number of}

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2.3.1 Exposure to TV advertising

TV advertising is described as paid messages from an identified sponsor, with the purpose of trying to persuade consumers, broadcasted on television (Belch & Belch, 2011). TV advertising is still a dominant and from a consumers’ perspective typical form of advertising. Consumers are highly familiar with television watching, as it is an important part of our daily lives from early childhood (Jin & Lutz, 2013). Sharp et al. (2009) stated that television still enjoys a high audience reach and even in this Internet age, time spent watching TV exceeds that spend online. Buchholz and Smith (1991) found that TV advertisements are highly effective in providing information to consumers about low involvement offerings. The external pacing of TV ads (advertiser controlling the speed of information transfer), moving images and verbal information can influence a consumer who does not want to spend a lot of energy into processing the commercial (Buchholz & Smith, 1991, Dijkstra et al., 2005). In order to sell low involvement offerings, marketers use emotional appeals. Because TV advertisements are very suitable for communicating emotional contents, TV ads are expected to be particularly effective in informing consumers on low involvement offerings (Wills et al., 1991, Leong et al., 1998). As it was hypothesised that searching for low involvement products have a positive effect on online search behaviour, it is expected that exposure to TV commercials strengthen the relation between low product involvement and online search behaviour. Therefore, the following hypotheses are tested:

H3a: The relation between searching for a low involvement product and online search behaviour is stronger when consumers are exposed to TV commercials.

H4a: The relation between searching for low involvement product and offline search behaviour is equally strong when consumers are exposed to TV commercials relative to this relation when consumers are not exposed to TV commercials.

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involvement and online and offline search behaviour. Therefore, the following hypotheses are tested:

H5a: The relation between searching for a high involvement product and online search behaviour is stronger when consumers are exposed to TV commercials.

H6a: The relation between high searching for a high involvement product and offline search behaviour is stronger when consumers are exposed to TV commercials.

2.3.2 Exposure to TV advertising and online advertising

First, a description of online advertising is discussed, followed by the effect of TV and online advertising on the two types of search behaviour. Consumers are exposed to online ads constantly when actively using online media, whether they are engaged in entertainment, information search, or shopping. Shankar & Hollinger (2007, p2) define online advertising as “advertising that uses the Internet or more generally, the electronic or online media to deliver marketing messages, attract new customers and retain existing customers”. Internet was the first “new” medium used as an advertising medium in the mid-1990s. Nowadays, online advertising includes a large number of options, such as banners, pop ups or product placement The development of internet technology has driven online advertising from simple text advertisements to interactive ads with sound and video (Winer, 2009). From the broad range of online advertisements, this study only uses banner advertisements during the online experiment. Manchanda et al. (2006, p98) describe banner ads as “a section of online advertising space that is typically 480 x 60 pixels in size. Generally, it consists of a combination of graphic and textual content and contains a link to the advertiser’s website by means of a click-through URL, which acts as a web address”. It was shown that these kind of ads lead to increased site visits (Dreze & Hussherr, 2003), in this study interpreted as increased search. However, the question is what effect the combination of two media types has on consumers’ search behaviour.

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As mentioned before, banners advertisements lead to increased site visits. It can be argued that websites are most effective in informing consumers about high involvement products (Dijkstra et al., 2005). Websites require consumers that are active, because interacting with and reading websites requires lots of cognitive effort (Voorveld et al., 2012) However, Holbrook (1978) proposes that a consumer wants to minimize its cognitive effort, and is therefore only willing to interact with websites when the information is relevant. Hence, only if a consumer is motivated to process information, because of product involvement, he or she is more motivated to interact with a website. So, the interactive characteristics of banners and websites are especially advantageous to marketers when consumers are willing to interact (Liu & Shrum, 2009, Yang, 2004). The second reason why webpages are most successful in influencing consumers about high involvement offerings has to do with external information search. The extent of involvement with an offering influences consumers’ search behaviour. In case of being highly involved with an offering, consumers engage in more extensive search behaviour. (Buchholz & Smith, 1991, Volk & Kraft, 2005).

For low involvement products, a TV commercial is best suited to persuade consumers. A TV commercial-banner exposure instead of only exposure to TV ads might be more effective, because multiple exposure to the same commercial makes consumers become satiated (Chang & Thorson, 2004). A TV commercial might first trigger interest and in the subsequent exposure to a banner ad leading to increased site visits, consumers might be more eager to interact with a website. This could result in consumers getting more interested in the banner and website visit (Liu & Shrum, 2009). Based on these findings, the following hypotheses are tested:

H3b: The relation between searching for a low involvement product and online search behaviour is stronger when consumers are exposed to a TV commercial and a banner advertisement.

H4b: The relation between searching for a low involvement product and offline search behaviour is equally strong when consumers are exposed to a TV commercials and a banner advertisement relative to this relation when consumers are not exposed to a TV commercial and a banner advertisement.

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appreciate the TV ad and banner ad leading them to visit the product’s website. Also, the banner ad might be positively evaluated because it is an additional source of information. Due to the preceding exposure to the TV commercial, the experience with the displayed offering on television could spill over into the a positive experience of the banner advertisement Ahluwalia et al., 2001). Chang and Thorson (2004) confirm this finding, by stating that consumers are more motivated to devote attention to multiple-source messages instead of repetitive messages. Additionally, they found that exposure to a TV commercial followed by a corresponding banner advertisement resulted in more cognitive responses. This could mean that consumers devote more time and energy to their search process in online and offline settings. Based on these arguments and findings, the following hypotheses are tested:

H5b: The relation between searching for a high involvement product and online search behaviour is stronger or when consumers are exposed to a TV commercials and a banner advertisement.

H6b: The relation between searching for a high involvement product and offline search behaviour is stronger or when consumers are exposed to a TV commercials and a banner advertisement.

2.4 Conceptual model

Based on the literature discussed in the previous subsection, the following conceptual models is used to investigate the relationships between the variables.

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Low and high product involvement is expected to have a positive influence on online search behaviour, while it is expected that low product involvement has an equal influence on offline search behaviour and a positive influence on online search behaviour. The moderating variables are expected to influence the relation between product involvement and consumers’ search behaviour. One group of respondents is exposed to two TV commercials, which is expected to strengthen the relationship between low and high product involvement on online and offline search behaviour. The other group of respondents is exposed to one TV commercial and a banner advertisement which is expected to strengthen the relationship between low product involvement on online search behaviour and a equally effective influence on offline search behaviour. In case of a high involvement product, exposure to multiple media positively influences both types of behaviour.

3. Data collection and method

This chapter discusses the method of data collection. In the first subsection, the design of the study is explained. In the following subsection the procedure of finding participants and the execution of the experiment is described. The last subsection describes the manipulations of the variables.

3.1 Study design

The study is a 2X2 between participants full factorial design. The independent variable and moderator each have two levels. The levels of the independent variable and moderator are crossed with each other, resulting in four experimental conditions. Please find below an overview:

Exposure to media

Single medium Multiple media Product involvement Low

High 3.2 Participants and procedure

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participate in an online experiment. In order to avoid any biased answers, the purpose of the experiment is not explained. Respondents will be told that participating is completely anonymous and that the results of this study are only used for the purpose of the current study. At the end, participants are thanked and debriefed.

The experiment is conducted in an online setting, using ThesisTools as a medium for the experiment. The experiment contains multiple pages in which participants are asked to rate their search behaviour based on the kind of product involvement and type of media to which they are exposed. In order to obtain a greater number of respondents, the online experiment to collect data is written in Dutch.

3.3 Manipulations

In the current study design, the independent variable and moderator are manipulated. In order to get an understanding whether involvement and exposure to single or multiple media has an influence on consumers’ search behaviour, the experiment has four versions as mentioned before. Thesistools randomly assigns respondents to one of the four versions in order to obtain optimal results. Before the survey was launched online, two product categories were chosen to manipulate product involvement. A television was considered as high involvement product and a DVD was considered as low involvement product. To test whether these products were correctly interpreted as high and low involvement products, a pre-test was conducted among 25 students (57.3% male, 43.7% female) with a mean age of 23.59 (SD = 1.67). The participants were informed to fill in eight items with on a five-point scale for both product categories. The results showed that, as expected, a television was perceived as a high involvement product (M = 3.91, SD = 0.59), while a DVD was perceived as low involvement product (M = 2.68, SD = 0.52, p < 0.001). In the experiment, a manipulation check was performed to find out whether the manipulation was successful).

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As mentioned before, the participants were randomly assigned to one of the four versions. In the first version, participants are shown a low involvement product by being exposed to two TV commercials (exposure to single medium) showing the low involvement product. The second version contains the same low involvement product, but in this condition, respondents are being exposed to one TV commercial and a banner advertisement (exposure to multiple media) displaying the same offering as was shown during the TV commercial. In case of high involvement products, participants are asked to imagine themselves in need of the offering shown by the media. This means the offering is relevant to them and probably fulfils their needs and values. The third version contains a high involvement product with participants being exposed to two TV commercials. The fourth version contains the same high involvement product, with respondents being exposed to one TV commercial, and a banner advertisement displaying the same offering as was shown during the commercial.

3.4 Measurement

The independent variable and moderator are used to find out in which kind of search behaviour consumers engage themselves. This can either be online or offline search behaviour. In order to measure this, respondents are asked to rate their online and offline behaviour on a 7-point semantic scale. The scales that were used came from previous research (Cheema & Papatla, 2010), and were divided into offline and online measures. Offline search behaviour was measured by asking participants whether they agreed or not on statements about visiting a store, wanting to see the product, being helped by a sales assistant, consulting friend/acquaintance’s for recommendations and consulting recommendations in articles/editorials. Online search behaviour was measured by statements about using internet as search tool, wanting to see a picture, being helped by an online sales assistant, consulting friend/acquaintance’s online recommendations and consulting online recommendations in articles/editorials.

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4. Results

This chapter describes the results of the analysis. In the first subsection, some psychographics are discussed in order to say something about the respondents who participated in this research. The next subsection describes the preparations for the analys3s. In the subsection 4.2, the main relations are analysed. The moderating roles of media exposure (TV + TV or TV + banner ad) are explained in subsection 4.4.

4.1 Psychographics

From the 197 respondents, 47.3% were male, and 52.7% were female. The average age of the respondents is 34.7, ranging between 17 and 75 years. According to Statistics Netherlands (CBS, 2013), the average age of a Dutch inhabitant is 40.8 years. The age category of 40 – 65 years represents the biggest category in The Netherlands, accounting for 35.5%, while this age category of the sample of this experiment is the second biggest age category, accounting for 28.5%. The biggest age category of this sample is 20-40 years, accounting for 65.9%, while this age category of The Netherlands accounts for 24.6%, being the second biggest age category. All in all, it can be concluded that the sample of this experiment is a rather representative sample of the Dutch population. In order to find out whether age is a confounding variable, it is checked whether the participants’ age of the four survey versions is the same. The results are as follows:

Survey version Mean age Difference with mean age of total sample

1 33.9 - 0.8

2 35.0 + 0.3

3 35.1 + 0.4

4 34.8 + 0.1

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4.2 Preparing for analysis

The survey data from ThesisTools required additional configuring prior to performing the analyses. Before the main effect could be statistically measured, the dependent variables offline and online search behaviour were checked on their reliability by using Cronbach’s Alpha. As the table shows, both variables have a value higher than .7 (Nunnally, 1978) which means that the items are internally consistent.

Dependent variable Number of items Cronbach’s Alpha

Offline search behaviour 5 .936

Online search behaviour 5 .722

4.2.1 Manipulation check

The reliability of the three items that measured the manipulation for product involvement was checked by using Cronbach’s Alpha, which showed a value of .869, meaning that the items are internally consistent. The independent samples t-test showed that, as expected, the high involvement product (television) was perceived as more involving than the low involvement product (DVD). For the high involvement product, M=5.45, SD=1.21 and for the low involvement product, M=2.93, SD=1.38 with a Sig. of .001. This means that product involvement is significant (p<.05) and was successfully manipulated.

4.3 Main effects: product involvement and search behaviour

In this part, the hypotheses of the main effects are described. However, before describing the results of the analyses, some assumptions had to be made. The design has to be balanced, meaning that the number of participants for low and high product involvement have to be the same.

N

Low 94

High 103

Total 197

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Levene statistic df 1 df 2 Sig.

Offline SB 5.394 1 195 .021

Online SB .075 1 195 .785

The results show, that for offline search behaviour, sig < 0.05, meaning that the null hypothesis is rejected and thus a difference exists between the variances of both groups. For online search behaviour, sig > 0.05, meaning that the null hypothesis is accepted and thus no differences exist between the variances of both groups. To conclude, for offline search behaviour, there exist a difference between the variances, meaning that the variances of this population are heterogeneous. For online search behaviour, the variances are equal.

To test whether low and high product involvement influenced offline and online search behaviour, a Two-Way ANOVA was performed. The primary purpose of this analysis is to find out whether or not there is an interaction between product involvement and exposure to media on online and offline search behaviour. First, the main effects will be discussed. The first hypothesis that is going to be tested is:

H1: Searching for a high involvement product has a greater positive effect relative to searching for a low involvement product on consumers’ online search behaviour.

Df F Sig.

Corrected model

3 1.894 .132

Involvement 1 2.826 .094

R squared = .029 (Adjusted R squared = .014)

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= .094. The non-significance can be explained by the small contrast estimate. Relatively seen, high involvement product involvement has a greater positive effect relative to low product involvement on online search behaviour. However, as the results are not significant (p = .094), H1 is not supported.

The second hypothesis that is tested is as follows:

H2: Searching for a high involvement product has a greater positive effect relative to searching for a low involvement product on consumers’ offline search behaviour.

Df F Sig.

Corrected model

3 208.02 .000

The model is overall significant (F = 208.02, p = .000), and 76 % of the variance is explained by the model when looking at the value for the adjusted R squared. There is a significant overall F for the independent variable product involvement (F(3, 208.02 = 610.04, p = .000). However, it is not clear which kind of involvement influences offline search behaviour. Therefore, a contrast analysis was performed. The contrast analysis shows the following estimated marginal means: 2.545 for low product involvement and 5.81 for high product involvement, which results in a contrast estimate of 3.265, with as mentioned before p = .000. This means there is a significant difference between the influence of high and low product involvement on offline search behaviour, with high involvement (M = 5.81) being relatively more influential on offline search behaviour than low involvement (M = 2.545). Therefore, H2 is not accepted.

4.4 Moderating effects

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H3a: The relation between low product involvement and online search behaviour is stronger when consumers are exposed to TV commercials.

H3b: The relation between low product involvement and online search behaviour is stronger when consumers are exposed to a TV commercials and a banner advertisement.

Df F Sig.

Corrected model 3 1.894 .132

Medium 1 .178 .674

Involvement*medium 1 1.889 .171

The model is not overall significant (F = 1.894, p = .132). There is no overall significant F for the moderating effect exposure to media (F(3, 1.894 = .1889, p = .171. However, it is still relevant to get to know which kind of exposure to single or multiple media influences the relation between low product involvement and online search behaviour. Therefore, a contrast analysis was performed. The contrast analysis shows the following estimated marginal means: 5.715 for exposure to multiple media (TV + banner ad) and 5.666 for exposure to a single medium (TV + TV) which results in a contrast estimate of .049 with p = .674. The non-significance can be explained by the small contrast estimate. Relatively seen, exposure to multiple media (M = 5.715 increases to M = 5.810) has a greater positive effect relative to exposure to single medium (M = 5.666 decreases to 5.487) on the relation between low product involvement and online search behaviour. However, as the results are not significant (p = .674), H3a and H3b are not supported.

Next, the following two hypotheses are tested:

H4a: The relation between searching for low involvement product and offline search behaviour is equally strong when consumers are exposed to TV commercials relative to this relation when consumers are not exposed to TV commercials.

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Df F Sig.

Medium 1 3.363 .068

Involvement*medium 1 .043 .836

The model is overall significant (F = 208.02, p = .000). There is no overall significant F for the moderating effect exposure to media (F(3, 208.02 = .043, p = .836. However, it is still relevant to get to know which kind of exposure to single or multiple media influences the relation between low product involvement and offline search behaviour. Therefore, a contrast analysis was performed. The contrast analysis shows the following estimated marginal means: 4.056 for exposure to multiple media (TV + banner ad) and 4.299 for exposure to a single medium (TV + TV) which results in a contrast estimate of .242 with p = .068 The non-significance can be explained by the small contrast estimate. The results show a negative influence of exposure to both types of media. Exposure to multiple media (M = 4.056 decreases to M = 2.410) and exposure to single media (M = 4.299 decreases to M = 2.680), thus have a negative effect on the aforementioned relationship. All in all, it was expected that exposure to single or multiple media has an equally strong effect on the relationship between low product involvement and offline search behaviour compared to this relation without exposure to single or multiple media. As the results are not significant (p = .068), H4a and H4b are not supported.

H5a: The relation between high product involvement and online search behaviour is stronger when consumers are exposed to TV commercials.

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Df F Sig.

Medium 1 .178 .674

Involvement*medium 1 1.889 .171

The model is not overall significant (F = 1.894, p = .132). There is no overall significant F for the moderating effect exposure to media (F(3, 1.894 = .1242, p = .171. However, it is still relevant to get to know which kind of exposure to single or multiple media influences the relation between high product involvement and online search behaviour. Therefore, a contrast analysis was performed. The contrast analysis shows the following estimated marginal means: 5.715 for exposure to multiple media (TV + banner ad) and 5.666 for exposure to a single medium (TV + TV) which results in a contrast estimate of .049 with p = .674. The non-significance can be explained by the small contrast estimate. Relatively seen, exposure to single media (M = 5.715 increases to 5.845) has a greater positive effect relative to exposure to multiple media (M = 5.666 increases to M = 5.733) on the relation between high product involvement and online search behaviour. All in all, exposure to single or multiple media has a positive effect on the relationship between high product involvement and online search behaviour, however, as the results are not significant (p = .674), H5a and H5b are not supported.

H6a: The relation between high product involvement and offline search behaviour is stronger when consumers are exposed to TV commercials.

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Df F Sig.

Medium 1 3.363 .068

Involvement*medium 1 .043 .836

The model is overall significant (F = 208.02, p = .000). There is no overall significant F for the moderating effect exposure to media (F(3, 208.02 = .043, p = .836. However, it is still relevant to get to know which kind of exposure to single or multiple media influences the relation between high product involvement and offline search behaviour. Therefore, a contrast analysis was performed. The contrast analysis shows the following estimated marginal means: 4.056 for exposure to multiple media (TV + banner ad) and 4.299 for exposure to a single medium (TV + TV) which results in a contrast estimate of .242 with p = .068 The non-significance can be explained by the small contrast estimate. The results show a positive influence of exposure to both types of media. Exposure to multiple media (M = 4.056 increases to M = 5.202) and exposure to single media (M = 4.299 increases to M = 5.325), thus have a positive effect on the aforementioned relationship. All in all, as was expected, exposure to single or multiple media has a positive effect on the relationship between high product involvement and offline search behaviour. However, as the results are not significant (p = .068), H6a and H6b are not supported.

The following table shows the outcomes of the hypotheses:

Hypothesis Result

H1 Not supported

H2 Supported

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5. Conclusion and Discussion

The aim of this study was to gain insights into the role of product involvement on search behaviour. The results indicated that overall, high product involvement relative to low product has the most influence on offline search behaviour. This means that consumers which search information for a high involvement product, put more time and effort in their search process for, in this case, a TV. As was mentioned before, a staggering 75% of the consumers visit a store to see and experience the product before they buy it (Oracle, 2011). Besides, searching for a high involvement product relative to searching for a low involvement product, influences online search behaviour more, however this results were not statistically significant. Concerning the level of involvement, this study offers empirical evidence for the studies of Petty et al. (1983) and Okazaki and Hirose (2009), who argued that when consumers feel that a product is important or relevant to them, they will be likelier to put more effort into information search and processing and search for as much information as possible.

Another theoretical implication of this research is the need to take other moderating variables into account when studying product involvement and search behaviour. The interaction effects of exposure to media did not lead to any significant moderating effects. This could be explained by the fact that consumers which evaluate a low involvement product via a TV commercial, are not triggered and thus not motivated enough to extend or even start searching and interacting with a website. For high involvement products, it could be possible that there might be other product, brand, or individual factors that moderate the relation between product involvement and consumer search behaviour. Therefore, future research should focus on such moderating variables. In order to conclude this research, the main findings from this study are the following:

• High product involvement relative to low product involvement influences offline search behaviour the most. Although high product involvement relative to low product involvement influences online search behaviour the most, this finding is not statistically significant. Hence, as was expected, high product involvement has the most effect on both types of search behaviour.

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6. Managerial Implications

This study has some important implications for marketing managers. As was mentioned in the introduction, consumers engage themselves in different kinds of search behaviours. While the internet has been increasingly used as an information source, this study shows that consumers also spend time offline in order to gather product information. This means that retailers and other sellers should not only consider investing in and expanding their online channels, but also focus on their offline channels. Especially for high involvement products, which carry higher risks compared to low involvement products, consumers spend time and energy in their search process and want to see and basically experience the product. From the results of the online experiment, it appears that for high involvement products, consumers consider the search process as a serious and important task, which requires careful thought. This shows that consumers spend a lot of effort in their search process for, in this case, a new television. Retailers and sellers should therefore anticipate on the fact that consumers search both online and offline, by providing them the same information in all their channels or to provide them with information in such as way as to complement their search channels.

When consumers consider buying low involvement products, in this case a DVD, they certainly do not choose to make use of offline channels, such as going to a brick-and-mortar store. Such consumers are focused on convenience and do not want to put much effort in their search process. As low involvement products influence online search behaviour more relative to offline search behaviour, it is of utmost for sellers to get consumers to want to interact with online channels by providing such consumers with easy to process information which is readily and easily accessible. This could be done by offer simple, low-arousal website for consumers so they can easily find their desired products in just a few mouse-clicks.

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behaviour. Friends’ or family’s recommendations or consumer reviews might be a source of influence that strengthens the aforementioned relationship.

7. Limitations and Further Research

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8. References

Ahluwalia, R., H.R. Unnava, and R.E. Burnkrant. 2001. The moderating role of commitment on the spillover effect of marketing communications. Journal of Marketing Research, 38: 458–70.

Atkinson, L., & Rosenthal, S. 2014. Signalling the green sell: the influence of eco-label source, argument specificity and product involvement on consumer trust. Journal of

Advertising, 43(1): 33-45.

Bakos, J.Y. 1997. Reducing buyer search costs: implications for electronic marketplaces.

Management Science, 43(12): 1676-1692.

Bayer, R-C., & Ke, C. 2013. Discounts and consumer search behaviour: the role of framing.

Journal of Economic Psychology, 39: 215-224.

Beatty, S.E., & Smith, S.S. 1987. External search effort: an investigation across several search categories. Journal of Consumers Research, 14: 83-95.

Buchholz, L.M., & Smith, R.E. 1991. The role of consumer involvement in determining cognitive response to broadcast advertising. Journal of Advertising, 20(1), 4-17. Carmon, Z., Wertenbroch, K., & Zeelenberg, M. 2003. Option attachment: when deliberating

makes choosing feel like losing. Journal of Consumer Research, 2003: 15-29.

Celsi, R.L., & Olson, J.C. 1998. The role of involvement in attention and comprehension processes. Journal of Consumer Research, 15(2): 210-224.

Chang, Y., & Thorson, E. 2004. Television and web advertising synergies. Journal of

Advertising, 33(2): 75-84.

Chaudhuri, A. 2000. A macro analysis of the relationship of product involvement and information search: the role of risk. Journal of Marketing Theory and Practice, 8: 1-15.

Cheema, A., & Papatla, P. 2010. Relative importance of online versus offline information for internet purchases: product category and internet experience effects. Journal of

Business Research, 63(9): 979 – 985.

Chocarro, R., Cortiñas, M., Villanueva, M-L. 2013. Situational variables in online versus offline channel choice. Electronic Commerce Research and Application, 12: 347-361.

Day, E., Stafford, M.R., & Camacho, A. 1995. Research note: opportunities for involvement research: scale development approach. Journal of Advertising, 24(3): 69-75.

Dennis, A.R., & Taylor, N.J. 2006. Information foraging on the web: the effects of “acceptable” Internet delays on multi-page information search behaviour. Decision

Support Systems, 42: 810-824.

De Paola, M., Scoppa, V. 2014. Media exposure and individual choices: evidence from lottery players. Economic Modelling, 28: 385-391.

Dholaki, U.M. 2001. A motivational process model of product involvement and consumer risk perception. European Journal of Marketing, 35: 1340-1362.

(31)

Dreze, X., Hussherr, F-X. 2003. Internet advertising: is anybody watching? Journal of

Interactive Marketing, 17(4): 8-23.

Edell, J. A., & K.L. Keller. 1989. The information processing of coordinated media campaigns. Journal of Marketing Research, 26 (2), 149-63.

Furse, D.H., Punj, G.N., & Stewart, D.W. 1984. A typology of individual search strategies among purchasers of new automobiles. Journal of Consumer Research, 10: 417-431. Hauser, J.R., Urban, G.L., & Weinberg, B.D. 1993. How consumers allocate their time when

searching for information. Journal of Marketing Research, 30: 452-466.

Holbrook, M.B. 1978. Beyond attitude structure: toward the informational determinants of attitude. Journal of Marketing Research, 15: 545–56.

Hoyer, W.D., MacInnis, D.J., & Pieters, R. 2013. Consumer Behavior. South-Western Cengage Learning.

Huang, P., Luro, N.H., & Mitra, S. 2009. Searching for experience on the web: an empirical examination of consumer behaviour for search and experience goods. Journal of

Marketing, 73: 55-69.

Joo. M., Wilbur, K.C., Cowgill, B. & Yi, Z. 2014. Television advertising and online search.

Management Science, 60(1): 56-73.

Kempf, D.S., & Smith, R.E. Consumer processing of product trial and the influence of prior advertising. Journal of Marketing Research, 1998: 325-338.

Kim, S., Haley, E., & Koo, G-Y. 2009. Comparison of the paths from consumer involvement types to ad responses between corporate and product advertising. Journal of

Advertising, 38(3), 67-80.

Kinley, T.L., Conrad, C.A., & Brown, G. 1999. Internal and external promotional references: an examination of gender and product involvement effects in the retail apparel setting.

Journal of Retailing and Consumer services, 6: 39-44.

Kleinmuntz, D.N., & Schkade, D.A. 1993. Information displays and decision processes.

Psychological Science, 4: 221-227.

Kollmann, T., Kuckertz, A., Kayser, I. 2012. Cannibalization or synergy? Consumers’ channel selection in online-offline multichannel systems. Journal or Retaling and

Consumer Services, 19: 186-194.

Laroche, M., Kiani, I., Economakis, N., & Richard, M-O. 2013. Effects of multi-channel marketing on consumers’ online search behaviour: the power of multiple points of connection. Journal of Advertising Research, 53: 431-443.

Laurent, G., & Kapferer, J-L. 1985. Measuring consumer involvement profiles, Journal of

Marketing Research, 22: 41 – 53.

Leong, E.K.F., Huang, X., & Stanners, P.J. 1998. Research, Comparing the effectiveness of website with traditional media. Journal of Advertising, 38: 44-51.

Liu, Y., & L.J. Shrum, 2009. A dual-process model of interactivity effects. Journal of

Advertising, 38: 53-68.

Manchanda, P., Dubé, J-P., Goh, K.Y., & Chintagunta, P.K. 2006. The effect of banner advertising on internet purchasing. Journal of Marketing Research, 43(1): 98-108. McKinsey & Company. McKinsey iConsumer 2012. Insight No.1: Digital Marketing. The

(32)

Michaelidou, N., & Dibb, S. 2008. Consumer involvement: a new perspective. The

Marketing Review, 8: 83-99.

Moorthy, S., Ratchford, B.T., & Talukdar, D. 1997. Consumer information search revisited: theory and empirical analysis. Journal of Consumer Research, 23(4): 263-277.

Muncy, J.A. 1990. Involvement and perceived brand similarities/differences: the need for process oriented models. Advances in Consumer Research, 17: 144-148.

Naik, P. A. 2007. Integrated Marketing Communications, in The Sage Handbook of

Advertising. G. J. Tellis & T. Ambler, eds. London: Sage Publications, 35–53.

Naik, P. A., & K. R. Raman. 2003. Understanding the impact of synergy in multimedia communications. Journal of Marketing Research, 40(4), 375-88.

Nunnally, J.C. 1978. Psychometric Theory, 2nd edition. New York: McGraw-Hill.

Okazaki S., & Hirose, M. 2009. Effects of displacement-reinforment between traditional media, PC internet and mobile internet. A quasi experiment in Japan. International

Journal of Advertising, 28: 77-104.

Oracle. Cross-Channel Commerce: the consumer view. November 2011.

Petty, R.E., Cacioppo, J.T., & Schumann, D. 1983. Central and peripheral routes to advertising effectiveness: the moderating role of involvement. Journal of Consumer

Research, 10: 135-146.

Punj, G.N., & Stealin, R. 1983. A model of consumer information search behaviour for new automobiles. Journal of Consumer Research, 9(3): 192-198.

Richins, M.L., & Bloch, P.H. 1986. After the new wears off: the temporal context of product involvement. Journal of Consumer Research, 13: 280-285.

van Rijnsoever, F., Farla, J., & Dijst, M.J. 2009. Consumer car preferences and information search channels. Transportation Research Part D: Transport and Environment, 14(5): 334-342.

Rojas-Méndez, J.I., & Davies, G. 2005. Avoiding television advertising: some explanations from time allocation theory. Journal of Advertising Research, 45(1), 34 – 48.

Rose, S., & Samouel, P. 2009. Internal psychological versus external market-driven determinants of the amount of consumer information search amongst online shoppers.

Journal of Marketing Management, 25(2): 171-190.

Shankar, V., & Hollinger, M. 2007. Online advertising: current scenario and emerging trends.

Marketing Science Institute Report, 1-49.

Shim, S., Eastlick, M., Lotz, S.L., & Warrington, P. 2001. An online prepurchase intentions model: the role of intention to search. Journal of Retaling, 77: 397-416.

Solomon, M.R., Bamossy, G.J., Askegaard, S. & Hogg, M. (2006) Consumer Behaviour: A

European Perspective. Prentice Hall Europe, New York.

Urbany, J.E., Dickson, P.R., & Kalapurakal, R. 1996. Price search in the retail grocery market. Journal of Marketing, 60: 91-104.

Volk, F., & F.B., Kraft. 2005. Consumers: The theoretical foundations on online behaviour.

Handbook of human factors in web design, ed. R.W. Proctor and K.P.L. Vu, 595–

612. London: Routledge.

Voorveld, H.A.M., Neijens, P.C., & Smit, E.G. 2012. The interacting role of media sequence and product involvement in cross-media campaigns. Journal of Marketing

(33)

Wills, J., Samli, A.C., Jacobs, L. 1991. Developing global products and marketing strategies: a construct and research agenda. Journal of the Academy of Marketing Science, 19: 1-10.

Winer, R.S. 2009. New communications approaches in marketing: issues and research directions. Journal of Interactive marketing, 23: 108-117.

Wu, L-L., & Lin, J-Y. 2012. The match between information control and motivation in the online context. Psychology and Marketing, 29(11): 822-835.

Yang, K.C.C. 2004. Effects of consumer motives on search behaviour using internet advertising. CyberPsychology & Behavior, 7: 430–442.

Zaichkowsky, J.L. 1985. Measuring the involvement construct. Journal of consumer

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9. Appendix A – Online experiment

Welcome to this experiment,

For my master marketing I am conducting an experiment on consumer search behavior. Filling in the questions takes approximately 5 minutes. Participating is completely anonymous and the results of this experiment are only used for this research. Thank you very much in advance for your participation.

Best regards, Henk Reinds

Imagine yourself the following situation:

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Question 1.

Question 2.

Question 3.

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Imagine yourself the following situation:

On the same evening, you are watching a movie. During the commercial break, you see the following commercial (press play to start the commercial).

Question 5.

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Question 7.

Question 8.

Question 9.

Question 10.

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Question 12.

Product involvement and search behaviour: searching in the good old offline environment or 21 century style in the online universe