Vertical line extensions

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Master Thesis – Robbert Fransen 3

MANAGEMENT SUMMARY

It is common for manufacturers to vertically extend their brands into upscale line extensions and downscale line extensions. The price image of a brand has a great affect on consumer decision making. It is something that manufacturers want to control to influence consumers. In this decision making process two shopping goals are singled out, consumers that shop with a browsing goal and consumers that shop with a buying goal. This research takes a closer look at the effect a vertical line extension has on the price image of a brand considering different shopping goals.

An experiment is held in which 224 participants are randomly assigned to a browsing shopping goal or to a buying shopping goal. Participants were introduced to 3 types of television brands. 1) a mediocre brand with no line extensions, 2) a brand with an upscale line extension and 3) a brand with a downscale line extension. Next to that, participants had to recommend a brand in a spending mode and in a saving mode. Both groups were controlled on various extraneous variables for valid results.

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PREFACE

This thesis is the conclusion of my study Business Administration with a specialization in Marketing Management at the University of Groningen. After completing all my courses, I did an internship at Henkel Nederland on brand‐ and productmanagement for the brand Schwarzkopf.

During my internship at Henkel Nederland I learned a lot about product introductions. I found out that, in some cases, it is not clear what the best price strategy is for a manufacturer when they want a successful product introduction. Especially, the effect the price of the new product has on the parent brand resulted in some interesting discussions. Therefore, I decided to take a closer look on how product introductions affect the price image of a brand.

I would like to thank my supervisor at the University Dr. Liane Voerman for her guidance and useful insights during the realization of this thesis. It would like to give special thanks for her flexible ways of communicating over the telephone and her very quick and constructive feedback in order to finalize my thesis in time. In addition, I would like to thank Dr. Karel‐Jan Alsem for performing the function of co‐assessor and helping me to define my research.

Last but not least, I would like to thank the participants of my research for taking the time and effort to participate in my experiment. I would like to thank all my friends and family for their support during my study. My special thanks go out to my parents who have always believed in me. Without their support and motivation, in good times and in bad times, I would not have successfully graduated like the way I did.

Robbert Fransen,

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Master Thesis – Robbert Fransen 5 TABLE OF CONTENTS MANAGEMENT SUMMARY...3 PREFACE...4 1. INTRODUCTION ...7 1.1 Background...7 1.2 Problem introduction ...8 1.2.1 Research objective...9 1.2.2 Research question...10 1.2.3 Sub questions...10 1.3 Relevance of study ...10 1.4 Structure of thesis ...11 2. THEORETICAL FRAMEWORK ... 12 2.1 Vertical line extensions...12 2.1.1 Upscale and downscale extensions ...13 2.1.2 Fit ...15 2.1.3 Variety...16 2.2 Price image of a brand ...19 2.2.1 Price knowledge ...19 2.2.3. The influence of price on perceived quality...21 2.3 Line extensions evaluations ...23 2.3.1 Decision context effects ...24 2.3.3 Shopping Goals ...25 2.4 Conclusion...26 2.5 Conceptual framework ...28 3. RESEARCH DESIGN... 29 3.1 Type of research...29 3.2 Research method...31 3.2.1 Independent variables and manipulation ...31 3.2.2 Dependent variable ...36 3.2.3 Test units...38 3.2.4 Extraneous variables...38

3.3 Methods of data analysis...40

3.3.1 Descriptive statistics...40 3.3.2 One‐way ANOVA...40 3.3.3 Crosstab ...40 4. RESULTS ... 42 4.1 Characteristics of participants ...42 4.3 Price image evaluation...46 4.4 Shopping goal motivation...48

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Master Thesis – Robbert Fransen 6 5. CONCLUSION ... 52 6. LIMITATIONS AND FURTHER RESEARCH ... 54 LITERATURE... 55 APPENDICES ... 59

Appendix I: Pre test Questionaire ...59

Appendix II: Output Statistics Pre-Test ...62

Appendix III Questionairre Browsing ...66

Appendix IV Questionnaire Buying ...72

Appendix V Output descriptive statistics ...78

Appendix VI Output ANOVA ... 88

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Master Thesis – Robbert Fransen 7 1. INTRODUCTION 1.1 Background In today’s world, if a consumer wants to buy a product he or she has numerous options to choose from. Not only are there multiple brands that offer the same product, but also a single brand has an extended assortment to choose from for a particular product. The products differ on all kind of things like quality, price, size and colour. For example, if you want to buy a Philips television, you can choose from a small 17 inch screen for € 300, ‐ to a full integrated home cinema 42 inch HD Plasma screen for € 4.500,‐. In this way a consumer can always find the product that he or she desires. Many firms are constantly working on such new product introductions. To keep their brands strong, firms make sure that consumers have more than one choice in the product line of a brand. Due to the high costs of new product launches an increasing number of firms use extensions for their new product strategy (Aaker and Keller, 1990; Park and Srinivasan, 1994). By using well‐known brands, the costs of launching a new product can be reduced drastically through marketing and distribution efficiencies (Muroma and Saari, 1996). In this context, well over one half of all new brands in the 1980’s were extensions marketed under existing brand names (Loken and Roedder John, 1993). Although launching new products can be an attractive growth strategy, this is not without risks. Some estimate that 30‐35% of all new products fail (Montoya‐Weiss and Calantone, 1994).

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whereas variation in the function or ‘category’ of the products is referred to as ‘horizontal’ differentiation (eg. Randall et al., 1998). Kim and Chhajed (2001) refer to line extensions as introducing additional products under the same brand name in a given product category. A line extension often adds a different flavour or ingredient variety, a different form or size or a different application for the brand. Some examples of successful line extensions over the last decade are the Philips Ambilight television, with a new light dimension in watching television; Prodent Toothpaste with added whitener for brighter teeth; Burger King Kobe Beef burger with a new flavour and the Sony Blue‐Ray Disk with a new technology for DVD players.

It has become common for manufacturers to vertically extend brand lines. All these introductions have an effect on the perception the consumer has of a brand. For a manufacturer it is important to manage these perceptions in order to create the most positive brand image possible.

1.2 Problem introduction

Consumers form complex networks of associations with the stores at which they shop and with the brands that they buy (Keller, 2008). Although these bundles of associations can include almost any kind of information, from quality perceptions (Berry, 1969) to assessments of corporate social responsibility (Brown and Dacin 1997), marketing researchers have previously singled out the price dimension, or price image, for special consideration (Hamilton et al, 2010; Van Heerde et al., 2008). This is interesting, because any entity with a brand image will also have a price image.

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vice versa for a downscale addition. They argue that consumer shopping goals have a significant influence on these evaluations. Consumers can take all products of a store in consideration or focus on one particular product. Generally, when consumers evaluate new products they have limited experience or knowledge about the new offering. Taylor and Bearden (2002) show in their research that in such situations, when objective information available about the new product is low, price is likely to be used as a cue to quality. Although a great deal is known about how consumers perceive individual prices (e.g. Adaval and Monroe, 2002; Janiszewski and Lichtenstein, 1999), there has been relatively little knowledge about how consumers integrate these price perceptions into an overall price image. Price image is important for manufacturers because it can influence many of the brand perceptions that consumers form and the decisions they make. It is interesting to see whether consumers focus on an entire product line or on one offer in particular. Therefore, this thesis will be dedicated to establish a clear image for manufacturers in managing the perception of the price image of their brand using vertical line extensions.

1.2.1 Research objective

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1.2.2 Research question

The research question of this research is formulated as follows:

What is the impact of vertical line extensions on the price image of a brand and how do consumer shopping goals influence this price perception?

1.2.3 Sub questions

To come to a complete and satisfying answer to the main research question, several sub questions should be answered. The following sub questions are formulated:  What are vertical line extensions?  How do consumers form a price image?  How do the consumer shopping goals influence price image? These sub questions will serve as a theoretical framework in this study. 1.3 Relevance of study

Prior academic literature has paid quitte some attention to vertical line extensions, especially into the field of company performance (eg. Draganska and Jain, 2005; Reddy et al., 1994) and consumer decision making (eg. Boatwright and Nunes, 2001; Simonson and Tversky, 1992). However, little of that research has focused on price image. This study will contribute to a wider knowledge in understanding the effects of vertical line extensions, in particular, on the formation of price image of a brand.

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1.4 Structure of thesis

The first part of this thesis is an exploratory research; this is conducted in chapter 2. By making use of extensive academic literature and books the three variables relevant for this research are described. The literature review takes a closer look at vertical line extensions, price image and consumer decision‐ making. The data obtained for this exploratory research can be defined as published secondary data (Malholtra, 2004). The obtained information is used to define the relations between the addressed variables and to bring them together in a conceptual framework. Furthermore, several hypotheses defining the expected causal relations between the variables in the framework are described at the end of chapter 2.

The second part of this thesis is a conclusive research with an experimental causal research design; this is described in chapter 3. It is used to research causal relationships between the variables and will be based on a large number of representative cases. It will be structured according to the theory of Malhotra (2004); it will represent a structured way of collecting data and analyses the data statistically. In chapter 4, the results from the experiment will be analyzed to test the presented hypotheses. Finally, in chapter 5, this will lead to the conclusions and recommendations.

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2. THEORETICAL FRAMEWORK

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2.1.1 Upscale and downscale extensions

When products differ in quality or price they are either introduced ‘upscale’ or ‘downscale’. For example, cosmetics brand Schwarzkopf has added the high‐end hair conditioners for coloured hair, priced much higher than the rest of its cosmetics line. Downscale extensions are even more common, with many retailers augmenting their product lines with low‐priced offerings, often in the form of private labels, to increase the overall sales volume (Hamilton and Chernev, 2010).

Randall et al. (1998) describe the vertical extent of the product line relative to the product under consideration (figure 1). The figure shows influences of the structure of the brand product family on the consumer decision process. It states that brand associations may also be derived from cues taken from the vertical extent of the product line. The presence of high‐end models may create a consumer belief that the brand possesses strong design and production capabilities. This vertical structure may influence both brand prestige and brand associations. The presence of high‐end models in a product line may contribute to an image of prestige and exclusivity. The consumer may further believe that such capabilities are likely to result in high product performance relative to difficult‐to‐observe product attributes, even for the non‐premium models in the product line. This influence could be viewed as a belief in a ‘trickle‐down’ of quality from the high‐end models to the low‐end models (Randall et al., 1998). For example, Mercedes is perceived as a prestigious brand in the United States primarily because of the high‐end models in its product line. This prestige persists to some degree even for the middle‐market models offered under the Mercedes brand. Contrary, just as high‐end models may enhance the prestige of a brand, low‐end models may diminish this prestige.

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Figure 2.1: ”Iinfluences of the structure of the brand product family on the consumer decision process.” (Randall et al., 1998)

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2.1.2 Fit

Though the introduction of line extensions has become prevalent such a practice does not necessarily guarantee success. Even those extensions that are not classified as failures do not necessarily enjoy equal success. An extension may cannibalize sales of existing products and dilute the image of the original brand over time (Reddy et al. 1994). Ahluwalia (2008) argues that an important determinant of extension success is it’s “fit” to the parent brand name. Fit has been conceptualized as the extension’s perceived similarity to the parent brand primarily on dimensions such as product category and attributes (e.g., benefits, image). In general, the higher the perceived fit of the extension with its parent brand, the more positive is the extension evaluation and the greater is the gain from introducing the new product as a brand extension than under a new brand name (Keller, 2008). Thus, extensions with lower levels of similarity may have a lower likelihood of succeeding in the marketplace, thus imposing a limitation on the brand’s stretchability (Ahluwalia, 2008). Delvecchio and Smith (2005) refer to fit as the degree of similarity between an extension product and existing products affiliated with the brand. Similarity between the existing products affiliated with the brand and the extension can be construed holistically in 4 terms: 1. Needs satisfied by the products (e.g., Smith and Park 1992) 2. Situations in which they are used (e.g., Dacin and Smith 1994) 3. Skills needed to manufacture them (e.g., Aaker and Keller 1990) 4. Physical features (e.g., Smith and Park 1992)

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(DelVecchio, 2000). As fit decreases, the boundaries of the brand stretchability are more served because, for example, other needs are served with the product. A way to fully explore the brand stretchability is to increase the variety in the assortment.

2.1.3 Variety

According to Klemperer (1995) the degree of variety a new extension contributes to the existing product line is an important aspect in successfully introducing line extensions. They argue that a strategy of offering a full line of variety is needed, in order to keep customers loyal to the brand and prevent switching to competitors. When brands are added, deleted, or repositioned within a choice set, the resulting change in brand choice is of obvious concern to marketers. Change in brand preference is of particular interest when there is a product line extension or repositioning of a brand within a product line as management attempts to capture share of brands of competitors rather than cannibalize share of its own brands. For example, Coca‐Cola will attempt to position and promote Cherry Coke so that it will draw share from Pepsi and other competitors rather than other Coca‐Cola brands (Burton and Zinkhan, 1987).

According to Draganska and Jain (2005), firms can offer variety in two ways; price and quality. For example, by offering a wide range of products ranging from the BMW 3‐series to the BMW 7‐series, BMW is able to target different segments of consumers. BMW’s strategy of vertical line extension involves price discrimination according to consumers’ willingness to pay for quality (Horsky and Nelson, 1992). Even if products do not differ in price and quality, product lines may serve as competitive tools. For example, Coca Cola, carries Diet Coke, Decaffeinated Coke, Diet Decaffeinated Coke, etc., which all have the same price and quality but vary in other attributes.

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of changes in an assortment might affect sales differently. Their results indicate that category sales tended to increase rather than decrease, on average, as a result of a reduction in items of the assortment. Regarding product assortment, the conventional wisdom among supermarket managers has been that ‘more is better’. The larger the selection, the more likely consumers are to find a product that matches their exact specifications (Baumol and Ide, 1956). However, more recent studies suggest that consumer choice is affected by the perception of variety among a selection, which depend on more than just the number of distinct product on the shelves. The consumer’s perception of variety can be influenced by the space devoted to the category, the presence or absence of the consumer’s favourite item (Broniarczyk et al, 1998), the arrangement of an assortment and the repetition of items (Hoch et al, 1999), and the number of acceptable alternatives (Kahn, 1995).

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Sloot et al. (2006) show that too large of an assortment may influence retail customers to refrain from buying products because of the high search complexity. Furthermore, they show that assortment satisfaction apparently can be improved through assortment reduction and that new category buyers can be attracted. One way of reducing the complexity for a consumer is to use commonality in line extensions. According to Kim and Chhajed (2001) it is a growing practice in many industries.

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2.2 Price image of a brand

The previous paragraph has described vertical line extensions extensively, this paragraph will take a closer look at the formation of a price image and how consumers are influenced by that. Magi and Julander (2005) argue that consumers vary extensively as to their level of price knowledge with a substantial share of consumers exhibiting fairly poor levels of knowledge. Paragraph 2.2.1 describes what price knowledge and price references a consumer uses in its judgements. As said previously, variety can be offered in price and quality, therefore, paragraph 2.2.2 elaborates further on the effect of prices on the perceived quality of a product.

2.2.1 Price knowledge

Consumer price knowledge has primarily been investigated by assessing consumers’ ability to recall prices for specific consumer products in the process of buying (Dickson & Sawyer 1990; Wakefield & Inman 1993). These studies show that many shoppers are unable to recall the price of a product they have just placed in their shopping cart, which has been taken as evidence of poor price knowledge among consumers. Monroe and Lee (1999) suggest that price recall may only measure one aspect of price knowledge since memory for price information might not be easily recalled, but still might influence consumer purchase decisions.

It is generally accepted that consumers compare a market price to an internal reference price when judging the attractiveness of the market price. Thaler (1985) states that "the measure of transaction utility depends on the price an individual pays compared to some reference price." Also, Kalyanaram and Winer (1995) support the notion that “individuals make judgments and choices based on the comparison of observed phenomena to an internal reference price."

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can be traced back to Helson's Adaptation‐Level Theory (1964). This is a theory of sensory perception that proposes that sensory judgments rely on a comparison of current sensation to the adaptation level of recent sensory experiences. Greenleaf (1995) integrates the Adaptation‐Level Theory into the Behavioral Pricing Theory and shows that the internal reference price has been hypothesized to be an adaptation level that depends on recent price experiences. For example, the internal reference price has been estimated as the most recent price paid, the weighted mean of the logarithms of past prices, and as an exponential smoothing of past prices (Briesch et al. 1997). Also, considerable effort has been devoted to understanding the stability of the internal reference price and the factors that can alter it (Kalyanaram and Winer 1995; Lichtenstein et al., 1991; Mazumdar and Jun 1992).

The Adaptation‐Level Theory represents only one view of how people make sensory judgments. Another account of how people make sensory judgments is the Range Theory (Volkmann, 1951). This is a theory of sensory perception that proposes the range of the values of the stimuli to be judged determines the perceived value of any one stimulus in the range. Applied to behavioral pricing issues. The Range Theory suggests that people use the range of remembered price experiences to set a lower and upper bound of price expectations, and that the attractiveness of a market price is a function of its relative location within this range.

Adaval and Monroe (2002) focus on subjective values that consumers form when acquiring price information. They argue that when consumers receive objective information about a product, for example a 100 % cashmere shawl for € 150,‐, they may translate it into subjective values like soft and expensive. This translation requires that they relate the information to a standard that could be based in part on the central tendency of the values that they have encountered either in the immediate situation or on the range of these values.

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2.2.3. The influence of price on perceived quality

All these price perceptions and references have an effect on the perceived quality of a product. Zeithaml (1988) defines perceived quality as “a judgement about the global excellence or superiority of a product offering.”

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extension that is judged to be dissimilar or less related to the core brand, core brand associations have less relevance to the extension and, therefore, should be perceived as less diagnostic of extension quality. Therefore, additional quality cues, such as price, are more likely to be perceived as diagnostic and enter into the quality judgement.

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2.3 Line extension evaluations

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2.3.1 Decision context effects

Previous research in decision making and marketing indicates that consumer preferences for options in a choice set are influenced by the decision context, leading to the widely studied attraction and compromise effects (Burton and Zinkhan, 1987; Simonson, 1989; Simonson and Tversky, 1992). Context effects imply that, when evaluating a focal option, individuals take into consideration characteristics of other comparative alternatives rather than only the features of that focal alternative, complicating the decision‐making process (Sheng, et al., 2005). This means that when a consumer is evaluating multiple options the respondents take the characteristics of all options into consideration. Simonson and Tversky (1992) come up with two principles that describe the influence of context on choice; ‘trade‐off contrast’ and ‘extremeness aversion’. Both principles will be discussed in more detail.

 Tradeoff contrast: Contrast effects are ubiquitous in perception and judgment. The same circle appears large when surrounded by small circles and small when surrounded by large ones. Similarly, the same product may appear attractive on the background of less attractive alternatives and unattractive on the background of more attractive alternatives.

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 Extremeness aversion: The presence of loss aversion: outcomes that are below the reference point (losses) are weighted more heavily than outcomes that are above the reference point (gains).

To explain the effect of context, the notion of loss aversion needs to be extended to advantages and disadvantages that are defined in relation to the other available alternatives, rather than in relation to a neutral reference point. A consumer who considers three washing machines that differ in quality and price, for example, is likely to evaluate the advantages and disadvantages of these products in relation to each other. Suppose x has the highest quality and price, z has the lowest quality and price, and y is intermediate on both attributes. The assumption that disadvantages loom larger than the respective advantages tends to favour the intermediate option y, because it has only small disadvantages in relation to the other options. 2.3.3 Shopping Goals Hamilton et al. (2010) have done research into the effects that different shopping goals have on the buying process. They argue that when acquiring price information, consumers tend to be motivated by one of two goals: browsing or buying. First both goals will be shortly described:

 Browsing: Consumers that are interested in gathering information for possible future use but not for the immediate purpose of picking one option to put into their shopping basket. Browsing often involves making a judgment, in which the objective is to construct an overall assessment of an alternative or set of alternatives.

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Browsing and buying goals are likely to lead to differences in consumers’ breadth of focus when evaluating price information. Browsing often entails allocating attention across many options rather than focusing on any one option in particular (Moe, 2003). In contrast, a buying goal tends to lead to a narrow allocation of attention, ultimately focusing consumers on the to‐be‐purchased alternative (Moe, 2003).

The research of Hamilton et al. (2010) is focused on the price image of separate stores, additional research is needed to find out whether these shopping goals have the same effect when evaluating brands. With the previous academic insights on shopping goals and context effect on the evaluation of vertical product line extension, it is interesting to see whether a consumer focuses on a single product or on an entire product line in the formation of price image of a brand. 2.4 Conclusion To finalize the theoretical framework, this paragraph will give conclusions that will lead to the hypotheses for this research.

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that upscale extensions can lower the overall price image and downscale extensions can increase it.

H1: Due to the tradeoff contrast effect, an upscale extension with a buying shopping goal leads to a lower price image for a brand.

H2: Due to the tradeoff contrast effect, a downscale extension with a buying shopping goal leads to a higher price image for a brand.

In contrast, when people have a relatively broad focus on a wider assortment, which is a browsing shopping goal, the likelihood increases that they will evaluate the products in relation to each other, called the extremeness aversion effect. Consumers will be more likely to integrate the price of the extension with the prices of the other available options. In the situation of introducing either upscale or downscale extensions, this will have another effect on the price image a consumer has of a brand. Therefore, due to the extremeness aversion effect, a browsing shopping goal will lead to a scenario in which the change in the price image will be directionally consistent with the extension type, such that upscale extensions lead to a higher price image and downscale extensions lead to a lower price image.

H3: Due to the extremeness aversion, an upscale extension with a browsing shopping goal leads to a higher price image for a brand.

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2.5 Conceptual framework

The hypotheses of this study are displayed in the following conceptual framework: Figure 2.2: “Conceptual framework of the influence of vertical line extensions on price image considering consumer shopping goals” Price Image

Downscale extension Upscale extension

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3. RESEARCH DESIGN

Chapter 3 describes the way this research is conducted. It will explain which research design and methods are used. Furthermore, it will describe the manner of data collection and which methods of data analysis are applied.

3.1 Type of research

According to Malholtra (2004), a research design is a framework or blueprint for conducting the marketing research project. It details the procedures necessary for obtaining the information needed to structure or to solve marketing research problems.

Research designs may be broadly classified as exploratory or conclusive (Malholtra, 2004). The primary objective of exploratory research is to provide insights into, and an understanding of, the research problem. It is often used as the initial step in the overall marketing research framework. The insights gained from exploratory research might be verified or quantified by conclusive research, which consists of descriptive research or causal research. Descriptive research is often used to describe market characteristics and functions. Causal research is used to establish causal relationships between variables. The outcomes of both descriptive and causal research are used as input into managerial decision making (Malholtra, 2004).

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contrasting a focal option with a vertical extension. Although descriptive research can determine the degree of association between variables, it is not appropriate for examining causal relationships. Such an examination requires a causal design, in which the causal or independent variables are manipulated in a relatively controlled environment (Malholtra, 2004).

According to Malholtra (2004), the main method of causal research is experimentation; this method is commonly used to infer causal relationships. Experimental designs may be classified as four different types: 1. pre‐experimental 2. true experimental 3. quasi‐experimental 4. statistical

Pre‐experimental designs do not employ randomization procedures to control for extraneous factors. In true experimental designs, it is possible to randomly assign test units and treatments to experimental groups. Quasi‐experimental designs result when the researcher is unable to achieve full manipulation of scheduling or allocation of treatments to test units but can still apply part of the apparatus of true experimentation. A statistical design is a series of basic experiments that allows for statistical control and analysis of external variables (Malholtra, 2004). The experimental design in this research is a statistical 2 x 2 factorial design. This design is used to measure the effects of two independent variables on two levels and to allow for interactions between variables. That is, the formation of price image will be tested with two independent variables; shopping goals and vertical line extensions.

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participants in two groups using the shopping goal variable. In both groups participants have to evaluate an upscale and a downscale vertical line extension.

To test the hypotheses participants of the experiment will be asked to fill in an online questionnaire. There will be two sorts of questionnaires; one with a browsing manipulation and one with a buying manipulation. Participants will be randomly assigned to one of the conditions. Within these conditions participants will be asked to evaluate several vertical line extensions. The complete questionnaire with a browsing manipulation can be found in Appendix III and the complete questionnaire with a buying manipulation can be found in Appendix IV.

This research will test durable goods in the category of televisions. Almost every consumer has bought a television once in their life and people are familiar with the product, therefore almost every consumer can easily participate in this research. With this product category it is expected to have a high response.

3.2 Research method

According to Malholtra (2004), an experiment is formed when the researcher manipulates one or more independent variables and measures their effect on one or more dependent variables, while controlling for the effect of extraneous variables. Furthermore, an experimental design should be a set of procedures specifying (1) what independent variables or treatments are to be manipulated, (2) what dependent variables are to be measured, (3) the test units and how these units are to be divided into homogeneous sub samples, and (4) how the extraneous variables are to be controlled. These 4 elements of the experimental design are described in the next paragraphs.

3.2.1 Independent variables and manipulation

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the levels of these variables are changed. These variables, also known as treatments, may include price levels, package designs, and advertising themes (Malholtra, 2004). In this research the independent variables are vertical line extensions and shopping goals. These variables both have two levels. The first independent variable, vertical line extension, is varied at two levels; upscale and downscale, and is manipulated within subjects. This means that participants will serve in both experimental conditions. To make sure participants evaluate the effect of an extension and not only see a high priced and a low priced brand, they are first asked to evaluate a moderately priced brand, called “Tube”. This brand has an assortment that consists of three televisions with the following prices; €399,‐, €479,‐ and €589,‐. After the brand Tube is evaluated, two other brands are introduced that have the same assortment as Tube, the only difference is that they introduce either an upscale or a downscale extension. The first brand is “Visionair”, it has an assortment that consists of 4 televisions with an upscale extension compared to Tube; € 399,‐, €479,‐, € 589,‐ and € 999,. The second brand is “Images”, it has also an assortment that consist of 4 televisions but has a downscale extension compared to Tube; € 149, € 399,‐, €479,‐ and € 589,‐.

All three brands are fictive so that participants are not influenced by brand preference and brand knowledge. The brand names only serve to make sure participants understand the difference between an upscale and a downscale extension. Therefore, the results show the effect of line extensions and not the effect of different brands. All participants see the same prices of each brand. The experiment will include only the prices of the televisions and no pictures or other specific information regarding the product. There are only different type numbers added to the price to make sure the participants understands that these are different products, but no extra information can be subtracted from these numbers. In this way, participants will make a choice only based on the price and not based on, for example, looks or size.

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Furthermore, the research of Taylor and Bearden (2002) shows that when objective information available about a product is low, price is likely to be used as a cue to quality. Therefore, participants will probably see an upscale extension with a higher price, as a television with better quality and a downscale extension with a lower price, as a television with poorer quality.

The second independent variable, shopping goal, is also varied at two levels; browsing and buying, and is manipulated between subjects. This means that participants serve in only one experimental condition. Participants will be randomly assigned to one of the conditions. Thus, a participant will either have a buying goal or a browsing goal. In this way, we can compare the results of participants that have completed the experiment with a browsing goal to the results of participants with a buying goal. The research of Hamilton et al. (2010) has singled out both these shopping goals and explains how consumers can be manipulated into this condition. According to their research a moderately priced option should be singled out in a buying condition. In this experiment the television priced at €479,‐ is singled out as being strongly considered for purchase. The introduction for the shopping goal Buying is stated as follows: “Your current television has suddenly broken down. Because you watch television very often, you immediately decide to buy a new one. However, you do not want to spend too much money for it. There are two brands in which you are interested; the brand VISIONAIR and the brand IMAGES. Both brands offer a television for € 479, (the VISIONAIR LD58 and the IMAGES HS58) that completely meet your wishes. You decide to purchase one of these two televisions. “

Contrary, in the browsing goal condition, participants are asked to consider the prices of all options (Hamilton et al, 2010). The introduction for the shopping goal Browsing is stated as follows:

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“You have an oldfashioned television in your room and you decide it is time to buy a new one. There are two brands of which you consider to buy a television from; the brand VISIONAIR and the brand IMAGES. You do not want to rush things and therefore you take your time to evaluate the products of both brands. When you have gathered enough information you make your decision later on. “

Because these two conditions should be really clear a pre test has been done to make sure participants serve the right condition. The two introductions at the beginning of the questionnaire that will have either a buying goal or a browsing goal are submitted to a pre test. The questionnaire of the pre test can be found in appendix I. A small test sample of 23 participants filled in an online questionnaire in which they had to answer three questions for both introductions. The answers are rated on a 5‐point Likert scale, anchored at 1 = not at all, and 5 = very much. According to Saunders et al. (2000), scale questions are often used to collect data on attitudes and beliefs of respondents. The 7‐point Likert scale is based on Malholtra (2004), which creates enough possibilities for the respondents to express their opinion and it is measurable. Furthermore, it is an odd number of scales in order to give the respondent the opportunity of a neutral answer. The results of the pre‐test are presented in table 3.1. Question Buying manipulation Browsing manipulation 1. “Do you feel like you are taking the whole

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3.2.2 Dependent variable

Dependent variables are the variables that measure the effect of the independent variables on the test units. These variables may include sales, profits and market shares (Malholtra, 2004). In this research the dependent variable will be price image. Before measuring the price image the participants have to be introduced to the three brand, Tube, Visionair and Images and experience the shopping goal they serve.

First, participants have to evaluate the moderate brand Tube without knowing there are two other brands that will introduce upscale and downscale extensions to the same assortment. This is done by asking the participants to rate either the set of prices as a whole of all televisions, in a browsing goal condition (question 4, Appendix III), or the price of the particular television selected for purchase, in the buying condition (question 4, Appendix IV). After that, they will be introduced to the brand Visionair, with an upscale extension and the brand Images, with a downscale extension. Then they have to answer the same question for those two brands (question 5 and 6, Appendix III en IV). All ratings will be made on an interval scale with a 7‐point Likert scale, anchored at 1 = very low and 7 = very high. There is an important difference in evaluation of the price image in the buying condition and the browsing condition. In the buying condition participants are asked to rate the price image of a single product and in the browsing condition participants are asked to rate the price image of the whole brand. Therefore, the results of these questions can not be compared to each other because they do not measure the same variable. The results of these questions will not be discussed. However, the questions are included in this experiment to make sure the participants experienced a buying or a browsing shopping goal.

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spending mode or a saving mode. By doing this it can be measured which brand has a relative higher or lower price image compared to the other. Participants will be asked to choose between the two brands, Visionair and Images, to advise to a close friend for another purchase (question 7 and 8, Appendix III and IV). To control this correctly the question needs some background information for the participant, because consumers do not automatically choose the low priced brand to advise for another purchase. The research of Ding et al (2010) argues that sometimes consumers rather pay more then less money for a product. Although a high priced product decreases utility because they have to pay more for the product, it also infers better service, quality or guarantee. Therefore, the “spending mode” of the participants must be manipulated into spending versus saving. Because participants have to make a judgement based on the overall price image they have of a brand, it is important to use the same brands but with other products. Therefore participants are told that the brands Visionair en Images also sell DVD players. By doing this the participants will not rely on specific prices of the televisions, which they have seen in the previous part, but on the price image each brand has in the mind of the participant.

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scale, anchored at 1 = very cheap and 5 = very expensive. The results are presented in table 3.3. Question Brand recommendation 1. “If you had to advice a brand to David, in which price category would you advice him?” 4.57 2. “If you had to advice a brand to Michael, in which price category would you advice him?” 1.30

Table 3.3: Results pre test on brand recommendation. 1 = ‘very cheap’ and 5 = ‘very expensive’.

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the results of the experiment. Extraneous variables may include store size, shopping motives, and competitive effort.

In this research there are several extraneous variables that could affect the outcome of this experiment. The first extraneous variable is buying experience, it is measured how many times a participant has been involved in buying a new television (question 1, Appendix III and IV). The second extraneous variable is product knowledge, it is measured how much knowledge a participant thinks he or she has of televisions. In question 3 (Appendix II and IV) participants have to rate statements about how much attention they pay to certain television product attributes; price, quality, functionality and appearance. These extraneous variables should be measured to see how participants normally shop for a new television and what product attributes they find important in this process. For example, if participants find appearance of a television much more important then price, this could influence the validity of the outcomes because in this experiment the only information they get is the price of a television. Furthermore in question 3, they have to rate a statement regarding a buying and a browsing intention when shopping for a television. It is important to know whether in a normal situation participants have a buying or browsing intention when shopping for a television. If participants overall have more of a browsing intention then the results of the buying group could be influenced because participants could find it harder to place themselves in this situation. These statements are measured on an interval scale with a 7‐point Likert scale, anchored at 1 = totally not important and 7 = totally important (Saunders et al, 2000).

The other extraneous variables are demographic characteristics to measure the similarity in both sample groups (question 9 up to 14, Appendix III and IV). The questions measure sex, age, income, education, social situation and household composition (Kotler, 1997).

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Master Thesis – Robbert Fransen 40 3.3 Methods of data analysis To analyze the results of the experiment SPSS 17.0 will be used (Malholtra, 2004). Several statistical methods will be used to describe the results. 3.3.1 Descriptive statistics

First the participants will be described by using descriptive statistics. It is important that both groups will be built up on the same participants that have the same demographic composition. The results will be presented with a mean for the overall sample group, a mean for the browsing group and a mean for the buying group. 3.3.2 Oneway ANOVA This statistical technique examines the differences among means for two or more populations. It tests the null hypothesis that all means are equal. An analysis of variance must have a dependent variable and one or more independent variables (Malholtra, 2004). In this research the one‐way ANOVA test will measure the difference between the groups with a browsing shopping goal and the group with the buying shopping goal. The one‐way ANOVA will test the difference in demographic variables and the difference in shopping goal motivations.

3.3.3 Crosstab

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Master Thesis – Robbert Fransen 42 4. RESULTS Chapter 4 discusses the results of the experiment. First the test sample will be described and the difference between both groups, then the results of the ANOVA test and the crosstab will be presented and the significant differences between both groups will be described. 4.1 Characteristics of participants

This paragraph shows the descriptive statistics of the participants of the experiment who filled in the online questionnaire. 231 people participated in this experiment by filling in the online questionnaire. Participants were randomly assigned to one of the two shopping goal conditions, browsing or buying. Seven people did not complete the questionnaire and are removed from the dataset, leaving 224 valid participants in this experiment. This resulted in 112 participants in the browsing group and 112 participants in the buying group.

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The results of the ANOVA test, presented in tabel 4.1, show that there is no significant difference on these variables between the buying group and the browsing group. All significance levels are greater then .05. 45% _42% 47% 55% 58% _53% 0% 20% 40% 60% 80% 100%

Overall Browsing Buying

Male Female Figure 4.1: Sex of participants The first statistic concerns sex of the participant. The results, presented in figure 4.1, show that overall the majority of the participants is female (55%) and 45% of the participants is male. 71% 71% 71% 21% 22% 21% 8% _6% 9% 0% 20% 40% 60% 80% 100%

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Master Thesis – Robbert Fransen 44 age of 18‐34 years old, 21% is in the age 35 – 54 years old and 8% is the age of 55 – 65 years old. As figure 4.2 shows, both sample groups also have almost the same distribution. In the overall group the average age is 32,4 years. 38% 38% 37% 18% 19% 18% 44% 43% 46% 0% 20% 40% 60% 80% 100%

>€20.000 €20.001 - €40.000 €40.001 < Figure 4.3 Income per year of participants The third statistic concerns the income of the participants. The results, presented in figure 4.3, show that overall the majority (44%) has an income per year higher then €40.001, 38% has an income per year less then € 20.000 and 18% has an income between €20.001 and €40.000. 0%1%6% 0%0%6% 1%3%6% 11% _8% 14% 23% 24% _22% 58% 62% _54% 0% 20% 40% 60% 80% 100%

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level. 23% of the participants has a HBO education level and the other lower education levels fill the remaining 19%. 33% 34% _31% 14% 15% _13% 46% 46% 45% 5%_1%_2% _2%_1%_2% 8%_1%_3% 0% 20% 40% 60% 80% 100%

Scholier/student

Part time werk

Full time werk

Huisvrouw/man Werkloos/arbeids ongeschikt Gepensioneerd Figure 4.5: Social situation of participants

The fifth statistic concerns the social situation of the participants. The results, presented in figure 4.5, show that overall the majority (46%) has a full time job, followed by 33% that is still a scholar or a student. 2% 1% 3% 13% 13% 14% 32%31% 31%34% 32%_29% 22% 21% 22% 0% 20% 40% 60% 80% 100%

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Master Thesis – Robbert Fransen 46 together with there partner. The third largest group are people who live together with there partner and children (22%). The results in this paragraph show that the demographic variables according to Kotler (1997) have no significant differences (p <.05) across both sample groups of buying and browsing. Therefore, any further statements regarding different outcomes between the two sample groups will have to be based on the manipulation of the participant into the buying or browsing condition. 4.3 Price image evaluation The results of the questions in which participants have to recommend a brand to a friend, show the overall price image of both brand in a buying condition and a browsing condition. In these questions the spending mode of the participants is manipulated into spending (question 7) and saving (question 8). The complete descriptive statistics for these variables can be found in appendix VII. Saving Mode Spending Mode Browsing

Goal(%) Goal (%) Buying Browsing Goal(%) Goal(%) Buying Visionair 21.4 56.3 82.1 41.1 Images 78.6 43.8 17.9 58.9 Table 4.1: Crosstab with choice of brand, shopping goal and spending mode

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The results for the downscale brand (Images) show that for participants in a saving mode, who chose the brand which they thought had a lower price image, the recommendations for the downscale brand were stronger among participants with a browsing goal (78.6%) then among those with a buying goal (43.8%). The recommendations were reversed for participants in a spending mode, who chose the brand that they thought had a higher price image. In this condition, 58.9% of the participants with a buying goal recommended the downscale brand, compared to 17.9% of the participants with a browsing goal.

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4.4 Shopping goal motivation

In the experiment participants had to fill in eight statements concering their shopping motivations when they have the intention to buy a new television. The questions can be found in appendix III for the browsing group, question 1 t/m 3, and in appendix IV for the buying group, question 1 t/m 3. These questions were submitted before the manipulation into the buying and browsing shopping goals and are therefore not influenced. Participants had to answer to the statements on a 7‐point Likert scale. With a ANOVA test the means of both groups can be compared and it can be measured whether there are significant differences between the browsing and buying group in their shopping motivations for an new television. An overview of the results of the ANOVA test is presented in table 4.2 and the complete descriptives can be found in appendix VIII. Mean* Shopping

motivation Browsing Buying

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Master Thesis – Robbert Fransen 50 4.5 Relationship between shopping goals and vertical line extensions With the results of the experiment the relationship between shopping goals and vertical line extensions can be described. The relationships are equal to the hypotheses for this research. The first hypothesis was stated as follows:

H1: Due to the tradeoff contrast effect, an upscale extension with a buying shopping goal leads to a lower price image for a brand.

The results presented in table 4.1 show that a majority of the participants in a saving mode (56.3%), who had to recommend the brand with the lowest price image, chooses the upscale brand Visionair. The literature research in chapter 2 has singled out that people with a buying shopping goal (Hamilton, 2010) evaluate their products with a single option, called the trade‐off contrast effect (Simonson, 1989). Therefore, this hypothesis is accepted.

The second hypothesis of this research was stated as follows:

H2: Due to the tradeoff contrast effect, a downscale extension with a buying shopping goal leads to a higher price image for a brand.

The results presented in table 4.1 show that a majority of the participants in a spending mode (58.9%), who had to recommend the brand with the highest price image, chooses the downscale brand Images. This hypothesis has the same literature background as the first hypothesis; people with a buying shopping goal (Hamilton, 2010) evaluate their products with a single option, called the trade‐ off contrast effect (Simonson, 1989). Therefore, this hypothesis is accepted. The third hypothesis of this research was stated as follows:

H3: Due to the extremeness aversion, an upscale extension with a browsing shopping goal leads to a higher price image for a brand.

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price image, chooses the upscale brand Visionair. The literature research in chapter 2 has singled out that people with a browsing shopping goal (Hamilton, 2010), integrate the price of the extension with the prices of the other available options (Simonson, 1989). Therefore, this hypothesis is accepted.

The fourth and last hypothesis of this research was stated as follows:

H4: Due to the extremeness aversion, a downscale extension with a browsing shopping goal leads to a lower price image for a brand.

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5. CONCLUSION

In this conclusion, both the findings of the literature study and the empirical research will be addressed. Eventually the main research question will be answered; what is the impact of vertical line extensions on the formation of a price image of a brand and how do consumer shopping goals influence this perception? After that the limitations of this research and implications for further research will be described. When manufacturers are trying to manage the price image of their brands, they often rely on a strategy that when a brand needs a higher price image, a few high‐ priced products should be introduced to the assortment, and when a brand needs a lower price image, a few low‐priced products should be introduced. This research has shown that this strategy may be effective in some cases but, more important, that there are situations that this strategy will cause the opposite effect. This is mainly caused by different shopping goals of consumers.

The literature study in chapter 2 shows that shopping goals relate to the context effects of comparison and attraction. It demonstrates that when people have a narrow focus on a single option, called a buying shopping goal, the likelihood that this option will be evaluated by comparing it with the other options in the set increases. This is called the trade‐off contrast effect. When people have a relatively broad focus on a wider assortment, called a browsing shopping goal, the likelihood increases that they will evaluate the products in relation to each other, called the extremeness aversion effect. When these shopping goals are related to the introduction of vertical line extensions, different effects on price image result for a downscale line extension and an upscale line extension.