PROF. DR. FLORIAN STAHL. Quantitative Marketing. Department of Business Administration. Submission Date: August 21, 2013

Transcription

1 BUNDLING OF DIGITAL INFORMATION GOODS: A COMPARISON OF DIFFERENT CONJOINT APPROACHES MASTER S THESIS MATTHIAS LAUBE Thesis Supervisor: Chair: Faculty: Institution: PROF. DR. FLORIAN STAHL Quantitative Marketing Department of Business Administration University of Zurich Submission Date: August 21, 2013 Author Details Full Name: Matthias Andreas Laube Matriculation No matthiaslaube@gmx.ch Mobile No

2 ABSTRACT This master s thesis compares the results of a Choice-Based Conjoint Analysis (A) and a Menu-Based Conjoint Analysis (A) study who investigated the same pricing issue for a European newspaper company. En route, I compile an exhaustive overview on the A literature and assemble the biggest theory reading on A to date. The two investigated studies find different bundling strategies to be optimal and I explain why this is the case and outline how the results can be compared. Specifically, I identify important differences in the market simulations of the bundling scenarios which can explain the different strategy proposals. Since the two methods also delivered different levels of maximum revenue forecasts, I conduct a crossreference analysis and discuss which method s forecasts should be trusted more. I deduce four hypotheses in the theory part which I subsequently test in Chapter 4. The most fascinating hypothesis-driven finding concerns price sensitivity. While A is expected to yield higher price sensitivity, this turns out to be untrue when using the slope of the demand curve as measure for price sensitivity. But when looking at the quantity demanded at the same price level, A is in fact more price sensitive. Finally, I draw four managerial implications from my findings, where the most important one addresses the hypothetical nature of both A and A. Acknowledgements First and foremost, I would like to thank my supervisor, Professor Stahl, for his guidance and support. I am also grateful to Katja Werder and Robert Ung who both kindly took time to answer my questions regarding study analysis details. I am further obliged to Bryan Orme and Brian McEwan from Sawtooth Software, who answered my questions in the Sawtooth Software forum on minimum sample sizes for HB estimation and attribute coding in A (cf. References). Additionally, I would like to express my gratitude towards Bryan Orme for sending me a copy of the 2003 ART Forum slides of Bakken and Bremer. II

3 TABLE OF CONTENTS 1 INTRODUCTION LITERATURE REVIEW THEORETICAL FOUNDATIONS Bundling Measuring the Willingness-to-Pay Conjoint Analysis: An Overview Definitions A Brief History of Conjoint Analysis Which Method Should Be Used? Choice-Based Conjoint Analysis Methodology Advantages of A Disadvantages of A Summary Menu-Based Conjoint Analysis Methodology Conceptual Differences between A and A Advantages of A Disadvantages of A Comparing the Simple and Extended Menu Approach Summary and Hypotheses COMPARISON BETWEEN AND STUDY RESULTS Study Overview Product and Bundle Overview Result Comparison Overview and Bundling Strategy III

4 4.3.2 Cross-Reference Analysis Cannibalization Potential and Substitutability Content and Form Utility Sensitivity Negatively Valued Attributes DISCUSSION Differences in the Study Designs Differences in the Study Analyses Theoretical Differences Conclusion MANAGERIAL IMPLICATIONS CONCLUDING REMARKS REFERENCES APPENDIX A APPENDIX B B B APPENDIX C APPENDIX D APPENDIX E IV

5 LIST OF TABLES Table 1: Advantages and Drawbacks of State-of-the-Art A Table 2: Overview of Data Analysis Methodologies Table 3: Advantages and Drawbacks of A Table 4: Product Overview and Categorization Table 5: Bundle Overview Table 6: Overview on the Maximizing Cases Table 7: Overview on the Maximizing Cases (Normalized) Table 8: s and Normalized s for Bundle 1 Components Table 9: s and Normalized s for Bundle 6 Components Table 10: Summary of the Cross-Reference Analysis Table 11: Difference Between Stand-Alone Estimation Approaches Bundles 1, 2 & Table 12: Difference Between Stand-Alone Estimation Approaches Bundles 4, 8 & Table 13: Pure Stand Alone s and Added Value of Additional Products Table 14: Added Value of Season Pass Table 15: Differences between Pure Bundles and Their Highest Valued Part Table 16: Pure Single Product for and Methods Table 17: Slopes of Pure Product and Pure Bundling Scenarios Table 18: Pure Bundling Possibly Negative Valued Attributes Table 19: Summary of (Quality) Indicators and Their Impact Table 20: Summary of the Analysis Indicators Table 21: Summary of the Theoretical Differences Applicable in This Study Table 22: Exhaustive Overview on A Literature Table 23: Comprehensive Literature Overview on Choice Modeling Across Multiple Categories Table 24: Calculating Weighted s (Bundling Scenario 1, Stand Alone Case ) Table 25: Comparing Weighted s (Bundling Scenario 8, Stand Alone Case) Table 26: Comparing and Mixed Bundling Results Table 27: Comparing Mixed Bundling Cases A and D in Bundle Scenario Table 28: s and Normalized for Bundle 2 Components Table 29: s and Normalized for Bundle 3 Components V

6 Table 30: s and Normalized for Bundle 4 Components Table 31: s and Normalized s for Bundle 5 Components Table 32: s and Normalized s for Bundle 7 Components Table 33: s and Normalized for Bundle 8 Components Table 34: s and Normalized for Bundle 9 Components Table 35: s and Normalized s for Bundle 10 Components Table 36: Preference Shares of the and Methods for Pure Bundling Cases 3, 5, 6 & Table 37: Preference Shares of the and Methods for Pure Bundling Cases 1, 2, 4, 8, 9 & Table 38: Preference Shares of the and Methods for Pure Single Product Cases Table 39: A Pure Single Product s Table 40: A Pure Single Product s Table 41: List of Product Attributes and Their Attribute Levels Table 42: List of Attributes and Their Attribute Levels Table 43: List of Attributes and Their Levels VI

7 LIST OF FIGURES Figure 1: Example of a Task Figure 2: Example of an Task Figure 3: Illustration of an Extended Menu Approach Figure 4: Typology of Market Research Methods Figure 5: Example of Reasonably Well Behaving Curves Figure 6: Example of an Erratic Behaving Curve VII

8 ABBREVIATIONS ABYO ACA A AL APT B BYO CVA A CSS DCM DP DYOP EA EBA EMA GP HB I ICE IIA ISC LC A Adaptive Build Your Own Adaptive Conjoint Analysis Adaptive Choice-Based Conjoint Auto-Logistic Alternatives per Task Browser Build Your Own Conjoint Value Analysis Choice-Based Conjoint Choice-Based Conjoint Analysis Choice Set Sampling Discrete Choice Modeling Day Pass Design Your Own Product Exhaustive Alternatives Elimination by Aspects Extended Menu Approach Game Pass Hierarchical Bayes Incentive-aligned Choice-Based Conjoint Individual Choice Estimation Independence of Irrelevant Alternatives Independent Serial Choice Latent Class Menu-Based Conjoint (also: Menu-Based Choice) Menu-Based Conjoint Analysis (also: Menu-Based Choice Analysis) VIII

9 MCM MNL MNP MVL MVP N NA n/a P PDF RFC SA SCE SEM SL SMA SP TA TPR VCM W WTP Menu Choice Modeling Multinomial Logit Multinomial Probit Multivariate Logit Multivariate Probit Newspaper Newspaper App not available Print epaper (PDF) Randomized First Choice Smartphone App Serial Cross-Effects Self-Explication Method Sports League Simple Menu Approach Season Pass Tablet App Tasks per Respondent Volumetric Model Website Willingness-to-Pay IX

10 1 INTRODUCTION Bundling of digital information goods has been a topic of academic interest since 1995 (Stahl, Schäfer, & Maass, 2004). While most of the research in the bundling literature is understandably concerned with optimal bundling strategies, this master s thesis is mainly concerned with how to obtain the willingness-to-pay (WTP) estimates which are needed as input into such calculations. More pronouncedly, I am going to compare two conjoint methods, Choice-Based Conjoint Analysis (A) and Menu-Based Conjoint Analysis (A). A is also often referred to as discrete choice modeling (DCM) and A is also often referred to as Menu-Based Choice Analysis or menu choice modeling (MCM). While A is considered the industry standard in conjoint analysis (Orme, 2010a), A is a quite recent development but already being praised as the next big innovation in conjoint analysis (Cordella, Borghi, van der Wagt, & Loosschilder, 2012b; Huisman, 2011; Orme, 2013a). So far, there are only four studies comparing these two methods and all of them have the same big limitation, namely just one menu task per respondent (cf. Chapter 2). Therefore, my thesis is the first attempt to compare A to A without that limitation. En route, I will first review the theory behind both methods, compiling the up-to-date most complete theoretical description available for A. Coming back to the topic of bundling, the comparison between the two methods, which have been analyzed in the scope of two separate master s theses, yielded different results regarding the optimal bundling strategy. I am therefore going to investigate why, looking at the issue from three angles: Differences in study design, differences in the study analysis, and theoretical differences. My imperative goal is to point out which result variations occur due to the differing theoretical frameworks behind both methods, therefore contributing to the literature of A. But I will also tackle this issue from a pragmatic point of view and provide some valuable insights to facilitate the ultimate pricing decision. The rest of this master s thesis is structured as follows: In Chapter 2 I am going to quickly review the literature and point out the unique contribution of my thesis to the conjoint literature. In Chapter 3, I am looking at theory which bears importance to this topic. Section 3.1 quickly reviews important bundling papers and deduces two hypotheses. Section 3.2 explains the hypothetical bias and why conjoint methods provide value over self-explicated WTP elicitation methods. Section 3.3 defines conjoint analysis and provides a quick overview on its history. 1

11 Section 3.4 reviews the A theory and compares A to the traditional conjoint analysis. Finally, Section 3.5 exhaustively looks at A theory and modeling techniques and compares A to A on a theoretical basis. Section 3.6 then infers two more hypotheses based on theoretical expectations regarding A. Chapter 4 then introduces, compares and comments the results obtained in the A and A studies. It also investigates the four hypotheses derived in the theory part, among some other interesting issues. In Chapter 5, I will discuss the study design differences, study analysis differences and theoretical differences and conclude the findings. Chapter 6 then draws some managerial implications and Chapter 7 concludes the thesis. 2 LITERATURE REVIEW As mentioned in the introduction, the added value from this master s thesis stems from comparing two studies which applied different conjoint approaches, specifically A and A. Sometimes, the distinction between discrete choice and menu choice can be blurred, as in Jedidi, Jagpal and Manchanda s (2003) model, where they analyze a bundle situation with two goods. Only when they extend their model to n > 2, the discrete choice character will become clear. Also, Orme (2010c) applies a study design which potentially combines discrete and menu choice (although in his case, the menu character was prevalent). In most cases though, these two approaches are quite distinct from each other, even implying different choice behavior as I will show in Section 3.5. While there is ample literature on A and DCM, not many papers have been written on A and MCM. Regarding the former, I would like to refer to the works of Green, Krieger, and Wind (2001) and Hensher, Rose and Greene (2005) for an overview. Regarding the latter, I compiled an exhaustive overview in Appendix A, which includes methodologies, contributions and limitations of each paper and study. I divided Appendix A into two tables, Table 22 summarizes MCM in the context of conjoint analysis, so henceforth A will refer to only that particular stream of MCM, while I will use the term MCM to refer to other menu choice literature. Table 23 summarizes MCM in the contexts of bundling (e.g. Bradlow & Rao, 2000; Chung & Rao, 2003) and shopping baskets (e.g. Russell & Petersen, 2000; Song & Chintagunta, 2006, 2007). This second stream of menu choice literature is not directly relevant to conjoint theory, but provides interesting insights on menu modeling techniques and their dis/advantages. 2

12 In this chapter, I d like to focus on the literature comparing A to A. As to date, there seem to be only four attempts for a direct comparison. The first one has been conducted by Bakken and Bayer (2001). Their respondents answered several Choice-Based Conjoint () tasks and one Menu-Based Conjoint () task each, hence they only compared price sensitivity and (predicted) preference shares. As a result, they found respondents to be more price sensitive in tasks. In both studies they looked at in their 2001 paper, Bakken and Bayer had a high sample size (n=967 and n=1170) but asked only one task per respondent. Bakken & Bremer (2003) conducted study with A, and added a self-explication task and an task. They were then able to estimate utilities and preference shares from the data using Bayesian modeling techniques. A and A share predictions differed, but not much explication was offered in the slides. In 2006, Rice and Bakken refined that comparison approach. They apparently used the same data as Bakken & Bremer, but a different estimation technique. Panelists had to answer a self-explication task, 25 tasks and one task. Rice and Bakken then hypothesized a conditional decision process for each attribute and panelist, allowing them to split the task into a series of attribute-specific tasks. They then estimated these binary models with /HB from Sawtooth Software. Again, they found A to deliver more price sensitive results than A. Furthermore, A predicted preference shares which are highly different from the A predictions for two out of four products. They had approximately n=500 respondents which again answered only one task each. Finally, Johnson, Orme and Pinnell (2006) conducted a study with n=605 respondents, asking each respondent to complete four and one task. They looked at different ways to code the data which yielded interesting insights (cf. Section 3.5.1). In their A to A comparison, Johnson et al. found context effects to be present in the data (respondents tended to avoid the lowest and highest attribute levels), and found that both methods might measure different things (cf. Section 3.5.3). Furthermore, both methods were measuring price sensitivity poorly, which Johnson et al. explained with low sample size, the questionnaire design (untypical for A, prices were shown for every attribute) and that data was an across-respondents treatment, not within-respondents treatment. With regard to hold-out task predictions the result was sobering, which Johnson et al. explain with the questionnaire design and the fact that only tasks were used as hold-out tasks, a potential disadvantage for A. 3

13 Therefore, this master s thesis is the first one to compare A and A with more than just one task per respondent. It also includes the highest number of respondents so far, namely n=1388 and n=1462, respectively. In order to not overburden test subjects, the idea of test subjects answering both and questionnaires was abandoned. Unlike in the three studies introduced above, the and data originates from different test subjects. 3 THEORETICAL FOUNDATIONS In this chapter I am going to present the most important theoretical foundations needed to understand and compare the studies conducted by Werder (2013) and Ung (2012). The most important facts on bundling will be summarized in Section 3.1, culminating in a hypothesis on the expected results of the two studies examined in Chapter 4. Stated preference data is discussed in Section 3.2 and conjoint analysis is looked at to a bigger extent in Sections 3.3 to 3.5 because one aim of this thesis is to advance the literature comparing A to A. Section 3.3 is going to provide a quick overview on the various conjoint methods in a chronological fashion and outline the most important forms. Since there already are comprehensive overviews on the traditional forms of conjoint analysis and their derivatives, sections 3.4 and 3.5 will therefore focus on A and A, respectively. In Section 3.5, I am also going to postulate two hypotheses which will subsequently be tested in Chapter Bundling The main focus of this Master s thesis is to compare two pre-analyzed studies which used different marketing research methodologies. So naturally, the main focus will be on those methodologies. However, as they both investigated three forms of bundling and came to diverging results regarding which form of bundling yields maximum revenues, an introduction to this topic is warranted. Bundling has been defined in narrower and broader ways. For this master s thesis, I prefer an encompassing definition as used by Guiltinan (1987) and endorsed by Yadav and Monroe (1993): Bundling is the practice of marketing two or more products and/or services in a single package for a special price (Guiltinan 1987, p. 74). Traditionally, three forms of bundling have been distinguished: no bundling (also known as unbundled sales, unbundling or stand-alone sales), pure bundling (only bundles are sold, their components aren t available separately) and mixed 4

14 bundling (bundles and their components are sold jointly). Hitt and Chen (2005) also introduced the term customized bundling, which refers to a situation where the customer has the right to buy a predefined amount of goods out of a (much) larger pool of goods for a fixed price. Although it has some interesting properties, 1 it won t be investigated here since it hasn t been analyzed by Werder (2013) or Ung (2012). Yet another form of bundling is called rebundling, which refers to offline content that has been split apart and recomposed for online channels, e.g. article dossiers or music playlists (Stahl et al., 2004). Bundling has been identified as a useful strategy for several reasons. For example, Koukova, Kannan and Ratchford (2008) identify the following demand side reasons: negatively correlated reservation prices among customers, 2 goods which are complementary in consumption and uncertainty in the valuation of a good s quality. Supply side reasons include cost saving via economies of scale or scope (Chuang & Sirbu, 1999), economies of aggregation (Bakos & Brynjolfsson, 2000) and network externalities (Arthur, 1996; Bakos & Brynjolfsson, 2000). In the context of this master s thesis, the interesting part will be the performance of the three traditional forms of bundling compared against one another. Schmalensee (1984) and McAfee, McMillan and Whinston (1989) showed that in a monopoly setting with two goods, pure bundling and unbundling generally are weakly dominated by mixed bundling. McAfee et al. also showed the conditions under which their results extend to an oligopoly case. Chuang and Sirbu (1999) analyzed an n-good model and also find mixed bundling to be the dominant strategy, with pure bundling and unbundling only being boundary cases. Li, Feng, Chen and Kou (2013) found partial mixed bundling to be optimal, i.e. offering bundles which don t include all products. Kopalle, Krishna and Assunção (1999) investigated competitive environments (with simultaneous decisions) and found that with decreasing scope for market expansion, the subgame perfect Nash equilibrium shifts from mixed bundling to unbundling, while pure bundling is never an equilibrium strategy. Jedidi et al. (2003) conducted two empirical studies, considering competition via a none-option. They find mixed bundling to be the optimal strategy in every scenario. 1 Hitt and Chen (2005) find the mathematical formulation of customized bundling to be identical to nonlinear pricing. Hence it is useful as a price discrimination tool. 2 I use the standard economic definition of reservation price as e.g. in Frank (2006): The price which makes an individual indifferent between paying and not paying for a good or service. 5

15 There is also literature specifically considering distribution channels, which is one of the insights the client of the study underlying this master s thesis aims to gain. Venkatesh and Chatterjee (2006) suggested that unbundling of online content (e.g. single articles) could be useful to skim consumers who are not interested in the full print product. Such a pricing model could even be combined with customized bundling. Koukova et al. (2008) distinguished between content utility and form utility of a good. For example, an article will have the same content utility, regardless if published online or offline. However, form utility will differ, depending on the usage situation like the ability to search for keywords or reading while traveling (Koukova et al., 2008). Therefore, Koukova et al. showed that content substitutes can become form complements, especially if the different usage situations are emphasized. Furthermore, they found that discounts play a crucial role for consumers to buy a bundle of content-substitute-form-complement goods. Finally, Ben-Akiva and Gershenfeld (1998) and Bakken and Bond (2004) pointed out that bundling can also be viewed from the consumer s perspective, i.e. that bundling can simplify the decision process by saving time and effort to evaluate other combinations. Similarly, Koukova, Kannan and Kirmani (2012) found in three experiments that if two otherwise similar products each dominate in a different salient attribute, consumers have to resolve a tie. This induces a significant number of test-subjects to counter-intuitively buying the bundle which consists of these two products (Koukova et al., 2012). Also related to this topic, Agarwal and Chatterjee (2003) discovered in their studies that as more products have to be evaluated per bundle, the higher the chance that the prospect will defer her decision. Finally, Myung and Mattila s (2010) study showed that consumers are more probable to choose a bundle which provides the highest savings, i.e. the difference between the bundle price and the sum of its components. In accordance with Thaler s (1985) mental accounting theory, this finding stresses the importance of reference prices and hence highlights another advantage of mixed bundling. From the bundling literature reviewed above, I infer two hypotheses: Hypothesis 1a: Pure bundling is never an optimal strategy, because it is weakly dominated by mixed bundling. Hypothesis 1b: Mixed bundling will be the optimal strategy. Even though the industry under consideration does not have much scope for market expansion, there is a certain degree of monopolistic competition. Also, even if stand-alone products are not often 6

16 chosen, they likely enhance the perceived value of a customer who buys the bundle (cf. Myung & Mattila, 2010). Furthermore, online pay walls are a relatively new phenomenon in this industry and thus might even be seen as a quality signal by consumers, because the firm feels confident enough to erect one. 3.2 Measuring the Willingness-to-Pay In order to price its product, a firm needs to know what a customer wants and how much she is willing to pay for it. So why not simply ask? Because there are several problems with stated preference data 3 to measure the willingness-to-pay: 4 Indifference Problem. Since hypothetical scenarios don t affect the respondent s welfare, the respondent may be so uninterested or careless that he or she might make irrational decisions (Morikawa, 1989). Policy-response bias. This bias arises if the respondent believes that he or she will benefit from answering in a certain way (Morikawa, 1989). 5 Justification bias. The extent to which respondents feel that they must justify past behavior by responding in a similar way during a survey (Morikawa, 1989). 6 -bargaining bias. This can occur if a respondent feels that by rejecting a higherpriced alternative he or she can influence the firm to charge a lower price (Ben-Akiva & Gershenfeld, 1998). Warm glow bias. In hypothetical settings, some respondents are more prone to support good causes and follow social norms than in reality (Diamond & Hausman, 1994; Ding, Grewal, & Liechty, 2005). Risk bias. Respondents are likely to behave less risk-averse in hypothetical settings (Ding et al., 2005). Budget bias. Participants discount budget constraints in hypothetical situations (Diamond & Hausman, 1994; Ding et al., 2005). 3 Stated preferences are derived from hypothetical behaviour, while revealed preferences are derived from actual behavior. 4 I define the maximum willingness-to-pay in a simple fashion as the reservation price of an agent. 5 For instance, overstating the intention to use a planned good or service in the hope it will be realized. 6 For example, a person downloading copyrighted files from the internet might overstate his or her preference for sharing. 7

17 Ding et al. simply used the term hypothetical bias as an umbrella term. One possibility to reduce the impact of the hypothetical bias is to calibrate models which use stated preference data with real data, if available (Ben-Akiva & Gershenfeld, 1998). Another possibility is to use traditional conjoint methods or Choice-Based Conjoint. These methods force respondents to make trade-offs between attributes and therefore conceal the direct price impact, which should alleviate several of these biases. Also, it should be noted that except for some street markets, products usually have a price tag. Hence Ben-Akiva and Gershenfeld s (1998) statement that stated preference exercises which are realistic and meaningful to the respondent tend to elicit responses which are more commensurate with actual choice behavior (p. 178) intuitively makes sense and gives additional support to conjoint analysis as opposed to self-explicated measures such as directly asking for the willingness to pay. However, Gibson (2001) defended self-explicated measures and points out that repetitive questioning in conjoint analysis also reveals the purpose of the study to respondents. Furthermore, the need to limit attributes in many forms of conjoint analysis may lead to missing out on potentially important information (Gibson, 2001). Green and Srinivasan (1990) on the other hand point out that self-explication methods usually take less time but redundancy may lead to double counting, among other problems. Riedesel (2003) tested A against the Marder style self-explication approach and found that A/HB only does marginally better in hold-out choice predictions but gave a more accurate prediction of preference shares. More important in the context of this master s thesis, Jedidi et al. (2003) found in a bundling study that discrete choice models clearly outperform self-explicated measures (by a margin of 8% to 43%). Ding et al. (2005) conducted an experiment in which they compared four methods: Hypothetical (), a hypothetical self-explication method (SEM 7 ), incentive-aligned (I) and incentive-aligned self-explication (BDM 8 ). In I, respondents had to purchase their preferred product/price combinations as calculated by the model. The results clearly showed that I outperformed the other methods, followed by, then BDM, and finally SEM. Ding et al. found respondents to be more price sensitive (budget bias), more risk averse and caring less about social norms in the incentive aligned versions than in the hypothetical counterparts. Miller, 7 Lieb (2013a) defines a self-explicated measure as when a respondent give[s] a specific value for an attribute (p. 4-3) as opposed to distributing points, ranking or choosing between items. 8 The BDM mechanism is described in Becker, DeGroot, and Marschak (1964). A price will be randomly determined. If it is below the respondents stated WTP, he or she must buy the product for that random price. If the random price is above their stated WTP, they will not be able to buy the product. 8

18 Hofstetter, Krohmer, and Zhang (2011) also compared above four methods and additionally used a simulated onlineshop (REAL) to emulate a buying situation as realistic as possible (although they admit some biases which might have remained). Using REAL as benchmark, Miller et al. found that BDM predicts WTP the best, followed by I, SEM and lastly. When it comes to forecasting pricing decisions though, slightly outperformed SE. Another interesting result they found was that much more respondents used the none-option in I (19%) as opposed to (5%). Finally, in line with Ding et al., Miller et al. report price sensitivity to be the highest for the two incentive-aligned methods, followed by the hypothetical methods and, surprisingly, the REAL setting as least price sensitive. It needs to be mentioned that their REAL benchmark was still an experimental setting, in addition to the fact that the product was new to the market, hence respondents didn t have any price experience. Also, the poor performance of and I might partially be explained with their small sample size and only five tasks per respondent to estimate the model. In summary, the literature available so far suggests that outperforms SEM and incentivealigned methods outperform hypothetical methods. The two pre-conducted market research experiments which will be analyzed here use A and A, both hypothetical measures. The managerial advantage of hypothetical measures is, that they cost much less than incentivealigned ones. More importantly, I am confident that A and A can outperform selfexplicated measures because consumer WTP is a context-sensitive construct (Thaler 1985), [therefore] the suitability of a WTP measurement method can depend on how well such a method approximates the actual purchasing context of the underlying product and/or category (Miller et al., 2011, p. 182) and because Ding and Huber (2009) made a persuading argument that A can alleviate hypothetical biases like social desirability. 3.3 Conjoint Analysis: An Overview This section will provide a general definition of conjoint analysis, explain its usefulness and review its origin and development to date. The aim is to familiarize the reader with the most important terms, forms, and concepts of the huge strand of literature concerned with conjoint analysis. For readers interested in the history of conjoint analysis and other conjoint methods, detailed overviews with references to influential papers can be found, for example, in the works of Green and Srinivasan (1990), Green, Krieger and Wind (2001), Lieb (2013a) or Gustafsson, Herrmann and Huber (2000). 9

19 3.3.1 Definitions A clear-cut definition for the term conjoint analysis is hard to find in the literature. In most newer papers, the term is only described and explained instead of clearly defined, whereas older definitions often don t encompass the whole scope of conjoint analysis. 9 Drawing on Mohr, Sengupta and Slater (2010), conjoint analysis is a survey research tool that can statistically predict which combination of product attributes across various brands and prices customers will prefer to buy (p. 193). They outlined the key mechanism of conjoint analysis as observing the trade-offs respondents are making between different combinations of product attributes in order to determine the importance and value of each attribute. Orme (2010a) identified the key characteristic of conjoint analysis as respondents evaluat[ing] product profiles composed of multiple conjoined elements (p. 29) and clarifies the widespread misconception that the term conjoint analysis is not an abbreviation of considered jointly, but rather descends from the verb to conjoin. In order to collect conjoint data, a researcher has to find respondents who need to complete an experimental design, often referred to as survey or interview. An experimental design consists of one or several tasks. For example, the full-profile conjoint analysis typically consists of one task, which is to sort a number of alternatives from most to least preferred. Choice-Based Conjoint Analysis on the other hand consists of several tasks, and in each task respondents have to choose one of several alternatives. An alternative, sometimes also referred to as a concept or profile, can be a bundle, a product profile, a partial product profile or a none-option. Each alternative is composed of several attributes. Attributes are sometimes also referred to as features or items. Typical attributes are brand, size, color and price. In the case of bundles, an attribute usually equals a single product. Finally, each attribute has one or several levels. For example, the attribute color could have the five levels blue, red, silver, black and yellow or the attribute air conditioning could exhibit the two levels included and not included. In Menu-Based Conjoint Analysis, tasks aren t consisted of alternatives, instead respondents can directly choose their preferred attributes and/or attribute levels in each task, therefore creating their own alternative (usually a product or bundle). 9 E.g. Green and Srinivasan (1978) or Morikawa (1989) include decompositional method as a key word in their broad definitions. However, A is a compositional method. 10

20 3.3.2 A Brief History of Conjoint Analysis Many authors such as Hauser and Rao (2004); Green, Krieger and Wind (2001); and Orme (2010a) recognized the work of Luce and Tukey (1964), published in the Journal of Mathematical Psychology, as the birth of conjoint analysis. From that point, it took seven more years until the seminal paper of Green and Rao (1971) introduced conjoint analysis to the field of marketing. This early form of conjoint analysis is also known as traditional conjoint analysis, full-profile conjoint analysis, card-sort conjoint analysis or Conjoint Value Analysis (CVA). It was initially based on respondents sorting a set of cards with product profiles printed on them from best to worst. Later improvements included to ask respondents to rate each card, for example on a tenpoint scale (Orme, 2010a). In the late sixties, Johnson (1974) independently came up with the idea of working with trade-off matrices which simplified the tasks for respondents significantly, as they only had to choose between two partial profile cards at a time (Orme, 2010a). With the rise of personal computers, Johnson founded Sawtooth Software and refined his trade-off analysis by exploiting the flexibility offered by personal computers (Orme, 2010a). He wrote a program where respondents first had to execute a self-explication task, making it a hybrid method (Green et al., 2001). The program could then adapt the trade-off survey in real time, presenting only the most relevant trade-off problems (Orme, 2010a). This more user friendly conjoint method encouraged more realistic responses (Orme, 2010a) and became later known as Adaptive Conjoint Analysis (ACA). In 1985, not only the ACA software was released but also a CVA software package; and both included market simulation tools. Subsequently, debates on which method is the better one ensued between the ACA and CVA camps which sparked research but also dampened practitioners enthusiasm (Orme, 2010a). The whole debate between the CVA and ACA camps became increasingly obsolete with the appearance of discrete choice models also known as Choice-Based Conjoint Analysis (A). A dates back to the precursory paper written by the econometrician McFadden (1974), using a multinomial logit model. His approach has then been extended by Louviere and Woodworth (1983), a paper which spawned subsequent research (Green et al., 2001). In A, respondents don t need to rate or rank anything, instead they just have to make a choice between a set of available alternatives. While making choices seems more natural and realistic to respondents, it is an inefficient way to ask questions since a choice doesn t indicate the strength of preference 11

21 (Orme, 2010a). Hence, initially there was typically not enough data to model individual preferences but aggregated preferences were subject to various problems, such as independence of irrelevant alternatives (IIA) 10 and ignorance of separate preferences for subgroups (Orme, 2010a). However, the development and introduction of latent class models (segmenting respondents into relatively homogenous preference groups) and Hierarchical Bayes (HB) models (estimating individual level preferences from discrete choice data) helped to alleviate those problems and by the late 1990s A emerged as the most accepted conjoint method and became the industry standard (Orme, 2010a). A good part of A s success is attributed to the availability of commercial software tools (Orme, 2010a) which were introduced by Sawtooth Software in One approach to make choice tasks more realistic and enjoyable for test-subjects is called design your own product (DYOP), which is also known as build your own (BYO). Bakken and Bayer (2001) made the proposition to increase the value of A with a BYO element; this idea was later refined into the Adaptive Choice-Based Conjoint (A) analysis, a hybrid conjoint method developed by Sawtooth Software and launched in This method asks the respondents a BYO task first, then follows up with screening tasks and in a last step applies the questionnaire (Johnson & Orme, 2007). Another attempt to improve choice tasks by making them more realistic and enjoyable incorporates the BYO approach as well, but was developed into another direction which later became known as Menu-Based Conjoint Analysis (A) or Menu-Based Choice Analysis. The idea has first been introduced by Ben-Akiva and Gershenfeld (1998) and was based on the observation that consumers nowadays have lots of bundle choices, often with the additional option(s) to mix them with à la carte items. Like A, demand can be modeled using discrete choice modeling techniques (Ben-Akiva & Gershenfeld, 1998) in the sense that picking or not picking an option is the discrete choice space. Up to date, A is in high demand and even praised as the next big trend in conjoint analysis (Huisman, 2011; Orme, 2013a; Cordella et al., 2012b). Since early 2012 there is a commercial software package available, and if one keeps in mind that A only took off after commercial software packages have been introduced to the market, the future of A might look bright indeed. 10 This is a problem because when for example Pepsi is added to a market consisting of Coke, Sprite and hot chocolate, it would take the same share proportion from all existing products, even though that seems illogical. 12

22 3.3.3 Which Method Should Be Used? In light of all those methods, the question which one should be applied quickly arises. Important factors are the goal of a study, the possible sample size and the number of attributes. Since the two studies discussed in this paper have already been designed and conducted by a marketing research company and analyzed within the scope of two other master s theses, I couldn t take any influence. So the question changes to: Were the chosen methods appropriate? According to a Sawtooth Software (2007) technical paper, ACA has an advantage in being able to handle large amounts of attributes but has shown weaknesses in pricing research, often underestimating the importance of the price. Since the goal of the underlying study was to gain pricing information, ACA would not have been a wise choice. A would not have been necessary either, since the number of attributes is only four (Newspaper, Website, Sports League and ) in the part of the underlying study, while the sample size is big enough for data to be analyzed in a meaningful fashion. Even though A interviews are more engaging to respondents, they take two to three times longer than similar interviews (Sawtooth Software, 2013). And since A enjoys many advantages over CVA, as outlined in the next section, the above stated question can be answered with yes. 3.4 Choice-Based Conjoint Analysis In this section, Choice-Based Conjoint Analysis (A) will be described in more detail. Since A historically emerged from and mostly replaced the traditional forms of conjoint analysis (CVA, ACA and their derivatives) this section focuses on pointing out advantages and shortcomings related to the older forms of conjoint analysis, while pros and cons regarding Menu-Based Conjoint Analysis (A) will be described in the next section Methodology As briefly outlined in the last section, tasks are structured in such a way that respondents have to choose one of several pre-designed alternatives. This induces them to make trade-offs between the different attribute levels the presented alternatives exhibit. Balderjahn, Hedergott, and Peyer (2009) point out that analyzes discrete choices instead of preference judgments like in CVA. Therefore, A is technically a discrete choice analysis employed on a conjoint design (Cohen, 1997). Figure 1 depicts how the tasks in the study later discussed in this thesis looked like. In every task, exactly one alternative had to be chosen. 13

23 Figure 1: Example of a Task Which offer would you choose? Offer 1 Offer 2 Offer 3 Offer 4 Website Website Website Website Website tablet app browser access smartphone app smartphone app Logo smartphone app tablet app tablet app tablet app Newspaper Newspaper Newspaper Newspaper Logo as animated tablet app as epaper (PDF) as printed edition as animated tablet app as printed edition as animated tablet app I would not choose any of these offers. Sports League Logo Inclusive: All videos of one season Inclusive: Video of a single game per match day Add-on option: Video for a single game for 0.99 per game Monthly Fee Source: Own illustration. With respect to questionnaire design, there are four major methods for Choice-Based Conjoint experiments. Chrzan and Orme (2000) investigated those four methods along with several others 11 in terms of relative efficiency and give some guidance which approach could be used in which situation; however those results should be used with caution considering new estimation techniques such as HB. The first two major methods are complete enumeration and the shortcut method. Both are randomized designs but coded to produce near-orthogonal 12 designs for each respondent while displaying minimal overlap 13 and level balance 14 (Sawtooth Software, 2013). While the high quality complete enumeration considers all possible alternatives, the faster shortcut method attempts to build alternatives by using attribute levels least frequently shown previously to the same respondent (Sawtooth Software, 2013). The random method utilizes 11 Such as modified versions of fractional factorial design plans (which are manually generated and hence not randomized among respondents) and the SPSS computer optimization method. 12 Orthogonality: Attribute levels are chosen independently from other attribute levels (Sawtooth Software, 2013). 13 Minimal overlap: Each attribute level is shown as few times as possible in a single task (Sawtooth Software, 2013). 14 Level balance: Shows each attribute level an approximately equal number of times (Sawtooth Software, 2013). 14

24 random sampling with replacement for choosing alternatives, therefore allowing attribute level overlap within tasks (Sawtooth Software, 2013). Finally, the balanced overlap method combines elements of the complete enumeration and random method, permitting roughly half the overlap of the random method (Sawtooth Software, 2013). According to Sawtooth Software (2013), the complete enumeration method estimates main effects best, but doesn t perform as well with interaction effects; the random method estimates interaction effects best, but is the least efficient with main effects; the balanced overlap model is nearly as efficient as complete enumeration in estimating main effects while measurably better with interaction effects. With respect to data analysis, there are five major methods. The simplest one is counting choices, also known as counts. It just divides total number of times attribute level X has been chosen by total appearance of attribute level X. According to Sawtooth Software (2013), aggregating those counts and taking their log should come close to the aggregate logit solutions. This intuitively makes sense, as logit is nothing else than log-odds. However, since the logit analysis iteratively finds the maximum likelihood solution for fitting a multinomial logit model to the data, it is slightly more accurate than counting choices (Sawtooth Software, 2013). One should be aware that aggregate logit still suffers from biases, for example the IIA property (Sawtooth Software, 2013) and the fictitious average consumer bias, e.g. if 50% of respondents highly like an attribute and 50% highly dislike it, aggregate analysis will yield a mid-level preference (Baumgartner & Steiner, 2009). Over the years, researchers developed several methods to overcome these shortcomings. The first is Latent Class (LC) analysis, which assumes that respondents can be clustered into homogenous segments. Segment maximum likelihood solutions are computed, as well as a probability estimate for each respondent regarding to which segment he or she belongs (Huber, 1998). This allows to estimate expected individual part-worths as probability weighted combinations of the segment part-worths (Huber, 1998). 15 Sawtooth Software refined this approach in a method called Individual Choice Estimation (ICE), by not constraining those weights to be positive and therefore allowing more deviations of the individual values from the segments (Huber, 1998). But with the emergence of more potent computers, Hierarchical Bayes became the predominant method. HB can provide estimates of individual part-worths with only a few choices per individual by borrowing information from the population data (Sawtooth Software, 2009b). Huber (1998) tested those three methods, finding 15 part-worth refers to attribute level utility 15

25 that LC has the worst performance in all three experiments, while HB outperforms ICE in one and ties with ICE in the two others. However, he underlines the theoretical superiority of HB over ICE and Orme (2005) finds that HB is more stable than ICE and can deliver more effective estimates, the fewer respondent choices are available. Orme (2010b) points out that all of these advanced techniques are multinomial logit estimations as they all employ the logit rule. 16 It should be noted, that even in HB, the choices at the individual level are still described by a multinomial logit (MNL) model (Orme, 2005), this is at least the way it is implemented in the Sawtooth Software /HB module. Theoretically though, there are other possibilities available like multinomial probit (MNP), hybrid logit and non-parametrical methods. But since discussing these methods for modeling would go beyond the scope of this master s thesis, I refer to Ben-Akiva et al. (1997) Advantages of A Moore (2010) identifies the main advantage of A as the fact that respondents perceive it as a much simpler task then older forms of conjoint analysis. But there are a number of other reasons, why is considered to be a superior approach to CVA and ACA (Johnson & Orme, 2003; Orme, 2009; Sawtooth Software 2007, 2013): First, it includes a none-option and second, it presents more realistic tasks. In real-world situations, customers either choose a product or don t buy anything, as opposed to ranking or rating different products. Specifically, the none-option is helpful for volume estimations, rather than just share-estimations (Sawtooth Software, 2007, 2013). Also, the none-option can be used to model the constant alternative, i.e. reflecting the continuance of a current situation (Moore, 2010). Third, because analysis can be done for groups, sufficient data is available to support measuring interaction effects, which is a concern in many pricing studies (Sawtooth Software, 2007, 2013). Fourth, allows for product- or alternative-specific attribute levels (Sawtooth Software, 2013), which allows to study two different products at the same time. Fifth, shares-of-choice can be calculated directly, instead of needing to stipulate a decision rule as in the case of CVA (Balderjahn et al., 2009) Disadvantages of A But as it is often the case, these advantages come at a cost. Drawbacks of are that it presents lots of data to test-subjects and hence should not include more than six attributes (Sawtooth 16 Basically, the logit rule says that using the anti-log of the utility of alternatives is proportional to the choice probabilities of those alternatives. 16

26 Software, 2007, 2013). But worse than that, it is not a very efficient method of data collection. In every task, all alternatives have to be evaluated by the test-subjects but just one will be chosen per task. In a CVA setting, each alternative will be ranked or rated and hence much more information is provided per test-subject (Sawtooth Software, 2013). A third drawback was the lack of individual-level analysis and its biases which aggregate logit models bring along, however, this has largely been remedied with the introduction of the HB method (Sawtooth Software, 2013). Though Lieb (2013a) critically stated that using a market specific technique, such as Choice-Based-Conjoint produces inaccurate or at least questionable individual response measurements (p. 4 5), because Bayesian procedures rely on prior distributions obtained from market or segment logit models. In a practical approach, Moore, Gray-Lee and Louviere (1998) compared the prediction powers of several CVA and methods. Their results show that individual-level estimates using /HB yields better predictions than any CVA method tested in that study, but they mention that their study design might have given an advantage. Another concern is that respondents, especially online, tend to answer choice tasks extremely quick (12 to 15 seconds per task); hence they likely simplify their choice procedures (Sawtooth Software, 2009a). Lastly, Balderjahn et al. (2009) pointed out that including a none-option might introduce the problem of decision avoidance. Carson et al. (1994) stated that decision avoidance is likely a function of respondent fatigue, task difficulty and respondent characteristics. Johnson and Orme (1996) found that respondents are indeed more likely to use the none-option in later tasks, but they are unsure if this is due to respondent fatigue or the fact, that respondents might be reluctant to choose mediocre product profiles after having seen superior ones. They test the decision avoidance hypothesis and find evidence against it, concluding that the use of the noneoption is usually a rational decision. Sawtooth Software (2009a) even states that respondents tend to avoid the none-option Summary In the early days of A, the drawbacks weighted quite heavy. However, over the years many of them could be resolved with the availability of more powerful computers and new developments in data analysis techniques. It has been shown that /HB is the best method as of yet to analyze data. A synoptic overview on the pros and cons of state-of-the-art A versus CVA and ACA are presented in Table 1. 17

27 Table 1: Advantages and Drawbacks of State-of-the-Art A Advantages tasks are more realistic includes a none-option able to measure interaction effects alternative specific designs possible no need to hypothesize a decision rule Disadvantages needs larger sample size ACA can handle more attributes respondents simplify their choice procedures Source: Own Illustration. 3.5 Menu-Based Conjoint Analysis In this section, Menu-Based Conjoint Analysis (A) is presented in more detail. Since its main competitor is A, their relative advantages and shortcomings are outlined and discussed here. Judging from the literature (Bakken & Bayer, 2001; Ben-Akiva & Gershenfeld, 1998; Liechty, Ramaswamy, & Cohen, 2001; Moore, 2010; Orme, 2010b) the idea of was inspired by the marketplace. When the mass customization trend emerged, academics and market researchers noticed that A wasn t fully adequate to model the consumer choice behavior for those menus. For example, Cohen and Liechty (2007) commented that the complexity of a menu situation makes traditional conjoint analysis approaches completely inadequate. Ben-Akiva and Gershenfeld (1998) have been the first researchers to link menu choices with conjoint analysis and to provide an analytical approach to solve this type of problem. Predestined fields of application for A include mass customization (a base model with additional options, e.g. cars), build your own (e.g. Dell Computers, myswisschocolate), bundle vs. à la carte choice situations (e.g. cell phone contracts, fast food restaurants) and picking items from a menu (e.g. restaurants). Consumers assembling their shopping basket by picking items from a store would be a more complex application Methodology A is not a conjoint method in the strict definition of e.g. Morikawa (1989) although included in more encompassing ones like Mohr et al. (2010). The same way A is about discrete choice modeling, A is about menu choice modeling. Ben-Akiva and Gershenfeld (1998) distinguish two types of menu modeling, the simple menu approach (SMA) and the 18

28 extended menu approach (EMA). The simple menu approach is pretty much the same as a BYO exercise: A list of bundle or product features is provided and the respondent can choose a particular attribute level for every feature on the list. Attributes could be binary (included/not included) or manifold (e.g. choosing a product color). In an extended menu approach, bundles of features are added to the list of features, often at a discount. So this approach comes closest to simulating mixed bundling offers. In the pre-designed and pre-analyzed study discussed in this master s thesis, a simple menu approach was chosen for the tasks as depicted in Figure 2. An example of an extended menu approach is provided in Figure 3. Figure 2: Example of an Task Please compile an offer which you would purchase. You are free to choose none, one or several of the represented products. The total price of your selection will be shown below. Website Newspaper Sports League (picture) (picture) (picture) browser access Newspaper as epaper (PDF) season pass for all videos 6.99 (picture) smartphone app 5.99 (picture) Newspaper as animated tablet app 1.99 (picture) videos of one match day 0.99 price per match day (picture) tablet app Total : 0.99 (picture) Newspaper as printed edition *. *price varies depending on selection and will be shown in the total price / Month + / sports league access (picture) video of a single game 0.99 price per single game I would not choose any of these options Source: Own illustration. 19

29 Figure 3: Illustration of an Extended Menu Approach Source: Orme (2013a), p. 7. With respect to questionnaire design, I was unable to find specific literature on it. Kamakura and Kwak (2012) encountered the same problem and identified this as a possible area for future research. Only a few pointers can be found scattered across papers. For example, Kamakura and Kwak reported that respondents get bored or are unwilling to do more than eight tasks with sixteen attributes each. Orme (2010c) observed that respondents are still fine with up to 16 tasks. And Orme (2010c) mentioned that if attribute prices are alternated in an uncorrelated fashion, then the analyst can estimate the price sensitivity of each attribute independent of the others, while cross-elasticities can be estimated as well. Orme (2013a) recommends using a purely randomized design, adhering to standard design principles such as excellent level balance and (near-)orthogonality. This can help reducing order and context effects (Orme, 2013a). 20