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Über dieses Buch

by Paul E. Green I am honored and pleased to respond to authors request to write a Fore­ word for this excellent collection of essays on conjoint analysis and related topics. While a number of survey articles and sporadic book chapters have appeared on the subject, to the best of my knowledge this book represents the first volume of contributed essays on conjoint analysis. The book re­ flects not only the geographical diversity of its contributors but also the variety and depth of their topics. The development of conjoint analysis and its application to marketing and business research is noteworthy, both in its eclectic roots (psychometrics, statistics, operations research, economics) and the fact that its development reflects the efforts of a large variety of professionals - academics, market­ ing research consultants, industry practitioners, and software developers. Reasons for the early success and diffusion of conjoint analysis are not hard to find. First, by the early sixties, precursory psychometric techniques (e.g., multidimensional scaling and correspondence analysis, cluster analy­ sis, and general multivariate techniques) had already shown their value in practical business research and application. Second, conjoint analysis pro­ vided a new and powerful array of methods for tackling the important problem of representing and predicting buyer preference judgments and choice behavior - clearly a major problem area in marketing.



1. Conjoint Analysis as an Instrument of Market Research Practice

The essay by the psychologist, Luce, and the statistician, Tukey (1964) can be viewed as the origin of conjoint analysis (Green and Srinivasan 1978; Carroll and Green 1995). Since its introduction into marketing literature by Green and Rao (1971) as well as by Johnson (1974) in the beginning of the 1970s, conjoint analysis has developed into a method of preference studies that receives much attention from both theoreticians and those who carry out field studies. For example, Cattin and Wittink (1982) report 698 conjoint projects that were carried out by 17 companies included in their survey in the period from 1971 to 1980. For the period from 1981 to 1985, Wittink and Cattin (1989) found 66 companies in the United States that were in charge of a total of 1062 conjoint projects. As regards Europe, Wittink, Vriens and Burhenne counted a total of 956 projects carried out by 59 companies in the period from 1986 to 1991 (Wittink, Vriens and Burhenne 1994 and Baier and Gaul 1999). A survey initiated in Germany in 1998 in which a total of 519 companies and chairs at universities of the country were to provide information on their activities in the field of conjoint analysis for the period from 1993 to 1998 shows that 52 institutions interested in the study design an average of 6 conjoint analyses per year (Melles and Holling 1999; for an earlier study Schubert 1991). If we project the number of analyses for the total period of five years, we get approx. 1531 projects.
Anders Gustafsson, Andreas Herrmann, Frank Huber

2. Measurement of Price Effects with Conjoint Analysis: Separating Informational and Allocative Effects of Price

One of the most frequent purpose of conjoint analysis is the measurement of price effects (Wittink and Cattin 1989; Wittink, Vriens, and Burhenne 1994). Usually this is be done by describing a number of product alternatives on a small number of attributes, including price, and collecting some kind of preference data for these product alternatives. From the estimated part-worth function for price one can infer price effects (Srinivasan 1979).
Vithala R. Rao, Henrik Sattler

3. Developing Business Solutions from Conjoint Analysis

Many companies claim to be consumer-driven or focused. They often support this claim with evidence from extensive customer research programmes. They run focus groups, send out questionnaires, monitor customer satisfaction scores and analyse sales data.
Sid Simmons, Mark Esser

4. Measuring the Credibility of Product-Preannouncements with Conjoint Analysis

Conjoint analysis is usually applied as a method for measuring preference structures (e.g. preferences of consumers with respect to new products). Preferences are determined by ratings, rankings, or choices concerning a number of profiles. This kind of preference measurement has been successfully applied in many fields of application (see chapter one of this book).
Henrik Sattler, Karsten Schirm

5. Market Simulation Using a Probabilistic Ideal Vector Model for Conjoint Data

In commercial applications of conjoint analysis to product design and product pricing it has become quite popular to further evaluate the estimated individual part-worth functions by predicting shares of choices for alternatives in hypothetical market scenarios (Wittink, Vriens and Burhenne 1994 and Baier 1999 for surveys on commercial applications). Wide-spread software packages for conjoint analysis (Sawtooth Software’s 1994 ACA system) already include specific modules to handle this so-called market simulation situation for which, typically, a threefold input is required: (I) The (estimated) individual part-worth functions have to be provided. (II) A definition of a hypothetical market scenario is needed that allows to calculate individual utility values for each available alternative. (III) A so-called choice rule has to be selected, which relates individual utility values to expected individual choice probabilities and, consequently, to market shares for the alternatives. In this context, the determination of an adequate choice rule seems to be the most cumbersome task. Well-known traditional choice rules are, e.g., the 1ST CHOICE rule (where the individuals are assumed to always select the choice alternative with the highest utility value), the BTL (Bradley,Terry, Luce) rule (where individual choice probabilities are related to corresponding shares of utility values), and the LOGIT rule (where exponentiated utility values are used). Furthermore, in newer choice rules implemented by various software developers, the similarity of an alternative to other alternatives is taken into account as a corrective when choice probabilities are calculated (Sawtooth Software 1994).
Daniel Baier, Wolfgang Gaul

6. A Comparison of Conjoint Measurement with Self-Explicated Approaches

Over the past two decades conjoint measurement has been a popular method for measuring customers’ preference structures. Wittink and Cattin (1989) estimate that about 400 commercial applications were carried out per year during the early 1980s. In the 1990s this number probably exceeds 1000. The popularity of conjoint measurement appears to derive, at least in part, from its presumed superiority in validity over simpler, less expensive techniques such as self-explication approaches (Leigh, MacKay and Summers 1984). However, when considered in empirical studies, this superiority frequently has not been found (e.g. Green and Srinivasan 1990; Srinivasan and Park 1997). This issue is of major practical relevance. If, at least in certain situations, conjoint measurement is not clearly superior in validity to self-explicated approaches, it becomes highly questionable whether future applications for measuring customers’ preferences should be done by conjoint measurement, as self-explicated approaches are clear advantageous in terms of time and money effort.
Henrik Sattler, Susanne Hensel-Börner

7. New Product Development in the Software Industry: The Role of Conjoint Analysis

The process of developing, introducing and disseminating a new product is one of the most frequently described and highly formalised subjects in marketing literature (Urban and Hauser 1993; Rogers 1995). The development of a software package, while not an exception to the more general models, presents a number of peculiarities which it is worth investigating (Urban and von Hippel 1988; Carmel 1995; Carmel and Becker 1995). The rate of technological progress in the IT industry is extremely rapid: this structural factor, combined with the low level of entry barriers, leads to intense competition within the sector. In this structural context one key performance factor is the ability to generate innovative ideas and put them into practice in the shortest possible time. It is in fact this time variable which plays a crucial role in securing a sustainable competitive advantage. Consequently, the reduction of time-to-market, i.e. the time lapse between the devising of the product and its marketing, acquires great importance.
Gian Luca Marzocchi, Sergio Brasini, Marcello Rimessi

8. Non-geometric Plackett-Burman Designs in Conjoint Analysis

Design of experiments is an established technique for product and process improvement that has its origin in the 1920s and the work of Sir Ronald Fisher. Conjoint analysis shares the same theoretical basis as traditional design of experiments, but was originally used within the field of psychology and it was not until the early 1970s that the methodology was introduced into marketing research to form what is called conjoint analysis (Luce and Tukey 1964; Green and Rao 1971; Johnson 1974). Today, conjoint analysis is an established technique for investigating customer preferences.
Ola Blomkvist, Fredrik Ekdahl, Anders Gustafsson

9. On the Influence of the Evaluation Methods in Conjoint Design — Some Empirical Results

It is the goal of conjoint analysis to explain and predict preferences of customers (Schweikl 1985). Variants of predefined manifestations of attributes of various product concepts (both real and hypothetical) are created, and these are presented to test persons for evaluation. The contributions (partial benefits) the various attributes make to overall preference (overall benefit) are estimated on the basis of overall preference judgments (Green and Srinivasan 1978).
Frank Huber, Andreas Herrmann, Anders Gustafsson

10. The Value of Extent-of-Preference Information in Choice-based Conjoint Analysis

It is clear that conjoint analysis has had a substantial impact upon research practice (Wittink and Cattin 1989; Wittink, Vriens and Burhenne 1994). Conjoint analysis has evolved, and along with that evolution has been a gradual shift in the types of responses collected, from rankings to ratings to choices.
Terry Elrod, Keith Chrzan

11. Confounding of Effects in Rank-Based Conjoint-Analysis

Conjoint analysis enjoys large popularity among marketing researchers, as it combines easy-to-handle data collection with sophisticated evaluation methods. Dating back to the 70th, rank-based conjoint analysis is traditionally used to approximate metric utility functions of individual respondents within its given limits (see overview in: Green and Srinivasan 1978, 1990). This approach constitutes the basis for many past academic research studies and practical applications (Schubert 1991) and thus shapes their findings. Today, rating scales are applied more often (Wittink et al. 1994), partly because of the scale requirements of new methods, such as hybrid models (e.g., Green 1984) or the standardized software package ACA (Green et al. 1991). Accordingly, this paper is intended for those who either (still) apply rank-based Conjoint-analyses or who want to compare their findings with those from past rank-based conjoint studies.
Thorsten Teichert

12. A Multi-trait Multi-Method Validity Test of Partworth Estimates

Conjoint analysis has already been widely accepted by marketing researchers as a popular instrument for the measurement of consumer preferences. Typical applications of conjoint analysis include new product design based on the relationship between product features and predicted choice behavior, benefit segmentation based on attribute preferences, etc. The popularity of conjoint analysis among marketing researchers hinges on the belief that it produces valid measurements of consumer preferences for the features of a product or service, and that it provides accurate predictions of choice behavior.
Wagner Kamakura, Muammer Ozer

13. Adaptive Conjoint Analysis: Understanding the Methodology and Assessing Reliability and Validity

It is widely known that preference is not an observable phenomenon per se but a construct and in so far „a term specifically designed for a special scientific purpose generally to organize knowledge and direct research in attempt to describe or explain some aspect of nature“ (Peter 1981, 134) for which „[...] measures can be developed which at least partially represent the constructs.“ (Peter and Churchill 1986, 1). Consequently, measuring a construct implies that „numbers are assigned to objects [...] in such a way that the relations between the numbers reflect the relations between the objects [...] with respect to the characteristic involved“ (Green, Tull and Albaum 1988, 243).
Andreas Herrmann, Dirk Schmidt-Gallas, Frank Huber

14. Conjoint Preference Elicitation Methods in the Broader Context of Random Utility Theory Preference Elicitation Methods

The purpose of this chapter is to place conjoint analysis techniques within the broader framework of preference elicitation techniques that are consistent with the Random Utility Theory (RUT) paradigm. This allows us to accomplish the following objectives: explain how random utility theory provides a level playing field on which to compare preference elicitation methods, and why virtually all conjoint methods can be treated as a special case of a much broader theoretical framework. We achieve this by:
  • discussing wider issues in modelling preferences in the RUT paradigm, the implications for understanding consumer decision processes and practical prediction, and how conjoint analysis methods fit into the bigger picture.
  • discussing how a level playing field allows meaningful comparisons of a variety of preference elicitation methods and sources of preference data (conjoint methods are only one of many types), which in turn allows us to unify many disparate research streams;
  • discussing how a level playing field allows sources of preference data from various elicitation methods to be combined, including the important case of relating sources of preference elicitation data to actual market behaviour;
  • discussing the pros and cons of relaxing the simple error assumptions in basic choice models, and how these allow one to capture individual differences without needing individual-level effects;
  • using three cases studies to illustrate the themes of the chapter.
Jordan Louviere, David Hensher, Joffre Swait

15. Conjoint Choice Experiments: General Characteristics and Alternative Model Specifications

Conjoint choice experimentation involves the design of product profiles on the basis of product attributes specified at certain levels, and requires respondents to repeatedly choose one alternative from different sets of profiles offered to them, instead of ranking or rating all profiles, as is usually done in various forms of classic metric conjoint studies. The Multinomial Logit (MNL) model has been the most frequently used model to analyze the 0/1 choice data arising from such conjoint choice experiments (e.g., Louviere and Woodworth 1983; Elrod, Louviere and Davey 1992). One of the first articles describing the potential advantages of a choice approach for conjoint analysis was by Madanski (1980). His conclusion was that conjoint analysts could adopt the random utility model approach to explain gross trends or predilections in decisions instead of each person’s specific decision in each choice presented. The real breakthrough for conjoint choice came with the Louviere and Woodworth (1983) article in which they integrated the conjoint and discrete choice approaches.
Rinus Haaijer, Michel Wedel

16. Using Conjoint Choice Experiments to Model Consumer Choices of Product Component Packages

Recent advances in flexibility and automation allow a growing number of manufacturers and service providers to ‘mass-customize’ their products and offer modules from which consumers can create their own individualized products (e.g., Gilmore and Pine 1997). Traditional production processes limit consumer choices to fixed products defined by suppliers, but new mass-customization processes allow consumers to create their own optimal combination of product components. Although mass-customization offers consumers increased flexibility and consumption utility, little is known about how consumer choices to package or bundle separate components differ (if at all) from choices among traditional fixed product options, much less what the impact of packaging product components will be on the market shares of such products or a producer’s overall share in the category.
Benedict G. C. Dellaert, Aloys W. J. Borgers, Jordan J. Louviere, Harry J. P. Timmermans

17. Latent Class Models for Conjoint Analysis

Conjoint analysis was introduced to market researchers in the early 1970s as a means to understand the importance of product and service attributes and price as predictors of consumer preference (e.g., Green and Rao 1971; Green and Wind 1973). Since then it has received considerable attention in academic research (see Green and Srinivasan 1978, 1990 for exhaustive reviews; and Louviere 1994 for a review of the behavioral foundations of conjoint analysis). By systematically manipulating the product or service descriptions shown to a respondent with an experimental design, conjoint analysis allows decision-makers to understand consumer preferences in an enormous range of potential market situations (see Cattin and Wittink 1982; Wittink and Cattin 1989; and Wittink, Vriens, and Burhenne 1994 for surveys of industry usage of conjoint analysis).
Venkatram Ramaswamy, Steven H. Cohen

18. A Generalized Normative Segmentation Methodology Employing Conjoint Analysis

Since the pioneering research of Wendell Smith (1956), the concept of market segmentation has been one of the most pervasive activities in both the marketing academic literature and practice. In addition to being one of the major ways of operationalizing the marketing concept, marketing segmentation provides guidelines for a firm’s marketing strategy and resource allocation among markets and products. Facing heterogeneous markets, a firm employing a market segmentation strategy can typically increase expected profitability as suggested by the classic price discrimination model which provides the major theoretical rationale for market segmentation (cf. Frank, Massey and Wind 1972).
Wayne S. DeSarbo, Christian F. DeSarbo

19. Dealing with Product Similarity in Conjoint Simulations

One of the reasons conjoint analysis has been so popular as a management decision tool has been the availability of a choice simulator. These simulators often arrive in the form of a software or spreadsheet program accompanying the output of a conjoint study. These simulators enable managers to perform ‘what if’ questions about their market — estimating market shares under various assumptions about competition and their own offerings. As examples, simulators can predict the market share of a new offering; they can estimate the direct and cross elasticity of price changes within a market, or they can form the logical guide to strategic simulations that anticipate short- and long-term competitive responses (Green and Krieger 1988).
Joel Huber, Bryan Orme, Richard Miller

20. Evaluating Brand Value A Conjoint Measurement Application for the Automotive Industry

At first, the automotive manager had only one simple question: “What price premium does the brand value of my models allow me to demand on the market?” In this article, we would like to show how conjoint measurement can be used to find an answer to that question. Conjoint measurement is not the only building block for determining brand value, yet it is the foundation on which the “brand simulation model”, which we will introduce here, is built.
Claus Kolvenbach, Stefanie Krieg, Claudio Felten

21. Continuous Conjoint Analysis

Conjoint analysis was introduced in the 1970’s to quantify consumer tradeoffs. The methodology has become very popular for market-based strategic decisions (see e.g. Green and Srinivasan 1990; Wittink, Vriens and Burhenne 1994). This popularity has both been influenced by and has led to the development of a substantial body of research on conjoint analysis. Surprisingly, however, conjoint applications are almost exclusively limited to one-shot, ad hoc surveys.
Dick R. Wittink, Sev K. Keil


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