Conjoint Analysis as an Instrument of Market Research Practice

  • Anders Gustafsson
  • Andreas Herrmann
  • Frank Huber

Abstract

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 in their survey of 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. Wittink, Vriens, and Burhenne counted a total of 956 projects in Europe carried out by 59 companies in the period from 1986 to 1991 (Wittink, Vriens, and Burhenne 1994; Baier and Gaul 1999). Based on a 2004 Sawtooth Software customer survey, the leading company in Conjoint Software, between 5,000 and 8,000 conjoint analysis projects were conducted by Sawtooth Software users during 2003. The validation of the conjoint method can be measured not only by the companies today that utilize conjoint methods for decision-making, but also by the 989,000 hits on www.google.com. The increasing acceptance of conjoint applications in market research relates to the many possible uses of this method in various fields of application such as the following:
  • new product planning for determining the preference effect of innovations (for example Bauer, Huber, and Keller 1997; DeSarbo, Huff, Rolandelli, and Choi 1994; Green and Krieger 1987; 1992; 1993; Herrmann, Huber, and Braunstein 1997; Johnson, Herrmann, and Huber 1998; Kohli and Sukumar 1990; Page and Rosenbaum 1987; Sands and Warwick 1981; Yoo and Ohta 1995; Zufryden 1988) or to

  • improve existing achievements (Green and Wind 1975; Green and Srinivasan 1978; Dellaert et al., 1995), the method can also be applied in the field of

  • pricing policies (Bauer, Huber, and Adam 1998; Currim, Weinberg, and Wittink 1981; DeSarbo, Ramaswamy, and Cohen 1995; Goldberg, Green, and Wind 1984; Green and Krieger 1990; Kohli and Mahajan 1991; Mahajan, Green, and Goldberg 1982; Moore, Gray-Lee, and Louviere 1994; Pinnell 1994; Simon 1992; Wuebker and Mahajan 1998; Wyner, Benedetti, and Trapp 1984),

  • advertising (Bekmeier 1989; Levy, Webster, and Kerin 1983; Darmon 1979; Louviere 1984; Perreault and Russ 1977; Stanton and Reese 1983; Neale and Bath 1997; Tscheulin and Helmig 1998; Huber and Fischer 1999), and

  • distribution (Green and Savitz 1994; Herrmann and Huber 1997; Oppewal and Timmermans 1991; Oppewal 1995; Verhallen and DeNooij 1982).

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References

  1. Acito, F. (1977), An Investigation of some Data Collection Issues in Conjoint Measurement, American Marketing Association Educators’ Proceedings, 82–85.Google Scholar
  2. Acito, F. (1979), Industrial Product Concept Testing, Industrial Marketing Management, 10, 157–164.CrossRefGoogle Scholar
  3. Agarwal, M. (1988), Comparison of Conjoint Methods, Proceedings of the Sawtooth Software Conference on Perceptual Mapping, Sun Valley, 51–57.Google Scholar
  4. Akaah, I. P. (1988), Cluster Analysis versus Q-Type Factor Analysis as a Disaggregation Method in Hybrid Conjoint Modeling: An empirical Investigation, Journal of the Academy of Marketing Science, 19, 309–314.CrossRefGoogle Scholar
  5. Akaah, I. P. and P. K. Korgaonkar (1983), An Empirical Comparison of the Predictive Validity of Self-Explicated, Huber-Hybrid, Traditional Conjoint and Hybrid-conjoint Models, Journal of Marketing Research, 20, 187–197.CrossRefGoogle Scholar
  6. Allenby, G. M., N. Arora, and J. L. Ginter (1995), Incorporating prior Knowledge into the Analysis of Conjoint Studies, Journal of Marketing Research, 32, 152–162.CrossRefGoogle Scholar
  7. Alpert, M. I., J. F. Betak, and L. L. Golden (1978), Data gathering Issues in Conjoint Measurement, Working paper, Graduate School of Business, The University of Texas at Austin.Google Scholar
  8. Arora, Neeraj and Joel Huber (2001), “Improving Parameter Estimates and Model Prediction by Aggregate Customization in Choice Experiments,” Journal of Consumer Research, 28, (September), 273–283.CrossRefGoogle Scholar
  9. Assmus, E. F. and J. K. Key (1992), Designs and their Codes, Cambridge.Google Scholar
  10. Baier, D., and W. Gaul (1996), Analyzing Paired Comparisons Data Using Probabilistic Ideal Point and Vector Models, in: Bock, H. H., Polasek, P., eds., Data Analysis and Information Systems, Berlin, 163–174.Google Scholar
  11. Baier, D., and W. Gaul (1999), Optimal Product Positioning Based on Paired Comparison Data, Journal of Econometrics, 89, 365–392.CrossRefGoogle Scholar
  12. Baier, D. and W. Gaul (1995), Classification and Representation using Conjoint Data, in W. Gaul and D. Pfeifer eds., From data to knowledge: Theoretical and Practical Aspects of Classification, Data Analysis, and Knowledge Organization, Berlin, 298–307.Google Scholar
  13. Bateson, J. E., D. Reibstein, and W. Boulding (1987), Conjoint Analysis Reliability and Validity: a Framework for future Research, in: American Marketing Association, ed., Review of Marketing, Chicago, 451–481.Google Scholar
  14. Bauer, H. H., F. Huber, and R. Adam (1998), Utility oriented design of service bundles in the hotel industry based on the conjoint measurement method, in: Fuerderer, R., Herrmann, A. and Wuebker, G., eds., Optimal Bundling-Marketing Strategies for Improving economic performance, Wiesbaden, 269–297.Google Scholar
  15. Bauer, H. H., F. Huber, and T. Keller (1997), Design of Lines as a product-policy Variant to retain Customers in the Automotive Industry, in Johnson, M., Herrmann, A., Huber, F. and Gustafsson, A., Customer Retention in the Automotive Industry-Quality, Satisfaction and Retention, Wiesbaden, 67–92.Google Scholar
  16. Carmone, F. J., P. E. Green, and A. K. Jain (1978), Robustness of Conjoint Analysis: Some Monté Carlo Results, Journal of Marketing Research, 15, 300–303.CrossRefGoogle Scholar
  17. Carroll, J. D. (1972), Individual Differences and Multidimensional Scaling, in: Shepard, R. N., Romney, A. K., Nerlove, S. B., eds., Multidimensional Scaling-Theory and applications in behavioral sciences, Vol. 1, New York.Google Scholar
  18. Cattin, P. and F, Bliemel (1978), Metric vs. Nonmetric Procedures for Multiattribute Modeling: Some Simulation Results, Decision Sciences, 9, 1978, 472–480.CrossRefGoogle Scholar
  19. Cattin, P. and M. Weinberger (1980), Some Validity and Reliability Issues in the Measurement of Attribute Utilities, in: Olsen, Jerry C., ed., Advances in Consumer Research, 7, 780–783.Google Scholar
  20. Cattin, P. and D. R. Wittink (1977), Further knowledge beyond Conjoint Measurement: Toward a comparison of methods, Advances in Consumer Research, 4, 41–45.Google Scholar
  21. Cattin, P. and D. R. Wittink (1982), Commercial Use of Conjoint Analysis: A Survey, Journal of Marketing, 46, 44–53.CrossRefGoogle Scholar
  22. Cerro, D. (1988), Conjoint Analysis by Mail, Proceedings of the Sawtooth Software Conference on perceptual mapping, Sun Valley, 139–143.Google Scholar
  23. Cochran, W. G. and G. M. Cox (1957), Experimental Designs, New York.Google Scholar
  24. Colberg, T. (1977), Validation of Conjoint Measurement Methods: a Simulation and empirical Investigation, Dissertation, University of Washington.Google Scholar
  25. Currim, I. S., C. B. Weinberg, and D. R. Wittink (1981), Design of Subscription Programs for a Performing Arts Series, Journal of Consumer Research, 8, 67–75.CrossRefGoogle Scholar
  26. Darmon, R. Y. (1979), Setting Sales Quotas with Conjoint Analysis, Journal of Marketing Research, 16, 133–140.CrossRefGoogle Scholar
  27. Davey, K. S. and T. Elrod (1991), Predicting Shares from Preferences for Multiattribute Alternatives, working paper, University of Alberta.Google Scholar
  28. De Soete, G., J. D. Carroll (1983), A Maximum Likelihood Method for Fitting the Wandering Vector Model, Psychometrika, 48, 553–566.CrossRefGoogle Scholar
  29. De Soete, G. and W. DeSarbo (1991), A latent Class Probit Model for Analyzing pick Any/N data, Journal of Classification, 8, 45–63.CrossRefGoogle Scholar
  30. De Soete, G. and S. Winsberg (1994) A latent Class Vector Model for Preference Ratings, Journal of Classification, 8, 195–218.Google Scholar
  31. Dellaert, B., A. Borgers and H. Timmermans (1995), A Day in the City: Using Conjoint Experiments to urband Tourists’Choice of Activity Packages, Tourism Management, 16, 347–353.CrossRefGoogle Scholar
  32. DeSarbo, W. S., J. D. Carroll, D. R. Lehmann, and J. O’Shaughness (1982), Three-way Multivariate Conjoint Analysis, Marketing Science, 1, 323–350.CrossRefGoogle Scholar
  33. DeSarbo, W. S., R. L. Oliver, and A. Rangaswamy (1989), A simulated annealing Methodology for Clusterwise Linear Regression, Psychometrika, 54, 707–736.CrossRefGoogle Scholar
  34. DeSarbo, W. S., A. Ramaswamy, and K. Chaterjee (1992), Latent Class Multivariate Conjoint Analysis with Constant Sum Ratings Data, working paper, University of Michigan.Google Scholar
  35. DeSarbo, W. S., V. Ramaswamy, and S. H. Cohen, (1995), Market Segmentation with Choice-based Conjoint Analysis, Marketing Letters, 6, 137–147.CrossRefGoogle Scholar
  36. DeSarbo, W. S., M. Wedel, M. Vriens, and V. Ramaswamy (1992), Latent Class Metric Conjoint Analysis, Marketing Letters, 3, 273–288.CrossRefGoogle Scholar
  37. DeSarbo, W., L. Huff, M. M. Rolandelli, and J. Choi (1994), On the Measurement of Perceived Service Quality, in: R. T. Rust, and R. L. Oliver (ed.), Service Quality: New directions in theory and practice, London, 201–222.Google Scholar
  38. Diamantopoulos, A., B. Schlegelmilch, and J. P. DePreez (1995), Lessons for Pan-European Marketing? The Role of Consumer Preferences in fine-tuning the Product Market Fit, International Marketing Review, 12, 38–52.CrossRefGoogle Scholar
  39. Finkbeiner, C. T. (1988), Comparison of Conjoint Choice Simulators, Proceedings of the Sawtooth Software Conference on perceptual mapping, Sun Valley, 75–105.Google Scholar
  40. Finkbeiner, C. T. and P. J. Platz (1986), Computerized versus Paper and Pencil Methods: a Comparison Study, paper presented at the Association of Consumer Research Conference, Toronto.Google Scholar
  41. Gaul, W. (1989), Probabilistic Choice Behavior Models and their Combination With Additional Tools Needed for Applications to Marketing, in: De Soete, G., Feger, H., Klauer, K.-H., eds., New Developments in Psychological Choice Modeling, Amsterdam, 317–337.Google Scholar
  42. Gaul, W. and E. Aust (1994), Latent Class Inequality Constrained Least Square Regression, working paper, University of Karlsruhe.Google Scholar
  43. Gaul, W., U. Lutz, and E. Aust (1994), Goodwill towards domestic Products as Segmentation Criterion: An empirical Study within the Scope of Research on country-of-origin effects, in: Bock H. H., Lenski, W. and Richter, M., eds., Information systems and Data Analysis, Studies in Classification and data analysis, and knowledge organization, 4, 415–424.Google Scholar
  44. Goldberg, S. M., P. Green, and Y. Wind (1984), Conjoint Analysis of Price Premiums for Hotel Amenities, Journal of Business, 57, 111–147.CrossRefGoogle Scholar
  45. Green, P. E. and V. R. Rao (1971), Conjoint Measurement for Quantifying Judgmental Data, Journal of Marketing Research, 8, 355–363.CrossRefGoogle Scholar
  46. Green, P. E. and V. Srinivasan (1978), Conjoint Analysis in Consumer Research: Issues and Outlook, Journal of Consumer Research, 5, 103–123.CrossRefGoogle Scholar
  47. Green, P. E. and V. Srinivasan (1990), Conjoint Analysis in Marketing: New Developments With Implications for Research and Practice, Journal of Marketing, 54, 3–19.CrossRefGoogle Scholar
  48. Green, P. E. and D. S. Tull (1982), Methoden und Techniken der Marketingforschung, Stuttgart.Google Scholar
  49. Green, P. E. and Y. Wind (1975), New Way to Measure Consumers’ Judgments, Harvard Business Review, 53, 107–117.Google Scholar
  50. Green, P. E. and A. M. Krieger (1990), A hybrid Conjoint Model for price-demand Estimation, European Journal of Operations Research, 44, 28–38.CrossRefGoogle Scholar
  51. Green, P. E. and K. Helsen (1989), Cross-validation Assessment of Alternatives to individual-level Conjoint Analysis: a case study, Journal of Marketing Research, 26, 346–350.CrossRefGoogle Scholar
  52. Green, P. E., K. Helsen, and B. Shandler (1988), Conjoint Internal Validity under alternative Profile Presentations, Journal of Consumer Research, 15, 392–397.CrossRefGoogle Scholar
  53. Green, P. E. and A. M. Krieger (1987), A simple Heuristic for Selecting ‘good’ Products in Conjoint Analysis, Application of Management Science, 5, 131–153.Google Scholar
  54. Green, P. E. and A. M. Krieger (1992), An Application to Optimal Product Positioning Model to Pharmaceutical Products, Marketing Science, 11, 117–132.CrossRefGoogle Scholar
  55. Green, P. E. and A. M. Krieger (1993), A simple Approach to Target Market Advertising Strategy, Journal of the Market Research Society, 35, 161–170.Google Scholar
  56. Green, P. E. and A. M. Krieger (1993), Conjoint Analysis with product-positioning Applications, J. Eliashberg, G. J. Lilien eds., Marketing, Handbooks in OR&MS, 5, 467–515.Google Scholar
  57. Green, P. E. and J. Savitz (1994), Applying Conjoint Analysis to Product Assortment and Pricing in Retailing Research, Pricing Strategy and Practice, 4–19.Google Scholar
  58. Hagerty, M. R. (1985), Improving the predictive Power of Conjoint Analysis: The use of Factor Analysis and Cluster Analysis, Journal of Marketing Research, 22, 168–184.CrossRefGoogle Scholar
  59. Hagerty, M. R. (1986), The cost of simplifying Preference Models, Marketing Science, 5, 298–324.CrossRefGoogle Scholar
  60. Herrmann, A., B. Franken, F. Huber, M. Ohlwein, and R. Schellhase (1999), The Conjoint Analysis as an Instrument for Marketing Controlling taking a public Theatre as an Example, International Journal of Arts Management, forthcoming.Google Scholar
  61. Herrmann, A. and F. Huber (1997), Utility orientated Product Distribution, The International Review of Retail, Distribution and Consumer Research, 8, 369–382.CrossRefGoogle Scholar
  62. Herrmann, A., F. Huber, and C. Braunstein (1997), Standardization and Differentiation of Services: a cross-cultural study based on Semiotics, Means End Chains and Conjoint Analysis, Academy of Marketing/American Marketing Association Proceedings of 31st Annual Conference 7th July 1997, Manchester Metropolitan University.Google Scholar
  63. Hruschka, H. (1986), Market definition and Segmentation Using Fuzzy Clustering Methods, International Journal of Research in Marketing, 3, 117–134.CrossRefGoogle Scholar
  64. Huber, F. and M. Fischer (1999), Measurement of Advertising Response-Results of a conjointanalytical Study, Proceedings of the Academy of Marketing Science World Conference, Malta.Google Scholar
  65. Huber, G. P. (1974), Multiattribute Utility Models: a Review of filed and field-like Studies, Management Science, 20, 1393–1402.CrossRefGoogle Scholar
  66. Huber, J. (1997), What we have learned from 20 Years of Conjoint Research: When to use self-explicated, graded pairs, full profiles or choice experiments, Sawtooth Software Conference Proceedings, Seattle, 243–256.Google Scholar
  67. Huber, J., D. Ariely, and G. Fischer (1997), The Ability of People to express Values with Choices, Matching and Ratings, working paper, Fuqua School of Business, Duke University.Google Scholar
  68. Jain, A. R., F. Acito, N. Malhorta, and V. Mahajan (1979), A Comparison of internal Validity of alternative Parameter Estimation Methods in decompositional Multiattribute Preference Models, Journal of Marketing Research, 16, 313–322.CrossRefGoogle Scholar
  69. Jain, A. R., N. Malhorta, and C. Pinson (1980), Stability and Reliability of part-worth utility in Conjoint Analysis: a longitudinal Investigation, working paper, European Institute of Business Administration, Brüssel.Google Scholar
  70. Johnson, M., A. Herrmann, and F. Huber (1998), Growth through Product Sharing Services, Journal of Service Research, 1, 167–177.CrossRefGoogle Scholar
  71. Johnson, R. M. (1974), Trade-Off Analysis of Consumer Values, Journal of Marketing Research, 11, 121–127.CrossRefGoogle Scholar
  72. Kahneman, D. and A. Tversky (1979), Prospect Theory: An Analysis of Decision under Risk, Econometrica, 47, 263–291.CrossRefGoogle Scholar
  73. Kamakura, W. A. (1988), A least squares Procedure for Benefit Segmentation with Conjoint Experiments, Journal of Marketing Research, 25, 157–167.CrossRefGoogle Scholar
  74. Kamakura, W. A. and R. K. Srivastava (1986), An ideal-point probabilistic Choice Model for heterogeneous Preferences, Marketing Science, 5, 199–218.CrossRefGoogle Scholar
  75. Kohli, R. and R. Sukumar (1990), Heuristics for Product-Line-Design using Conjoint Analysis, Management Science, 36, 1464–1478.CrossRefGoogle Scholar
  76. Kohli, R. and V. Mahajan (1991), A reservation-price Model for optimal Pricing of Mulitattribute Products in Conjoint Analysis, Journal of Marketing Research, 28, 347–354.CrossRefGoogle Scholar
  77. Krishnamurthi, L. (1988), Conjoint Models of Family Decision Making, International Journal of Research in Marketing, 5, 185–198.CrossRefGoogle Scholar
  78. Krishnamurthi, L. and D. R. Wittink (1991), The Value of Idiosyncratic Functional Forms in Conjoint Analysis, International Journal of Research in Marketing, 8, 301–313.CrossRefGoogle Scholar
  79. Kuhfeld, W. D. (1997), Efficient Experimental Designs using Computerized Searches, Sawtooth Software Conference Proceedings, Seattle, 71–86.Google Scholar
  80. Levy, M., J. Webster, and R. A. Kerin (1983), Formulating Push Marketing Strategies: a Method and Application, Journal of Marketing, 47, 25–34.CrossRefGoogle Scholar
  81. Louviere, J. (1984), Using discrete Choice Experiments and mulitnominal Logit Models to forecast Trial in a competitive Retail Environment: a fast food Restaurant Illustration, Journal of Retailing, 60, 81–107.Google Scholar
  82. Luce, R. D. and J. W. Tukey (1964), Simultaneous Conjoint Measurement-A New Type of Fundamental Measurement, Journal of Mathematical Psychology, 1, 1–27.CrossRefGoogle Scholar
  83. Mahajan, V., P. E. Green, and S. M. Goldberg (1982), A Conjoint Model for Measuring Self and Cross-Price/Demand Relationships, Journal of Marketing Research, 19, 334–342.CrossRefGoogle Scholar
  84. McCullough, J. and R. Best (1979), Conjoint Measurement: Temporal Stability and Structural Reliability, Journal of Marketing Research, 16, 26–31.CrossRefGoogle Scholar
  85. Mishra, S., U. N. Umesh, and D. E. Stem (1989), Attribute Importance weights in Conjoint Analysis: Bias and Precision, Advances in Consumer Research, 16, 605–611.Google Scholar
  86. Mohn, N. C. (1989), Simulated purchase ‘Chip’ testing versus trade-off (conjoint) analysis, Proceedings of the Sawtooth Software Conference on perceptual mapping, Sun Valley, 53–63.Google Scholar
  87. Montgomery, D. B. and D. R. Wittink (1980), The predictive Validity of Conjoint Analysis for alternative Aggregation Schemes, Market Science Institute, ed., Market Measurement and Analysis, Cambridge, 298–309.Google Scholar
  88. Montgomery, D. B., D. R. Wittink, and T. Glaze (1977), A predictive Test of individual level Concept Evaluation and trade-off Analysis, Research paper No. 415, Graduate School of Business, Stanford University.Google Scholar
  89. Moore, W. L., J. Gray-Lee, and J. J. Louviere (1994), A cross-validity Comparison of Conjoint Analysis and Choice Models at different levels of Aggregation, working paper, University of Utah, Salt Lake City.Google Scholar
  90. Moore, W. L. and M. B, Holbrook (1990), Conjoint Analysis on objects with environmentally correlated Attributes: The questionable Importance of representative Design, Journal of Consumer Research, 6, 490–497.CrossRefGoogle Scholar
  91. Neal, W. D. and S. Bathe (1997), Using the Value Equation to evaluate Campaign Effectiveness, Journal of Advertising Research, 37, 80–85.Google Scholar
  92. Ogawa, K. (1987), An Approach to Simultaneous Estimation and Segmentation in Conjoint Analysis, Marketing Science, 6, 66–81.CrossRefGoogle Scholar
  93. Oppedijk van veen, W. M. and D. Beazley (1977), An Investigation of alternative Methods of Applying the trade-off Model, Journal of Market Research Society, 19, 2–9.Google Scholar
  94. Oppewal, H. (1995), Conjoint experiments and retail planning: Modeling consumer choice of shopping centre and retailer reactive behavior, thesis, Eindhoven.Google Scholar
  95. Orme, B. K., M. I. Alpert, and E. Chistensen (1997), Assessing the validity of Conjoint Analysis-continued, Sawtooth Software Conference Proceedings, Seattle, 209–226.Google Scholar
  96. Page, A. and H. F. Rosenbaum (1987), Redesigning Product Lines with Conjoint Analysis: how Sunbeam does it, Journal of Product Innovation Management, 4, 120–137.CrossRefGoogle Scholar
  97. Page, A. and H. F. Rosenbaum (1989), Redesigning Product Lines with Conjoint Analysis: a reply to Wittink, Journal of Product Innovation Management, 6, 293–296.CrossRefGoogle Scholar
  98. Parker, B. R. and V. Srinivasan (1976), A consumer Preference Approach to the Planning of rural primary health-care facilities, Operations Research, 24, 991–1025.CrossRefGoogle Scholar
  99. Pearson, R. W. and R. F. Boruch (1986), Survey Research designs: Towards a better Understanding of their Cost and Benefits, Berlin.Google Scholar
  100. Pekelman, D. and S. K. Senk (1979): Improving prediction in conjoint analysis, Journal of Marketing Research, 16, 211–220.CrossRefGoogle Scholar
  101. Perreault, W. D. and F. A. Russ (1977), Improving Physical Distribution Service Decisions with trade-off Analysis, International Journal of physical Distribution and Materials Management, 7, 3–19.Google Scholar
  102. Pinnell, J. (1994), Multi-Stage Conjoint Methods to Measure Price Sensitivity, in: Weiss, S., ed., Sawtooth News, 10, 5–6.Google Scholar
  103. Pullman, M. E., K. J. Dodson, and W. L. Moore (1999),. A comparison of conjoint methods when there are many attributes, Marketing Letters, 10(2), 125–138.CrossRefGoogle Scholar
  104. Punj, G. and D. W. Stewart (1983), Cluster Analysis in Marketing Research: Review and Suggestions for Application, Journal of Marketing Research, 20, 134–148.CrossRefGoogle Scholar
  105. Robinson, P. J. (1980), Applications of Conjoint Analysis to Pricing Problems, in: D. B. Montgomery and D. R. Wittink eds., Proceedings of the first ORSA/TMS Special interest conference on market measurement and analysis, Report 80-103, Cambridge, 183–205.Google Scholar
  106. Rosko, M. D. and W. F. McKenna (1983), Modelling consumer choices of health plans: A comparison of two techniques, Social Sciences and Medicine, 17, 421–429.CrossRefGoogle Scholar
  107. Safizadeh, M. H. (1989), The internal Validity of the trade-off Method of Conjoint Analysis, Decision Science, 20, 451–461.CrossRefGoogle Scholar
  108. Sands, S. and K. Warwick (1981), What product Benefits to offer to whom: an Application of Conjoint Segmentation, California Management Review, 24, 69–74.CrossRefGoogle Scholar
  109. Segal, M. N. (1982), Reliability of Conjoint Analysis: contrasting Data Collection Procedures, Journal of Marketing Research, 13, 211–224.Google Scholar
  110. Shah, K. R. and B. K. Sinha (1989), Theory of Optimal Designs, Berlin.Google Scholar
  111. Simon, H. (1992b), Pricing Opportunities-And How to Exploit Them, Sloan Management Review, 34, 55–65.Google Scholar
  112. Slovic, P., D. Fleissner, and S. Bauman (1972), Analyzing the use of Information in Investment Decision Making: a methodological proposal, Journal of Business, 45, 283–301.CrossRefGoogle Scholar
  113. Srinivasan, V., A. K. Jain, and N. K. Malhotra (1983), Improving predictive Power of Conjoint Analysis by constrained Parameter Estimation, Journal of Marketing Research, 20, 433–438.CrossRefGoogle Scholar
  114. Srinivasan, V. and C. S. Park (1997), Surprising Robustness of the self-explicated Approach to Customer Preference Structure Measurement, Journal of Marketing Research, 34, 286–291.CrossRefGoogle Scholar
  115. Srinivasan, V. and A. D. Shocker (1973), Linear Programming Techniques for Multidimensional Analysis of Preferences, Psychometrika, 38, 337–369.CrossRefGoogle Scholar
  116. Srinivasan, V., A. D. Shocker, and A. G. Weinstein (1973), Measurement of a Composite Criterion of Managerial Success, Organizational Behavior and Human Performance, 9, 147–167.CrossRefGoogle Scholar
  117. Stahl, B. (1988), Conjoint Analysis by Telephone, Proceedings of the Sawtooth Software Conference on perceptual mapping, Sun Valley, 131–138.Google Scholar
  118. Stanton, W. W. and R. M. Reese (1983), Three Conjoint Segmentation Approaches to the Evaluation of Advertising Theme Creation, Journal of Business Research, 11, 201–216.CrossRefGoogle Scholar
  119. Steckel, J. H., W. DeSarbo, and V. Mahjan (1990), On the Creation of acceptable Conjoint Analysis Experimental Designs, Decision Sciences, 22, 435–442.CrossRefGoogle Scholar
  120. Steenkamp, J. B. and M. Wedel (1991), Segmenting Retail Markets on Store Image using a consumer-based Methodology, Journal of Retailing, 7, 300–320.Google Scholar
  121. Steenkamp, J. B. and M. Wedel (1993), Fuzzy clusterwise Regression in Benefit Segmentation Application and Investigation into its Validity, Journal of Business Research, 26, 237–249.CrossRefGoogle Scholar
  122. Tscheulin, D. K. and B, Helmig. (1998), The optimal Design of Hospital Advertising by Means of Conjoint Measurement, Journal of Advertising Research, 38, 35–46.Google Scholar
  123. Tscheulin, D. K. and C. Blaimont (1993), Die Abhängigkeit der Prognosegüte von Conjoint-Studien von demographischen Probanden-Charakteristika, Zeitschrift für Betriebswirtschaftslehre, 63, 839–847.Google Scholar
  124. Tversky, A. and D. Kahneman (1991), Loss Aversion and Riskless Choice: A Reference Dependent Model, Quarterly Journal of Economics, 6, 1039–1061.CrossRefGoogle Scholar
  125. Van der Lans, I. A., P. W. Verlegh, and H. N. Schifferstein (1999), An Empirical Comparison of various individual-level Hybrid Conjoint Analysis Models, in: Hildebrandt, L., Annacker, D. and Klapper, D., eds., Proceedings of the 28th EMAC Conference, Berlin.Google Scholar
  126. Verhallen, T. and G. J. DeNooij (1982), Retail Attributes and shopping Patronage, Journal of Economic Psychology, 2, 439–455.Google Scholar
  127. Vriens, M. (1995), Conjoint analysis in Marketing, Ph. D thesis, Capelle.Google Scholar
  128. Vriens, M., H. Oppewal, and M. Wedel (1998), Ratings-based versus choice-based Latent Class Conjoint Models-an empirical comparison, Journal of the Market Research Society, 40, 237–248.Google Scholar
  129. Vriens, M., H. R. van der Scheer, J. C. Hoekstra, and J. P. Bult (1998), Conjoint Experiments for direct mail Response Optimization, European Journal of Marketing, 32, 323–339.CrossRefGoogle Scholar
  130. Vriens, M., M. Wedel, and T. Wilms (1996), Metric Conjoint Segmentation Methods: a Monte Carlo comparison, Journal of Marketing Research, 33, 73–85.CrossRefGoogle Scholar
  131. Vriens, M. and D. Wittink (1992), Data Collection in Conjoint Analysis, unpublished manuscript.Google Scholar
  132. Wedel, M. and C. Kistemaker (1989), Consumer Benefit Segmentation using clusterwise Linear Regression, International Journal of Research in Marketing, 6, 45–59.CrossRefGoogle Scholar
  133. Wedel, M. and J. B. Steenkamp (1989), Fuzzy clusterwise Regression Approach to Benefit Segmentation, International Journal of Research in Marketing, 6, 241–258.CrossRefGoogle Scholar
  134. Wedel, M. and J. B. Steenkamp (1991), A clusterwise Regression Method for simultaneous fuzzy market structuring and Benefit Segmentation, Journal of Research in Marketing, 28, 385–396.CrossRefGoogle Scholar
  135. Winer, B. J. (1973), Statistical Principles in Experimental Design, New York.Google Scholar
  136. Witt, K. J. (1997), Best Practice in Interviewing via the Internet, Sawtooth Software Conference Proceedings, Seattle, 15–34.Google Scholar
  137. Wittink, D. R. and P. Cattin (1981), Alternative Estimation Methods for Conjoint Analysis: A Monté Carlo Study, Journal of Marketing Research, 18, 101–106.CrossRefGoogle Scholar
  138. Wittink, D. R. and P. Cattin (1989), Commercial Use of Conjoint Analysis: An Update, Journal of Marketing, 53, 91–96.CrossRefGoogle Scholar
  139. Wittink, D. R. and D. Montgomery (1979), Predicting validity of trade-off analysis for alternative Segmentation Schemes, American Marketing Association Educator’s Conference, Chicago, 69–73.Google Scholar
  140. Wittink, D. R., M. Vriens, and W. Burhenne (1994), Commercial Use of Conjoint Analysis in Europe: Results and Critical Reflections, International Journal of Research in Marketing, 11, 41–52.CrossRefGoogle Scholar
  141. Wright, P. and M. A. Kriewall (1980), State-of-mind Effects on the Accuracy with which Utility Functions predict marketplace Choice, Journal of Marketing Research, 17, 277–293.CrossRefGoogle Scholar
  142. Wuebker, G. and V. Mahajan (1998), A conjoint analysis-based Procedure to measure Reservation Price and to optimally Price Product Bundles, in: Fuerderer, R., Herrmann, A. and Wuebker, G., eds., Optimal Bundling-Marketing Strategies for Improving economic performance, Wiesbaden, 157–176.Google Scholar
  143. Wyner, G. A., L. H. Benedetti, and B. M. Trapp (1984), Measuring the quantity and mix of Product Demand, Journal of Marketing, 48, 101–109.CrossRefGoogle Scholar
  144. Yoo, D. I, and H. Ohta (1995), Optimal Pricing and Product Planning for new Mulitattribute Products based on Conjoint Analysis, International Journal of Production Economics, 38, 245–254.CrossRefGoogle Scholar
  145. Young, F. W. (1972), A model for polynomial Conjoint Analysis algorithms, in: Shepard, R., Romney, A. K. and Nerlove, S. B., eds., Multidimensional Scaling-Theory and Applications in Behavioral Sciences, New York, 69–104.Google Scholar
  146. Zandan, P. and L. Frost (1989), Customer Satisfaction Research using disks-by-mail, Proceedings of the Sawtooth Software Conference on perceptual mapping, Sun Valley, 5–17.Google Scholar
  147. Zufryden, F. (1988), Using Conjoint Analysis to predict trial and repeat-purchase Patterns of new frequently purchased Products, Decision Sciences, 19, 55–71.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Anders Gustafsson
    • 1
    • 2
  • Andreas Herrmann
    • 3
  • Frank Huber
    • 4
  1. 1.Service Research CenterKarlstad UniversitySweden
  2. 2.Department of Quality Technology and ManagementLinköping UniversitySweden
  3. 3.Center of Business MetricsUniversity of St. GallenSwitzerland
  4. 4.Center of Market-Oriented Product and Production ManagementUniversity of MainzGermany

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