Conjoint Analysis as an Instrument of Market Research Practice

  • Anders Gustafsson
  • Andreas Herrmann
  • Frank Huber


The essay by the psychologist, Luce, and the statistician, Tukey (1964) can be viewed as the origin of conjoint analysis (Carroll and Green 1995; Green and Srinivasan 1978). 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 (Baier and Gaul 1999; Wittink, Vriens and Burhenne 1994). 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.


Market Research Conjoint Analysis Conjoint Measurement Adaptive Conjoint Analysis Conjoint Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Acito, F. (1977), An Investigation of some Data Collection Issues in Con joint 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. Addelman, S. (1962), Orthogonal Main Effects Plans for Asymmetrical Factorial Experiments,Technometrics, 4, 21–46.CrossRefGoogle Scholar
  4. Agarwal, M. (1988), Comparison of Conjoint Methods,Proceedings of the Sawtooth Software Conference on Perceptual Mapping, Sun Valley, 51–57.Google Scholar
  5. 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
  6. Albers, S. (1983), Schätzung von Nachfragereaktionen auf Variationen des Tarif-und Leistungsangebots im öffentlichen Personennahverkehr,Zeitschrift für Verkehrswissenschaft, 54, 207–230.Google Scholar
  7. Albers, S. and Bielert, W. (1996), Kostenminimale Gestaltung von finanziellen Nebenleistungen für Führungskräfte, Zeitschrift für Betriebswirtschaft, 66, 460–472.Google Scholar
  8. Albers, S. and Brockhoff, K. (1985), Die Gültigkeit der Ergebnisse eines Testmarktsimulators bei unterschiedlichen Daten und Auswertungsmethoden,Schmalenbachs Zeitschrift für betriebswirtschaftliche Forschung, 37, 191–217.Google Scholar
  9. Allenby, G. M., Arora, N. and Ginter, J. L. (1995), Incorporating prior Knowledge into the Analysis of Conjoint Studies,Journal of Marketing Research, 32, 152–162.CrossRefGoogle Scholar
  10. Alpert, M. I., Betak, J. F. and Golden, L. L. (1978),Data gathering Issues in Conjoint Measurement, Working paper, Graduate School of Business, The University of Texas at Austin.Google Scholar
  11. Assmus, E. F. and Key, J. K. (1992),Designs and their Codes, Cambridge.Google Scholar
  12. Aust, E. (1996),Simultane Conjointanalyse, Benefitsegmentierung, Produktlinien-und Preisgestaltung, Frankfurt.Google Scholar
  13. Backhaus, K., Erichson, B., Plinke, W. and Weiber, R. (1996), Multivariate Analysemethoden - Eine anwendungsorientierte Einführung, Berlin.Google Scholar
  14. Baier, D. and Gaul, W. (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.CrossRefGoogle Scholar
  15. Baier, D. and Gaul, W. (1999), Optimal Product Positioning Based on Paired Comparison Data,Journal of Econometrics, 89, 365–392.CrossRefGoogle Scholar
  16. Baier, D. and Gaul, W. (1995), Classification and Representation using Conjoint Data, in Gaul, W. and Pfeifer, D., eds.,From data to knowledge: Theoretical and Practical Aspects of Classification, Data Analysis, and Knowledge Organization, Berlin, 298–307.Google Scholar
  17. Baier, D. and Zirn, M. (1995), Benefitsegmentierung und Neuproduktdesign bei touristischen Problemstellungen, in: Baier, D. and Decker, R., eds.,Marketingprobleme–Innovative Lösungsansätze aus Forschung und Praxis, Karlsruhe, 19–31.Google Scholar
  18. Balderjahn, I. (1991), Ein Verfahren zur empirischen Bestimmung von Preisresponsefunktionen,Marketing ZFP, 13, 33–42.Google Scholar
  19. Balderjahn, I. (1993),Marktreaktionen von Konsumenten: Ein theoretisch methodisches Konzept zur Analyse der Wirkung marketingpolitischer Instrumente, Hannover.Google Scholar
  20. Balderjahn, I. (1994), Der Einsatz der Conjoint-Analyse zur empirischen Bestimmung von Preisresponsefunktionen,Marketing ZFP, 16, 12–Google Scholar
  21. Bateson, J. E., Reibstein, D. and Boulding, W. (1987),Conjoint Analysis Reliability and Validity: a Framework for future Research, in: American Marketing Association, ed.,Review of Marketing, Chicago, 451–481.Google Scholar
  22. Bauer, H. H. and Thomas, U. (1984), Die Präferenzen von Arbeitnehmern gegenüber Tarifvertragskomponenten,Schmalenbachs Zeitschrift für betriebswirtschaftliche Forschung, 36, 200–228.Google Scholar
  23. Bauer, H. H., Huber, F. and Adam, R. (1998), Utility oriented design of service bundles in the hotel industry based on the conjoint measurement method, in: Fuerderer, R., Hellmann, A. and Wuebker, G., eds.,Optimal Bundling —ng Strategies for Improving economic performance, Wiesbaden, 269–297.Google Scholar
  24. Bauer, H. H., Huber, F., Jung, S. and Rapp, M. (1997), Erfolgsgrössen bei der Gewinnung von Reisemittlerorganisationen durch Reiseveranstalter, working paper, Institut für Marketing, Universität Mannheim.Google Scholar
  25. Bauer, H. H., Huber, F. and Keller, T. (1997), Design of Lines as a product-policy Variant to retain Customers in the Automotive Industry, in Johnson, M., Hellmann, A., Huber, F. and Gustafsson, A.,Customer Retention in the Automotive Industry–Quality, Satisfaction and Retention, Wiesbaden, 67–92.Google Scholar
  26. Bekrneier, S. (1989),Nonverbale Kommunkation in der Fernsehwerbung, Heidelberg.CrossRefGoogle Scholar
  27. Böcker, F. (1986), Präferenzforschung als Mittel marktorientierter Unternehmensführung,Schmalenbachs Zeitschrift für betriebswirtschaftliche Forschung, 38, 543–574.Google Scholar
  28. Buchtele, F. and Holzmüller, H. H. (1990), Die Bedeutung der Umweltverträglichkeit von Produkten für die Käuferpräferenz–Ergebnisse einer Conjoint-Analyse bei Holzschutzmitteln,GfK–Jahrbuch der Absatz-und Verbrauchsforschung, 36, 86–102.Google Scholar
  29. Büschken, J. (1994), Conjoint-Analyse–Methodische Grundlagen und Anwendungen in der Marktforschungspraxis,Thexis–Fachbuch für Marketing: Marktforschung, 13, 72–89.Google Scholar
  30. Carmone, F. J., Green, P. E. and Jain, A. K. (1978), Robustness of Conjoint Analysis: Some Monté Carlo Results,Journal of Marketing Research, 15, 300–303.CrossRefGoogle Scholar
  31. 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
  32. Cattin, P. and Bliemel, F. (1978), Metric vs. Nonmetric Procedures for Multiattribute Modeling: Some Simulation Results,Decision Sciences, 9, 1978, 472–480.CrossRefGoogle Scholar
  33. Cattin, P. and Weinberger, M. (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
  34. Cattin, P. and Wittink, D. R. (1977), Further knowledge beyond Conjoint Measurement: Toward a comparison of methods,Advances in Consumer Research, 4, 41–45.Google Scholar
  35. Cattin, P. and Wittink, D. R. (1982), Commercial Use of Conjoint Analysis: A Survey,Journal of Marketing, 46, 44–53.CrossRefGoogle Scholar
  36. Cerro, D. (1988), Conjoint Analysis by Mail,Proceedings of the Sawtooth Software Conference on perceptual mapping, Sun Valley, 139–143.Google Scholar
  37. Cochran, W. G. and Cox, G. M. (1957),Experimental Designs, New York.Google Scholar
  38. Colberg, T. (1977), Validation of Conjoint Measurement Methods: a Simulation and empirical Investigation, Dissertation, University of Washington.Google Scholar
  39. Currim, I. S., Weinberg, C. B. and Wittink, D. R. (1981), Design of Subscription Programs for a Performing Arts Series,Journal of Consumer Research, 8, 67–75.CrossRefGoogle Scholar
  40. Dahan, E. (2000): The predictive power of Internet-based product concept ttesting using visual depiction and animation,The Journal of Product Innovation Management, Vol. 17, 99–114.CrossRefGoogle Scholar
  41. Darmon, R. Y. (1979), Setting Sales Quotas with Conjoint Analysis,Journal of Marketing Research, 16, 133–140.CrossRefGoogle Scholar
  42. Davey, K. S. and Elrod, T. (1991),Predicting Shares from Preferences for Multiattribute Alternatives, working paper, University of Alberta.Google Scholar
  43. De Soete, G. and Carroll, J. D. (1983), A Maximum Likelihood Method for Fitting the Wandering Vector Model,Psychometrika, 48, 553–566.CrossRefGoogle Scholar
  44. De Soete, G. and De Sarbo, W. (1991), A latent Class Probit Model for Analyzing pick Any/N data,Journal of Classifcation, 8, 45–63.CrossRefGoogle Scholar
  45. De Soete, G. and Winsberg, S. (1994) A latent Class Vector Model for Preference Ratings,Journal of Classification, 8, 195–218.Google Scholar
  46. Dellaert, B., Borgers, A. and Timmermans, H. (1995), A Day in the City: Using Conjoint Experiments to urband Tourists’Choice of Activity Packages,Tourism Management, 16, 347–353.CrossRefGoogle Scholar
  47. DeSarbo, W. S., Carroll, J. D., Lehmann, D. R. and O’Shaughness, J. (1982), Three-way Multivariate Conjoint Analysis,Marketing Science, 1, 323–350.CrossRefGoogle Scholar
  48. DeSarbo, W. S., Oliver, R. L. and Rangaswamy, A. (1989), A simulated annealing Methodology for Clusterwise Linear Regression,Psychometrika, 54, 707–736.CrossRefGoogle Scholar
  49. DeSarbo, W. S., Ramaswamy, A. and Chaterjee, K. (1992),Latent Class Multivariate Conjoint Analysis with Constant Sum Ratings Data, working paper, University of Michigan.Google Scholar
  50. DeSarbo, W. S., Ramaswamy, V. and Cohen, S. H. (1995), Market Segmentation with Choice-based Conjoint Analysis,Marketing Letters, 6, 137–147.CrossRefGoogle Scholar
  51. DeSarbo, W. S., Wedel, M., Vriens, M. and Ramaswamy, V. (1992), Latent Class Metric Conjoint Analysis,Marketing Letters, 3, 273–288.CrossRefGoogle Scholar
  52. DeSarbo, W., Huff, L., Rolandelli, M.M. and Choi, J. (1994), On the Measurement of Perceived Service Quality, in: Rust, R.T. and Oliver, R.L., ed.,Service Quality: New directions in theory and practice, London, 201–222.Google Scholar
  53. Diamantopoulos, A., Schlegelmilch, B. and DePreez, J. P. (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
  54. Diller, H. (1991),Preispolik, Stuttgart.Google Scholar
  55. Diller, H. (1991), Preispolitik, Stuttgart. Eggenberger, C. and Hauser, C. (1996), Conjoint Measurement zur Gestaltung von internationalen Telefondienstleistungen,Schmalenbachs Zeitschrift für betriebswirtschaftliche Forschung, 48, 541–859.Google Scholar
  56. Finkbeiner, C. T. (1988), Comparison of Conjoint Choice Simulators,Proceedings of the Sawtooth Software Conference on perceptual mapping, Sun Valley, 75–105.Google Scholar
  57. Finkbeiner, C. T. and Platz, P. J. (1986), Computerized versus Paper and Pencil Methods: a Comparison Study, paper presented at theAssociation of Consumer Research Conference, Toronto.Google Scholar
  58. 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.CrossRefGoogle Scholar
  59. Gaul, W. and Aust, E. (1994),Latent Class Inequality Constrained Least Square Regression, working paper, University of Karlsruhe.Google Scholar
  60. Gaul, W., Lutz, U. and Aust, E. (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
  61. Goldberg, S. M., Green, P. and Wind, Y. (1984), Conjoint Analysis of Price Premiums for Hotel Amenities,Journal of Business, 57, 111–147.Google Scholar
  62. Green, P. E. and Rao, V. R. (1971), Conjoint Measurement for Quantifying Judgemental Data,Journal of Marketing Research, 8, 355–363.CrossRefGoogle Scholar
  63. Green, P. E. and Srinivasan, V. (1978), Conjoint Analysis in Consumer Research: Issues and Outlook,Journal of Consumer Research, 5, 103–123.CrossRefGoogle Scholar
  64. Green, P. E. and Srinivasan, V. (1990), Conjoint Analysis in Marketing: New Developments With Implications for Research and Practice,Journal of Marketing, 54, 3–19.CrossRefGoogle Scholar
  65. Green, P. E. and Tull, D. S. (1982),Methoden und Techniken der Marketingforschung, Stuttgart.Google Scholar
  66. Green, P. E. and Wind, Y. (1975), New Way to Measure Consumers’ Judgments,Harvard Business Review, 53, 107–117.Google Scholar
  67. Green, P. E. and Krieger, A. M. (1990), A hybrid Conjoint Model for price-demand Estimation,European Journal of Operations Research, 44, 28–38.CrossRefGoogle Scholar
  68. Green, P. E. and Helsen, K. (1989), Cross-validation Assessment of Alternatives to individual-level Conjoint Analysis: a case study,Journal of Marketing Research, 26, 346–350.CrossRefGoogle Scholar
  69. Green, P. E., Helsen, K. and Shandler, B. (1988), Conjoint Internal Validity under alternative Profile Presentations,Journal of Consumer Research, 15, 392–397.CrossRefGoogle Scholar
  70. Green, P. E. and Krieger, A. M. (1987), A simple Heuristic for Selecting ‘good’ Products in Conjoint Analysis,Application of Management Science, 5, 131–153.Google Scholar
  71. Green, P. E. and Krieger, A. M. (1992), An Application to Optimal Product Positioning Model to Pharmaceutical Products,Marketing Science, 11, 117–132.CrossRefGoogle Scholar
  72. Green, P. E. and Krieger, A. M. (1993), A simple Approach to Target Market Advertising Strategy,Journal of the Market Research Society, 35, 161–170.Google Scholar
  73. Green, P. E. and Krieger, A. M. (1993), Conjoint Analysis with product-positioning Applications, Eliashberg, J., Lilien, G.L., eds.,Marketing, Handbooks in OR & MS, 5, 467–515.Google Scholar
  74. Green, P. E. and Savitz, J. (1994), Applying Conjoint Analysis to Product Assortment and Pricing in Retailing Research,Pricing Strategy and Practice, 4–19.Google Scholar
  75. Gutsche, J. (1995),Produktpräferenzanalyse: Ein modelltheoretisches und methodisches Konzept zur Marktsimulation mittels Präferenzerfassungsmodellen, Berlin.Google Scholar
  76. 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
  77. Hagerty, M. R. (1986), The cost of simplifying Preference Models,Marketing Science, 5, 298–324.CrossRefGoogle Scholar
  78. Hausruckinger, G. and Herker, A. (1992), Die Konstruktion von Schätzdesigns für Conjoint-Analysen auf der Basis von Paarvergleichen,Marketing ZFP, 14, 99–110.Google Scholar
  79. Herrmann, A., Franken, B., Huber, F., Ohlwein, M. and Schellhase, R. (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
  80. Herrmann, A. and Huber, F. (1997), Utility orientated Product Distribution,The International Review of Retail, Distribution and Consumer Research, 8, 369–382.CrossRefGoogle Scholar
  81. Heirmmiann, A., Huber, F. and Braunstein, C. (1997), Standardization and Differentiation of Services: a crosscultural 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
  82. Hruschka, H. (1986), Market definition and Segmentation Using Fuzzy Clustering Methods,International Journal of Research in Marketing, 3, 117–134.CrossRefGoogle Scholar
  83. Huber, F. and Fischer, M. (1999), Measurement of Advertising Response - Results of a conjointanalytical Study,Proceedings of the Academy of Marketing Science World Conference, Malta.Google Scholar
  84. Huber, G. P. (1974), Multiattribute Utility Models: a Review of filed and field-like Studies,Management Science, 20, 1393–1402.CrossRefGoogle Scholar
  85. 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, Seatle, 243–256.Google Scholar
  86. Huber, J., Ariely, D. and Fischer, G. (1997),The Ability of People to express Values with Choices, Matching and Ratings, working paper, Fuqua School of Business, Duke University.Google Scholar
  87. Hujer, R., Gramming, J., Fryns, H. and Hetrich, R. (1996), Preisfindung und optimale Marketingstrategien für neue pharamzeutische Produkte,Schmalenbachs Zeitschrift für betriebswirtschaftliche Forschung, 48, 219–232.Google Scholar
  88. Jain, A. R., Acito, F., Malhorta, N. and Mahajan, V. (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
  89. Jain, A. R., Malhorta, N. and Pinson, C. (1980),Stability and Reliability of part-worth utility in Conjoint Analysis: a longitudinal Investigation, working paper, European Institute of Business Administration, Brüssel.Google Scholar
  90. Johnson, M., Hellmann, A. and Huber, F. (1998), Growth through Product Sharing Services,Journal of Service Research, 1, 167–177.CrossRefGoogle Scholar
  91. Johnson, R. M. (1974), Trade-Off Analysis of Consumer Values,Journal of Marketing Research, 11, 121–127.CrossRefGoogle Scholar
  92. Kahneman, D. and Tversky, A. (1979), Prospect Theory: An Analysis of Decision under Risk,Econometrica, 47, 263–291.CrossRefGoogle Scholar
  93. Kamakura, W. A. (1988), A least squares Procedure for Benefit Segmentation with Conjoint Experiments,Journal of Marketing Research, 25, 157–167.CrossRefGoogle Scholar
  94. Kamakura, W. A. and Srivastava, R. K. (1986), An ideal-point probabilistic Choice Model for heterogeneous Preferences,Marketing Science, 5, 199–218.CrossRefGoogle Scholar
  95. Kohli, R. and Sukumar, R. (1990), Heuristics for Product-Line-Design using Conjoint Analysis,Management Science, 36, 1464–1478.CrossRefGoogle Scholar
  96. Kohli, R. and Mahajan, V. (1991), A reservation-price Model for optimal Pricing of Mulitattribute Products in Conjoint Analysis,Journal of Marketing Research, 28, 347–354.CrossRefGoogle Scholar
  97. Krishnamurthi, L. (1988), Conjoint Models of Family Decision Making,International Journal of Research in Marketing, 5, 185–198.CrossRefGoogle Scholar
  98. Krishnamurthi, L. and Wittink, D. R. (1991), The Value of Idiosyncratic Functional Forms in Conjoint Analysis,International Journal of Research in Marketing, 8, 301–313.CrossRefGoogle Scholar
  99. Kruskal, J. B. (1965), Analysis of Factorial Experiments by Estimating Monotone Transformations of the Data,Journal of the Royal Statistical Society, Series B, 251–263.Google Scholar
  100. Kucher, E. (1991), Preisfindung bei neuen Produkten,Die Pharmazeutische Industrie, 53, 3–8.Google Scholar
  101. Kucher, E. and Simon, H. (1987), Conjoint-Measurement–Durchbruch bei der Preisentscheidung,HARVARDmanager, 9, 28–36.Google Scholar
  102. Kuhfeld, W. D. (1997), Efficient Experimental Designs using Computerized Searches,Sawtooth Software Conference Proceedings, Seatle, 71–86.Google Scholar
  103. Laakmann, K. (1995), Value added Services als Profilierungsinstrument im Wettbewerb, Wiesbaden.Google Scholar
  104. Levy, M., Webster, J. and Kerin, R. A. (1983), Formulating Push Marketing Strategies: a Method and Application,Journal of Marketing, 47, 25–34.CrossRefGoogle Scholar
  105. Louviere, J. (1984), Using discrete Choice Experiments and mulitnominal Logit Models to forecast Trial in a cometitive Retail Environment: a fast food Restaurant Illustration,Journal of Retailing, 60, 81–107.Google Scholar
  106. Luce, R. D. and Tukey, J. W. (1964), Simultaneous Conjoint Measurement A New Type of Fundamental Measurement,Journal of Mathematical Psychology, 1, 1–27.CrossRefGoogle Scholar
  107. Mahajan, V., Green, P. E. and Goldberg, S. M. (1982), A Conjoint Model for Measuring Self and Cross-Price/Demand Relationships,Journal of Marketing Research, 19, 334–342.CrossRefGoogle Scholar
  108. Mazanec, J., Porzer, P. and Wiegele, 0. (1976), Präferenzmessung im mehrdimensionalen Einstellungsraum,Der Markt, 57, 1–32.Google Scholar
  109. McCullough, J. and Best, R. (1979), Conjoint Measurement: Temporal Stability and Structural Reliability,Journal of Marketing Research, 16, 26–31.CrossRefGoogle Scholar
  110. Mengen, A. (1993),Konzeptgestaltung von Dienstleistungsprodukten — Eine Conjoint-Analyse im Luftfrachtmarkt unter Berücksichtigung der Qualitütsunsicherheit beim Dienstleistungskauf, Stuttgart.Google Scholar
  111. Mishra, S., Umesh, U. N. and Stem, D. E. (1989), Attribute Importance weights in Conjoint Analysis: Bias and Precision,Advances in Consumer Research, 16, 605–611.Google Scholar
  112. 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
  113. Montgomery, D. B. and Wittink, D. R. (1980), The predictive Validity of Conjoint Analysis for alternative Aggregation Schemes, Market Science Institute, ed.,Market Measurement and Analysis, Cambridge, 298–309.Google Scholar
  114. Montgomery, D. B., Wittink, D. R. and Glaze, T. (1977),A predictive Test of individual level Concept Evaluation and trade-off Analysis, Research paper No. 415, Graduate School of Business, Standford University.Google Scholar
  115. Moore, W. L., Gray-Lee, J. and Louviere, J. J. (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
  116. Moore, W. L. and Holbrook, M. B. (1990), Conjoint Analysis on objects with environmentally correlated Attributes: The questionable Importance of representative Design,Journal of Consumer Research, 6, 490–497.CrossRefGoogle Scholar
  117. Müller-Hagedorn, L., Sewing, E. and Toporowski, W. (1993), Zur Validität von Conjoint-Analysen,Schmalenbachs Zeitschrift für betriebswirtschaftliche Forschung, 45, 123–148.Google Scholar
  118. Neal, W. D. and Bathe, S. (1997), Using the Value Equation to evaluate Campaign Effectiveness,Journal of Advertising Research, 37, 80–85.Google Scholar
  119. Ogawa, K. (1987), An Approach to Simultaneous Estimation and Segmentation in Conjoint Analysis,Marketing Science, 6, 66–81.CrossRefGoogle Scholar
  120. Oppedijk van veen, W. M. and Beazley, D. (1977), An Investigation of alternative Methods of Applying the trade-off Model,Journal of Market Research Society, 19, 2–9.Google Scholar
  121. Oppewal, H. (1995),Conjoint experiments and retail planning: Modeling consumer choice of shopping centre and retailer reactive behavior, thesis, Eindhoven.Google Scholar
  122. Orme, B. K., Alpert, M. I. and Chistensen, E. (1997), Assessing the validity of Conjoint Analysis–continued,Sawtooth Software Conference Proceedings, Seatle, 209–226.Google Scholar
  123. Page A. and Rosenbaum, H. F. (1987), Redesigning Product Lines with Conjoint Analysis: how Sunbeam does it,Journal of Product Innovation Management, 4, 120–137.CrossRefGoogle Scholar
  124. Page, A. and Rosenbaum, H. F. (1989), Redesigning Product Lines with Conjoint Analysis: a reply to Wittink,Journal of Product Innovation Management, 6, 293–296.CrossRefGoogle Scholar
  125. Parker, B. R. and Srinivasan, V. (1976), A consumer Preference Approach to the Planning of rural primary health-care facilities,Operations Research, 24, 991–1025.CrossRefGoogle Scholar
  126. Pearson, R. W. and Boruch, R. F. (1986),Survey Research designs: To-, wards a better Understanding of their Cost and Benefits, Berlin.Google Scholar
  127. Perreault, W. D. and Russ, F. A. (1977), Improving Physical Distribution Service Decisions with trade-off Analysis,International Journal of physical Distribution and Materials Management, 7, 3–19.Google Scholar
  128. Perrey, J. (1996), Erhebungsdesign-Effekte bei der Conjoint-Analyse,Marketing ZFP, 18, 105–116.Google Scholar
  129. Pinnell, J. (1994), Multi-Stage Conjoint Methods to Measure Price Sensitivity, in Weiss, S., ed.,Sawtooth News, 10, 5–6.Google Scholar
  130. Punj, G. and Stewart, D. W. (1983), Cluster Analysis in Marketing Research: Review and Suggestions for Application,Journal of Marketing Research, 20, 134–148.CrossRefGoogle Scholar
  131. Reutterer, T. (2000): The use of conjoint-analysis for measuring preferences in supply chain design,Industrial Marketing Management, Vol. 29, 1, 27–43.CrossRefGoogle Scholar
  132. Robinson, P. J. (1980), Applications of Conjoint Analysis to Pricing Problems, in: Montgomery, D. B. and Wittink, D. R., eds.,Proceedings of the first ORSA/TMS Special interest conference on market measurement and analysis, Report 80–103, Cambridge, 183–205.Google Scholar
  133. Safizadeh, M. H. (1989), The internal Validity of the trade-off Method of Conjoint Analysis,Decision Science, 20, 451–461.CrossRefGoogle Scholar
  134. Sands, S. and Warwick, K. (1981), What product Benefits to offer to whom: an Application of Conjoint Segmentation,California Management Review, 24, 69–74.CrossRefGoogle Scholar
  135. Schubert, B. (1991),Entwicklung von Konzepten für Produktinnovationen mittels Conjoint-Analyse, Stuttgart.Google Scholar
  136. Schubert, B. and Wolf, A. (1993), Erlebnisorientierte Produktgestaltung, in: Arnold, U. and Eierhoff, K., eds., Marketingfocus: Produktmanagment, Stuttgart, 121–151.Google Scholar
  137. Schweikl, H. (1985), Computergestützte Präferenzanalyse mit individuell wichtigen Produktmerkmalen, Berlin.Google Scholar
  138. Segal, M. N. (1982), Reliability of Conjoint Analysis: contrasting Data Collection Procedures, Journal of Marketing Research, 13, 211–224.Google Scholar
  139. Shah, K. R. and Sinha, B. K. (1989), Theory of Optimal Designs, Berlin.CrossRefGoogle Scholar
  140. Simon, H. (1992a), Preismanagement, Wiesbaden.Google Scholar
  141. Simon, H. (1992b), Pricing Opportunities–And How to Exploit Them,Sloan Management Review, 34, 55–65.Google Scholar
  142. Simon, H. and Kucher, E. (1988), Die Bestimmung empirischer Preisabsatzfunktionen,Zeitschrift für Beriebswirtschaft, 58, 171–183.Google Scholar
  143. Simon, H. and Tacke, G. (1992), Mit nichtlinearer Preisbildung zu höherem Gewinn,HARVARDmanager, 14, 48–62.Google Scholar
  144. Slovic, P., Fleissner, D. and Bauman, S. (1972), Analyzing the use of Information in Investment Decision Making: a methodological proposal,Journal of Business, 45, 283–301.CrossRefGoogle Scholar
  145. Srinivasan, V., Jain, A. K. and Malhotra, N. K. (1983), Improving predictive Power of Conjoint Analysis by constrained Parameter Estimation,Journal of Marketing Research, 20, 433–438.CrossRefGoogle Scholar
  146. Srinivasan, V. and Park, C. S. (1997), Surprising Robustness of the self-explicated Approach to Customer Preference Structure Measurement,Journal of Marketing Research, 34, 286–291.CrossRefGoogle Scholar
  147. Srinivasan, V. and Shocker, A. D. (1973), Linear Programming Techniques for Multidimensional Analysis of Preferences,Psychometrika, 38, 337–369.CrossRefGoogle Scholar
  148. Srinivasan, V., Shocker, A. D. and Weinstein, A. G. (1973), Measurement of a Composite Criterion of Managerial Success,Organizational Behavior and Human Performance, 9, 147–167.CrossRefGoogle Scholar
  149. Stahl, B. (1988), Conjoint Analysis by Telephone,Proceedings of the Sawtooth Software Conference on perceptual mapping, Sun Valley, 131–138.Google Scholar
  150. Stanton, W. W. and Reese, R. M. (1983), Three Conjoint Segmentation Approaches to the Evaluation of Advertising Theme Creation,Journal of Business Research, 11, 201–216.CrossRefGoogle Scholar
  151. Steckel, J. H., DeSarbo, W. and Mahjan, V. (1990), On the Creation of acceptable Conjoint Analysis Experimental Designs,Decision Sciences, 22, 435–442.CrossRefGoogle Scholar
  152. Steenkamp, J. B. and Wedel, M. (1991), Segmenting Retail Markets on Store Image using a consumer-based Methodology,Journal of Retailing, 7, 300–320.Google Scholar
  153. Steenkamp, J. B. and Wedel, M. (1993), Fuzzy clusterwise Regression in Benefit Segmentation Application and Investigation into its Validity,Journal of Business Research, 26, 237–249.CrossRefGoogle Scholar
  154. Tacke, G. (1989),Nichtlineare Preisbildung, Wiesbaden.CrossRefGoogle Scholar
  155. Teichert, Th. (1998), Schätzgenauigkeit von Conjoint-Analysen,Zeitschrift für Betriebswirtschaft, 68 (11), 1245–1266.Google Scholar
  156. Thomas, L. (1983), Der Einfluß von Kindern auf die Produktpräferenz ihrer Mütter, Berlin.Google Scholar
  157. Thomas, U. and Dröll, C. (1989), Der Einfluß von Informationen auf die Präferenzstruktur von Verbrauchern, Marketing ZFP, 10, 239–248.Google Scholar
  158. Tscheulin, D. K. and Helmig, B. (1998), The optimal Design of Hospital Advertising by Means of Conjoint Measurement, Journal of Advertising Research, 38, 35–46.Google Scholar
  159. Tscheulin, D. K. and Blaimont, C. (1993), Die Abhängigkeit der Prognosegüte von Conjoint-Studien von demographischen Probanden-Charakteristika, Zeitschrift für Betriebswirtschaftslehre, 63, 839847.Google Scholar
  160. Tversky, A. and Kahneman, D. (1991), Loss Aversion and Riskless Choice: A Reference Dependent Model, Quarterly Journal of Economics, 6, 1039–1061.CrossRefGoogle Scholar
  161. Van der Lans, I. A., Verlegh, P. W. and Schifferstein, H. N. (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
  162. Verhallen, T. and DeNooij, G. J. (1982), Retail Attributes and shopping Patronage, Journal of Economic Psychology, 2, 439–455.Google Scholar
  163. Vriens, M. (1995), Conjoint analysis in Marketing, Ph. D thesis, Capelle.Google Scholar
  164. Vriens, M., Oppewal, H. and Wedel, M. (1998), Ratings-based versus choice-based Latent Class Conjoint Models–an empirical comparison, Journal of the Market Research Society, 40, 237–248.Google Scholar
  165. Vriens, M., van der Scheer, H. R., Hoekstra, J. C. and Bult, J. R. (1998), Conjoint Experiments for direct mail Response Optimization, European Journal of Marketing, 32, 323–339.CrossRefGoogle Scholar
  166. Vriens, M., Wedel, M. and Wilms, T. (1996), Metric Conjoint Segmentation Methods: a Monte Carlo comparison, Journal of Marketing Re search, 33, 73–85.Google Scholar
  167. Vriens, M. and Wittink, D. (1992), Data Collection in Conjoint Analysis, unpublished manuscript.Google Scholar
  168. Waldmann, K. H. (1992), Qualitätsregelkarten mit Gedächtnis, eine einführende Darstellung moderner Methoden der Fertigungsüberwachung, Schmalenbachs Zeitschrift für betriebswirtschaftliche Forschung, 44, 867–883.Google Scholar
  169. Wedel, M. and Kistemaker, C. (1989), Consumer Benefit Segmentation using clusterwise Linear Regression, International Journal of Research in Marketing, 6, 45–59.CrossRefGoogle Scholar
  170. Wedel, M. and Steenkamp, J. B. (1989), Fuzzy clusterwise Regression Approach to Benefit Segmentation, International Journal of Research in Marketing, 6, 241–258.CrossRefGoogle Scholar
  171. Wedel, M. and Steenkamp, J. B. (1991), A clusterwise Regression Method for simultaneous fuzzy market structuring and Benefit Segmentation, Journal of Research in Marketing, 28, 385–396.CrossRefGoogle Scholar
  172. Weisenfeld, U. (1989), Die Einflüsse von Verfahrensvariationen und der Art des Kaufentscheidungsprozesses auf die Reliabilität der Ergebnisse bei der Conjoint-Analyse, Berlin.Google Scholar
  173. Wetzels, M. (2000): Measuring service quality trade-offs in Asian distribution channels: A multi-layer perspective, Total Quality Manage ment, Vol. 11, 3, 307–318.CrossRefGoogle Scholar
  174. Winer, B. J. (1973), Statistical Principles in Experimental Design, New York.Google Scholar
  175. Wirth, U. (1996), Kundenorientierte Produktgestaltung mittels Conjoint-Measurement: Neuproduktplanung bei Mercedes-Benz, in Bauer, H. H., Dichtl., E. and Heimann, A., eds., Automboilmarktforschung, München, 53–66.Google Scholar
  176. Witt, K. J. (1997), Best Practice in Interviewing via the Internet, Sawtooth Software Conference Proceedings, Seatle, 15–34.Google Scholar
  177. Wittink, D. R. and Cattin, P. (1981), Alternative Estimation Methods for Conjoint Analysis: A Monté Carlo Study, Journal of Marketing Research, 18, 101–106.CrossRefGoogle Scholar
  178. Wittink, D. R. and Cattin, P. (1989), Commercial Use of Conjoint Analysis: An Update, Journal of Marketing, 53, 91–96.CrossRefGoogle Scholar
  179. Wittink, D. R. and Montgomery, D. (1979), Predicting validity of trade-off analysis for alternative Segmentation Schemes, American Marketing Association Educator’s Conference, Chicago, 69–73.Google Scholar
  180. Wittink, D. R., Vriens, M. and Burhenne, W. (1994), Commercial Use of Conjoint Analysis in Europe: Results and Critical Reflections, International Journal of Research in Marketing, 11, 41–52.CrossRefGoogle Scholar
  181. Wright, P. and Kriewall, M. A. (1980), State-of-mind Effects on the Accuracy with which Utility Functions predict marketplace Choice, Journal of Marketing Research, 17, 277–293.CrossRefGoogle Scholar
  182. Wuebker, G. and Mahajan, V. (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
  183. Wyner, G. A., Benedetti, L. H. and Trapp, B. M. (1984), Measuring the quantity and mix of Product Demand, Journal of Marketing, 48, 101–109.CrossRefGoogle Scholar
  184. Yoo, D. I and Ohta, H. (1995), Optimal Pricing and Product Planning for new Mulitattribute Products based on Conjoint Analysis, International Journal of Production Economics, 38, 245–254.CrossRefGoogle Scholar
  185. Young, F. W. (1972), A model for polynomial Conjoint Analyisis 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
  186. Zandan, P. and Frost, L. (1989), Customer Satisfaction Research using disks-by-mail, Proceedings of the Sawtooth Software Conference on perceptual mapping, Sun Valley, 5–17.Google Scholar
  187. Zufryden, F. (1988), Using Conjoint Analysis to predict trial and repeatpruchase Patterns of new frequently purchased Products, Decision Sciences, 19, 55–71.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Anders Gustafsson
  • Andreas Herrmann
  • Frank Huber

There are no affiliations available

Personalised recommendations