Skip to main content
SpringerLink
Log in

Conjoint Analysis, Related Modeling, and Applications

  • Chapter
Marketing Research and Modeling: Progress and Prospects

Part of the book series: International Series in Quantitative Marketing ((ISQM,volume 14))

Abstract

Conjoint analysis has as its roots the need to solve important academic and industry problems. Paul Green’s work on conjoint analysis grew out of his contributions to the theory and practice of multidimensional scaling (MDS) to address marketing problems, as discussed in Chapter 3. MDS offered the ability to represent consumer multidimensional perceptions and consumer preferences relative to an existing set of products.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Acito, E, and Jain, A. K. (1980), “Evaluation of conjoint analysis results: A comparison of methods,” Journal of Marketing Research, 17 (February), 106–112.

    Article  Google Scholar 

  • Addelman, S. (1962), “Orthogonal main-effect plans for asymmetrical factorial experiments,” Technometrics, 4, 1 (February), 21–46.

    Google Scholar 

  • Addelman, S. (1962), “Symmetrical and asymmetrical fractional factorial plans,” Technometrics, 4, 1 (February), 47–58.

    Google Scholar 

  • Akaah, I. P., and Korgaonkar, P. K. (1983), “An empirical comparison of the predictive validity of self-explicated, Huber-hybrid, traditional conjoint, and hybrid conjoint models,” Journal of Marketing Research, 20 (May), 187–197.

    Article  Google Scholar 

  • Allenby, G. M., and Rossi, P. E. (1999), “Marketing models of consumer heterogeneity,” Journal of Econometrics, 89 (March/April), 57–78.

    Article  Google Scholar 

  • Andrews, R. L., Ansari, A., and Currim, I. S. (2002), “Hierarchical Bayes versus finite mixture conjoint analysis models: A comparison of fit, prediction, and partworth recovery,” Journal of Marketing Research, 39 (February), 87–98.

    Article  Google Scholar 

  • Arora, N., Allenby, G. M., and Ginter, J. L. (1998), “A hierarchical Bayes model of primary and secondary demand,” Marketing Science, 17, 1, 29–44.

    Article  Google Scholar 

  • Arora, N., and Huber, J. (2001), “Improving parameter estimates and model prediction by aggregate customization in choice experiments,”Journal of Consumer Research,28 (September), 273–283.

    Google Scholar 

  • Bateson, J. E. G., Reibstein, D., and Boulding, W. (1987),”Conjoint analysis reliability and validity: A framework for future research,”in M. Houston (ed.), Review of Marketing,451–481.

    Google Scholar 

  • Ben-Akiva, M., and Lerman, S. R. (1985), Discrete Choice Analysis, Cambridge, MA: MIT Press.

    Google Scholar 

  • Berry, S., Levinsohn J., and Pakes, A. (1995),”Automobile prices in market equilibrium,”Econometrica 63 (4), 841–890.

    Google Scholar 

  • Berry, S., Levinsohn, J., and Pakes, A. (1998), ”Differentiated products demand systems from a combination of micro and macro data: The new car market,”Working Paper, New Haven, CT: Yale University.

    Google Scholar 

  • Bucklin, R. E., and Srinivasan, V. (1991),”Determining interbrand substitutability through survey measurement of consumer preference structures,”Journal of Marketing Research,28 (February), 58–71.

    Article  Google Scholar 

  • Buckman, R. (2000),”Knowledge Networks’ Internet polls will expand to track Web surfers,”Wall Street Journal (September 7).

    Google Scholar 

  • Carmone, F. J., and Green, P. E. (1981), “Model misspecification in multiattribute parameter estimation,” Journal of Marketing Research, 18 (February), 87–93.

    Article  Google Scholar 

  • Carmone, F. J., Green, P. E., and Jain, A. K. (1978), “Robustness of conjoint analysis: Some Monte Carlo results,” Journal of Marketing Research, 15 (May), 300–303.

    Article  Google Scholar 

  • Carroll, J. D., and Green, P. E. (1995), “Psychometric methods in marketing research: Part I,. conjoint analysis,” Journal of Marketing Research, 32 (November), 385–391.

    Article  Google Scholar 

  • Cattin, P., and Punj, G. (1984), “Factors influencing the selection of preference model form for continuous utility functions in conjoint analysis,” Marketing Science, 3, 1 (Winter), 73–82.

    Google Scholar 

  • Cattin, P., and Wittink, D. R. (1982), “Commercial use of conjoint analysis: A survey,” Journal of Marketing, 46 (Summer), 44–53.

    Article  Google Scholar 

  • Corstjens, M. L., and Gautschi, D. A. (1987), “Formal choice models in marketing,” Marketing Science, 2 (Winter), 19–56.

    Article  Google Scholar 

  • Dahan, E., and Hauser, J. R. (2002), “The Virtual Customer,” forthcoming, Journal of Product Innovation Management, 19, 5 (September), 332–354.

    Google Scholar 

  • Dahan, E., and Srinivasan, V. (2000), “The predictive power of Internet-based product concept testing using visual depiction and animation,” Journal of Product Innovation Management, 17, 99–109.

    Article  Google Scholar 

  • Dawes, R. M., and Corrigan, B. (1974), “Linear models in decision making,”Psychological Bulletin 81 (March), 95–106.

    Google Scholar 

  • Einhorn, H. J. (1971), “Use of nonlinear, noncompensatory, models as a function of task and amount of information,”Organizational Behavior and Human Performance, 6, 1–27.

    Google Scholar 

  • Eliashberg, J. (1980), “Consumer preference judgments: An exposition with empirical applications,” Management Science, 26,1 (January), 60–77.

    Google Scholar 

  • Eliashberg, J., and Hauser, J. R. (1985), “A measurement error approach for modeling consumer risk preference,” Management Science, 31, 1 (January), 1–25.

    Google Scholar 

  • Elrod, T., Louviere, J., and Davey, K. S. (1992), “An empirical comparison of ratings-based and choice-based conjoint models,”Journal of Marketing Research 29, 3 (August), 368–377.

    Google Scholar 

  • Evgenoiu, T., Boussios, C., and Zacharia, G. (2002), ”Generalized robust conjoint estimation,” Working Paper, Fontainebleau, France: INSEAD.

    Google Scholar 

  • Farquhar, P. H. (1977), “A survey of multiattribute utility theory and applications,” Studies in Management Sciences, 59–89.

    Google Scholar 

  • Foutz, Y., Zhang N., Rao V. R., and Yang, S. (2002), “Incorporating consumer reference effects into choice-based conjoint analysis: An application to online and offline channel choices”Working paper, Ithaca, NY: Johnson Graduate School of management, Cornell University.

    Google Scholar 

  • Franke, N., and von Hippel, E. (2002), “Satisfying heterogeneous user needs via innovation toolkits: The case of Apache security software,” Working Paper #4341–02, Cambridge, MA: MIT Sloan School of Management.

    Google Scholar 

  • Freund, R. (1993), “Projective transformations for interior-point algorithms, and a superlinearly convergent algorithm for the w-center problem,” Mathematical Programming, 58, 385–414.

    Article  Google Scholar 

  • Gonier, D. E. (1999), “The emperor gets new clothes,” Paper presented at the Advertising Research Foundation’s On-line Research Day and available at www.dmsdallas.com. (January).

    Google Scholar 

  • Green, P. E. (1974), “On the design of choice experiments involving multifactor alternatives,” Journal of Consumer Research, 1 (September).

    Google Scholar 

  • Green, P. E. (1984), “Hybrid models for conjoint analysis: An expository review,” Journal of Marketing Research, 21 (May), 155–169.

    Article  Google Scholar 

  • Green, P. E., Carmone, F. J., and Fox, L. B. (1969), “Television programme similarities: An application of subjective clustering,” Journal of the Market Research Society, 11, 1, 70–90.

    Google Scholar 

  • Green, P. E., Carroll, J. D., and Carmone, F. J. (1977–78), “Superordinate factorial designs in the analysis of consumer judgments,” Journal of Economics and Business, 30, 197–778.

    Google Scholar 

  • Green P. E., Carroll, J. D., and Goldberg, S. M. (1981),”A general approach to product design optimization via conjoint analysis,” Journal of Marketing,45 (Summer), 17–37.

    Article  Google Scholar 

  • Green, P. E., DeSarbo, W. S, and Kedia, P. K. (1980),”On the insensitivity of brand choice simulations to attribute importance weights,” Decision Sciences, (July), 11, 439–450.

    Google Scholar 

  • Green P. E., and Devita, M. (1974),”A complementary model of consumer utility for item collections,” Journal of Consumer Research,1 (December), 56–67.

    Article  Google Scholar 

  • Green, P. E., and Devita, M. T. (1975), “An interaction model of consumer utility,”Journal of Consumer Research,2 (September), 146–153.

    Article  Google Scholar 

  • Green, P. E., Goldberg, S. M., and Montemayor, M. (1981), “A hybrid utility estimation model for conjoint analysis,” Journal of Marketing, 45, 1 (Winter), 33–41.

    Google Scholar 

  • Green, P. E., Goldberg, S. M., and Wiley, J. B. (1982), “A cross validation test of hybrid conjoint models,” in Advances in Consumer Research, 10, R. P. Bagozzi and A. M. Tybout (eds.), Ann Arbor, MI: Association for Consumer Research, pp. 147–150.

    Google Scholar 

  • Green, P. E., and Helsen, K. (1989), “Cross-validation assessment of alternatives to individual-level conjoint analysis: A case study,”Journal of Marketing Research,24, 3 (August), 346–350.

    Google Scholar 

  • Green, P. E., Helsen, K., and Shandler, B. (1988), “Conjoint internal validity under alternative profile presentations,” Journal of Consumer Research, 15 (December), 392–397.

    Article  Google Scholar 

  • Green P. E., and Krieger, A. M. (1991), “Product design strategies for target-market positioning,” Journal of Product Innovation Management, 8 (Fall), 189–202.

    Article  Google Scholar 

  • Green P. E., and Krieger, A. M. (1992), “An application of a product positioning model to pharmaceutical products,” Marketing Science, 11, 2 (Spring), 117–132.

    Article  Google Scholar 

  • Green, P. E., and Krieger, A. (1996), “Individual hybrid models for conjoint analysis,” Management Science, 42, 6 (June), 850–867.

    Google Scholar 

  • Green, P. E., and Krieger, A. (1985), “Choice rules and sensitivity analysis in conjoint simulations,” Journal of the Academy of Marketing Science, (Spring) 1988.

    Google Scholar 

  • Green, P. E., and Krieger, A. (1985), “Models and heuristics for product line selection,” Marketing Science, 4, 1 (Winter), 1–19.

    Google Scholar 

  • Green, P. E., and Krieger, A. (1989), “Recent contributions to optimal product positioning and buyer segmentation,” European Journal of Operational Research, 41, 2, 127–141.

    Article  Google Scholar 

  • Green, P. E., Krieger, A., and Agarwal, M. K. (1991), “Adaptive conjoint analysis: Some caveats and suggestions,” Journal of Marketing Research, 23, 2 (May), 215–222.

    Google Scholar 

  • Green, P. E., Krieger, A., and Bansal, P. (1988), “Completely unacceptable levels in conjoint analysis: A cautionary note,” Journal of Marketing Research, 25, 3 (August), 293–300.

    Google Scholar 

  • Green P. E., Krieger, A. M., and Vavra, T. (1999), “Evaluating E-Z Pass: Using conjoint analysis to assess consumer response to a new tollway technology,” Marketing Research 11 (Summer)„ 5–16.

    Google Scholar 

  • Green, P. E., Krieger, A. M., and Wind, Y. (2001), “Thirty Years of Conjoint Analysis: Reflections and Prospects,” Interfaces, 31, 3, Part 2 (May-June), S56–S73.

    Google Scholar 

  • Green, P. E., and McMennamin, J. L. (1973), “Market position analysis,” in S. H. Britt and N. F. Guess (eds.), Marketing Manager’s Handbook, Chicago IL: Dartnell Press), pp. 543554.

    Google Scholar 

  • Green, P. E., and Rao, V. R. (1971), “Conjoint measurement for quantifying judgmental data,” Journal of Marketing Research, 8 (August), 355–363.

    Article  Google Scholar 

  • Green, P.E., and Rao, V. R. (1972), Applied Multidimensional Scaling,Dryden Press.

    Google Scholar 

  • Green, P. E., Rao, V. R., and DeSarbo, W. (1978), “A procedure for incorporating group-level similarity judgments in conjoint analysis,” Journal of Consumer Research, 5 (December), 187–193.

    Article  Google Scholar 

  • Green, P. E., and Srinivasan, V. (1990), “Conjoint analysis in marketing: New developments with implications for research and practice,” Journal of Marketing, 54, 4 (October), 3–19.

    Google Scholar 

  • Green, P. E., and Srinivasan, V. (1978), “Conjoint analysis in consumer research: Issues and outlook,” Journal of Consumer Research, 5, 2 (September), 103–123.

    Google Scholar 

  • Green, P. E., and Wind, J. (1975), “New way to measure consumers’ judgments,” Harvard Business Review, (July-August),107–117.

    Google Scholar 

  • Green, P. E., Wind, Y., and Jain, A. K. (1972), “Preference measurement of item collections,” Journal of Marketing Research, 9 (November), 371–377.

    Article  Google Scholar 

  • Green, P. E., and Wind, J. (1973), Multiattribute Decisions in Marketing,Dryden Press.

    Google Scholar 

  • Griffin, A. J., and Hauser, J. R. (1993),“The voice of the customer,” Marketing Science,12 (Winter), 1–27.

    Article  Google Scholar 

  • Haaijer, R., Wedel, M., Vriens, M., and Wansbeek, T. (1998), “Utility covariances and context effects in conjoint MNP models,” Marketing Science, 17, 3, 236–252.

    Article  Google Scholar 

  • Haaijer, R., Kamakura, W., and Wedel, M. (2000), “Response latencies in the analysis of conjoint choice experiments,” Journal of Marketing Research 37 (August), 376–382.

    Article  Google Scholar 

  • Hauser, J. R., and Koppelman, F. S. (1979), “Alternative perceptual mapping techniques: Relative accuracy and usefulness,” Journal of Marketing Research, 16, 4 (November), 495–506.

    Google Scholar 

  • Hauser, J. R., and Shugan, S. M. (1980), “Intensity measures of consumer preference,” Operation Research, 28, 2 (March-April), 278–320.

    Google Scholar 

  • Hauser, J. R., Simester, D. I., and Toubia, O. (2002), “Configurators, utility balance, and managerial use,” working paper, Cambridge, MA: Center for Innovation in Product Development, MIT, (June).

    Google Scholar 

  • Hauser, J. R., and Urban, G. L. (1977), “A normative methodology for modeling consumer response to innovation,” Operations Research, 25, 5 (July-August), 579–619.

    Google Scholar 

  • Hauser, J. R., and Urban, G. L. (1979), “Assessment of attribute importances and consumer utility functions: von Neumann-Morgenstern theory applied to consumer behavior,” Journal of Consumer Research, 5 (March), 251–262.

    Article  Google Scholar 

  • Huber, J. (1975), “Predicting preferences on experimental bundles of attributes: A comparison of models,” Journal of Marketing Research, 12 (August), 290–297.

    Article  Google Scholar 

  • Huber, J. (1987), “Conjoint analysis: How we got here and where we are,” Proceedings of the Sawtooth Software Conference on Perceptual Mapping, Conjoint Analysis, and Computer Interviewing, pp. 237–252.

    Google Scholar 

  • Huber, J., Wittink, D. R., Fiedler, J. A., and Miller, R. (1993),“The effectiveness of alternative preference elicitation procedures in predicting choice,” Journal of Marketing Research,105–114.

    Google Scholar 

  • Huber, J., and Zwerina, K. (1996),“The importance of utility balance in efficient choice designs,” Journal of Marketing Research,33 (August), 307–317.

    Google Scholar 

  • Jain, A. K., Acito, F., Malhotra, N. K., and Mahajan, V. (1979), “A comparison of the internal validity of alternative parameter estimation methods in decompositional multiattribute preference models,” Journal of Marketing Research, 16 (August), 313322.

    Google Scholar 

  • Johnson, R. (1974), “Tradeoff analysis of consumer values,” Journal of Marketing Research, (May), 121–127.

    Google Scholar 

  • Johnson, R. (1987) “Accuracy of utility estimation in ACA,” Working Paper, Sequim, WA: Sawtooth Software, (April).

    Google Scholar 

  • Johnson, R. (1991),“Comment on adaptive conjoint analysis: Some caveats and suggestions,” Journal of Marketing Research 28 (May), 223–225.

    Google Scholar 

  • Johnson, R. (1999), “The joys and sorrows of implementing HB methods for conjoint analysis,” Working Paper, Sequim, WA: Sawtooth Software, ( November).

    Google Scholar 

  • Kadiyali, V., Sudhir, K., and Rao, V. R. (2000), “Structural analysis of competitive behavior,” International Journal of Research in Marketing, 18, 161–185.

    Google Scholar 

  • Keeney, R., and Raiffa, H. (1976), Decisions with Multiple Consequences: Preferences and Value Tradeoffs, New York, NY: John Wiley amp; Sons.

    Google Scholar 

  • Klein, N. M. (1988), “Assessing unacceptable attribute levels in conjoint analysis,” Advances in Consumer Research, 14, 154–158.?

    Google Scholar 

  • Koza, J. R. (1992), Genetic Programming, Cambridge, MA: The MIT Press.

    Google Scholar 

  • Krantz, D. H., Luce, R. D., Suppes, P., and Tversky, A. (1971), Foundations of Measurement, New York, NY: Academic Press.

    Google Scholar 

  • Kuhfeld, W. E., Tobias, R. D., and Garratt, M. (1994), “Efficient experimental design with marketing research applications,” Journal of Marketing Research, 31, 4 (November), 545–557.

    Google Scholar 

  • Lenk, P. J., DeSarbo, W. S., Green, P. E., and Young, M. R. (1996), “Hierarchical Bayes conjoint analysis: Recovery of partworth heterogeneity from reduced experimental designs,” Marketing Science, 15, 2, 173–191.

    Article  Google Scholar 

  • Liechty, J., Ramaswamy, V., and Cohen, S. (2001), “Choice-menus for mass customization: An experimental approach for analyzing customer demand with an application to a web-based information service,” Journal of Marketing Research, 38, 2 (May).

    Google Scholar 

  • Louviere, J. J., Hensher, D. A., and Swait, J. D. (2000), Stated Choice Methods: Analysis and Application, New York, NY: Cambridge University Press.

    Book  Google Scholar 

  • Luce, R. D., and Tukey, J. W. (1964), “Simultaneous conjoint measurement: A new type of fundamental measurement,” Journal of Mathematical Psychology, 1, 1–27.

    Article  Google Scholar 

  • Malhotra, N. (1982), “Structural reliability and stability of nonmetric conjoint analysis,” Journal of Marketing Research, 19 (May), 1999–207.

    Article  Google Scholar 

  • Malhotra, N. (1986), “An approach to the measurement of consumer preferences using limited information,” Journal of Marketing Research, 23 (February), 33–40.

    Article  Google Scholar 

  • McFadden, D. (1974), “Conditional logit analysis of qualitative choice behavior,” in P. Zarembka (ed.), Frontiers in Econometrics, New York: Academic Press, pp. 105142.

    Google Scholar 

  • McFadden, D. (2000), “Disaggregate behavioral travel demand’s RUM side: A thirty-year retrospective,” Working Paper, University of California, Berkeley, ( July).

    Google Scholar 

  • Montgomery, D. B., and Wittink, D. R. (1980), “The predictive validity of conjoint analysis for alternative aggregation schemes,” in D. B. Montgomery and D. R. Wittink (eds.), Market Measurement and Analysis: Proceedings of the 1979 ORSAITIMS Conference on Marketing,Cambridge, MA: Marketing Science Institute, pp. 298–309.

    Google Scholar 

  • Moore, W. L. (1980), “Levels of aggregation in conjoint analysis: An empirical comparison,” Journal of Marketing Research, 17, 4 (November), 516–523.

    Article  Google Scholar 

  • Moore, W.L., and Semenik, R. J. (1988), “Measuring preferences with hybrid conjoint analysis: The impact of a different number of attributes in the master design,” Journal of Business Research, 261–274.

    Google Scholar 

  • Nadilo, R. (1999), “On-line research: The methodology for the next millennium,” Advertising Research Foundation Journal, (Spring). Available at www.greenfield.com.

    Google Scholar 

  • Neslin, S. A. (1981), “Linking product features to perceptions: Self-stated versus statistically revealed importance weights,” Journal of Marketing Research, 18 (February), 80–86.

    Article  Google Scholar 

  • Nesterov, Y., and Nemirovskii, A. (1994), “Interior-point polynomial algorithms in convex programming,” SIAM, Philadelphia.

    Google Scholar 

  • Oppewal, H., Louviere, J. J., and Timmermans, H. J. P. (1994), “Modeling hierarchical conjoint processes with integrated choice experiments,” Journal of Marketing Research, 31 (February), 92–105.

    Article  Google Scholar 

  • Orme, B. (1999), “ACA, CBC, or both?: Effective strategies for conjoint research,” Working Paper, Sequim, WA: Sawtooth Software..

    Google Scholar 

  • Rao, V. R. (1977) “ Conjoint measurement in marketing analysis,” in J. Sheth (ed.), Multivariate Methods for Market and Survey Research, Chicago: American Marketing Association, pp. 257–286.

    Google Scholar 

  • Rao, V. R., and Katz, R. (1971), “Alternative multidimensional scaling methods for large stimulus sets,” Journal of Marketing Research, 8 (November), 488–494.

    Article  Google Scholar 

  • Rao, V. R., and Sattler, H. (2000), “Measurement of informational and allocative effects of price,” in A. Gustafsson, A. Herrmann, and F. Huber (eds.), Conjoint Measurement: Methods and Applications, Berlin: Springer-Verlag, pp. 47–66.

    Chapter  Google Scholar 

  • Reibstein, D., Bateson, J. E. G., and Boulding, W. (1988), “Conjoint analysis reliability: Empirical findings,” Marketing Science, 7, 3 (Summer), 271–286.

    Google Scholar 

  • Sandor, Z., and Wedel, M. (2001), “Designing conjoint choice experiments using managers’ prior beliefs,” Journal of Marketing Research, 38, 4 (November), 430–444.

    Google Scholar 

  • Sawtooth Software, Inc. (1996), “ACA system: Adaptive conjoint analysis,” ACA Manual, Sequim, WA: Sawtooth Software, Inc.

    Google Scholar 

  • Sawtooth Software, Inc. (1999), “The ACA/HB module for Hierarchical Bayes estimation,” ACA/HB Manual, Sequim, WA: Sawtooth Software, Inc.

    Google Scholar 

  • Segal, M. N. (1982), “Reliability of conjoint analysis: Contrasting data collection procedures,” Journal of Marketing Research, 19, 139–143.

    Article  Google Scholar 

  • Silk, Al. J., and Urban, G. L. (1978), “Pre-test-market evaluation of new packaged goods: A model and measurement methodology,” Journal of Marketing Research, 15 (May), 171–191.

    Article  Google Scholar 

  • Sonnevend, G. (1985a), “An ‘analytic’ center for polyhedrons and new classes of global algorithms for linear (smooth, convex) programming,” Proceedings of the 12 th IFIP Conference on System Modeling and Optimization, Budapest.

    Google Scholar 

  • Sonnevend, G. (1985b), “A new method for solving a set of linear (convex) inequalities and its applications for identification and optimization,” Preprint, Department of Numerical Analysis, Institute of Mathematics, E6tv•s University, Budapest, 1985.

    Google Scholar 

  • Srinivasan, V. (1988), “A conjunctive-compensatory approach to the self-explication of multiattributed preferences,” Decision Sciences, 19 (Spring), 295–305.

    Article  Google Scholar 

  • Srinivasan, V., and Park, C. S. (1997), “Surprising robustness of the self-explicated approach to customer preference structure measurement,” Journal of Marketing Research, 34 (May), 286–291.

    Article  Google Scholar 

  • Srinivasan, V., and Shocker, A. D. (1973a), “Estimating the weights for multiple attributes in a composite criterion using pairwise judgments,” Psychometrika, 38, 4 (December), 473–493.

    Google Scholar 

  • Srinivasan, V., and Shocker, A. D. (1973b), “Linear programming techniques for multidimensional analysis of preferences,” Psychometrika, 38, 3 (September), 337369.

    Google Scholar 

  • Srinivasan, V., and Wyner, G. A. (1988), “Casemap: Computer-assisted self-explication of multiattributed preferences,” in W. Henry, M. Menasco, and K. Takada (eds.), Handbook on New Product Development and Testing, Lexington, MA: D. C. Heath, pp. 91–112.

    Google Scholar 

  • Ter Hofstede, F., Kim, Y., and Wedel, M. (2002), “Bayesian prediction in hybrid conjoint analysis,” Journal of Marketing Research, 36 (May), 253–261.

    Article  Google Scholar 

  • Toubia, O., Simester, D., Hauser, J. R, and Dahan, E. (2003), “Fast polyhedral adaptive conjoint estimation,” forthcoming Marketing Science, 22.

    Google Scholar 

  • Toubia, O., Simester, D., and Hauser, J. R. (2003), “Adaptive choice-based conjoint analysis with polyhedral methods,” Working Paper, Cambridge, MA: Center for Innovation in Product Development, MIT, ( February).

    Google Scholar 

  • Urban, G. L., and Hauser, J. R. (2002), “’Listening in’ to find consumer needs and solutions,” Working Paper, Cambridge, MA: Center for Innovation in Product Development, MIT, ( January).

    Google Scholar 

  • Urban, G. L., and Katz, G. M. (1983), “Pre-test market models: Validation and managerial implications,” Journal of Marketing Research, 20 (August), 221–34.

    Article  Google Scholar 

  • Vaidja, P. (1989), “A locally well-behaved potential function and a simple Newton-type method for finding the center of a polytope,” in N. Megiddo (ed.), Progress in Mathematical Programming: Interior Points and Related Methods, New York: Springer, pp. 79–90.

    Chapter  Google Scholar 

  • Vavra, T. G., Green, P. E., and Krieger, A. (1999), “Evaluating E-Z Pass,” Marketing Research, 11, 3 (Summer), 5–16.

    Google Scholar 

  • von Hippel, E. (2001), “Perspective: User toolkits for innovation,” Journal of Product Innovation Management, 18, 247–257.

    Article  Google Scholar 

  • Wilkie, W. L., and Pessemier, E. A. (1973), “Issues in marketing’s use of multi-attribute attitude models,” Journal of Marketing Research, 10 (November), 428–441.

    Article  Google Scholar 

  • Wind, J., Green, P. E., Shifflet, D., and Scarbrough, M. (1989), “Courtyard by Marriott: Designing a hotel facility with consumer-based marketing models,” Interfaces, 19, 25–47.

    Article  Google Scholar 

  • Wittink, D. R., and Cattin, P. (1981), “Alternative estimation methods for conjoint analysis: A Monte Carlo study,” Journal of Marketing Research, 18 (February), 101–106.

    Article  Google Scholar 

  • Wittink, D. R., and Cattin, P. (1989), “Commercial use of conjoint analysis: An update,” Journal of Marketing, 53, 3 (July), 91–96.

    Google Scholar 

  • Wittink, D. R., and Montgomery, D. B. (1979), “Predictive validity of tradeoff analysis for alternative segmentation schemes,” in N. Beckwith (ed.), 1979 AMA Educators’ Conference Proceedings, Chicago, IL: American Marketing Association.

    Google Scholar 

  • Wright, P., and Kriewall, M. A. (1980), “State-of-mind effects on accuracy with which utility functions predict marketplace utility,” Journal of Marketing Research, 17 (August), 277–293.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Massachusetts Institute of Technology Cornell University, USA

    John R. Hauser & Vithala R. Rao

Authors
  1. John R. Hauser
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vithala R. Rao
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. The Wharton School, University of Pennsylvania, USA

    Yoram Wind  & Paul E. Green  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer Science+Business Media New York

About this chapter

Cite this chapter

Hauser, J.R., Rao, V.R. (2004). Conjoint Analysis, Related Modeling, and Applications. In: Wind, Y., Green, P.E. (eds) Marketing Research and Modeling: Progress and Prospects. International Series in Quantitative Marketing, vol 14. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-28692-1_7

Download citation

  • DOI: https://doi.org/10.1007/978-0-387-28692-1_7

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-24308-5

  • Online ISBN: 978-0-387-28692-1

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation