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An Interdisciplinary Review of Research in Conjoint Analysis: Recent Developments and Directions for Future Research

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  • James Agarwal
  • Wayne DeSarbo
  • Naresh K. Malhotra
  • Vithala Rao

Abstract

This review article provides reflections on the state of the art of research in conjoint analysis—where we came from, where we are, and some directions as to where we might go. We review key articles, mostly contemporary published since 2000, but some classic, drawn from the major marketing as well as various interdisciplinary academic journals to highlight important areas related to conjoint analysis research and identify more recent developments in this area. We develop an organizing framework that attempts to integrate various threads of research in conjoint methods and models. Our goal is to (a) emphasize the major developments in recent years, (b) evaluate these developments, and (c) to identify several potential directions for future research. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • James Agarwal & Wayne DeSarbo & Naresh K. Malhotra & Vithala Rao, 2015. "An Interdisciplinary Review of Research in Conjoint Analysis: Recent Developments and Directions for Future Research," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 2(1), pages 19-40, March.
    • Handle: RePEc:spr:custns:v:2:y:2015:i:1:p:19-40
    DOI: 10.1007/s40547-014-0029-5
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    1. Raghuram Iyengar & Christophe Van den Bulte & Thomas W. Valente, 2011. "Opinion Leadership and Social Contagion in New Product Diffusion," Marketing Science, INFORMS, vol. 30(2), pages 195-212, 03-04.
    2. Oded Netzer & James M. Lattin & V. Srinivasan, 2008. "A Hidden Markov Model of Customer Relationship Dynamics," Marketing Science, INFORMS, vol. 27(2), pages 185-204, 03-04.
    3. Joel Huber and Kenneth Train., 2000. "On the Similarity of Classical and Bayesian Estimates of Individual Mean Partworths," Economics Working Papers E00-289, University of California at Berkeley.
    4. Michalek, Jeremy J. & Ebbes, Peter & Adigüzel, Feray & Feinberg, Fred M. & Papalambros, Panos Y., 2011. "Enhancing marketing with engineering: Optimal product line design for heterogeneous markets," International Journal of Research in Marketing, Elsevier, vol. 28(1), pages 1-12.
    5. Ding, Min & Ross, William T. & Rao, Vithala R., 2010. "Price as an Indicator of Quality: Implications for Utility and Demand Functions," Journal of Retailing, Elsevier, vol. 86(1), pages 69-84.
    6. Tversky, Amos & Kahneman, Daniel, 1992. "Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
    7. Vithala R. Rao, 2008. "Developments in Conjoint Analysis," International Series in Operations Research & Management Science, in: Berend Wierenga (ed.), Handbook of Marketing Decision Models, chapter 0, pages 23-53, Springer.
    8. Frenkel Ter Hofstede & Michel Wedel & Jan-Benedict E.M. Steenkamp, 2002. "Identifying Spatial Segments in International Markets," Marketing Science, INFORMS, vol. 21(2), pages 160-177, July.
    9. Yu, Jie & Goos, Peter & Vandebroek, Martina, 2011. "Individually adapted sequential Bayesian conjoint-choice designs in the presence of consumer heterogeneity," International Journal of Research in Marketing, Elsevier, vol. 28(4), pages 378-388.
    10. Daria Dzyabura & John R. Hauser, 2011. "Active Machine Learning for Consideration Heuristics," Marketing Science, INFORMS, vol. 30(5), pages 801-819, September.
    11. Cawley, John, 2008. "Contingent valuation analysis of willingness to pay to reduce childhood obesity," Economics & Human Biology, Elsevier, vol. 6(2), pages 281-292, July.
    12. Duncan Fong & Peter Ebbes & Wayne DeSarbo, 2012. "A Heterogeneous Bayesian Regression Model for Cross-sectional Data Involving a Single Observation per Response Unit," Psychometrika, Springer;The Psychometric Society, vol. 77(2), pages 293-314, April.
    13. Olivier Toubia & Duncan I. Simester & John R. Hauser & Ely Dahan, 2003. "Fast Polyhedral Adaptive Conjoint Estimation," Marketing Science, INFORMS, vol. 22(3), pages 273-303.
    14. Elie Ofek & V. Srinivasan, 2002. "How Much Does the Market Value an Improvement in a Product Attribute?," Marketing Science, INFORMS, vol. 21(4), pages 398-411, June.
    15. Vithala R. Rao, 2014. "Applied Conjoint Analysis," Springer Books, Springer, edition 127, number 978-3-540-87753-0, June.
    16. Min Ding & Rajdeep Grewal & John Liechty, 2005. "Incentive-aligned conjoint analysis," Framed Field Experiments 00139, The Field Experiments Website.
    17. Franziska Voelckner, 2006. "An empirical comparison of methods for measuring consumers’ willingness to pay," Marketing Letters, Springer, vol. 17(2), pages 137-149, April.
    18. Arnaud De Bruyn & John C. Liechty & Eelko K. R. E. Huizingh & Gary L. Lilien, 2008. "Offering Online Recommendations with Minimum Customer Input Through Conjoint-Based Decision Aids," Marketing Science, INFORMS, vol. 27(3), pages 443-460, 05-06.
    19. Wayne DeSarbo & Duncan Fong & John Liechty & Jennifer Coupland, 2005. "Evolutionary preference/utility functions: A dynamic perspective," Psychometrika, Springer;The Psychometric Society, vol. 70(1), pages 179-202, March.
    20. Linda Court Salisbury & Fred M. Feinberg, 2010. "Alleviating the Constant Stochastic Variance Assumption in Decision Research: Theory, Measurement, and Experimental Test," Marketing Science, INFORMS, vol. 29(1), pages 1-17, 01-02.
    21. Dan Ariely & George Loewenstein & Drazen Prelec, 2003. ""Coherent Arbitrariness": Stable Demand Curves Without Stable Preferences," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(1), pages 73-106.
    22. Bond, Samuel D. & Carlson, Kurt A. & Meloy, Margaret G. & Russo, J. Edward & Tanner, Robin J., 2007. "Information distortion in the evaluation of a single option," Organizational Behavior and Human Decision Processes, Elsevier, vol. 102(2), pages 240-254, March.
    23. Alexandre Belloni & Robert Freund & Matthew Selove & Duncan Simester, 2008. "Optimizing Product Line Designs: Efficient Methods and Comparisons," Management Science, INFORMS, vol. 54(9), pages 1544-1552, September.
    24. J. Wesley Hutchinson & Gal Zauberman & Robert Meyer, 2010. "—On the Interpretation of Temporal Inflation Parameters in Stochastic Models of Judgment and Choice," Marketing Science, INFORMS, vol. 29(1), pages 23-31, 01-02.
    25. Dong, Songting & Ding, Min & Huber, Joel, 2010. "A simple mechanism to incentive-align conjoint experiments," International Journal of Research in Marketing, Elsevier, vol. 27(1), pages 25-32.
    26. David Godes & Dina Mayzlin, 2004. "Using Online Conversations to Study Word-of-Mouth Communication," Marketing Science, INFORMS, vol. 23(4), pages 545-560, June.
    27. Oded Netzer & Olivier Toubia & Eric Bradlow & Ely Dahan & Theodoros Evgeniou & Fred Feinberg & Eleanor Feit & Sam Hui & Joseph Johnson & John Liechty & James Orlin & Vithala Rao, 2008. "Beyond conjoint analysis: Advances in preference measurement," Marketing Letters, Springer, vol. 19(3), pages 337-354, December.
    28. Wiktor Adamowicz & David Bunch & Trudy Cameron & Benedict Dellaert & Michael Hanneman & Michael Keane & Jordan Louviere & Robert Meyer & Thomas Steenburgh & Joffre Swait, 2008. "Behavioral frontiers in choice modeling," Marketing Letters, Springer, vol. 19(3), pages 215-228, December.
    29. Kamel Jedidi & Z. John Zhang, 2002. "Augmenting Conjoint Analysis to Estimate Consumer Reservation Price," Management Science, INFORMS, vol. 48(10), pages 1350-1368, October.
    30. Mohamed Lachaab & Asim Ansari & Kamel Jedidi & Abdelwahed Trabelsi, 2006. "Modeling preference evolution in discrete choice models: A Bayesian state-space approach," Quantitative Marketing and Economics (QME), Springer, vol. 4(1), pages 57-81, March.
    31. Joffre Swait & Rick L. Andrews, 2003. "Enriching Scanner Panel Models with Choice Experiments," Marketing Science, INFORMS, vol. 22(4), pages 442-460, September.
    32. Olivier Toubia & John R. Hauser, 2007. "—On Managerially Efficient Experimental Designs," Marketing Science, INFORMS, vol. 26(6), pages 851-858, 11-12.
    33. David Laibson, 1997. "Golden Eggs and Hyperbolic Discounting," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 112(2), pages 443-478.
    34. Luc Wathieu & Marco Bertini, 2007. "Price as a Stimulus to Think: The Case for Willful Overpricing," Marketing Science, INFORMS, vol. 26(1), pages 118-129, 01-02.
    35. Jeffrey D. Camm & James J. Cochran & David J. Curry & Sriram Kannan, 2006. "Conjoint Optimization: An Exact Branch-and-Bound Algorithm for the Share-of-Choice Problem," Management Science, INFORMS, vol. 52(3), pages 435-447, March.
    36. Shiling Ruan & Steven MacEachern & Thomas Otter & Angela Dean, 2008. "The Dependent Poisson Race Model and Modeling Dependence in Conjoint Choice Experiments," Psychometrika, Springer;The Psychometric Society, vol. 73(2), pages 261-288, June.
    37. Romero, Jaime & van der Lans, Ralf & Wierenga, Berend, 2013. "A Partially Hidden Markov Model of Customer Dynamics for CLV Measurement," Journal of Interactive Marketing, Elsevier, vol. 27(3), pages 185-208.
    38. Riccardo Scarpa & Mara Thiene & Kenneth Train, 2008. "Utility in Willingness to Pay Space: A Tool to Address Confounding Random Scale Effects in Destination Choice to the Alps," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 90(4), pages 994-1010.
    39. Joo Heon Park & Douglas L. MacLachlan, 2008. "Estimating Willingness to Pay with Exaggeration Bias-Corrected Contingent Valuation Method," Marketing Science, INFORMS, vol. 27(4), pages 691-698, 07-08.
    40. Theodoros Evgeniou & Constantinos Boussios & Giorgos Zacharia, 2005. "Generalized Robust Conjoint Estimation," Marketing Science, INFORMS, vol. 24(3), pages 415-429, May.
    41. Riccardo Scarpa & Timothy J. Gilbride & Danny Campbell & David A. Hensher, 2009. "Modelling attribute non-attendance in choice experiments for rural landscape valuation," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 36(2), pages 151-174, June.
    42. Johannesson, Magnus & Blomquist, Glenn C. & Blumenschein, Karen & Johansson, Per-olov & Liljas, Bengt & O'Conor, Richard M., 1999. "Calibrating Hypothetical Willingness to Pay Responses," Journal of Risk and Uncertainty, Springer, vol. 18(1), pages 21-32, April.
    43. Garrett P. Sonnier & Leigh McAlister & Oliver J. Rutz, 2011. "A Dynamic Model of the Effect of Online Communications on Firm Sales," Marketing Science, INFORMS, vol. 30(4), pages 702-716, July.
    44. Venkatraman, Srinivasan & Aloysius, John A. & Davis, Fred D., 2006. "Multiple prospect framing and decision behavior: The mediational roles of perceived riskiness and perceived ambiguity," Organizational Behavior and Human Decision Processes, Elsevier, vol. 101(1), pages 59-73, September.
    45. Dan K. Hsu & J. Michael Haynie & Sharon A. Simmons & Alexander McKelvie, 2014. "What matters, matters differently: a conjoint analysis of the decision policies of angel and venture capital investors," Venture Capital, Taylor & Francis Journals, vol. 16(1), pages 1-25, January.
    46. Vishal Narayan & Vithala R. Rao & Carolyne Saunders, 2011. "How Peer Influence Affects Attribute Preferences: A Bayesian Updating Mechanism," Marketing Science, INFORMS, vol. 30(2), pages 368-384, 03-04.
    47. Xinfang (Jocelyn) Wang & Jeffrey D. Camm & David J. Curry, 2009. "A Branch-and-Price Approach to the Share-of-Choice Product Line Design Problem," Management Science, INFORMS, vol. 55(10), pages 1718-1728, October.
    48. John C. Liechty & Duncan K. H. Fong & Wayne S. DeSarbo, 2005. "Dynamic Models Incorporating Individual Heterogeneity: Utility Evolution in Conjoint Analysis," Marketing Science, INFORMS, vol. 24(2), pages 285-293, November.
    49. Rose, John M. & Bliemer, Michiel C.J. & Hensher, David A. & Collins, Andrew T., 2008. "Designing efficient stated choice experiments in the presence of reference alternatives," Transportation Research Part B: Methodological, Elsevier, vol. 42(4), pages 395-406, May.
    50. David Godes & Dina Mayzlin & Yubo Chen & Sanjiv Das & Chrysanthos Dellarocas & Bruce Pfeiffer & Barak Libai & Subrata Sen & Mengze Shi & Peeter Verlegh, 2005. "The Firm's Management of Social Interactions," Marketing Letters, Springer, vol. 16(3), pages 415-428, December.
    51. Jonathan Levav & Mark Heitmann & Andreas Herrmann & Sheena S. Iyengar, 2010. "Order in Product Customization Decisions: Evidence from Field Experiments," Journal of Political Economy, University of Chicago Press, vol. 118(2), pages 274-299, April.
    52. Bettman, James R & Luce, Mary Frances & Payne, John W, 1998. "Constructive Consumer Choice Processes," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 25(3), pages 187-217, December.
    53. Zsolt Sándor & Michel Wedel, 2002. "Profile Construction in Experimental Choice Designs for Mixed Logit Models," Marketing Science, INFORMS, vol. 21(4), pages 455-475, February.
    54. Scarpa, R. & Thiene, M. & Train, K., 2008. "Appendix to Utility in WTP space: a tool to address confounding random scale effects in destination choice to the Alps," American Journal of Agricultural Economics APPENDICES, Agricultural and Applied Economics Association, vol. 90(4), pages 1-9, January.
    55. Qing Liu & Neeraj Arora, 2011. "Efficient Choice Designs for a Consider-Then-Choose Model," Marketing Science, INFORMS, vol. 30(2), pages 321-338, 03-04.
    56. Rajeev Kohli & Kamel Jedidi, 2007. "Representation and Inference of Lexicographic Preference Models and Their Variants," Marketing Science, INFORMS, vol. 26(3), pages 380-399, 05-06.
    57. Terry Elrod & Gerald Häubl & Steven Tipps, 2012. "Parsimonious Structural Equation Models for Repeated Measures Data, with Application to the Study of Consumer Preferences," Psychometrika, Springer;The Psychometric Society, vol. 77(2), pages 358-387, April.
    58. Theodoros Evgeniou & Massimiliano Pontil & Olivier Toubia, 2007. "A Convex Optimization Approach to Modeling Consumer Heterogeneity in Conjoint Estimation," Marketing Science, INFORMS, vol. 26(6), pages 805-818, 11-12.
    59. Wayne S. DeSarbo & Christian F. DeSarbo, 2007. "A Generalized Normative Segmentation Methodology Employing Conjoint Analysis," Springer Books, in: Anders Gustafsson & Andreas Herrmann & Frank Huber (ed.), Conjoint Measurement, edition 0, chapter 16, pages 321-345, Springer.
    60. Sung S. Kim & Naresh K. Malhotra, 2005. "A Longitudinal Model of Continued IS Use: An Integrative View of Four Mechanisms Underlying Postadoption Phenomena," Management Science, INFORMS, vol. 51(5), pages 741-755, May.
    61. Danny Campbell & David A. Hensher & Riccardo Scarpa, 2011. "Non-attendance to attributes in environmental choice analysis: a latent class specification," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 54(8), pages 1061-1076, December.
    62. Denzil G. Fiebig & Michael P. Keane & Jordan Louviere & Nada Wasi, 2010. "The Generalized Multinomial Logit Model: Accounting for Scale and Coefficient Heterogeneity," Marketing Science, INFORMS, vol. 29(3), pages 393-421, 05-06.
    63. Avi Goldfarb & Teck-Hua Ho & Wilfred Amaldoss & Alexander Brown & Yan Chen & Tony Cui & Alberto Galasso & Tanjim Hossain & Ming Hsu & Noah Lim & Mo Xiao & Botao Yang, 2012. "Behavioral models of managerial decision-making," Marketing Letters, Springer, vol. 23(2), pages 405-421, June.
    64. Timothy J. Gilbride & Greg M. Allenby, 2004. "A Choice Model with Conjunctive, Disjunctive, and Compensatory Screening Rules," Marketing Science, INFORMS, vol. 23(3), pages 391-406, October.
    65. Gregan-Paxton, Jennifer & John, Deborah Roedder, 1997. "Consumer Learning by Analogy: A Model of Internal Knowledge Transfer," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 24(3), pages 266-284, December.
    66. Green, Paul E & Srinivasan, V, 1978. "Conjoint Analysis in Consumer Research: Issues and Outlook," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 5(2), pages 103-123, Se.
    67. Martijn G. de Jong & Donald R. Lehmann & Oded Netzer, 2012. "State-Dependence Effects in Surveys," Marketing Science, INFORMS, vol. 31(5), pages 838-854, September.
    68. Eleanor McDonnell Feit & Mark A. Beltramo & Fred M. Feinberg, 2010. "Reality Check: Combining Choice Experiments with Market Data to Estimate the Importance of Product Attributes," Management Science, INFORMS, vol. 56(5), pages 785-800, May.
    69. Kamel Jedidi & Sharan Jagpal & Puneet Manchanda, 2003. "Measuring Heterogeneous Reservation Prices for Product Bundles," Marketing Science, INFORMS, vol. 22(1), pages 107-130, July.
    70. Peter Stüttgen & Peter Boatwright & Robert T. Monroe, 2012. "A Satisficing Choice Model," Marketing Science, INFORMS, vol. 31(6), pages 878-899, November.
    71. Kahneman, Daniel, 2002. "Maps of Bounded Rationality," Nobel Prize in Economics documents 2002-4, Nobel Prize Committee.
    72. Min Ding & Young-Hoon Park & Eric T. Bradlow, 2009. "Barter Markets for Conjoint Analysis," Management Science, INFORMS, vol. 55(6), pages 1003-1017, June.
    73. Olivier Toubia & John Hauser & Rosanna Garcia, 2007. "Probabilistic Polyhedral Methods for Adaptive Choice-Based Conjoint Analysis: Theory and Application," Marketing Science, INFORMS, vol. 26(5), pages 596-610, 09-10.
    74. P. K. Kannan & Barbara Kline Pope & Sanjay Jain, 2009. "—Pricing Digital Content Product Lines: A Model and Application for the National Academies Press," Marketing Science, INFORMS, vol. 28(4), pages 620-636, 07-08.
    75. Olivier Toubia & Martijn G. de Jong & Daniel Stieger & Johann Füller, 2012. "Measuring Consumer Preferences Using Conjoint Poker," Marketing Science, INFORMS, vol. 31(1), pages 138-156, January.
    76. Eric T. Bradlow & Young-Hoon Park, 2007. "Bayesian Estimation of Bid Sequences in Internet Auctions Using a Generalized Record-Breaking Model," Marketing Science, INFORMS, vol. 26(2), pages 218-229, 03-04.
    77. Greg Allenby & Geraldine Fennell & Joel Huber & Thomas Eagle & Tim Gilbride & Dan Horsky & Jaehwan Kim & Peter Lenk & Rich Johnson & Elie Ofek & Bryan Orme & Thomas Otter & Joan Walker, 2005. "Adjusting Choice Models to Better Predict Market Behavior," Marketing Letters, Springer, vol. 16(3), pages 197-208, December.
    78. Schlosser, Ann E & Shavitt, Sharon, 2002. "Anticipating Discussion about a Product: Rehearsing What to Say Can Affect Your Judgments," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 29(1), pages 101-115, June.
    79. Oliver J. Rutz & Garrett P. Sonnier, 2011. "The Evolution of Internal Market Structure," Marketing Science, INFORMS, vol. 30(2), pages 274-289, 03-04.
    80. Min Ding & Jehoshua Eliashberg, 2008. "A Dynamic Competitive Forecasting Model Incorporating Dyadic Decision Making," Management Science, INFORMS, vol. 54(4), pages 820-834, April.
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    12. Gensler, Sonja & Hinz, Oliver & Skiera, Bernd & Theysohn, Sven, 2012. "Willingness-to-pay estimation with choice-based conjoint analysis: Addressing extreme response behavior with individually adapted designs," European Journal of Operational Research, Elsevier, vol. 219(2), pages 368-378.
    13. Denis Sauré & Juan Pablo Vielma, 2019. "Ellipsoidal Methods for Adaptive Choice-Based Conjoint Analysis," Operations Research, INFORMS, vol. 67(2), pages 315-338, March.
    14. Pauwels, Koen & Aksehirli, Zeynep & Lackman, Andrew, 2016. "Like the ad or the brand? Marketing stimulates different electronic word-of-mouth content to drive online and offline performance," International Journal of Research in Marketing, Elsevier, vol. 33(3), pages 639-655.
    15. John R. Hauser & Felix Eggers & Matthew Selove, 2019. "The Strategic Implications of Scale in Choice-Based Conjoint Analysis," Marketing Science, INFORMS, vol. 38(6), pages 1059-1081, November.
    16. Olivier Toubia & Martijn G. de Jong & Daniel Stieger & Johann Füller, 2012. "Measuring Consumer Preferences Using Conjoint Poker," Marketing Science, INFORMS, vol. 31(1), pages 138-156, January.
    17. Dong, Songting & Ding, Min & Huber, Joel, 2010. "A simple mechanism to incentive-align conjoint experiments," International Journal of Research in Marketing, Elsevier, vol. 27(1), pages 25-32.
    18. Daria Dzyabura & Srikanth Jagabathula & Eitan Muller, 2019. "Accounting for Discrepancies Between Online and Offline Product Evaluations," Marketing Science, INFORMS, vol. 38(1), pages 88-106, January.
    19. Mohammed H. Alemu & Søren B. Olsen, 2017. "Can a Repeated Opt-Out Reminder remove hypothetical bias in discrete choice experiments? An application to consumer valuation of novel food products," IFRO Working Paper 2017/05, University of Copenhagen, Department of Food and Resource Economics.
    20. Ghaderi, Mohammad & Kadziński, Miłosz, 2021. "Incorporating uncovered structural patterns in value functions construction," Omega, Elsevier, vol. 99(C).

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