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Product line optimization in the presence of preferences for compromise alternatives

Author

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  • Bechler, Georg
  • Steinhardt, Claudius
  • Mackert, Jochen
  • Klein, Robert

Abstract

Recent advances in customer choice analysis demonstrated the strong impact of compromise alternatives on the behaviour of decision-makers in a wide range of decision situations. Compromise alternatives are characterized by an intermediate performance on some of the relevant attributes. For instance, price compromises are well known in the sense that customers tend to buy neither the cheapest, nor the most expensive alternative, but the mid-priced one. However, thus far, the literature on product line optimization has not considered such context effects.

Suggested Citation

  • Bechler, Georg & Steinhardt, Claudius & Mackert, Jochen & Klein, Robert, 2021. "Product line optimization in the presence of preferences for compromise alternatives," European Journal of Operational Research, Elsevier, vol. 288(3), pages 902-917.
    • Handle: RePEc:eee:ejores:v:288:y:2021:i:3:p:902-917
    DOI: 10.1016/j.ejor.2020.06.029
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    1. Kraus, Ursula G. & Yano, Candace Arai, 2003. "Product line selection and pricing under a share-of-surplus choice model," European Journal of Operational Research, Elsevier, vol. 150(3), pages 653-671, November.
    2. Simonson, Itamar, 1989. "Choice Based on Reasons: The Case of Attraction and Compromise Effects," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 16(2), pages 158-174, September.
    3. G. E. Fruchter & A. Fligler & R. S. Winer, 2006. "Optimal Product Line Design: Genetic Algorithm Approach to Mitigate Cannibalization," Journal of Optimization Theory and Applications, Springer, vol. 131(2), pages 227-244, November.
    4. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, September.
    5. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    6. 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.
    7. Gregory Dobson & Shlomo Kalish, 1988. "Positioning and Pricing a Product Line," Marketing Science, INFORMS, vol. 7(2), pages 107-125.
    8. Day, Jamison M. & Venkataramanan, M.A., 2006. "Profitability in product line pricing and composition with manufacturing commonalities," European Journal of Operational Research, Elsevier, vol. 175(3), pages 1782-1797, December.
    9. Wu, Tai-Hsi, 1997. "A note on a global approach for general 0-1 fractional programming," European Journal of Operational Research, Elsevier, vol. 101(1), pages 220-223, August.
    10. A. Charnes & W. W. Cooper, 1962. "Programming with linear fractional functionals," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 9(3‐4), pages 181-186, September.
    11. Guillermo Gallego & Huseyin Topaloglu, 2019. "Revenue Management and Pricing Analytics," International Series in Operations Research and Management Science, Springer, number 978-1-4939-9606-3, April.
    12. Moon, Ilkyeong & Park, Kun Soo & Hao, Jing & Kim, Dongwook, 2017. "Joint decisions on product line selection, purchasing, and pricing," European Journal of Operational Research, Elsevier, vol. 262(1), pages 207-216.
    13. Kyle D. Chen & Warren H. Hausman, 2000. "Technical Note: Mathematical Properties of the Optimal Product Line Selection Problem Using Choice-Based Conjoint Analysis," Management Science, INFORMS, vol. 46(2), pages 327-332, February.
    14. Strauss, Arne K. & Klein, Robert & Steinhardt, Claudius, 2018. "A review of choice-based revenue management: Theory and methods," European Journal of Operational Research, Elsevier, vol. 271(2), pages 375-387.
    15. Huber, Joel & Payne, John W & Puto, Christopher, 1982. "Adding Asymmetrically Dominated Alternatives: Violations of Regularity and the Similarity Hypothesis," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 9(1), pages 90-98, June.
    16. Caspar Chorus & Michel Bierlaire, 2013. "An empirical comparison of travel choice models that capture preferences for compromise alternatives," Transportation, Springer, vol. 40(3), pages 549-562, May.
    17. Paul E. Green & Abba M. Krieger, 1985. "Models and Heuristics for Product Line Selection," Marketing Science, INFORMS, vol. 4(1), pages 1-19.
    18. Mortenson, Michael J. & Doherty, Neil F. & Robinson, Stewart, 2015. "Operational research from Taylorism to Terabytes: A research agenda for the analytics age," European Journal of Operational Research, Elsevier, vol. 241(3), pages 583-595.
    19. Federico Echenique & Kota Saito, 2019. "General Luce model," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 68(4), pages 811-826, November.
    20. Dimitris Bertsimas & Velibor V. Mišić, 2019. "Exact First-Choice Product Line Optimization," Operations Research, INFORMS, vol. 67(3), pages 651-670, May.
    21. Matthew J. Liberatore & Wenhong Luo, 2010. "The Analytics Movement: Implications for Operations Research," Interfaces, INFORMS, vol. 40(4), pages 313-324, August.
    22. Gregory Dobson & Shlomo Kalish, 1993. "Heuristics for Pricing and Positioning a Product-Line Using Conjoint and Cost Data," Management Science, INFORMS, vol. 39(2), pages 160-175, February.
    23. Richard D. McBride & Fred S. Zufryden, 1988. "An Integer Programming Approach to the Optimal Product Line Selection Problem," Marketing Science, INFORMS, vol. 7(2), pages 126-140.
    24. Amos Tversky & Itamar Simonson, 1993. "Context-Dependent Preferences," Management Science, INFORMS, vol. 39(10), pages 1179-1189, October.
    25. Ben-Akiva, Moshe & McFadden, Daniel & Train, Kenneth, 2019. "Foundations of Stated Preference Elicitation: Consumer Behavior and Choice-based Conjoint Analysis," Foundations and Trends(R) in Econometrics, now publishers, vol. 10(1-2), pages 1-144, January.
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    Cited by:

    1. Yan, Xiaoming & Zhao, Wenhan & Yu, Yugang, 2022. "Optimal product line design with reference price effects," European Journal of Operational Research, Elsevier, vol. 302(3), pages 1045-1062.
    2. Georg Bechler & Claudius Steinhardt & Jochen Mackert, 2021. "On the Linear Integration of Attraction Choice Models in Business Optimization Problems," SN Operations Research Forum, Springer, vol. 2(1), pages 1-13, March.
    3. Zhen-Yu Chen & Xin-Li Liu & Li-Ping Yin, 2023. "Data-driven product configuration improvement and product line restructuring with text mining and multitask learning," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 2043-2059, April.
    4. Follett, Lendie & Naald, Brian Vander, 2023. "Heterogeneity in choice experiment data: A Bayesian investigation," Journal of choice modelling, Elsevier, vol. 46(C).

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