IDEAS home Printed from https://ideas.repec.org/a/kap/qmktec/v22y2024i3d10.1007_s11129-024-09280-5.html
   My bibliography  Save this article

Testing a theory of strategic multi-product choice

Author

Listed:
  • Edward J. Fox

    (Cox School of Business)

  • Hristina Pulgar

    (Cox School of Business)

  • John H. Semple

    (Cox School of Business)

Abstract

This paper tests a theory of strategic multi-product choice (SMPC) using empirical evidence from a large-scale choice experiment, two smaller longitudinal choice experiments, and multi-market panel data. Multi-product choice involves two stages. In the first stage, the consumer chooses a set of substitutable products, where “set” refers to both the variety of alternatives and the quantities of each. In the second stage, the set is consumed. Assuming consumers are strategic, their consumption decisions will consider both the utility of whichever product is selected for consumption and the expected utility (i.e., value) of the set that remains. SMPC therefore requires a dynamic model. We test two such dynamic models in this paper. These models are derived from a basic random utility framework with a stochastic error term for the utility of each product alternative at the moment of consumption. Despite maintaining state variables for the quantity of every alternative, these SMPC dynamic models offer both a value function and optimal consumption policy in closed form. These structures allow us to test for strategic consumption in the second stage and for optimality of the choice sets selected in the first stage. Data from the large-scale choice experiment and the smaller longitudinal choice experiments support strategic consumer decision-making, consistent with SMPC theory. SMPC theory further predicts that the amount of variety consumers select will be higher for lower consumption rates and lower for higher consumption rates. Evidence from panel data of yogurt purchases supports this prediction. While we find that consumption choices are consistent with SMPC theory, they are not consistent with alternative explanations such as variety seeking or diversification bias. Viewed in its entirety, the empirical evidence presented in this paper confirms that both the choice set selected and the way it is consumed are consistent with dynamic models of future preference uncertainty.

Suggested Citation

  • Edward J. Fox & Hristina Pulgar & John H. Semple, 2024. "Testing a theory of strategic multi-product choice," Quantitative Marketing and Economics (QME), Springer, vol. 22(3), pages 257-289, September.
  • Handle: RePEc:kap:qmktec:v:22:y:2024:i:3:d:10.1007_s11129-024-09280-5
    DOI: 10.1007/s11129-024-09280-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11129-024-09280-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11129-024-09280-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Igal Hendel & Aviv Nevo, 2006. "Measuring the Implications of Sales and Consumer Inventory Behavior," Econometrica, Econometric Society, vol. 74(6), pages 1637-1673, November.
    2. Tülin Erdem & Susumu Imai & Michael Keane, 2003. "Brand and Quantity Choice Dynamics Under Price Uncertainty," Quantitative Marketing and Economics (QME), Springer, vol. 1(1), pages 5-64, March.
    3. Igal Hendel & Aviv Nevo, 2006. "Sales and consumer inventory," RAND Journal of Economics, RAND Corporation, vol. 37(3), pages 543-561, September.
    4. McAlister, Leigh & Pessemier, Edgar, 1982. "Variety Seeking Behavior: An Interdisciplinary Review," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 9(3), pages 311-322, December.
    5. Liang Guo, 2010. "Capturing Consumption Flexibility in Assortment Choice from Scanner Panel Data," Management Science, INFORMS, vol. 56(10), pages 1815-1832, October.
    6. Sanghak Lee & Greg M. Allenby, 2014. "Modeling Indivisible Demand," Marketing Science, INFORMS, vol. 33(3), pages 364-381, May.
    7. Richards, Timothy J. & Gómez, Miguel I. & Pofahl, Geoffrey, 2012. "A Multiple-discrete/Continuous Model of Price Promotion," Journal of Retailing, Elsevier, vol. 88(2), pages 206-225.
    8. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    9. Jean-Pierre Dubé, 2004. "Multiple Discreteness and Product Differentiation: Demand for Carbonated Soft Drinks," Marketing Science, INFORMS, vol. 23(1), pages 66-81, September.
    10. Simonson, Itamar & Winer, Russell S, 1992. "The Influence of Purchase Quantity and Display Format on Consumer Preference for Variety," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 19(1), pages 133-138, June.
    11. Igal Hendel & Aviv Nevo, 2006. "Sales and Consumer Inventory," RAND Journal of Economics, The RAND Corporation, vol. 37(3), pages 543-561, Autumn.
    12. Kim, Jaehwan & Allenby, Greg M. & Rossi, Peter E., 2007. "Product attributes and models of multiple discreteness," Journal of Econometrics, Elsevier, vol. 138(1), pages 208-230, May.
    13. John W. Walsh, 1995. "Flexibility in Consumer Purchasing for Uncertain Future Tastes," Marketing Science, INFORMS, vol. 14(2), pages 148-165.
    14. Linda Court Salisbury & Fred M. Feinberg, 2008. "Future Preference Uncertainty and Diversification: The Role of Temporal Stochastic Inflation," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 35(2), pages 349-359, March.
    15. Pessemier, Edgar A, 1978. "Stochastic Properties of Changing Preferences," American Economic Review, American Economic Association, vol. 68(2), pages 380-385, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pradeep K. Chintagunta & Harikesh S. Nair, 2011. "Structural Workshop Paper --Discrete-Choice Models of Consumer Demand in Marketing," Marketing Science, INFORMS, vol. 30(6), pages 977-996, November.
    2. Victor Aguirregabiria & Margaret Slade, 2017. "Empirical models of firms and industries," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 50(5), pages 1445-1488, December.
    3. Yonezawa, Koichi & Richards, Timothy J., 2016. "Competitive Package Size Decisions," Journal of Retailing, Elsevier, vol. 92(4), pages 445-469.
    4. Matthew Osborne, 2011. "Consumer learning, switching costs, and heterogeneity: A structural examination," Quantitative Marketing and Economics (QME), Springer, vol. 9(1), pages 25-70, March.
    5. Kris Johnson Ferreira & Joel Goh, 2021. "Assortment Rotation and the Value of Concealment," Management Science, INFORMS, vol. 67(3), pages 1489-1507, March.
    6. Ribeiro, Ricardo, 2010. "Consumer demand for variety: intertemporal effects of consumption, product switching and pricing policies," MPRA Paper 25812, University Library of Munich, Germany.
    7. Hinnosaar, Marit, 2016. "Time inconsistency and alcohol sales restrictions," European Economic Review, Elsevier, vol. 87(C), pages 108-131.
    8. Susumu Imai & Yuka Ohno, 2018. "Bridging Marketing and Economics: Introduction to Special Issue," The Japanese Economic Review, Springer, vol. 69(3), pages 255-257, September.
    9. Christopher Hansman & Harrison Hong & Áureo de Paula & Vishal Singh, 2020. "A Sticky-Price View of Hoarding," NBER Working Papers 27051, National Bureau of Economic Research, Inc.
    10. Aguirregabiria, Victor & Gu, Jiaying & Luo, Yao, 2021. "Sufficient statistics for unobserved heterogeneity in structural dynamic logit models," Journal of Econometrics, Elsevier, vol. 223(2), pages 280-311.
    11. Chan, Tat Y. & Narasimhan, Chakravarthi & Yoon, Yeujun, 2017. "Advertising and price competition in a manufacturer-retailer channel," International Journal of Research in Marketing, Elsevier, vol. 34(3), pages 694-716.
    12. Stephan Seiler, 2013. "The impact of search costs on consumer behavior: A dynamic approach," Quantitative Marketing and Economics (QME), Springer, vol. 11(2), pages 155-203, June.
    13. Gonca P. Soysal & Lakshman Krishnamurthi, 2012. "Demand Dynamics in the Seasonal Goods Industry: An Empirical Analysis," Marketing Science, INFORMS, vol. 31(2), pages 293-316, March.
    14. Ching, Andrew T. & Erdem, Tülin & Keane, Michael P., 2014. "A simple method to estimate the roles of learning, inventories and category consideration in consumer choice," Journal of choice modelling, Elsevier, vol. 13(C), pages 60-72.
    15. Aguirregabiria, Victor & Mira, Pedro, 2010. "Dynamic discrete choice structural models: A survey," Journal of Econometrics, Elsevier, vol. 156(1), pages 38-67, May.
    16. Raymond Deneckere & James Peck, 2012. "Dynamic Competition With Random Demand and Costless Search: A Theory of Price Posting," Econometrica, Econometric Society, vol. 80(3), pages 1185-1247, May.
    17. Stephan Seiler & Song Yao, 2017. "The impact of advertising along the conversion funnel," Quantitative Marketing and Economics (QME), Springer, vol. 15(3), pages 241-278, September.
    18. Jean‐Pierre Dubé & Günter J. Hitsch & Peter E. Rossi, 2010. "State dependence and alternative explanations for consumer inertia," RAND Journal of Economics, RAND Corporation, vol. 41(3), pages 417-445, September.
    19. Xiaodan Pan & Martin Dresner & Benny Mantin & Jun A. Zhang, 2020. "Pre‐Hurricane Consumer Stockpiling and Post‐Hurricane Product Availability: Empirical Evidence from Natural Experiments," Production and Operations Management, Production and Operations Management Society, vol. 29(10), pages 2350-2380, October.
    20. Robert Donnelly & Francisco J.R. Ruiz & David Blei & Susan Athey, 2021. "Counterfactual inference for consumer choice across many product categories," Quantitative Marketing and Economics (QME), Springer, vol. 19(3), pages 369-407, December.

    More about this item

    Keywords

    Multi-product choice; Dynamic programming; Discrete choice modeling;
    All these keywords.

    JEL classification:

    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:qmktec:v:22:y:2024:i:3:d:10.1007_s11129-024-09280-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.