IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v43y2024i1p213-228.html
   My bibliography  Save this article

The Power of Commitment in Group Search

Author

Listed:
  • Xinyu Cao

    (Stern School of Business, New York University, New York, New York 10012; Chinese University of Hong Kong, Hong Kong)

  • Yuting Zhu

    (National University of Singapore Business School, Singapore 119245)

Abstract

In this paper, we build a two-member, two-period model to show that, when a group of people with different preferences conducts a search and makes a decision using the majority voting rule, they can benefit from making a commitment on the number of products to search ex ante (i.e., conducting a fixed-sample search) when the search cost is small enough or relatively large. The underlying mechanism is that, because of the preference inconsistency between group members, they tend to search fewer products and, thus, have lower expected utility in group search than in single-agent search, and making a commitment on the number of products to search helps mitigate the preference inconsistency problem in group search, especially when the search cost is small enough or relatively large. We further show that, under alternative voting rules, there also exist ranges of search cost in which fixed-sample search works better than sequential search as long as the voting rule is exogenously determined. If the group can endogenously choose the voting rule that maximizes their expected utility, then sequential search is always preferable to fixed-sample search. We also consider several extensions to show the robustness of our finding.

Suggested Citation

  • Xinyu Cao & Yuting Zhu, 2024. "The Power of Commitment in Group Search," Marketing Science, INFORMS, vol. 43(1), pages 213-228, January.
  • Handle: RePEc:inm:ormksc:v:43:y:2024:i:1:p:213-228
    DOI: 10.1287/mksc.2023.1447
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.2023.1447
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mksc.2023.1447?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
    ---><---

    References listed on IDEAS

    as
    1. Rothschild, Michael, 1974. "Searching for the Lowest Price When the Distribution of Prices Is Unknown," Journal of Political Economy, University of Chicago Press, vol. 82(4), pages 689-711, July/Aug..
    2. Yuxin Chen & Song Yao, 2017. "Sequential Search with Refinement: Model and Application with Click-Stream Data," Management Science, INFORMS, vol. 63(12), pages 4345-4365, December.
    3. Jos van Ommeren & Giovanni Russo, 2014. "Firm Recruitment Behaviour: Sequential or Non-sequential Search?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(3), pages 432-455, June.
    4. Weitzman, Martin L, 1979. "Optimal Search for the Best Alternative," Econometrica, Econometric Society, vol. 47(3), pages 641-654, May.
    5. Davis, Harry L, 1976. "Decision Making within the Household," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 2(4), pages 241-260, March.
    6. Bart J. Bronnenberg & Jun B. Kim & Carl F. Mela, 2016. "Zooming In on Choice: How Do Consumers Search for Cameras Online?," Marketing Science, INFORMS, vol. 35(5), pages 693-712, September.
    7. Fernando Branco & Monic Sun & J. Miguel Villas-Boas, 2012. "Optimal Search for Product Information," Management Science, INFORMS, vol. 58(11), pages 2037-2056, November.
    8. T. Tony Ke & Zuo-Jun Max Shen & J. Miguel Villas-Boas, 2016. "Search for Information on Multiple Products," Management Science, INFORMS, vol. 62(12), pages 3576-3603, December.
    9. Olivier Compte & Philippe Jehiel, 2010. "Bargaining and Majority Rules: A Collective Search Perspective," Journal of Political Economy, University of Chicago Press, vol. 118(2), pages 189-221, April.
    10. Raluca M. Ursu, 2018. "The Power of Rankings: Quantifying the Effect of Rankings on Online Consumer Search and Purchase Decisions," Marketing Science, INFORMS, vol. 37(4), pages 530-552, August.
    11. Chen, Xiaohong & Hong, Han & Shum, Matthew, 2007. "Nonparametric likelihood ratio model selection tests between parametric likelihood and moment condition models," Journal of Econometrics, Elsevier, vol. 141(1), pages 109-140, November.
    12. J. J. McCall, 1970. "Economics of Information and Job Search," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 84(1), pages 113-126.
    13. Chen, Zhiqi & Woolley, Frances, 2001. "A Cournot-Nash Model of Family Decision Making," Economic Journal, Royal Economic Society, vol. 111(474), pages 722-748, October.
    14. Telser, L G, 1973. "Searching for the Lowest Price," American Economic Review, American Economic Association, vol. 63(2), pages 40-49, May.
    15. Asher Wolinsky, 1986. "True Monopolistic Competition as a Result of Imperfect Information," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 101(3), pages 493-511.
    16. Jun B. Kim & Paulo Albuquerque & Bart J. Bronnenberg, 2010. "Online Demand Under Limited Consumer Search," Marketing Science, INFORMS, vol. 29(6), pages 1001-1023, 11-12.
    17. Michael Rothschild, 1974. "Searching for the Lowest Price When the Distribution of Prices Is Unknown: A Summary," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 3, number 1, pages 293-294, National Bureau of Economic Research, Inc.
    18. Joseph L. Gastwirth, 1976. "On Probabilistic Models of Consumer Search for Information," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 90(1), pages 38-50.
    19. Han Hong & Matthew Shum, 2006. "Using price distributions to estimate search costs," RAND Journal of Economics, RAND Corporation, vol. 37(2), pages 257-275, June.
    20. Babur De Los Santos & Ali Hortacsu & Matthijs R. Wildenbeest, 2012. "Testing Models of Consumer Search Using Data on Web Browsing and Purchasing Behavior," American Economic Review, American Economic Association, vol. 102(6), pages 2955-2980, October.
    21. , & ,, 2013. "Specialization and partisanship in committee search," Theoretical Economics, Econometric Society, vol. 8(3), September.
    22. Burdett, Kenneth & Judd, Kenneth L, 1983. "Equilibrium Price Dispersion," Econometrica, Econometric Society, vol. 51(4), pages 955-969, July.
    23. Dmitri Kuksov & J. Miguel Villas-Boas, 2010. "When More Alternatives Lead to Less Choice," Marketing Science, INFORMS, vol. 29(3), pages 507-524, 05-06.
    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. Rafael P. Greminger, 2022. "Optimal Search and Discovery," Management Science, INFORMS, vol. 68(5), pages 3904-3924, May.
    2. Xing Zhang & Tat Y. Chan & Ying Xie, 2018. "Price Search and Periodic Price Discounts," Management Science, INFORMS, vol. 64(2), pages 495-510, February.
    3. Raluca M. Ursu & Qingliang Wang & Pradeep K. Chintagunta, 2020. "Search Duration," Marketing Science, INFORMS, vol. 39(5), pages 849-871, September.
    4. Marcu, Emanuel & Noussair, Charles, 2018. "Sequential Search with a Price Freeze Option - Theory and Experimental Evidence," Other publications TiSEM dacf4815-c001-44c3-bda3-f, Tilburg University, School of Economics and Management.
    5. repec:smu:ecowpa:1301 is not listed on IDEAS
    6. Elisabeth Honka & Pradeep Chintagunta, 2017. "Simultaneous or Sequential? Search Strategies in the U.S. Auto Insurance Industry," Marketing Science, INFORMS, vol. 36(1), pages 21-42, January.
    7. Leon Yang Chu & Hamid Nazerzadeh & Heng Zhang, 2020. "Position Ranking and Auctions for Online Marketplaces," Management Science, INFORMS, vol. 66(8), pages 3617-3634, August.
    8. Ke, T. Tony & Villas-Boas, J. Miguel, 2019. "Optimal learning before choice," Journal of Economic Theory, Elsevier, vol. 180(C), pages 383-437.
    9. T. Tony Ke & Song Lin, 2020. "Informational Complementarity," Management Science, INFORMS, vol. 66(8), pages 3699-3716, August.
    10. Mantian (Mandy) Hu & Chu (Ivy) Dang & Pradeep K. Chintagunta, 2019. "Search and Learning at a Daily Deals Website," Marketing Science, INFORMS, vol. 38(4), pages 609-642, July.
    11. Liang Guo, 2021. "Endogenous Evaluation and Sequential Search," Marketing Science, INFORMS, vol. 40(3), pages 413-427, May.
    12. Yuxin Chen & Song Yao, 2017. "Sequential Search with Refinement: Model and Application with Click-Stream Data," Management Science, INFORMS, vol. 63(12), pages 4345-4365, December.
    13. Pedro M. Gardete & Carlos D. Santos, 2020. "No data? No problem! A Search-based Recommendation System with Cold Starts," Papers 2010.03455, arXiv.org.
    14. Bart J. Bronnenberg & Jun B. Kim & Carl F. Mela, 2016. "Zooming In on Choice: How Do Consumers Search for Cameras Online?," Marketing Science, INFORMS, vol. 35(5), pages 693-712, September.
    15. Anocha Aribarg & Thomas Otter & Daniel Zantedeschi & Greg M. Allenby & Taylor Bentley & David J. Curry & Marc Dotson & Ty Henderson & Elisabeth Honka & Rajeev Kohli & Kamel Jedidi & Stephan Seiler & X, 2018. "Advancing Non-compensatory Choice Models in Marketing," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(1), pages 82-92, March.
    16. Babur De Los Santos & Ali Hortacsu & Matthijs R. Wildenbeest, 2012. "Testing Models of Consumer Search Using Data on Web Browsing and Purchasing Behavior," American Economic Review, American Economic Association, vol. 102(6), pages 2955-2980, October.
    17. Babur De los Santos & Ali Hortacsu & Matthijs R. Wildenbeest, 2009. "Testing Models of Consumer Search Using Data on Web Browsing Behavior," Working Papers 09-23, NET Institute, revised Aug 2009.
    18. Tat Y. Chan & Young-Hoon Park, 2015. "Consumer Search Activities and the Value of Ad Positions in Sponsored Search Advertising," Marketing Science, INFORMS, vol. 34(4), pages 606-623, July.
    19. Greminger, Rafael, 2019. "Optimal Search and Awareness Expansion," Other publications TiSEM ac47e6ff-42a4-4d70-addd-6, Tilburg University, School of Economics and Management.
    20. Daria Dzyabura & John R. Hauser, 2019. "Recommending Products When Consumers Learn Their Preference Weights," Marketing Science, INFORMS, vol. 38(3), pages 417-441, May.
    21. Timothy J. Richards & Stephen F. Hamilton & Koichi Yonezawa, 2017. "Variety and the Cost of Search in Supermarket Retailing," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 50(3), pages 263-285, May.

    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:inm:ormksc:v:43:y:2024:i:1:p:213-228. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.