IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v44y2025i3p516-524.html

Frontiers: Recommending What to Search: Sales Volume and Consumption Diversity Effects of a Query Recommender System

Author

Listed:
  • Shuang Zheng

    (School of Economics and Management, Dalian University of Technology, Dalian City 116024, China)

  • Siliang (Jack) Tong

    (Nanyang Business School, Nanyang Technological University, Singapore 639798)

  • Hyeokkoo Eric Kwon

    (Nanyang Business School, Nanyang Technological University, Singapore 639798)

  • Gordon Burtch

    (Questrom School of Business, Boston University, Boston, Massachusetts 02215)

  • Xianneng Li

    (School of Economics and Management, Dalian University of Technology, Dalian City 116024, China; and Institute for Advanced Intelligence, Dalian University of Technology, Dalian City 116024, China)

Abstract

This study examines the impact of a query recommender system on user search behavior, sales volume, and consumption diversity within a leading mobile food delivery app in Asia. We find that access to a query recommender increases consumer purchase volumes by 1%–2% over 30 days while broadening consumption diversity at both the individual and market levels. Exploring the mechanisms by which these effects arise, we highlight the complementary, balancing role of query auto-completion features. Whereas the query recommender helps to expand a user’s consideration set by suggesting alternative and adjacent queries, the auto-complete feature helps to extend and refine the queries in a personalized manner. Our findings highlight the potential of query recommenders for increasing demand while enhancing consumer exploration and consumption diversity, particularly when deployed in tandem with auto-complete. Our study contributes to the literature on search behavior and recommendation systems, offering actionable insights for platform managers into the strategic design and integration of query recommenders to improve user engagement and market outcomes.

Suggested Citation

  • Shuang Zheng & Siliang (Jack) Tong & Hyeokkoo Eric Kwon & Gordon Burtch & Xianneng Li, 2025. "Frontiers: Recommending What to Search: Sales Volume and Consumption Diversity Effects of a Query Recommender System," Marketing Science, INFORMS, vol. 44(3), pages 516-524, May.
  • Handle: RePEc:inm:ormksc:v:44:y:2025:i:3:p:516-524
    DOI: 10.1287/mksc.2024.1121
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.2024.1121
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mksc.2024.1121?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. Bo Zhou & Tianxin Zou, 2023. "Competing for Recommendations: The Strategic Impact of Personalized Product Recommendations in Online Marketplaces," Marketing Science, INFORMS, vol. 42(2), pages 360-376, March.
    2. Ratchford, Brian T., 2009. "Consumer Search Behavior and Its Effect on Markets," Foundations and Trends(R) in Marketing, now publishers, vol. 3(1), pages 1-74, March.
    3. Prabuddha De & Yu (Jeffrey) Hu & Mohammad S. Rahman, 2010. "Technology Usage and Online Sales: An Empirical Study," Management Science, INFORMS, vol. 56(11), pages 1930-1945, November.
    4. Punj, Girish N & Staelin, Richard, 1983. "A Model of Consumer Information Search Behavior for New Automobiles," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 9(4), pages 366-380, March.
    5. Dokyun Lee & Kartik Hosanagar, 2019. "How Do Recommender Systems Affect Sales Diversity? A Cross-Category Investigation via Randomized Field Experiment," Service Science, INFORMS, vol. 30(1), pages 239-259, March.
    6. Guy Aridor & Duarte Goncalves & Daniel Kluver & Ruoyan Kong & Joseph Konstan, 2022. "The Informational Role of Online Recommendations: Evidence from a Field Experiment," Papers 2211.14219, arXiv.org, revised Dec 2024.
    7. Xitong Li & Jörn Grahl & Oliver Hinz, 2022. "How Do Recommender Systems Lead to Consumer Purchases? A Causal Mediation Analysis of a Field Experiment," Information Systems Research, INFORMS, vol. 33(2), pages 620-637, June.
    8. Tianshu Sun & Zhe Yuan & Chunxiao Li & Kaifu Zhang & Jun Xu, 2024. "The Value of Personal Data in Internet Commerce: A High-Stakes Field Experiment on Data Regulation Policy," Management Science, INFORMS, vol. 70(4), pages 2645-2660, April.
    9. Xiang (Shawn) Wan & Anuj Kumar & Xitong Li, 2024. "How Do Product Recommendations Help Consumers Search? Evidence from a Field Experiment," Management Science, INFORMS, vol. 70(9), pages 5776-5794, September.
    10. Anindya Ghose & Avi Goldfarb & Sang Pil Han, 2013. "How Is the Mobile Internet Different? Search Costs and Local Activities," Information Systems Research, INFORMS, vol. 24(3), pages 613-631, September.
    11. Daniel Fleder & Kartik Hosanagar, 2009. "Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity," Management Science, INFORMS, vol. 55(5), pages 697-712, May.
    12. Erik Brynjolfsson & Yu (Jeffrey) Hu & Duncan Simester, 2011. "Goodbye Pareto Principle, Hello Long Tail: The Effect of Search Costs on the Concentration of Product Sales," Management Science, INFORMS, vol. 57(8), pages 1373-1386, August.
    13. Robert Donnelly & Ayush Kanodia & Ilya Morozov, 2024. "Welfare Effects of Personalized Rankings," Marketing Science, INFORMS, vol. 43(1), pages 92-113, January.
    14. Hinz, Oliver & Eckert, Jochen & Skiera, Bernd, 2011. "Drivers of the Long Tail Phenomenon: An Empirical Analysis," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 56544, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    15. Catherine Tucker & Juanjuan Zhang, 2011. "How Does Popularity Information Affect Choices? A Field Experiment," Management Science, INFORMS, vol. 57(5), pages 828-842, May.
    16. Sai Chand Chintala & Jūra Liaukonytė & Nathan Yang, 2024. "Browsing the Aisles or Browsing the App? How Online Grocery Shopping is Changing What We Buy," Marketing Science, INFORMS, vol. 43(3), pages 506-522, 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. Dongwon Lee & Anandasivam Gopal & Sung-Hyuk Park, 2020. "Different but Equal? A Field Experiment on the Impact of Recommendation Systems on Mobile and Personal Computer Channels in Retail," Information Systems Research, INFORMS, vol. 31(3), pages 892-912, September.
    2. Xitong Li & Jörn Grahl & Oliver Hinz, 2022. "How Do Recommender Systems Lead to Consumer Purchases? A Causal Mediation Analysis of a Field Experiment," Information Systems Research, INFORMS, vol. 33(2), pages 620-637, June.
    3. Tobias Kretschmer & Christian Peukert, 2020. "Video Killed the Radio Star? Online Music Videos and Recorded Music Sales," Information Systems Research, INFORMS, vol. 31(3), pages 776-800, September.
    4. Yi, Sangyoon & Kim, Dongyeon & Ju, Jaehyeon, 2022. "Recommendation technologies and consumption diversity: An experimental study on product recommendations, consumer search, and sales diversity," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    5. Tianshu Sun & Zhe Yuan & Chunxiao Li & Kaifu Zhang & Jun Xu, 2024. "The Value of Personal Data in Internet Commerce: A High-Stakes Field Experiment on Data Regulation Policy," Management Science, INFORMS, vol. 70(4), pages 2645-2660, April.
    6. Kevin Zielnicki & Guy Aridor & Aur'elien Bibaut & Allen Tran & Winston Chou & Nathan Kallus, 2025. "The Value of Personalized Recommendations: Evidence from Netflix," Papers 2511.07280, arXiv.org, revised Jun 2026.
    7. Tom Fangyun Tan & Serguei Netessine & Lorin Hitt, 2017. "Is Tom Cruise Threatened? An Empirical Study of the Impact of Product Variety on Demand Concentration," Information Systems Research, INFORMS, vol. 28(3), pages 643-660, September.
    8. Dokyun Lee & Kartik Hosanagar, 2021. "How Do Product Attributes and Reviews Moderate the Impact of Recommender Systems Through Purchase Stages?," Management Science, INFORMS, vol. 67(1), pages 524-546, January.
    9. Chenshuo Sun, 2025. "How Does Prepopulating Search Bars with Keywords Affect Online Consumer Behavior? A Field Experiment," Marketing Science, INFORMS, vol. 44(6), pages 1217-1231, November.
    10. Xiaopeng Luo & Cheng He & Yu Jeffrey Hu & Xitong Li & Yuan Cheng, 2025. "The Impact of Mobile Data Cost on Consumer Price Sensitivity: A Study of a Hotel Booking App," Information Systems Research, INFORMS, vol. 36(3), pages 1912-1925, September.
    11. Sai Chand Chintala & Jūra Liaukonytė & Nathan Yang, 2024. "Browsing the Aisles or Browsing the App? How Online Grocery Shopping is Changing What We Buy," Marketing Science, INFORMS, vol. 43(3), pages 506-522, May.
    12. Yinbo Feng & Ming Hu, 2017. "Blockbuster or Niche? Competitive Strategy under Network Effects," Working Papers 17-13, NET Institute.
    13. repec:ces:ceswps:_12257 is not listed on IDEAS
    14. Zhe Yuan & AJ Yuan Chen & Yitong Wang & Tianshu Sun, 2025. "How Recommendation Affects Customer Search: A Field Experiment," Information Systems Research, INFORMS, vol. 36(1), pages 84-106, March.
    15. Panagiotis (Panos) Adamopoulos, 2024. "The Spillover Effect of Fraudulent Reviews on Product Recommendations," Management Science, INFORMS, vol. 70(12), pages 8818-8832, December.
    16. Hoskins, Jake D., 2020. "The evolving role of hit and niche products in brick-and-mortar retail category assortment planning: A large-scale empirical investigation of U.S. consumer packaged goods," Journal of Retailing and Consumer Services, Elsevier, vol. 57(C).
    17. Konstantin Bauman & Alexander Tuzhilin, 2022. "Know Thy Context: Parsing Contextual Information from User Reviews for Recommendation Purposes," Information Systems Research, INFORMS, vol. 33(1), pages 179-202, March.
    18. Miguel Godinho de Matos & Pedro Ferreira, 2020. "The Effect of Binge-Watching on the Subscription of Video on Demand: Results from Randomized Experiments," Information Systems Research, INFORMS, vol. 31(4), pages 1337-1360, December.
    19. Weeds, Helen, 2012. "Superstars and the long tail: The impact of technology on market structure in media industries," Information Economics and Policy, Elsevier, vol. 24(1), pages 60-68.
    20. Anuj Kumar & Kartik Hosanagar, 2019. "Measuring the Value of Recommendation Links on Product Demand," Information Systems Research, INFORMS, vol. 30(3), pages 819-838, September.
    21. Joan Calzada & Nestor Duch-Brown & Ricard Gil, 2021. "Do search engines increase concentration in media markets?," UB School of Economics Working Papers 2021/415, University of Barcelona School of Economics.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    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:inm:ormksc:v:44:y:2025:i:3:p:516-524. 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.