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Measuring the Impact of Crowdsourcing Features on Mobile App User Engagement and Retention: A Randomized Field Experiment

Author

Listed:
  • Zhuojun Gu

    (Information Systems and Operations Management, The University of Texas at Arlington, Arlington, Texas 76010)

  • Ravi Bapna

    (Information and Decision Sciences, University of Minnesota, Minneapolis, Minnesota 55455)

  • Jason Chan

    (Information and Decision Sciences, University of Minnesota, Minneapolis, Minnesota 55455)

  • Alok Gupta

    (Information and Decision Sciences, University of Minnesota, Minneapolis, Minnesota 55455)

Abstract

The most commonly cited issues with mobile apps are low user engagement and retention levels. In this paper, we use a randomized control trial to test the efficacy of crowdsourcing on enhancing user engagement and retention in the context of mobile gaming apps. We examine two specific crowdsourcing features: the ability to submit content and the ability to access crowdsourced content. We find that the content submission significantly increases engagement and retention by reducing users’ hazard of ending a session by approximately 11% relative to the baseline and reducing the hazard of abandoning the game app by 14%. In contrast, giving users the ability to access crowdsourced content has no significant effect on engagement but has a positive effect on retention by reducing the hazard of abandoning the game app by 13%. Surprisingly, we find that the interaction effect of these two crowdsourced features is negative on user engagement. Individually, the submission feature manifests itself via empowering users to control their product use experience, whereas the access feature’s positive effect on retention is mediated by diversity and novelty of content. However, the two effects are not complementary. It turns out when these two features are given together, the empowerment enabled by one’s own submission is crowded out by others’ submissions, and this dominates the diversity benefit. Crowdsourcing features have heterogeneous impact on different user segments, with heavy users and users of longer tenure being more affected by the crowdsourcing features.

Suggested Citation

  • Zhuojun Gu & Ravi Bapna & Jason Chan & Alok Gupta, 2022. "Measuring the Impact of Crowdsourcing Features on Mobile App User Engagement and Retention: A Randomized Field Experiment," Management Science, INFORMS, vol. 68(2), pages 1297-1329, February.
    • Handle: RePEc:inm:ormnsc:v:68:y:2022:i:2:p:1297-1329
    DOI: 10.1287/mnsc.2020.3943
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    References listed on IDEAS

    as
    1. Assael, Henry, 2005. "A Demographic and Psychographic Profile of Heavy Internet Users and Users by Type of Internet Usage," Journal of Advertising Research, Cambridge University Press, vol. 45(1), pages 93-123, March.
    2. Eva Ascarza & Oded Netzer & Bruce G. S. Hardie, 2018. "Some Customers Would Rather Leave Without Saying Goodbye," Marketing Science, INFORMS, vol. 37(1), pages 54-77, January.
    3. Dokyun Lee & Kartik Hosanagar & Harikesh S. Nair, 2018. "Advertising Content and Consumer Engagement on Social Media: Evidence from Facebook," Management Science, INFORMS, vol. 64(11), pages 5105-5131, November.
    4. McAlister, Leigh, 1982. "A Dynamic Attribute Satiation Model of Variety-Seeking Behavior," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 9(2), pages 141-150, September.
    5. Sam Ransbotham & Gerald C. Kane & Nicholas H. Lurie, 2012. "Network Characteristics and the Value of Collaborative User-Generated Content," Marketing Science, INFORMS, vol. 31(3), pages 387-405, May.
    6. Pei-Yu (Sharon) Chen & Lorin M. Hitt, 2002. "Measuring Switching Costs and the Determinants of Customer Retention in Internet-Enabled Businesses: A Study of the Online Brokerage Industry," Information Systems Research, INFORMS, vol. 13(3), pages 255-274, September.
    7. Raghuram Iyengar & Kamel Jedidi & Skander Essegaier & Peter J. Danaher, 2011. "The Impact of Tariff Structure on Customer Retention, Usage, and Profitability of Access Services," Marketing Science, INFORMS, vol. 30(5), pages 820-836, September.
    8. Heather L. O'Brien & Elaine G. Toms, 2010. "The development and evaluation of a survey to measure user engagement," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(1), pages 50-69, January.
    9. Yan Huang & Param Vir Singh & Kannan Srinivasan, 2014. "Crowdsourcing New Product Ideas Under Consumer Learning," Management Science, INFORMS, vol. 60(9), pages 2138-2159, September.
    10. Kevin J. Boudreau & Nicola Lacetera & Karim R. Lakhani, 2011. "Incentives and Problem Uncertainty in Innovation Contests: An Empirical Analysis," Management Science, INFORMS, vol. 57(5), pages 843-863, May.
    11. Khim-Yong Goh & Cheng-Suang Heng & Zhijie Lin, 2013. "Social Media Brand Community and Consumer Behavior: Quantifying the Relative Impact of User- and Marketer-Generated Content," Information Systems Research, INFORMS, vol. 24(1), pages 88-107, March.
    12. Philipp Afèche & Mojtaba Araghi & Opher Baron, 2017. "Customer Acquisition, Retention, and Service Access Quality: Optimal Advertising, Capacity Level, and Capacity Allocation," Manufacturing & Service Operations Management, INFORMS, vol. 19(4), pages 674-691, October.
    13. Lars Bo Jeppesen & Karim R. Lakhani, 2010. "Marginality and Problem-Solving Effectiveness in Broadcast Search," Organization Science, INFORMS, vol. 21(5), pages 1016-1033, October.
    14. Heather L. O'Brien & Elaine G. Toms, 2008. "What is user engagement? A conceptual framework for defining user engagement with technology," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 59(6), pages 938-955, April.
    15. Seshadri Tirunillai & Gerard J. Tellis, 2012. "Does Chatter Really Matter? Dynamics of User-Generated Content and Stock Performance," Marketing Science, INFORMS, vol. 31(2), pages 198-215, March.
    16. 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.
    17. Anindya Ghose & Sang Pil Han, 2014. "Estimating Demand for Mobile Applications in the New Economy," Management Science, INFORMS, vol. 60(6), pages 1470-1488, June.
    18. Gordon Burtch & Yili Hong & Ravi Bapna & Vladas Griskevicius, 2018. "Stimulating Online Reviews by Combining Financial Incentives and Social Norms," Management Science, INFORMS, vol. 64(5), pages 2065-2082, May.
    19. Heather L. O'Brien & Elaine G. Toms, 2010. "The development and evaluation of a survey to measure user engagement," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(1), pages 50-69, January.
    20. Yingjie Zhang & Beibei Li & Xueming Luo & Xiaoyi Wang, 2019. "Personalized Mobile Targeting with User Engagement Stages: Combining a Structural Hidden Markov Model and Field Experiment," Information Systems Research, INFORMS, vol. 30(3), pages 787-804, September.
    21. Shlomo Benartzi & Richard H. Thaler, 2002. "How Much Is Investor Autonomy Worth?," Journal of Finance, American Finance Association, vol. 57(4), pages 1593-1616, August.
    22. Ruth N. Bolton, 1998. "A Dynamic Model of the Duration of the Customer's Relationship with a Continuous Service Provider: The Role of Satisfaction," Marketing Science, INFORMS, vol. 17(1), pages 45-65.
    23. Joel O. Wooten & Karl T. Ulrich, 2017. "Idea Generation and the Role of Feedback: Evidence from Field Experiments with Innovation Tournaments," Production and Operations Management, Production and Operations Management Society, vol. 26(1), pages 80-99, January.
    24. Eva Ascarza & Bruce G. S. Hardie, 2013. "A Joint Model of Usage and Churn in Contractual Settings," Marketing Science, INFORMS, vol. 32(4), pages 570-590, July.
    25. Barry L. Bayus, 2013. "Crowdsourcing New Product Ideas over Time: An Analysis of the Dell IdeaStorm Community," Management Science, INFORMS, vol. 59(1), pages 226-244, June.
    26. Mollen, Anne & Wilson, Hugh, 2010. "Engagement, telepresence and interactivity in online consumer experience: Reconciling scholastic and managerial perspectives," Journal of Business Research, Elsevier, vol. 63(9-10), pages 919-925, September.
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