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Switching decision, timing, and app performance: An empirical analysis of mobile app developers’ switching behavior between monetization strategies

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  • Lee, Young-Jin
  • Ghasemkhani, Hossein
  • Xie, Karen
  • Tan, Yong

Abstract

Mobile application developers switch their monetization strategies for strategic purposes. We investigate the determinants and consequences of app developers’ switching behavior between paid and free strategies over time. Using large-scale daily app rank data from the iOS App Store, our estimations reveal that the prior performance of an app and its duration of staying in a monetization strategy significantly impact the developers’ decision of switching. We also find that better-performing apps tend to have longer stays in the paid strategy, whereas developers tend to switch their monetization strategy to free when the performance of a paid app declines. Furthermore, app developers’ switching behavior and duration of the app in each strategy have significant effects on the subsequent app performance in the App Store. Finally, we find the impact of switching behavior varies by app category. Managerial implications with estimated economic outcomes on strategic moves by app developers towards higher app performance are provided.

Suggested Citation

  • Lee, Young-Jin & Ghasemkhani, Hossein & Xie, Karen & Tan, Yong, 2021. "Switching decision, timing, and app performance: An empirical analysis of mobile app developers’ switching behavior between monetization strategies," Journal of Business Research, Elsevier, vol. 127(C), pages 332-345.
    • Handle: RePEc:eee:jbrese:v:127:y:2021:i:c:p:332-345
    DOI: 10.1016/j.jbusres.2021.01.027
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    1. Biraglia, Alessandro & Bowen, Karen T. & Gerrath, Maximilian H.E.E. & Musarra, Giuseppe, 2022. "How need for closure and deal proneness shape consumers’ freemium versus premium price choices," Journal of Business Research, Elsevier, vol. 143(C), pages 157-170.
    2. Davazdahemami, Behrooz & Kalgotra, Pankush & Zolbanin, Hamed M. & Delen, Dursun, 2023. "A developer-oriented recommender model for the app store: A predictive network analytics approach," Journal of Business Research, Elsevier, vol. 158(C).
    3. Numminen, Emil & Sällberg, Henrik & Wang, Shujun, 2022. "The impact of app revenue model choices for app revenues: A study of apps since their initial App Store launch," Economic Analysis and Policy, Elsevier, vol. 76(C), pages 325-336.

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