| 摘要(英) |
With the advancing technology, the number of smartphone users is increasing, which in turn leads to a rise in users of application platforms such as Google Play and App Store. For users and app developers, these platforms serve as intermediaries where developers can provide services to users, who can, in turn, obtain the desired services. The platform profits from this arrangement and strives to find a balance between users and developers through pricing decisions to maximize their own profit. Previous academic research has primarily focused on studying the decision differences among platform, advertisers, and app developers, aiming to maximize the overall profit of the platform ecosystem through the analysis of profit functions. In recent years, due to the growing awareness of personal privacy, platform are required to adhere to regulations and impose restrictions on app developers, limiting their access to user data to ensure the protection of users′ personal information.
This paper aims to utilize an economic model to explore the platform′s optimal profit and privacy protection decisions in the context of user data being required to be protected. The study also analyzes the impact of different factors on the platform′s optimal privacy protection decisions through the analysis of profit functions. The research reveals that when the platform owner simultaneously acts as a smartphone brand and does not charge app developers a share of the advertising revenue, they would set the privacy settings to be the most stringent in order to maximize profit. This can explain why applications in App Store proactively ask users for permission to access their data, while Google Play does not. An important finding is that when the revenue sharing exceeds a certain proportion, if the platform owner does not have their own smartphone brand, they would focus on attracting more platform users to maximize profit. However, if the platform owner has their own smartphone brand, they would choose to attract more app developers and advertisers rather than platform users. |
| 參考文獻 |
1. Tiwana, A., Konsynski, B., & Ashley a. bush. (2010). Research Commentary—Platform Evolution: Coevolution of Platform Architecture, Governance, and Environmental Dynamics. Information Systems Research, 21(4).
2. Balebako, R., et al. (2013). “Little Brothers Watching You”: Raising Awareness of Data Leaks on Smartphones. SOUPS ’13: Proceedings of the Ninth Symposium on Usable Privacy and Security, 1–11.
3. Regulation (EU) 2016/679 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 27 April 2016 on the Protection of Natural Persons with Regard to the Processing of Personal Data and on the Free Movement of Such Data, and Repealing Directive 95/46/EC (General Data Protection Regulation). (n.d.). https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:32016R0679
4. App Tracking Transparency (ATT). (n.d.). AppsFlyer. https://www.appsflyer.com/glossary/app-tracking-transparency/
5. Balasubramanian, M. (2022, May 2). App Tracking Transparency Opt-In Rate - Monthly Updates. Flurry. https://www.flurry.com/blog/att-opt-in-rate-monthly-updates/
6. Dutton, S. (2021, May 18). FLoC. Chrome Developers. https://developer.chrome.com/en/docs/privacy-sandbox/floc/
7. Ax sharma. (2021, April 14). Vivaldi, Brave, DuckDuckGo Reject Google’s FLoC Ad Tracking Tech. BleepingComputer. https://www.bleepingcomputer.com/news/security/vivaldi-brave-duckduckgo-reject-googles-floc-ad-tracking-tech/
8. Cyphers, B. (2021, March 3). Google’s FLoC Is a Terrible Idea. Electronic Frontier Foundation. https://www.eff.org/deeplinks/2021/03/googles-floc-terrible-idea
9. Subin, B. (2022, January 30). What Is Google’s Topics API and How Is It Different from FLoC? Beebom. https://beebom.com/what-is-google-topics-api-how-different-from-floc/
10. Cruz, J. (n.d.). Impact of Privacy Policy Changes On The Advertising Industry. Greenwich Capital Group. https://greenwichgp.com/2022/09/02/impact-of-privacy-policy-changes-on-the-advertising-industry/
11. Claussen, J., et al. (2013). The Effects of Rewarding User Engagement: The Case of Facebook Apps. Information Systems Research, 24(1).
12. What Are Apple and Google’s Fees and Revenue Share Percentage on in-App Purchases and Subscriptions? (n.d.). MobiLoud. https://www.mobiloud.com/help/knowledge-base/what-are-apple-and-googles-fees-and-revenue-share-percentage-on-in-app-purchases-and-subscriptions
13. Wang, R. (2018). Advertising Strategies for Mobile Platforms With “Apps.” IEEE Transactions on Systems, 48(5), 767–778.
14. Avinadav, T. (2021). Revenue-Sharing between Developers of Virtual Products and Platform Distributors. European Journal of Operational Research, 290(3), 927–945.
15. Rysman, M. (2009). The Economics of Two-Sided Markets. Journal of Economic Perspectives, 23(3).
16. Economides, N., & Katsamakas, E. (2006). Two-Sided Competition of Proprietary vs. Open Source Technology Platforms and the Implications for the Software Industry. Management Science, 52(7).
17. Kagan, J. (2022, April 26). Two-Sided Market: Definition and Examples. Investopedia. https://www.investopedia.com/terms/t/two-sidedmarket.asp
18. Hao, L., et al. (2016). A Mobile Platform’s In-App Advertising Contract Under Agency Pricing for App Sales. Production and Operations Management, 26(2), 189–202.
19. Wang, M., & You, Z. (2020). Regulation of App Users’ Privacy Security Based on Three-Party Evolutionary Game Model. 2020 Management Science Informatization and Economic Innovation Development Conference.
20. Kahneman, D., & Tversky, A. (2013). Prospect Theory: An Analysis of Decision Under Risk (Vol. 4).
21. Ji, Y. (2019). Monetization on Mobile Platforms: Balancing in-App Advertising and User Base Growth. Production and Operations Management, 28(9), 2202–2220.
22. Ghose, A., & Han, S. pil. (2014). Estimating Demand for Mobile Applications in the New Economy. Management Science, 60(6), 1351–1616.
23. Boudreau, K. (2010). Open Platform Strategies and Innovation: Granting Access vs. Devolving Control. Management Science, 56(10), 4–1872.
24. Economides, N., & Katsamakas, E. (2006). Two-Sided Competition of Proprietary vs. Open Source Technology Platforms and the Implications for the Software Industry. Management Science, 52(7), 4–1128.
25. Albuquerque, P., et al. (2012). Evaluating Promotional Activities in an Online Two-Sided Market of User-Generated Content. Marketing Science, 31(3), 369–547.
26. Lee, C., et al. (2013). Designing Freemium: A Model of Consumer Usage, Upgrade, and Referral Dynamics.
27. Lin, M., et al. (2014). Vertical Differentiation and a Comparison of Online Advertising Models. Journal of Management Information Systems, 29(1), 195–236.
28. Balebako, R., et al. (2014). Improving App Privacy: Nudging App Developers to Protect User Privacy. IEEE Security & Privacy, 12(4), 55–58.
29. Gu, J., et al. (2017). Privacy Concerns for Mobile App Download: An Elaboration Likelihood Model Perspective. Decision Support Systems, 94, 19–28.
30. Hao, L., et al. (2017). A Mobile Platform’s In-App Advertising Contract Under Agency Pricing for App Sales. Production and Operations Management, 26(2), 189–202.
31. Ghose, A., et al. (2014). Estimating Demand for Mobile Applications in the New Economy. Management Science, 60(6), 1351–1616. |
[Endnote RIS 格式]
[相關文章]
[文章引用]
[完整記錄]
[館藏目錄]
至系統瀏覽論文 (2028-6-30以後開放)
plurk
funp
live
udn
HD
myshare
netvibes
friend
youpush
delicious
baidu
Google bookmarks
del.icio.us
hemidemi
facebook
twitter
google
reddit