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Copycats vs. Original Mobile Apps: A Machine Learning Copycat-Detection Method and Empirical Analysis

Author

Listed:
  • Quan Wang

    (Heinz College, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

  • Beibei Li

    (Heinz College, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

  • Param Vir Singh

    (Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

Abstract
While the growth of the mobile apps market has created significant market opportunities and economic incentives for mobile app developers to innovate, it has also inevitably invited other developers to create rip-offs. Practitioners and developers of original apps claim that copycats steal the original app’s idea and potential demand, and have called for app platforms to take action against such copycats. Surprisingly, however, there has been little rigorous research analyzing whether and how copycats affect an original app’s demand. The primary deterrent to such research is the lack of an objective way to identify whether an app is a copycat or an original. Using a combination of machine learning techniques such as natural language processing, latent semantic analysis, network-based clustering, and image analysis, we propose a method to identify apps as original or copycat and detect two types of copycats: deceptive and nondeceptive. Based on the detection results, we conduct an econometric analysis to determine the impact of copycat apps on the demand for the original apps on a sample of 10,100 action game apps by 5,141 developers that were released in the iOS App Store over five years. Our results indicate that the effect of a specific copycat on an original app’s demand is determined by the quality and level of deceptiveness of the copycat. High-quality nondeceptive copycats negatively affect demand for the originals. By contrast, low-quality, deceptive copycats positively affect demand for the originals. Results indicate that in aggregate the impact of copycats on the demand of original mobile apps is statistically insignificant. Our study contributes to the growing literature on mobile app consumption by presenting a method to identify copycats and providing evidence of the impact of copycats on an original app’s demand. The online appendix is available at https://doi.org/10.1287/isre.2017.0735 .

Suggested Citation

  • Quan Wang & Beibei Li & Param Vir Singh, 2018. "Copycats vs. Original Mobile Apps: A Machine Learning Copycat-Detection Method and Empirical Analysis," Information Systems Research, INFORMS, vol. 29(2), pages 273-291, June.
    • Handle: RePEc:inm:orisre:v:29:y:2018:i:2:p:273-291
    DOI: isre.2017.0735
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    14. Sun, Xiaochi & Cui, Xuebin & Sun, Yacheng, 2023. "Understanding the sequential interdependence of mobile app adoption within and across categories," International Journal of Research in Marketing, Elsevier, vol. 40(3), pages 659-678.
    15. Hou, Pengwen & Zhen, Ziyan & Pun, Hubert, 2020. "Combating copycatting in the luxury market with fighter brands," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 140(C).
    16. Yuchen Zhang & Jingjing Li & Tony W. Tong, 2022. "Platform governance matters: How platform gatekeeping affects knowledge sharing among complementors," Strategic Management Journal, Wiley Blackwell, vol. 43(3), pages 599-626, March.
    17. Hanyao Gao & Gang Kou & Haiming Liang & Hengjie Zhang & Xiangrui Chao & Cong-Cong Li & Yucheng Dong, 2024. "Machine learning in business and finance: a literature review and research opportunities," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-35, December.
    18. Subrahmanyam Aditya Karanam & Ashish Agarwal & Anitesh Barua, 2023. "Design for Social Sharing: The Case of Mobile Apps," Information Systems Research, INFORMS, vol. 34(2), pages 721-743, June.
    19. Shunyuan Zhang & Dokyun Lee & Param Vir Singh & Kannan Srinivasan, 2022. "What Makes a Good Image? Airbnb Demand Analytics Leveraging Interpretable Image Features," Management Science, INFORMS, vol. 68(8), pages 5644-5666, August.
    20. Burström, Thommie & Parida, Vinit & Lahti, Tom & Wincent, Joakim, 2021. "AI-enabled business-model innovation and transformation in industrial ecosystems: A framework, model and outline for further research," Journal of Business Research, Elsevier, vol. 127(C), pages 85-95.
    21. Malik, Nikhil & Wei, Yanhao Max & Appel, Gil & Luo, Lan, 2023. "Blockchain technology for creative industries: Current state and research opportunities," International Journal of Research in Marketing, Elsevier, vol. 40(1), pages 38-48.
    22. Ka Chung Ng & Ping Fan Ke & Mike K. P. So & Kar Yan Tam, 2023. "Augmenting fake content detection in online platforms: A domain adaptive transfer learning via adversarial training approach," Production and Operations Management, Production and Operations Management Society, vol. 32(7), pages 2101-2122, July.
    23. Feng Zhu, 2019. "Friends or foes? Examining platform owners’ entry into complementors’ spaces," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 28(1), pages 23-28, January.
    24. Pallab Sanyal & Shun Ye, 2024. "An Examination of the Dynamics of Crowdsourcing Contests: Role of Feedback Type," Information Systems Research, INFORMS, vol. 35(1), pages 394-413, March.
    25. Ming-Hui Huang & Roland T. Rust, 2021. "A strategic framework for artificial intelligence in marketing," Journal of the Academy of Marketing Science, Springer, vol. 49(1), pages 30-50, January.

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