The DOI matches its Crossref record; 40 of the 43 references it cites were found in a registry or an index.
Copycats vs. Original Mobile Apps: A Machine Learning Copycat-Detection Method and Empirical Analysis
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 .
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A strategic framework for artificial intelligence in marketing
Huang, M.-H., & Rust, R. T. (2021) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-020-00749-9 · Cited by 2,059
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Metadata
- Authors
- Wang, Q., Li, B., & Singh, P. V.
- Affiliations
- Carnegie Mellon University (US)
- Venue
- Information Systems Research
- Published
- 2018-06
- Volume
- 29
- Issue
- 2
- Pages
- 273-291
- ISSN
- 1047-7047 (ISSN-L)
- 1526-5536
- OpenAlex
- W3121821896
- Cited by
- 162
- References
- 43
- Citations by year
- 2026: 13 · 2025: 23 · 2024: 19 · 2023: 24 · 2022: 21 · 2021: 19 · 2020: 30 · 2019: 9 · 2018: 3
- FWCI
- 25.48
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Information Systems Research (publishedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Strategy and Management › Digital Platforms and Economics
- Social Sciences › Business, Management and Accounting › Marketing › Copyright and Intellectual Property
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Keywords
- iOS App Store, app developers, app quality, market competition
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