The Impact of Copycats on an Original Mobile App's Demand: Empirical Analysis and a Method for Detecting Copycat Apps
Social Science Research Network(2015)
摘要
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 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 similarities between different apps. 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 compare apps and detect two types of copycats: deceptive and non-deceptive. 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 final results indicate that the effect of copycats on an original app’s demand is determined by the quality and level of imitation of the copycat. High-quality, non-deceptive copycats negatively affect demand for the originals. In contrast, low-quality, deceptive copycats positively affect demand for the originals. 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.
更多查看译文
关键词
mobile
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络