Zoom in iOS Clones: Examining the Impact of Copycat Apps on Original App Downloads

With the rapidly growth of mobile app market, a large amount of mobile app developers are encouraged to invest in app innovation. However, it inevitably invites other developers to imitate the design and appearance of innovative and original apps. In this paper, we examine the prevailing copycat app phenomenon and its impact. Using a combination of machine learning techniques such as natural language processing, latent semantic analysis, network-based clustering and image analysis, we are able to detect two types of copycats: deceptive and nondeceptive. Based on the detection results, we conduct econometric analysis to understand the major impacts on the download of original apps. Our analysis is validated on a unique dataset containing detailed information about 10,100 action game apps by 5,141 developers released at iOS App Store for five years. Our final results indicate significant heterogeneity in the interactions between copycats and original apps over time. In particular, our findings suggest that the copycat apps can be either friends or foes of the original apps. Specifically, high quality copycats tend to compete with the original app, especially if the copycat apps are non-deceptive. Interestingly, for low quality copycats, we find a significant and positive effect from the deceptive copycats on the original app downloads, suggesting a potential positive spillover effect.