Zoom in iOS Clones: Examining the Antecedents and Consequences of Mobile App Copycats

In this paper, we examine the emerging copycat issue in the mobile apps market. Using machine learning techniques on large-scale unstructured data, we detect two types of copycats (deceptive and non-deceptive) from 10,100 action game apps from iOS App Store over five years. Based on our detected copycats, we model the key drivers of mobile app copycats as well as their major impacts. Our results indicate significant heterogeneity in the interactions between copycats and original apps over time: (1) Non-deceptive copycats are reluctant to enter the market when the original app is popular and free. However, this negative effect does not hold in other cases; (2) Copycats can be either friends or foes of the original apps. High quality copycats always have a negative effect on the original app downloads. Interestingly, low quality deceptive copycats have a positive effect on the original app downloads, suggesting a potential positive spillover effect.