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Keywords

Differentiation, Legitimacy, Machine learning, Optimal distinctiveness, Revenue models

Abstract

The optimal distinctiveness literature highlights a fundamental trade-off in product positioning within market categories: Products should be distinct to minimize competition, but similar to build legitimacy. Most recently, this research has focused on understanding sources of variance in the distinctiveness–performance relationship. We extend this literature with an examination of digital products and argue that the relationship depends on products' revenue models: We theorize the relationship is inverted U-shaped for paid products but U-shaped for free products, owing to heightened privacy concerns of free product customers. We further argue that this latter relationship becomes flatter for free products that provide greater monetization transparency by publishing a privacy statement or adopting a freemium revenue approach. Hypotheses are tested using a sample of 250,000-plus Apple App Store apps.

Note

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License

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