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Research Summary 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. Managerial Summary How should firms in the digital space position their products for optimal performance? We study this question in the Apple App Store, and suggest that the optimal positioning of digital products depends on their revenue model. Paid products should be moderately differentiated from competing products. By contrast, free products benefit most from very low or very high levels of differentiation. We attribute the different performance effects of differentiation to customers' privacy concerns over free products. Firms can partially ameliorate those privacy concerns by providing greater monetization transparency by publishing a privacy statement or by adopting a freemium revenue approach, making moderate levels of differentiation more viable. Our findings help managers align choices of positioning and revenue model, two critical aspects of the firm's business model.

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Abstract-Platforms and their marketplaces with complementarities are prominent in the software industry. As the proprietary platform itself exhibits elementary or generic functionality, platform owners depend on a complementor ecosystem populated by third-parties. At present, little is known about mechanisms at play in proprietary ecosystems. Addressing this deficiency, this paper investigates the Google Apps ecosystem through statistical and network analysis. Results show that the Google Apps ecosystem is sparsely connected, the majority of complementors develops one application (83%) and does not have visible relationships (73%). Furthermore, there is a positive relationship between the number of applications a complementor develops and the number of relationships it establishes. The research method and results presented can be used by practitioners as a reference to evaluate their structural position in the ecosystem, while it provides researchers with a quantification of ecosystem characteristics.

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