Research Summary: While revenue models are strategically important, research is incomplete. Thus, we ask: "What is the optimal choice of revenue model?"Using a novel theory-building method combining machine learning and multi-case theory building, we unpack optimal revenue model choice for a wide range of products on the App Store. Our primary theoretical contribution is a framework of high-performing revenue model-activity system configurations. Our core insight is the fit between value capture (revenue models) and value creation (activities) at the heart of successful business models. Contrastingly, low-performing products avoid complex value capture (i.e., freemium) and misunderstand value creation (e.g., overweight effort). Overall, we contribute a theoretically accurate and empirically grounded view of successful business models using a pioneering method for theory building using large, quantitative data sets. Managerial Summary: Revenue models are critical for product performance. Yet, the high-performing choice is often unclear. We combine machine learning with multiple-case deep-dives to unpack optimal revenue model choice for a wide range of products on the App Store, a significant setting in the digital economy.Our primary insight is that high-performing products fit value capture (revenue models) and value creation

Research summary Analytic models are a powerful approach for developing theory, yet are often poorly understood in the strategy and organizations community. Our goal is to enhance the influence of the method by clarifying for consumers of modeling research how to understand and appreciate analytic modeling and use modeling results to enhance their own research. Our primary contribution is a guide for reading analytic models. Using comparisons with other methods and exemplar analytic models, we explore key features as well as counterintuitive aspects and common misconceptions. We also add by illuminating strengths and weaknesses of analytic modeling relative to other theory‐building methods. Finally, we identify under‐exploited opportunities for pairing analytic models with complementary methods. Overall, our aim is enhancing the influence of analytic modeling by better‐informing consumers. Managerial summary In this paper, we explore the use of analytic (mathematical) models for developing strategy and organizations theory. Analytic modeling is common in related fields like economics but is often poorly understood among the broader of strategy and organizations community. Whereas existing resources on analytic modeling are geared towards modelers, our aim is to enhance understanding and appreciation of the method among potential consumers of modeling research. We offer three specific contributions in this regard. The first is a guide for reading analytic models, including key features, counterintuitive aspects, and common misconceptions. Second, we clarify the strengths and weaknesses of analytic modeling relative to other theory‐building methods. Finally, we discuss promising opportunities for pairing methods.

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Revenue models are important. They are the monetization approach by which a firm captures value (Casadesus-Masanell and Zhu, 2010; Johnson et al., 2008). Revenue models are also an emerging source of innovation (Casadesus-Masanell and Zhu, 2013; Gilbert, 2005; Snihur and Zott, 2014), in particular with the rise of experience and information goods, which make pricing more challenging (Shapiro and Varian, 1998). At the same time, blurring industry lines, lower barriers to entry, and Internet penetration have created opportunities for complementors and users (not just producer-firms) to create value (e.g., platforms, ecosystems) (Hannah and Eisenhardt, 2018; Gambardella and McGahan, 2010). Yet this complicates value capture (Massa et al., 2017; Zott et al., 2011). Indeed, figuring out a revenue model is increasingly a primary strategic challenge (Massa et al., 2017), and reshaping revenue models (and restructuring related activity systems) are important avenues for success (Gilbert, 2005) and even survival (Seamans and Zhu, 2017).While prior research suggests broad factors (e.g., product quality, consumer ad-aversion, and advertising rates) that may shape the optimal choice of revenue model, this research is often incomplete (e.g., glosses over the advertiser perspective), too simple (e.g., under-theorizes freemium models), and conflicting (e.g., inconsistent results for product quality). More deeply, this work separates value capture (i.e., revenue model) from value creation (i.e., underlying activity system), studying the two in isolation. As a result, this work leaves open which are the most relevant revenue models, why similar products (e.g., Pandora and Spotify, StitchFix and Rent the Runway, Netflix and Hulu, 23andMe and Pathway Genomics) have different revenue models, and when particular revenue models are likely to be effective. Overall, theoretical insights into an important empirical puzzle -when and why is each revenue model preferred -remain limited. We address this gap with question-driven research. We ask, "When should particular revenue models be used?"We tackle our research question by studying 66,652 mobile products ("apps") on Apple's App Store. This setting is appropriate for several reasons. First, it is economically significant, generating over $70 billion in cumulative developer earnings (Apple, 2017; Arora et al., 2017). Second, it covers a wide range of products in multiple industries (Bresnahan et al., 2014; Hallen et al., 2018), likely yielding a rich understanding of revenue models in different settings. Third, it is more than just a sales channel, and is in fact the only channel for many firms (e.g., Uber, Waze, Candy Crush). Finally, the App Store provides accurate measures of relevant constructs (Yin et al., 2014), making it particularly attractive for our study.We develop a novel theory-building methodology. We began with exploratory data analysis (EDA). We first measured several variables from our App Store data that are likely to be important for our research, including performanc...

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