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Research Summary: We spotlight the use of machine learning in two‐stage matching models to deal with sample selection bias. Recent advances in machine learning have unlocked new empirical possibilities for inductive theorizing. In contrast, the opportunities to use machine learning in regression studies involving large‐scale data with many covariates and a causal claim are still less well understood. Our core contribution is to guide researchers in the use of machine learning approaches to choosing matching variables for enhanced causal inference in propensity score matching models. We use an analysis of real‐world technology invention data of public–private relationships to demonstrate the method and find that machine learning can provide an alternative approach to ad hoc matching. However, as with any method, it is also important to understand its limitations. Managerial Summary: This article explores the use of machine learning to enhance decision‐making, particularly in addressing sample selection bias in large‐scale datasets. The rapid development of AI and machine learning offers new, powerful tools especially for digital ecosystems where complex data and causal relationships are complex to analyze. We offer managers and stakeholders insight into the effective integration of machine learning for selecting critical variables in propensity score matching models. Through a detailed examination of real‐world data on technology inventions within public–private relationships, we demonstrate the effectiveness of machine learning as a robust alternative to traditional matching methods. Video abstract [ABSTRACT FROM AUTHOR]

Entrepreneurial and established firms form collaborative relationships to commercialize products. Through such ties, entrepreneurs seek (a) development help to hone ideas into marketable products and (b) access to markets. In most cases, entrepreneurs face a trade-off: they can be a big fish in a small pond, getting more attention and development help from a smaller firm with less market access, or a small fish in a big pond, getting less attention and help from a larger firm with more market access. Our study investigates what goes into choosing between these options. Drawing from resource dependence theory and an empirical study of tie formation between developers and publishers of PlayStation 2 video games, we develop and test a framework that identifies the key decision variables and focuses on two moderators--resource need evolution and resource uncertainty related to competition--that explain whether a big fish (more development help) or a big pond (more market access) drives tie formation. Our findings point to prospective peers as one of the significant decision criteria at tie formation and highlight the dynamic nature of resource dependence. Altogether, the results give resource dependence theory a dynamic element it has lacked in the past. [ABSTRACT FROM AUTHOR]

Research Summary: This study examines whether "unblocking" competition through antitrust intervention against a dominant platform can spur complementor innovation in platform ecosystems. Using a novel dataset on enterprise infrastructure software and a difference‐in‐differences design, we examine the relation between the U.S. antitrust intervention against Microsoft (dominant enterprise platform) and subsequent innovation and profitability of infrastructure applications firms (complementors). The data show that innovation among complementors—particularly ones with low market share—soared when the competitive pressure on the dominant platform amplified. However, the profitability of these complementors dropped. Our results contribute to understanding links between competition and innovation in platform ecosystems, as well as the opportunities and threats related to dominant platforms in those ecosystems. Managerial Summary: Complementors (apps, services) that are owned by their platforms often have an unfair advantage. Antitrust action challenges the power of such dual platform‐complementors, reasoning that unfair advantage blocks fair competition, and, in turn, reduces firms' incentives to innovate and thus limits consumer choice. We examine whether reducing anticompetitive barriers and platform‐complementors' power revitalizes the platform ecosystem. Using a landmark antitrust case, we find mixed results: while complementors do innovate more, their profits go down. In particular, the low‐share complementors that bring in the most innovation are also the ones that lose the most financially, suggesting that they may have over‐relied on the platform for key assets. To develop a healthy ecosystem in the long run, platform owners may want to resist the temptation to keep complementors weak and instead help support their development to stand on their own. [ABSTRACT FROM AUTHOR]

We develop and test an attention-based theory of search by top management teams and the influence on firm innovativeness. Using an in-depth field study of 61 publicly traded high-technology firms and their top executives, we find that the location selection and intensity of search independently and jointly influence new product introductions. We have three important findings. First, in contrast to the portrait of local managerial search, we find teams that select locations that contain novel, vivid, and salient information introduce more new products. Next, unlike information-gathering approaches that merely "satisfice," persistent search intensity may lead to increases in new product introductions. Finally, level of search intensity must fit the selected location of search to maximize new product introductions. [ABSTRACT FROM AUTHOR]