Abstract Research Summary Regulatory anticipation is a nonmarket response whereby firms, foreseeing future penalties, adjust their behavior when peers are targeted by regulators. Prior research defines peers using broad jurisdictional boundaries. Instead, I argue that regulatory anticipation may emerge locally, driven by two channels: proximity to peer scrutiny and firms' perceived sanction risks. Examining U.S. facilities' GHG emissions, I exploit variation in local‐peer scrutiny arising from a change in the EPA's High‐Priority‐Violation policy. Difference‐in‐differences estimates show that heightened scrutiny of county peers is associated with 7% lower emissions among non‐targeted firms, driven by those facing higher sanction risks. Distance‐decay analyses indicate that these anticipation patterns weaken with geographic separation. The findings encourage managerial attention to local regulatory conditions and suggest that avoiding regulatory deserts could improve policy effectiveness. Managerial Summary This paper examines how stricter regulatory scrutiny of one firm can prompt nearby, non‐targeted firms to reduce their emissions. Using U.S. data on facilities' greenhouse gas (GHG) emissions, I find that when a county peer faces heightened oversight, non‐targeted firms are associated with about 7% lower GHG emissions on average. These patterns are stronger for firms already at higher risk of environmental penalties, declining as geographic distance from scrutinized peers increases. For managers, the findings highlight the importance of monitoring local regulatory activity and the behavior of nearby peers, as local comparisons can shape stakeholder expectations. For policymakers, the results suggest that avoiding regulatory ‘deserts’ may enhance the effectiveness of climate‐related and environmental regulation.

Abstract Research Summary How should organizations manage learning dynamics? Strategy theories suggest “more‐is‐better”—fast, frictionless sharing enhances performance—while organizational learning theory warns that “less‐is‐more,” as fast learning causes premature convergence. We reconcile this tension by showing that the “less‐is‐more” prediction depends critically on a key assumption in classic computational models: that information about agents' performance is not continuously updated. When performance information is timely, fast learning enhances exploration. The mechanism is Target Diversity: fast learning allows many individuals to rapidly reach the performance frontier, increasing the set of imitation targets. Organizations thus learn from a diverse, shifting set of targets. The implication is that organizations achieve superior performance not by restricting information or slowing learning, but by making data on choices and performance available more quickly. Managerial Summary Innovation relies on recombining diverse knowledge, yet facilitating this is challenging. Organizations often encourage copying stars with established track records or reputations, but this can lead to suboptimal results. We demonstrate a superior approach: provide up‐to‐date performance data and spotlight emergent top performers, regardless of their history. When feedback is timely, rapid learning allows “underdogs”—employees starting from lower positions—to quickly catch up to the frontier via unique knowledge combinations. Spotlighting these emergent successes creates “Target Diversity,” providing the organization with a continually renewed and diverse set of imitation targets. Such a design enhances the exploitation of diverse knowledge and improves long‐run performance.

Abstract Research Summary The trade‐off between scale and scope has long posed a strategic dilemma, especially in digital settings, where specialization enables hyperscaling. Drawing on a longitudinal case study of ByteDance, we theorize how digital firms can overcome this constraint through the use of artificial intelligence (AI) combined with an adaptive organizational design. AI evolves and improves through self‐learning and cross‐fertilization across domains, becoming increasingly valuable as learning accumulates. This, however, is contingent on access to structurally related data that allow learning to transfer across domains. We show how AI reverses the conventional logic of the resource‐based view: rather than valuable resources enabling diversification, diversification amplifies the value of resources. AI thus transforms the scale‐scope nexus from being a trade‐off into a source of strategic advantage. Managerial Summary The growing centrality of AI and digital platforms is reshaping how firms pursue and sustain growth. This study examines how ByteDance leveraged AI and adaptive organizational design not only to scale rapidly but also to diversify across industries and markets. Rather than incurring rising costs or coordination complexity, the firm's AI capabilities improved with each deployment through cross‐fertilization across domains, enabling more efficient growth across multiple domains. For managers, the findings highlight how dynamic combinations of AI and organizational structure can help overcome traditional trade‐offs between scale and scope, opening new pathways for scalable, cross‐market expansion in increasingly competitive environments.

Abstract Research Summary In evaluative contexts, evaluatees typically seek to present themselves in a favorable light, while evaluators ask penetrating questions to assess these claims. Here we develop a framework to identify curveball questions : ones that are on‐topic yet perplexing (i.e., difficult to predict) relative to past discourse. We develop a language‐based measure of curveball questions and apply it to a corpus of quarterly earnings calls. After validating this question‐level measure, we next demonstrate that a call‐level curveball measure predicts absolute returns, absolute abnormal returns, and changes in a firm's average analyst rating. Finally, we identify the types of analysts who are most likely to pose curveball questions, the types of firms that are most likely to receive them, and the conditions under which they tend to arise. Managerial Summary Even a carefully crafted presentation can be derailed by a challenging question. What makes a question challenging in ways that can be disruptive and how can such a question be measured? We propose that such questions, which we label curveballs , are on‐topic, and thus difficult to dismiss or deflect, yet difficult to predict based on prior knowledge. We harness the tools of computational linguistics to develop a measure of curveball questions and apply it to the context of quarterly earnings calls. We show that this measure predicts consequential economic outcomes and highlight the conditions under which curveball questions tend to arise. Our measurement strategy can be readily extended to other evaluative contexts such as job interviews and venture capital pitches.

Abstract While prior research on the strategic framing of innovation highlights the cognitive mechanisms underlying novelty evaluation, we know little about the corresponding emotional mechanisms. Drawing on appraisal theory, construal level theory, and the literature on emotions, we theorize two distinct appraisal‐emotion pathways through which the framing of novel ideas evokes hope and awe among investors with different motives. We argue that high‐construal framing supplements economically motivated investors' low‐level feasibility orientation, which evokes hope and increases their willingness to invest. By contrast, low‐construal framing supplements non‐economically motivated investors' high‐level desirability orientation, which evokes awe and increases their willingness to invest. We test and find some support for these within‐motive pathways across three studies. Our study offers new theoretical and practical insights on the strategic framing of novel ideas.

Abstract Research Summary This study investigates how firms engage in information‐seeking lobbying to address trade policy uncertainty. I argue that lobbying enables firms to gain early insights into forthcoming tariff actions, allowing them to strategically stockpile products likely to be targeted. Using shipping records of US firms during the 2018 US–China trade war, I find that lobbying firms increased imports of soon‐to‐be‐tariffed products before tariff lists were publicly released, compared to non‐lobbying firms. This selective stockpiling pattern disappeared after tariff announcements. Further analysis shows that lobbying firms were less likely to request tariff exemptions for products they had preemptively stockpiled, suggesting that information‐seeking lobbying during policy formulation provides an additional benefit by reducing the need for costly government engagement during the implementation phase. Managerial Summary To manage heightened trade policy uncertainty, firms often adjust global supply chains or engage with the government—but how can they integrate these strategies? This study proposes that firms can engage in lobbying not just to influence policy outcomes, but to gain early insights into pending trade actions. Using data from the 2018 US–China trade war, I find that lobbying firms stockpiled more of the products that were later targeted by tariffs before those tariffs were publicly announced. These firms were also less likely to request tariff exemptions for products they had preemptively stockpiled, indicating potential cost savings by avoiding expensive government engagement. The findings underscore the strategic value of early‐stage lobbying and highlight the importance of coordination between government affairs and operation departments.

Abstract Research Summary CEO promises are powerful but understudied communication tools. We develop a dual‐mechanism framework theorizing that while CEO promises elevate stakeholder expectations, they simultaneously constrain strategic flexibility. We argue that CEO promise‐making is shaped by two competing pressures: making more promises when the need to manage expectations upward is heightened and fewer promises when the need to preserve flexibility is increased. Further, we predict that under heightened uncertainty, CEOs preserve flexibility through strategic ambiguity (i.e., issuing promises with extended or vague time horizons and lower specificity). Leveraging Large Language Models (LLMs) to analyze over 69,000 earnings‐call transcripts from S&P 1500 firms (2010–2022), we identify 74,017 CEO promises and find support for our predictions. We contribute an original, publicly available dataset of CEO promises. Managerial Summary When CEOs publicly promise positive future results, they shape how investors and analysts view the company. These promises are a double‐edged sword: they can boost investor and stakeholder confidence, but they also lock the company into a pre‐determined path and make later shortfalls or pivots reputationally costly. Analyzing more than 69,000 earnings calls (2010–2022), we find that CEOs make more promises when needing to prove their legitimacy, such as during early tenure, when facing gender bias, or after missing earnings targets. However, in uncertain environments, CEOs often pull back from promise‐making or employ “strategic ambiguity” by making promises with vaguer timelines and details to maintain flexibility. Our analysis suggests that failing to deliver on these public commitments significantly increases the likelihood of CEO dismissal.

Abstract Research Summary We extend ecosystem theory to cases in which platforms are complementors to each other: inter‐platform ecosystems. Analyzing web traffic data on 241 European platforms, we identify and characterize demand‐side inter‐platform ecosystems, and propose a theory of why they emerge. We posit that demand‐side inter‐platform ecosystems solve matching problems generated by externalities platforms impose on each other. We describe four strategies platforms implement to solve these problems: network fusion (hosting competitor content), user‐community‐driven interactions (facilitating cross‐posting), meta‐platform (aggregating another platform), and platform concatenation (referring users to another platform for customized complementary services). We link these strategies to the nature of the externalities, the types of platforms involved and their competitive relationship. We conclude with implications for theory and suggestions for further research. Managerial Summary Platforms increasingly act as complementors to each other, creating “inter‐platform ecosystems” to boost cross‐platform interactions and reduce search costs. Using web traffic data on 241 European platforms, we identify four strategies they use to that end: network fusion (hosting competitor content), user‐community‐driven interactions (facilitating cross‐posting), meta‐platform (aggregating another platform), and platform concatenation (referring users to another platform for customized complementary services). These strategies pose three main management challenges. First, because no single platform necessarily orchestrates the ecosystem, platforms must navigate complex multi‐party coordination through bilateral agreements. Second, platforms face coopetition tensions: cooperating multiplies interactions but increases disintermediation risk. Third, pricing structures must account for cross‐platform interdependencies, where a platform's value depends on how its users interact with other platforms’.