Knowledge that Transforms

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.

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.

Research Summary Organizations grant stakeholders who provide valuable resources insider status in governance, excluding less valuable outsiders. Firms thereby assemble a value‐maximizing resource portfolio but face challenges when environmental shifts require adaptation that harms some insiders. We combine and extend new stakeholder and social movement theories, hypothesizing how various stakeholders influence such adaptation. Outsiders can enable adaptation depending on organizational governance and the array of insider stakeholders. For‐profit firms are less open to outsider influence, but a wider array of insiders enables outsiders to align with certain groups to overcome the opposition of others who resist change. The nature of these alignments shapes whether adaptation involves transformative divestments or exploratory investments. We test our theory in the context of the Beyond Coal movement to divest coal plants. Managerial Summary To gain access to valuable resources, organizations commit to stakeholders who provide these resources. However, this creates problems when adapting to changes in the environment that undermine the value of these resources. How can managers balance their stakeholder commitments with the need to integrate the concerns of other stakeholders demanding adaptive changes? We find that organizational governance and the configuration of stakeholder interests can create openings for adaptation. Studying a period of rapid environmental change in the US electric utility industry, for‐profit utilities were more likely to retire coal generators when activists aligned with consumer advocates, and more likely to invest in solar generators when activists aligned with prosumers.

Research Summary This exploratory paper introduces a new type of family business by studying the investment strategies of family‐managed venture capital funds (“Family VCs”) across a multi‐country setting. It shows that Family VCs are more likely to invest in (syndicate with) geographically proximate startups (investors), indicating a preference for local investments. This tendency is stronger when the VC is named after the family and the family is closely involved in the decision‐making process of the fund. I provide suggestive evidence that this pattern reflects both superior local knowledge (rational response) and home bias (non‐rational response), with the latter becoming more pronounced when performance pressure is lower. Managerial Summary Family‐managed VC funds (Family VCs), managing roughly $29 billion, represent an important segment of the venture capital industry. I show that family control shapes both the selection of startups and syndicate partners. Family VCs are indeed more likely to support ventures and partner with investors from their communities, particularly when family members are highly involved in investment decisions and the fund is named after the family. I provide suggestive evidence that this local preference stems from both rational factors, like superior local knowledge and networks, and a non‐rational preference for local investments. Their local focus becomes relatively less pronounced when families must demonstrate strong financial performance, such as when a follow‐on fund has not yet been raised and during competitive market conditions.

Research Summary We study an important, but largely overlooked, non‐market strategy used by firms in the enforcement stage of policy: “snitching,” that is, providing intelligence about potential violations of their rivals in an attempt to persuade regulators to fine them. Building on political marketplace theory, we develop and test a theoretical model of how firms use snitching during regulatory enforcement. We show that in equilibrium, firms snitch when the rival's violations are likely to cause significant harm to the population. We then derive several boundary conditions outlining when firms will engage in more or less snitching. We find support for our theory in panel data on enforcement actions by the U.S. Environmental Protection Agency for more than 8000 facilities over 12 years. Managerial Summary Firms can use their corporate political activity (CPA) not only to help themselves, but also to snitch on their rivals. Using a formal model, we look at how CPA influences regulatory enforcement. We find firms are most likely to snitch on their rivals when their rivals' potential violations are likely to cause significant harm, since this is when regulators care the most. Our model also outlines when this is most and least likely to occur. We then test our theory using data from the U.S. Environmental Protection Agency (EPA) and find support for our claims. The EPA is more likely to fine facilities for infractions that process many toxic chemicals, but this effect is much greater when a firm's rivals are actively lobbying the EPA.

Research Summary Strategy research examines the drivers of firm performance. Variance decomposition studies have analyzed industry's impact, with little attention paid to the industry to which a firm is assigned. This study investigates how industry assignment affects the findings about industry's impact. We analyze existing industry classification schemes and introduce a machine learning‐based scheme that leverages an embedding model to process text from annual reports. We find that industry assignment differs substantially across schemes, and that certain firms are more consistently grouped together. Schemes that group more similar firms together exhibit stronger industry effects. Given the literature's frequent reliance on the criticized Standard Industrial Classification scheme, industry's role in firm performance has likely been systematically underestimated. Managerial Summary Understanding what drives a company's success is crucial. Prior research has surprisingly found that a firm's industry plays little role in explaining a firm's performance. However, this study suggests that industry's importance is likely understated due to how firms are grouped into industries. Comparing several existing industry classification schemes alongside a new one we built using machine learning, we find that grouping similar firms in the same industry increases reported industry importance and decreases firm importance. Being more precise about industry membership reveals that industry matters more than previously thought. The implication is that understanding the industry is more important for a firm's performance and could be key to understanding sustained success.

Research Summary We explore how the emergence of digital distribution platforms (DDPs), platforms that facilitate transactions between consumers and incumbents, affects industry structure and competition. Studying the effect of delivery platforms on restaurants in the United States from 2012 to 2018, we show that DDP emergence increases exit and concentration and reduces business dynamism. We provide speculative evidence that these changes are driven by both an increase in inter‐firm competition as well as increased margin pressure from the platform. We show that strategic positioning shapes how establishments experience such changes to competition and that DDP emergence can cause a revaluation of resources within an establishment's business model. This research documents how technology‐enabled intermediaries can disrupt an industry by altering the basis of competition. Managerial Abstract The emergence of platforms like Grubhub or Instacart that connect consumers to existing incumbent establishments can alter industry structure and within‐industry competition. We study the effect of delivery platforms on restaurants in the United States and show that the emergence of such platforms has increased exit and concentration within the restaurant industry. We present suggestive evidence that these changes are driven by intensified inter‐firm competition and increased margin pressure from the platforms. We then explore which restaurants are better able to weather the emergence of delivery platforms, showing that establishments' strategic positioning and prior investments shape the extent to which delivery platform penetration is beneficial versus detrimental. Through this research, we document how digital distribution platforms can reshape competition within an industry.

Research Summary Scholars have theorized many factors shaping whether category spanning helps or hurts producers. We first synthesize evidence by meta‐analyzing 25 years of empirical research, which reveals a null effect of spanning on average, yet with significant subsample heterogeneity. To unpack it, we theorize and find that spanning hurts producers more (i) when spanning occurs within bounded (vs. unbounded) category systems, (ii) when category associations are made by third parties (vs. insiders), (iii) when spanning is located at the product (vs. producer) level, and (iv) when spanning is occurring at once (vs. over time). Two mechanisms underpin these relationships: on the audience side, spanning influences evaluations when it is salient; on the producer side, spanning results in more positive outcomes when producers can control how their spanning behavior is conveyed to audiences. Because these two mechanisms operate jointly, their combined influence can result in spanning having a null, a positive, or a negative net effect. Our study identifies, across 16 theoretical configurations, where these three baseline effects should be expected and clarifies why. Managerial Summary Firms face a strategic dilemma: should they span across categories or stay focused? Prior studies show mixed results on whether spanning helps or hurts performance. Our findings suggest that outcomes depend on how salient spanning is to audiences and how controllable it is for producers. Spanning tends to hurt performance (i) when the categorization system is bounded (vs. unbounded), (ii) when third parties (vs. insiders) highlight the diversification, (iii) when individual products (vs. producers) span categories, and (iv) when spanning occurs simultaneously (vs. over time). Firms can benefit from spanning, but success depends on managing these four factors to reduce saliency and enhance controllability.

Research Summary The emergence of large language models (LLMs) offers new opportunities for AI integration in research, particularly for data annotation and text classification. However, researchers lack guidance on implementation best practices, as the benefits and risks of these tools remain poorly understood. We develop a foundational framework for effective LLM implementation in management research, providing structured guidance on key decisions throughout the research process. We illustrate this framework through an empirical application: classifying sustainability claims in crowdfunding projects. While LLMs can match or exceed traditional methods' performance at lower cost, we find that variations in prompt design can significantly affect results and downstream analyses. We develop procedures for sensitivity analysis and provide detailed documentation to help researchers implement robustness while maintaining methodological integrity. Managerial Summary Large language models (LLMs) offer powerful new tools for business research, especially for analyzing and categorizing text data. However, managers and researchers lack clear guidance on how to use these tools effectively and reliably. This study creates a practical framework for implementing LLMs in management research, covering key decisions from model selection to result validation. Using a real‐world example of analyzing sustainability claims in crowdfunding campaigns, we demonstrate that LLMs can match traditional methods while being faster and cheaper. However, small changes in how you instruct the AI can significantly alter results and business conclusions. We provide systematic procedures for testing result reliability and offer practical tools to help managers implement AI‐powered analysis while maintaining rigorous standards and avoiding misleading findings.

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.