As the AI Task Force for Organization Science, we provide an early account of artificial intelligence’s (AI) impact on both submissions and reviews at a major academic journal. Submission volume has risen 42% since the late 2022 release of ChatGPT, while writing quality has declined. The rise in AI-generated writing accounts for nearly all of these trends. AI-generated writing in reviews has also increased, and is characterized by lower writing quality and less topical diversity than human-generated writing. We are, to our knowledge, the first journal to report these early impacts of AI in the review process. Conversations with editors across scientific disciplines, however, suggest that what we observe is not limited to our journal or to the social sciences. At this early stage of AI adoption, we cannot make a normative assessment about appropriate or ideal levels of AI usage. We can, however, conclude that the current state of AI tools, amplified by existing publish-or-perish incentives, appears to be pushing the system toward an equilibrium of more rather than better research. Reaching an equilibrium in which AI serves as a critical engine of innovation will require that our institutions and the incentive structures they create adapt. Funding: S. Hasan used research funding from Duke University’s Fuqua School of Business. C. Gartenberg used research funding from University of Pennsylvania’s Wharton School. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2026.ed.v37.n3 .

Research SummaryWe develop an ex ante measure of commercial potential of science, an otherwise unobservable variable driving the performance of innovation-intensive firms. To do so, we rely on large language models and neural networks to predict whether scientific articles will influence firms' use of science. Incorporating time-varying models and the quantification of uncertainty, the measure is validated through both traditional methods and out-of-sample exercises, leveraging a major university's technology transfer data. To illustrate the methodological contributions of our measure, we apply it to examining the impact of university reputation and university privatization of science, finding that firms' reliance on reputation may lead to foregone opportunities, and privatization (i.e., patenting) appears to increase firms' use of the science of one university. We make our measure and method available to researchers.Managerial SummaryUsing machine learning, we develop a measure that estimates the probability that a scientific discovery will contribute to a commercially valuable innovation. This work addresses a key challenge: the inability to observe what scientific discoveries are worth pushing forward into commercial application. We illustrate the usefulness of this measure with two examples: 1.) firms' use of research from prestigious universities over equally promising work from less prominent ones; and 2.) how patenting affects the diffusion of commercially relevant science across firms. For practitioners, this measure can inform R&D, licensing, and other innovation related decisions by guiding firms' search for commercially relevant scientific research. The measure and the associated code are publicly available.

This paper introduces the Generality-Accuracy-Simplicity (GAS) framework to analyze how large language models (LLMs) are reshaping organizations and competitive strategy. We argue that viewing AI as a simple reduction in input costs overlooks two critical dynamics: (a) the inherent trade-offs among generality, accuracy, and simplicity, and (b) the redistribution of complexity across stakeholders. While LLMs appear to defy the traditional trade-off by offering high generality and accuracy through simple interfaces, this user-facing simplicity masks a significant shift of complexity to infrastructure, compliance, and specialized personnel. The GAS trade-off, therefore, does not disappear but is relocated from the user to the organization, creating new managerial challenges, particularly around accuracy in high-stakes applications. We contend that competitive advantage no longer stems from mere AI adoption, but from mastering this redistributed complexity through the design of abstraction layers, workflow alignment, and complementary expertise. This study advances AI strategy by clarifying how scalable cognition relocates complexity and redefines the conditions for technology integration.

Prior research suggests that a firm's workforce age composition is a key factor influencing its performance through effects on technological adoption, organizational learning, employee job satisfaction, and turnover. However, the existing literature often assumes that the phenomenon of the "aging firm" - that is, an older workforce age composition within firms-primarily reflects broader demographic trends such as population aging or immigration patterns. This paper offers a new framework for understanding how firms may actively make strategic decisions about their workforce age composition. We argue that firms may, directly or indirectly, select a balance of younger and older workers based on a range of internal and external factors, including their production function (e.g., task requirements), human capital capabilities (e.g., hiring and training), and shifts in technology and policy (e.g., automation and retirement policy). By integrating these factors into a cohesive framework, we argue that a firm's workforce composition should result from a system of interrelated decisions that firms make that depend on their internal capabilities and evolve in response to external shocks.

Entrepreneurship and innovation are pivotal drivers of economic growth and societal progress, yet they are inherently risky endeavors. This chapter explores the role of field experiments in understanding and improving these risky processes, bridging the gap between controlled studies and real-world complexities. Through a critical review of existing literature, it shows how field experiments advance our understanding of entrepreneurship and innovation, and contribute to mitigating key frictions in these areas. Organized into frameworks, it categorizes existing studies to facilitate systematic understanding and identify opportunities for future research, paving the way for more effective strategies and interventions in these critical domains. The authors' experience is drawn upon in discussing practical considerations in designing and implementing field experiments, offering insights often overlooked in experimental articles.

The prevalence of racial bias in policing has long concerned social scientists and policymakers. This article studies a predecessor mechanism that constitutes an important source of policing bias in American society: calls by individuals to the police to investigate “suspicious” behaviors, often involving neighbors. We construct a novel data set of more than 39 million 911 calls across 14 U.S. cities from 2011 to 2020. These data, obtained through the digitization initiatives of local governments, provide us with a unique opportunity to study neighborhood-level trust and social cohesion and demonstrate how changes to a neighborhood’s composition lead to systematic increases in the prevalence of “unfounded” suspicion calls to the police. Across a range of specifications, the proportion of unfounded suspicion calls increases as more non-Black residents move into neighborhoods with historically high levels of Black residents. This relationship is exacerbated in gentrifying neighborhoods and those with public spaces that enable more contact between community members. However, we also find some evidence that Black leadership and public support of Black citizens in communities mitigate the association between non-Black residents and the proportion of unfounded 911 calls. We discuss our results and implications for future research and policy. This paper was accepted by Anindya Ghose, information systems. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.00362 .

Ratings of organizations and firms have become ubiquitous. These ratings, often produced by intermediaries (including private and public organizations), are designed to aid consumers and other stakeholders in their decision making while guiding rated organizations toward performance improvement or compliance. In doing so, these intermediaries introduce new information to markets. However, disparities may exist in the ability to strategically capture the value from such ratings, often due to differential access to complementary assets among stakeholders. Consequently, this differential ability can lead to outcomes contrary to the rating institutions’ intentions. Reflecting on this dynamic, we analyze how widespread access to a prevalent type of rating—school performance information, often produced to enhance transparency and equity in educational access—has affected existing economic and social disparities in America. We leverage the staged rollout of GreatSchools.org school ratings from 2006 to 2015 to answer this question. Across various outcomes and specifications, we find that the availability of school ratings has accelerated the divergence in housing values, income distributions, education levels, and racial and ethnic composition across communities. Affluent and more educated families were better positioned to strategically leverage this new information to capture educational opportunities in communities with top schools. The uneven benefits we observe highlight how ratings can unintentionally deepen existing inequalities, thereby complicating their intended impacts. Supplemental Material: The online appendix is available at https://doi.org/10.1287/stsc.2023.0113 .

Research Summary: We analyze firm-driven labor market search, where firms "hunt" for talent rather than rely on workers to apply for vacancies. We leverage three approaches. We develop a model of firm-driven search and derive equilibrium conditions under which firms use this channel. We test our model's predictions using two data sources. Data from a nationally representative survey of 10,000 workers shows that the percentage hired through recruiting has increased from 4.9% in 1991 to 14.3% in 2022. This share is larger for higher-skilled workers and those with online profiles on LinkedIn. We complement this analysis with data on the near universe of online job postings from 2010 through 2020. Consistent with our model and worker survey evidence, we find firms that demand higher-skilled workers or operate in labor markets with heavy LinkedIn use are more likely to "hunt for talent."Managerial Summary: We study the phenomenon of "hunting" for talent, where firms fill open positions by searching for workers and inviting them to a recruiting process, rather than relying on workers to apply directly. We find that the percentage of workers hired through hunting has increased from 4.9% in 1991 to 14.3% in 2022. We propose that firms that rely more on high-skilled workers and/or operate within industries with a higher share of available candidates with online profiles are more likely to hunt for their talent. We find support for this conjecture using two data sets, documenting the worker and firm side of the labor market. Data from a nationally representative survey of 10,000 workers shows they are more likely to have been "hunted" by their employer if they work in an occupation that requires more skills, or if their industry has more candidates with online profiles. Moreover, data on US-wide job postings over the past decade shows that firms in need of highly skilled workers are more likely to invest in outbound recruiting capabilities.

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Crafting high-quality ideas is crucial for entrepreneurs to succeed, yet evidence about the factors that shape the idea-generation process is scarce. A long-standing question is whether differences across entrepreneurs in market judgment—the ability to evaluate business ideas—explain differences in ideas’ quality and composition. We conduct an experiment with an intervention that improves subjects’ ability to evaluate an idea’s market potential, finding that improved judgment leads subjects to generate ideas 15% higher in quality and more complete, with stronger effects among initially poorly-calibrated subjects. Our results support a potential mechanism: individuals with developed judgment mentally test more ideas and better filter them before committing to one. Simple training can improve judgment and idea quality, complementing ex-post, experimental methods by reducing the costs of testing ideas.

Many organizations have embraced formal experimentation, i.e., A/B testing, to improve the performance of their products and services. Experimentation, some have argued, should democratize innovation inside organizations by creating a platform to test new ideas, regardless of origin. In this article, we argue that experimentation's promise hinges on having the proper organizational decision-making process that encourages innovation while mitigating the risk of unanticipated failures. We study this question by developing a model of experimentation inside organizations, where decisions to implement are either centralized or decentralized—a tension identified by practitioners and scholars alike. Organizations with centralized mechanisms that rely too much on the input of other teams benefit least from experimentation, as do ones with completely decentralized ones. In contrast, organizations with mostly decentralized decisions, with a single authority that sets consistent thresholds for implementation, achieve growth but with less downside risk. Thus, without considering the organizational decision-making structure, the benefits of experimentation may be limited.

The last two decades have seen increasing interest in entrepreneurial experimentation, both from academia and practice, with a focus on better understanding the efficacy of adopting an experimental approach to guide entrepreneurs’ decision-making. While the survival and performance benefits of adopting different types of experimental approaches are increasingly well-understood, relatively little research examines different underpinning processes both at the organizational and individual levels that guide experimentation processes and choices as well as their performance implications. The papers in this proposed symposium address the gap in existing literature through multiple perspectives. First, they explore how different strategic choices support entrepreneurial experimentation. Such choices include organizational decision-making processes, management of internal and external key stakeholders, real-options-based decision-making, and the adoption of a scientific approach for opportunity creation. Second, these papers draw upon different theoretical perspectives to enrich our understanding of experimentation. We expect this symposium to add value to the field by facilitating discussion across different aspects of experimentation and generating insights for future research. Third, all of these papers consider experimentation as “part of a larger system,” investigating different factors and processes that underpin a focal venture’s experimentation. Collectively, the papers to be presented in this symposium highlight different pathways that entrepreneurs might undertake to better manage experimentation. The Politics of Experimentation Author: Todd Hall; U. of Kansas Author: Sharique Hasan; Fuqua School of Business, Duke U. Real Options and Business Model Experimentation Author: Yong Li; U. of Nevada, Las Vegas Author: Joseph T. Mahoney; U. of Illinois at Urbana-Champaign Theory-driven Experimentation: Crafting Theories for Optimal Unknown-unknown Exploration Author: Arnaldo Camuffo; Bocconi U. Towards the System of Entrepreneurial Experimentation: A Review and Research Agenda Author: Hyeonsuh Lee; West Virginia U. Author: Gianluigi Viscusi; Linköping U. Author: Christopher L. Tucci; Imperial College Business School

Taking a network perspective to study teams has been popular and fruitful in the past decades. Yet, the changing nature of how work teams are organized and managed in the new era brings unprecedented challenges to this line of work. For example, nowadays, many teams have fuzzy boundaries. And social exchanges and collaborations between groups are far more frequent and intensive than they traditionally were. Teams are also becoming increasingly diverse due to the globalization trend and the recognition of the value of diversity. These changing features of teams are likely to influence or interact with intra- and inter-team networks and exert a collective, integrated impact on individual members’ and teams’ cognitions, behaviors, and outcomes, which have not been thoroughly understood and examined. Our symposium highlights the recent efforts to investigate new emergent features of teams and explore how they interact with networks within and between teams. Two papers directly tap into the members’ social relations within and between teams, the diversity of these social relations, and associated team performance outcomes. Another two papers look at dynamic entrepreneurial teams, where each team constitutes the entire organization. Each explores a different element of diversity as a function of how networks are strategically used in these budding firms. The fifth paper switches the gear to focus on individuals’ intrapersonal diversity and network structural features and provides insights into how their linkage may shape team dynamics. Complementarities of Members’ Structural Roles in Team Success: The Moderating Role of Experience Author: Shihan Li; Heinz College - Carnegie Mellon U. Author: Brandy Aven; Carnegie Mellon U. The Social Underpinnings of Effective Organizational Interteam Relations Author: Martin J. Kilduff; UCL School of Management Author: Andreas Wilhelm Richter; U. of Cambridge Author: Ronald Clarke; Rennes School of Business Multicultural Experience and Social Network Brokerage Author: Eva Hsin-Lian Lin; London Business School Author: Raina A. Brands; UCL School of Management Author: Adrienne Wood; U. of Virginia Author: Adam M. Kleinbaum; Dartmouth College, Tuck School of Business Showcasing strategies: The Role of Entrepreneurial Networking in Quest for Venture Capital Funding Author: Damiano Maria Morando; Imperial College Business School Author: Anne L.J. Ter Wal; Imperial College Business School Author: Stefano Breschi; Bocconi U. Recruiting for your team: Network hiring and match-specific performance in firms Author: Ines Black; - Author: Sharique Hasan; Fuqua School of Business, Duke U.

Advancements in information technology (IT) can have profound impacts on the development and management of human capital. Extant research has examined how human capital in markets and organizations are shaped by increased digitization and the deployment of strategies relying on collection and analysis of big data, cloud computing, social media, Internet of Things (IoT), and the like. In knowledge-intensive settings, however, IT-enabled mechanisms have heterogenous impacts on issues related to human capital. Although IT potentially expands capacity for collection, storage, and utilization of knowledge, some firms and individuals successfully leverage technology and information systems while others do not--or are even possibly left worse off. This symposium contributes to examining why such discrepancies may exist by shedding light on the market and organizational processes that underlie the intersection of IT and knowledge-based processes, networks, and outcomes as they relate to issues in strategic human capital. Can Social Media Alleviate Inequality? Evidence from Venture Capital Financing Presenter: Gavin Wang; Wharton OPIM Presenter: Lynn Wu; The Wharton School, U. of Pennsylvania Presenter: Lorin Hitt; U. of Pennsylvania Are Knowledge Sharing and Learning Tradeoffs? Linking Performance Incentives with KMS Usage Presenter: Sae-Seul Park; Carnegie Mellon U. - Tepper School of Business Cloud Adoption and Strategic Human Capital: Evidence from Norway Presenter: Derrick Choe; - Presenter: Amir Sasson; BI Norwegian Business School Presenter: Robert Channing Seamans; NYU Stern Hunting For Talent: Firm-Driven Labor Market Search in the United States Presenter: Ines Black; - Presenter: Sharique Hasan; Fuqua School of Business, Duke U.

Recent scholarship argues that experimentation should be the organizing principle for entrepreneurial strategy. Experimentation leads to organizational learning, which drives improvements in firm performance. We investigate this proposition by exploiting the time-varying adoption of A/B testing technology, which has drastically reduced the cost of testing business ideas. Our results provide the first evidence on how digital experimentation affects a large sample of high-technology start-ups using data that tracks their growth, technology use, and products. We find that, although relatively few firms adopt A/B testing, among those that do, performance improves by 30%–100% after a year of use. We then argue that this substantial effect and relatively low adoption rate arises because start-ups do not only test one-off incremental changes, but also use A/B testing as part of a broader strategy of experimentation. Qualitative insights and additional quantitative analyses show that experimentation improves organizational learning, which helps start-ups develop more new products, identify and scale promising ideas, and fail faster when they receive negative signals. These findings inform the literatures on entrepreneurial strategy, organizational learning, and data-driven decision making. This paper was accepted by Toby Stuart, entrepreneurship and innovation.

The prevalence of anti-Black bias in policing has long been of concern to social scientists and policymakers. This article studies a predecessor mechanism that constitutes an important source of policing bias in American society: calls by individuals to police to investigate "suspicious" behaviors, often of neighbors. We construct a novel data set of over 39 million 911 calls across 14 US cities from 2011 to 2019. This data, obtained through the digitization initiatives of local governments, provides us a unique opportunity to study neighborhood-level social cohesion and demonstrate how changes to a neighborhood’s composition lead to systematic biases against members of the minority community. Even in our most demanding specifications that used fixed-effect and instrumental variables, we find that the proportion of suspicion 911 calls and unfounded suspicion calls increase as more Non-Black residents move into neighborhoods, controlling for the population of other races and the overall crime in a tract. This effect is exacerbated in gentrifying neighborhoods and areas with high levels of internet penetration and Non-Black residents but can be mitigated through online activism, as in the case of the online #BlackLivesMatter movement. We conclude with a discussion of our results and implications for future work at the intersection of technology and policy.

We report results from a field experiment testing hypotheses that examine what drives firms to seek new learning opportunities. Specifically, we draw on behavioral theory of the firm to predict how prior performance affects the likelihood a firm enrolls in business training. We also evaluate cognitive mechanisms connecting recruitment messaging and CEO growth orientation to firm participation. Our study randomly allocates over 10,000 firms to one of three experimental conditions—prevention, pro-motion, and neutral messaging—that vary the framing of a recruitment message for an innovation program for small and medium enterprises (SMEs) in Singapore. We lever-age pre-treatment heterogeneity in firm performance and CEO orientation to better understand the differential impact of the three message types. We find that businesses with declining performance are 64% more likely to register than those with performance improving year over year. In addition, we find mixed evidence of a congruence effect—where messages (i.e., promotion) resonate more with CEOs with matching orientations. Surprisingly, we find that the neutral messaging performs 46% better than the promotion message and 115% better than the prevention message in spurring enrollment. Our work sheds light on both the frictions and remedies for scaling up the diffusion of new knowledge to businesses. Specifically, we find that subtle differences in recruitment strategy affect who enrolls and the overall demand for business training. Overall, our findings suggest that targeted firm performance-heterogeneity and the varied experimental recruitment efforts significantly affect enrolment. Researchers must pay careful attention to selection in attempting to understand who benefits from the training.