ASQ's March Issue and AI Guidelines
Fresh articles and book reviews, as well as ASQ's new guidelines on AI use for authors and reviewers
March 2026, Volume 71, Number 1
We kick off our first issue in volume 71 with two articles that explore firms’ responses to social issues and two that investigate workers’ reputations and relationships. Empirical investigations of two very different settings—CrossFit gyms and LGBT social movement organizations—demonstrate that the structure of firms’ environments, from local community embeddedness to the interorganizational division of labor, has an impact on firms’ responses to social issues. Two papers build theory around workers’ relationships, showing how platform creatives manage how they respond to their audiences and how power-line workers share information about potential co-workers. Another article takes a microhistorical perspective on strategy emergence, showing how Nokia’s anticipation and reaction to events that do not occur shape their later strategies. Finally, we learn about how evaluative stigma associated with employer failures influences the careers of non-executive employees. Happy reading!
Near-Histories and Strategy Emergence: A Microhistorical Perspective
Juha-Antti Lamberg, Eero Vaara, Pasi Nevalainen, and Henrikki Tikkanen
This study shows that near-histories, or strategic decisions and actions that do not ultimately occur, can spur dynamics that lead to profound strategic consequences for organizations. Focusing on two key episodes in Nokia Corporation’s evolution in the 1970s and 1980s, the authors create a process model showing how anticipatory reactions, mobilization of networks, revision of expectations, and the emergence of a new strategic direction can influence organizational strategy. The study shows how near-history episodes, which result in affective reorientation and relationship restructuring, can be generative forces that shape organizational trajectories.
CrossFit in the Crosshairs: A Community-Embedded Theory of Firm Responsiveness to Social Issues
Enrico Forti, Alessandro Piazza, and Joost Rietveld
When and why do firms act on social issues? Whereas most research studies these questions by focusing on firm-level or issue characteristics, here the authors find that structural aspects of local community are relevant to firms’ behavior. Focusing on the CrossFit CEO’s controversial statements after the murder of George Floyd at the hands of police in 2020, the authors develop a theory that links network closure, segregation patterns, and issue connectedness in communities to local gyms disaffiliating from CrossFit. They find that the social issue was less salient in local communities with stronger inward-focused ties and with greater ethnic segregation. In contrast, firms were more likely to respond when they were in communities connected to populations affected by Floyd’s death and when they were more dependent on the local community.
How Activists Collaboratively Divide the Labor of Making Change: The Case of LGBT Rights
Lisa Buchter and Elise Lobbedez
This study contributes to emerging research on how insider and outsider activists in social movement organizations (SMOs) collaborate to pursue common agendas. Studying the LGBT movement in French workplaces, the authors introduce the concept of collaborative division of labor, showing how SMO actors adopt different roles and leverage positions, postures, and expertise to reach their goals. The study examines how SMO actors interact as they practice four strategies: amplifying one another’s actions, compelling organizations to respond, threatening organizations’ reputations, and engaging in complementary datactivism. The authors reveal new forms of division of labor among social movement actors, show that such division is multidirectional, and demonstrate how these new forms help people to achieve outcomes.
Reputation on the Line: How the Third-Party Dilemma Shapes Trust in High-Risk Work
Luke N. Hedden and Michael G. Pratt
How workers build trust has mainly focused on the trustor (the one making a trust decision) and the trustee (the one the trustor is deciding to trust or not). But in some contexts, third parties deeply influence whether and how workers build trust. These authors study linemen—the men and women who work on power lines—and reveal how third-party assessments of potential co-workers are essential for maintaining safety in an interdependent job with a high rate of injury and death. They find that line workers grapple with a third-party dilemma: the simultaneous need to report accurate reputational information about these workers in order to keep trustors safe and the need to protect their own reputations, which can suffer if a recommended worker fails to meet expectations. The dynamics resulting from workers’ communication practices in the face of this dilemma tend to diminish trust within the triad. The study emphasizes the importance of work context and of going beyond dyadic trust models.
Beyond Blame: Evaluative Stigma, Attribution, and Employee Careers after Employer Failure
Tristan L. Botelho and Matt Marx
Prior studies have explored how employer failure affects organizational leaders, but what about the outcomes for non-executive employees? Using data from the U.S. Census and the automatic speech recognition industry, the authors develop a theoretical framework to reveal career outcomes for employees, finding that employer failure does not harm employees’ subsequent wages and career opportunities as it can for organizational leaders. Exceptions include when the failure involves scandal or when employees belong to certain marginalized demographic groups. By considering all employees, not only those in the upper echelons of organizations, the study expands understanding of how employer failure affects employees’ careers, and integrates relevant research on careers, evaluations, stigma, and attribution.
Audience Entanglement: How Independent Creative Workers Experience the Pressures of Widespread Appeal on Digital Platforms
Julianna Pillemer, Spencer Harrison, Chad Murphy, and Yejin Park
Many creative workers on digital platforms work hard to gain widespread appeal, but when they do gain it their relationships with their audiences can change. In this inductive study, the authors theorize that after obtaining a large audience, platform creators go through stages of audience entanglement and dysfunctional entanglement; in response, some creators then develop entanglement management strategies, which can lead to a new stage: functional entanglement. This new stage allows creators to simultaneously draw meaning from their audience and to feel that their platform work is sustainable. The study breaks new ground by unpacking the interplay between audience responses and creator meaning-making, showing how creators manage audiences while remaining true to their work.
Book Reviews
Robert W. Fairlie, Zachary Kroff, Javier Miranda, and Nikolas Zolas. The Promise and Peril of Entrepreneurship: Job Creation and Survival Among U.S. Startups
Maryann Feldman and Jing Deng
François-Xavier de Vaujany, Robin Holt, and Albane Grandazzi (Eds.). Organization as Time: Technology, Power and Politics
Blagoy Blagoev
Baruch Fischhoff. Bounded Disciplines and Unbounded Problems: A Vision for Management Science
Alan D. Meyer
Lizhi Liu. From Click to Boom: The Political Economy of E-Commerce in China
Shuang L. Frost
Jerald Hage, Joseph J. Valadez, and Wilbur C. Hadden. Saving Societies From Within: Innovation and Equity Through Inter-Organizational Networks
Arkangel M. Cordero
Marion Fourcade and Kieran Healy. The Ordinal Society
Michael Sauder
Our student-run ASQ Blog features interviews with ASQ authors that offer insights into the research and writing process
ASQ Guidelines on the Use of AI
For 70 years, ASQ has been committed to publishing scholarship in organization theory and management that reflects the highest standards of intellectual rigor and theoretical contribution. As generative AI tools become more embedded in academic work, we have taken time—through an extended process of discussion and consultation—to consider what their use should mean for our community. In developing our new guidance for authors and reviewers, we reviewed policies adopted by peer journals in our field and leading outlets in adjacent fields. Our aim is neither to resist new tools nor to embrace them uncritically but to articulate principles that protect the intellectual labor at the heart of our craft.
The policy reflects a simple premise: AI can assist scholars, but it cannot substitute for scholarly judgment. Used thoughtfully, generative AI may improve efficiency and clarity. Used carelessly, it risks displacing the theorizing, analytic control, and interpretive responsibility that define excellent work. Authors remain accountable for everything in their manuscripts, and reviewers remain responsible for the substance of their evaluations. Disclosure, transparency, and human judgment are essential.
We also recognize that the AI landscape is evolving rapidly. Tools are changing, norms are emerging, and our understanding is deepening. For that reason, this guidance is not static. We expect to revisit and refine it as both the technology and its uses in scholarship develop. We invite the ASQ community to approach this moment with the same care, curiosity, and rigor that characterize our best research.
ASQ Guidance for Authors’ AI Use
Generative AI marks a turning point in scholarly production, offering the ability to streamline not only the technical mechanics of our craft but also the intellectual labor of inquiry itself. Based on our conversations with many scholars, the ways in which people are incorporating generative AI tools to facilitate their research are many, all-encompassing, and changing rapidly. Like statistical software and word processing before it, generative AI is a tool that has the potential to transform the research process and produce better output. But when generative AI is used without thoughtful oversight and deep engagement by authors, our scholarship suffers. We worry about a future in which AI assistance makes it ever easier for authors to churn out work that does not meet the field’s standards. Our goal is to give our community a grounded sense of what appropriate uses are.
In our view, appropriate uses include making programming and analysis more efficient, copy editing and improving readability, and finding the best sources to inform your theory and methods. But taken too far, each of these can lead to what we regard as bad research. Having AI refine and debug your code might be valuable, while having it take your data, generate a script to analyze it, and produce a table based on the most significant effects it finds cedes analytical control. Leveraging AI to code textual data in accordance with a deductively derived set of categories may make sense, but relying on it to analyze and interpret qualitative data inductively or abductively undermines critical processes of theorizing. Using AI to refine your argument to be less repetitive can strengthen your writing, but letting it develop entire arguments and write paragraphs for you abdicates authorship. And prompting AI to identify important works in a given area can be a helpful starting point for a literature review, but asking it to generate the review and synthesize key themes substitutes automated output for scholarly judgment. In each instance, the former supports high-quality scholarship, while the latter displaces the judgment and intellectual labor that make research credible.
The hardest things that we do in our research are also the most critical ones to get right. While generative AI may seem as though it can make these difficult tasks easier, we advise caution. Careful thinking and writing are essential for producing excellent scholarship. When human researchers encounter something we don’t know, we engage in inquiry; when generative AI encounters something it doesn’t know, it engages in fabrication. By inviting productive frictions in your use of AI, you not only preserve your authorial voice but also maintain accountability for your scholarly output.
Of course, you are always accountable for everything that goes into a manuscript that you submit to ASQ. Authors, not tools, are responsible for the originality, accuracy, and integrity of all statements and should independently check any claims and analyses supported or assisted by AI. In the submission process, we now ask you to disclose AI use since knowing about it could help readers understand how your research was produced.
We want to publish your research, and we can do so only if it is thoughtful, high-quality work. We review your papers carefully and are deliberate about providing thoughtful, developmental feedback. We recognize the opportunity that new tools provide us to do better research. But we must not let the tools supplant our own ability as researchers to read, think, analyze, or develop insights. At its core, these are the essence of rigorous scholarship.
ASQ AI Guidance for Reviewers
We have a similar perspective on reviewers’ use of AI. When you accept a handling editor’s invitation to review a submission, you accept the responsibility to read the manuscript and engage in the important work of sharing your thoughts about it. You may use AI tools to improve the language or structure of the review you create. You may not use AI tools to read and summarize the article for you or to generate feedback.
Uploading a submitted manuscript into AI tools creates confidentiality risks and copyright issues. For this reason, both Cornell University (ASQ’s owner) and Sage (ASQ’s publishing partner) prohibit this use of AI, and we follow this policy. Never feed another scholar’s unpublished work into a tool that could use that content in its future outputs to other users.
If you use an AI tool to improve your review’s language or structure, you must read the output carefully before you submit it; you are responsible for the review’s content. If an AI tool introduces errors, such as fake citations, you must catch and correct them.
If an ASQ editor believes that you have used AI to create any portion of your review, we reserve the right not to use your review and to ask for an explanation. Depending on your response, we also reserve the right to mark you as an ineligible reviewer in our ScholarOne system.
If, as a reviewer, you have concerns about the use of AI in a manuscript you are reviewing, please share those concerns with the handling editor and, as appropriate, with the author. We do not expect you to always have a definitive response if an author acknowledges the use of AI in their manuscript. If you are not sure whether an author’s disclosed use has compromised the quality of the submission, note your thoughts in your comments to the handling editor and/or comments to the author. If you are not confident about the quality of parts of the manuscript that were assisted by AI, say so; the handling editor will take that feedback into consideration.
Similarly, if you suspect undisclosed use of AI in a submission and are concerned about that, please raise your concerns with the handling editor either in your review or in an email.
We are all actively learning how AI changes the tasks we do, including reviewing manuscripts. By sharing with the handling editor and/or author what prompted your reaction (whether positive or negative) to aspects of the research assisted by AI, your input will add to our collective learning over time.