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In this essay, we address the intersection of trust and modularity in organization design. We argue that, while advanced digital technologies favor more modular organizational arrangements, contemporary trust scholarship has largely failed to adopt the network-based approach that is necessary to understand relationships in such settings. Addressing this void, the article introduces a framework that differentiates between and elaborates on within- and between-module trust dynamics. Our argument offers insights into the challenges and opportunities presented by modular designs, particularly regarding the concerns they raise surrounding trust pluralism and organizational coherence. The discussion extends to practical implications for organizational designers, suggesting strategies for navigating trust in modular organizations. We also point to recursive effects of trust on the emergence of modular structures. By advancing theoretical discussions on modularity and trust, our work serves as a foundation for future theoretical and empirical research aimed at refining the strategies organizations can employ to leverage modularity while fostering a trustworthy environment. 

As generative artificial intelligence (AI) has found its way into various work tasks, questions about whether its usage should be disclosed and the consequences of such disclosure have taken center stage in public and academic discourse on digital transparency. This article addresses this debate by asking: Does disclosing the usage of AI compromise trust in the user? We examine the impact of AI disclosure on trust across diverse tasks—from communications via analytics to artistry—and across individual actors such as supervisors, subordinates, professors, analysts, and creatives, as well as across organizational actors such as investment funds. Thirteen experiments consistently demonstrate that actors who disclose their AI usage are trusted less than those who do not. Drawing on micro-institutional theory, we argue that this reduction in trust can be explained by reduced perceptions of legitimacy, as shown across various experimental designs (Studies 6–8). Moreover, we demonstrate that this negative effect holds across different disclosure framings, above and beyond algorithm aversion, regardless of whether AI involvement is known, and regardless of whether disclosure is voluntary or mandatory, though it is comparatively weaker than the effect of third-party exposure (Studies 9–13). A within-paper meta-analysis suggests this trust penalty is attenuated but not eliminated among evaluators with favorable technology attitudes and perceptions of high AI accuracy. This article contributes to research on trust, AI, transparency, and legitimacy by showing that AI disclosure can harm social perceptions, emphasizing that transparency is not straightforwardly beneficial, and highlighting legitimacy’s central role in trust formation. 

The literature on dynamic capabilities has long faced the challenge of empirically investigating a concept that is inherently intangible, complex, and organization-specific. Empirical clarity is crucial because dynamic capabilities are argued to be essential for achieving sustainable competitive advantage in changing environments, and only empirical research can validate this claim. Despite significant advancements, issues related to measurement, methodologies, and the robustness of findings persist. This article addresses these concerns by offering key recommendations for empirical research in this domain. First, we underscore the importance of construct validity and the need for clear definitions and focused dimensions of dynamic capabilities. Second, we recommend empirical designs with time lags between the measurement of dynamic capabilities and their antecedents or outcomes, and sector-specific studies to capture the temporal dynamics and contextual nuances. Third, we encourage leveraging established survey measures, close proxies in archival research, and innovative experiments and qualitative methods. Finally, we highlight the potential of multimethod approaches to triangulate findings and enhance theoretical insight. Our hope is to help future researchers address the unique challenges of studying dynamic capabilities empirically, paving the way for continued progress in this significant field of strategic management. 

ABSTRACT This article contributes to research on dynamic managerial capabilities, which examines managerial impact on firms' strategic change. Specifically, we study the role of managerial social capital—a key underpinning of dynamic managerial capabilities—in organizations' strategic change. We propose a temporally contingent account that elucidates how the fitness of dynamic managerial capabilities unfolds across life cycle stages and varying degrees of change pressures. We start by testing our theoretical model using data on 20,593 individuals in 5522 new ventures over a period of 5 years. A key insight from our quantitative analysis is that dynamic managerial capabilities are particularly valuable during convergent periods, when both external pressures to accomplish strategic change and inertial forces are increasing. Our subsequent interpretivist study, aimed at elaborating the mechanisms underlying the social capital‐funding effect, reveals that the types of resources and strategic changes spurred by social capital differ markedly across time. Overall, our paper enriches dynamic capabilities scholarship by highlighting that the effects of these capabilities are substantially time‐variant.