Akhil Bhardwaj

Man-made disasters never fail to shock, especially when accompanied by loss of life. The degree of shock is even greater when they involve a highly regulated organization, as in the case of Boeing that has a decades-long reputation for engineering excellence and safety. This study seeks to uncover the underlying dynamics that can result in such disasters, using system dynamics modelling. We consider the institutional (regulatory) context, as well as the effect of size when an organization is “too big to fail”. Our analysis indicates that organizations may be tempted to take risks with the complicity of the regulator in that the latter does not pay sufficient attention to their operations. This oversight is compounded by previous instances of risk-taking having paid off, leading to an erroneous belief that continuing to take risks in the future will pay off – a phenomenon known as “the confidence trap”. Being too big to fail introduces a moral hazard problem that “supercharges” the confidence trap, prompting unwarranted risk-taking by the organization and regulator inaction. This ultimately results in greater frequency and magnitude of disasters. To attenuate the consequences of these dynamics, we suggest two policy levers. First, financial clawbacks that entail executives having to pay back part of their renumeration in the event of a disaster. This is intended to weaken the “too big to fail” effect by subjecting individual decision makers to market mechanisms and better aligning the objectives of the operator and regulator, making disaster avoidance a shared priority. Second, third-party monitoring and auditing that serve to mitigate regulatory oversight and better utilize expertise. These levers are expected to significantly reduce the likelihood and impact of disasters. We discuss the implications of our study for high-risk industries as well as its limitations.

The growth of knowledge in strategic management hinges on developing novel research programs. However, the path from the discovery to the justification of research programs is fraught with hazards-most research directions that scholars pursue are unlikely to be fruitful. This paper introduces the logic of pursuit to strategy science to aid researchers in navigating these hazards and ascertaining whether they should continue working on a particular line of inquiry. Specifically, this paper considers theorizing as a problem-solving activity. It provides a five-phase trial-and-error approach that focuses on resolving doubt generated by anomalies and a lack of understanding of complex phenomena. At each phase of this journey, non-empirical indicators such as the economy of research guide scholars on whether to pursue their research program or to try alternative paths. Research programs that effectively address the problem they were devised are considered highly warranted and worthy of pursuit.

Artificial Intelligence (AI) is increasingly positioned as a transformative force across public and private sectors. Yet, its adoption raises critical questions about reasoning, accountability, and the limits of machine cognition. This study draws on the philosophy of science as well as well as theories of organizations to argue that current AI systems—despite their predictive and generative capabilities—lack essential human faculties such as ability to engage in abductive reasoning, grasp analogies and metaphors, interpret sparse or nuanced data. These limitations have profound implications for decision-making, particularly in democratic societies where legal and ethical accountability are paramount. We propose a pragmatic framework for the responsible use of AI, distinguishing between ‘reliable’ and ‘frontier’ technologies, and aligning their deployment with sector-specific obligations. By situating AI within broader epistemic and institutional contexts, the framework offers actionable guidance for aligning technological innovation with democratic values and ethical governance.

In this essay, I invite scholars to reflect on what it means to be a scientist in the age of AI. This essay is inspired by Max Weber’s 1917 lecture “Science as a vocation.”

Using Plato’s allegory of the cave, this essay explores the problem of “digital shadows” by drawing parallels between its prisoners’ perception of reality and the way modern AI systems interpret human behavior. Just as the cave dwellers mistake shadows for reality, machine learning (ML) algorithms operate solely on digital traces—tokenized, reductive representations of human life. These shadows, while useful for pattern recognition, fail to capture the embodied, social, and historical dimensions of human existence. The use of AI (ML) in this manner risks constructing a world optimized for shadows rather than people—an improvised reality that, like Plato’s cave, blinds us to the truth of human experience. Examples from credit scoring and predictive policing are employed to illustrate how digital shadows can entrench inequality.

In a marketplace of ideas where theories can act as substitutes, theorists seek to persuade peers to engage with their theories. Given this critical role of persuasion, how do theorists do so? To address this question, the current study adopts a pragmatist perspective and employs the Toulmin model of arguments to examine how Oliver Williamson persuaded his peers to engage with transaction cost economics. The study unpacks how Williamson structured his arguments, introduced new constructs and language, and employed analogies and metaphors to foster a consensus, giving rise to an epistemic community. The study highlights that not only do values influence how arguments are crafted and evaluated, but also appealing to them plays a key role in persuasion. In doing so, the study considers both the rational and non-rational aspects of theorizing and persuasion. Finally, the study discusses the significance of argumentation in the context of AI and theorizing in strategic management.

Problem definition: Reducing the likelihood of man-made disasters that cause harm to life, property, and the environment is a key societal goal. To that end, regulatory agencies are responsible for ensuring man-made disasters do not occur. In practice, to accomplish this task, regulators have to rely on and cooperate with operators. However, how much cooperation is optimal? In this study, we explore the prudent level of cooperation between operators and regulators to avoid man-made disasters and the role whistleblowers and other policy levers can play in maintaining it within a Goldilocks range. Methodology/results: We synthesize various theories and accounts of man-made disasters to construct a system dynamics model of the mechanisms that lead up to these unwanted outcomes. We employ simulations to uncover how changes in crucial parameters related to cooperation lead to different outcomes and influence the likelihood of the occurrence of man-made disasters. Managerial implications: We resist explanations for man-made disasters rooted in regulatory capture and offer instead a nuanced account rooted in excess cooperation between operators and regulators emerging as a result of everyday operational imperatives and constraints. Our findings indicate that absence of disasters leads operators and regulators to fall into a “confidence trap” that perpetuates limited regulatory oversight and excess cooperation and eventually leads to a disaster. To mitigate this tendency, we investigate, in particular, the role that timely whistleblowing and other policy levers can play in mitigating man-made disasters. We provide managerial and policy implications such as incentivizing and safeguarding whistleblowers, limits on the revolving door between operators and regulators, and more stringent operating and safety scientific standards. Overall, we offer a new frame and potentially fruitful frontier for the operations management community to explore.

Ostensibly, the evolving science of strategic management is geared towards addressing vexing
managerial problems. In practice, however, scholars in the field have a marked tendency to formulate problems to fit existing theoretical and methodological frameworks, even at the expense of committing type III errors. While the tendency to do so is often attributed to institutional pressures and the like, we submit that an equally or more compelling reason is the absence of guidance on how to engage in problem-driven inquiry and formulate problems to explore theoretical frontiers. In the strategic management field’s problem-solving spirit, we provide an approach for problem formulation and
theorizing inspired by Oliver Williamson and two of his accomplished advisees. We abduce five principles and six dialectic conversations. We synthesize these principles and dialectics into five protocols to enable canonical problem formulation directed at exploring theoretical frontiers, that is, a “white space.” Using a recently rejected manuscript, we show how our Williamson in

For economic agents seeking to organize collectively, opportunism is a central concern – any invasion by opportunistic agents can threaten an organization’s viability. To understand how economic agents address this concern, we examine Magnum Photos, an enduring elite cooperative of freelance photographers that screened prospective members based on shared values. We identify two mechanisms through which these shared values engendered non-calculative trust that renders collective organizing economically viable. We unpack how this values-based trust attenuates hazards of opportunism and promotes risk bearing and innovation when property rights are appropriately assigned to individual members and the size of the collective organization remains relatively small. We maintain that Magnum can be regarded as a form of collective entrepreneurship adopted by risk bearing agents bound together by shared values. We provide unique insight into how economic agents in industries beset by measurement problems can organize collectively around shared values to mitigate opportunism and innovate.