Knowledge that Transforms

Organizations increasingly use artificial intelligence (AI) to solve previously unexplored problems. While routine tasks can be automated, the intricate nature of exploratory tasks, such as solving new problems, demands a hybrid approach that integrates human intelligence with AI. We argue that the outcomes of this human–AI collaboration are contingent on the processes employed to combine human intelligence and AI. Our model unpacks three hybrid problem-solving processes and their outcomes: Compared to human problem-solving, autonomous search generates more distant solutions, sequential search enables more local solutions, and interactive search promotes more recombinative ones. Collectively, these hybrid problem-solving processes broaden the range of organizational search outcomes. We enrich the behavioral theory of the firm with a technology-conscious perspective of organizational problem-solving that complements its traditional human-centric perspective. Additionally, we contribute to the literature on AI in management by extending its scope from using predictive AI for routine tasks to generative AI applications for more exploratory tasks.

Deliberative democracy is a prominent political approach that is increasingly attracting the interest of management scholars. While many deliberative democracy scholars acknowledge that expertise improves the epistemic quality of deliberation, some have recognized that experts can become “problematic participants” in deliberations. Through an analysis of Henrik Ibsen’s ([1882] 2007) play An Enemy of the People, I discuss four difficulties of including expertise in public deliberation: manipulations in the deliberative setting, exploitation of the vulnerability of experts, disregard for the limitations of expertise, and inability to translate and enroll. I also argue that the play’s ending leads readers to question the practicality of expert withdrawal. Furthermore, characters in the play suggest two other possibilities for overcoming the obstacles associated with expertise: “epistocracy,” and finding new ways to increase deliberation and participation. To advance this latter option, I call for a bidirectional view of translation, following scholars in both deliberative democracy and science and technology studies, and underscore the complexities of building trust when boundary crossing between expertise and non-expertise. These insights enrich the stream of management studies using deliberative democracy, and reinforce recent claims that management scholars should be more involved in the public sphere.

Time-based theorizing has recognized the importance of temporal complexity but tends to reduce it to tensions and conflicts among but two static temporal demands. We reconceptualize temporal complexity as a dynamic interplay of multiple—that is, more than two—divergent temporal demands. Based on this reconceptualization, we develop a theory of organizational Eigenzeit, which illuminates how organizations “carve out” a time of their own amid temporal complexity. We propose a process model that explains how organizations navigate distinct constellations of temporal complexity by enacting varying degrees of temporal uncoupling and differentiation. Our model enables us to derive four generic modes of Eigenzeit—entrained, ambitemporal, agile, and pluritemporal—and theorize their characteristics and implications. We argue that grand challenges, such as climate change, require organizations to shift to more advanced modes of Eigenzeit, and detail why such a shift might be difficult to enact. Our paper contributes to time-based theorizing by specifying the distinct nature of temporal complexity and redirecting extant research toward examining different modes of Eigenzeit and their implied dynamics and consequences.

Modern artificial intelligence (AI) technologies based on deep learning architectures are often perceived as agentic to varying degrees—typically, as more agentic than other technologies but less agentic than humans. We theorize how different levels of perceived agency of AI affect human trust in AI. We do so by investigating three causal pathways. First, an AI (and its designer) perceived as more agentic will be seen as more capable, and therefore will be perceived as more trustworthy. Second, the more the AI is perceived as agentic, the more important are trustworthiness perceptions about the AI relative to those about its designer. Third, because of betrayal aversion, the anticipated psychological cost of the AI violating trust increases with how agentic it is perceived to be. These causal pathways imply, perhaps counterintuitively, that making an AI appear more agentic may increase or decrease the trust that humans place in it: success at meeting the Turing test may go hand in hand with a decrease of trust in AI. We formulate propositions linking agency perceptions to trust in AI, by exploiting variations in the context in which the human–AI interaction occurs and the dynamics of trust updating.

We develop a process model of how and why entrepreneurial framing can lead to deception and result in the loss of legitimacy in new ventures. We draw on the literature on framing and temporal construal theory to theorize how the emergence of a gap between expectations set during start-up and the realities that entrepreneurs encounter during implementation can trigger entrepreneurial deception when audiences seek concrete details in exchange for their continued support. Entrepreneurs may engage in further deception and moral disengagement to the extent that the gap remains as they pursue harder-to-accomplish stretch goals to maintain support. We also theorize what happens when entrepreneurial deception is publicly called out, resulting in a potentially catastrophic loss of legitimacy. Overall, we offer a cautionary note on entrepreneurship by exploring one aspect of its dark side.