Organizational Behaviour Seminars

Political polarization is recognized as a global risk. Although emerging studies on political dissimilarity at work document meaningful effects, findings are at times inconsistent and often treated as if they were stable over time. To provide a more nuanced understanding of when and why political dissimilarity disrupts workplace interactions, we draw on the social identity approach and threat processing to examine how political dissimilarity shapes perceptions of work relationships and behavior before and after election events. Across three studies, we demonstrate that political dissimilarity's effects depend on political macro events and thus become temporally activated. Study 1, an experience sampling field study during the 2020 U.S. presidential election, showed no significant impact of perceived political dissimilarity on negative interpersonal interactions before the election, but significance emerged on election day and persisted for six days post-election. In Study 2, an online experiment during the 2022 U.S. midterm elections, we found that actual political dissimilarity indirectly influenced negative interpersonal interactions via reduced social mindfulness after the election but not beforehand. Study 3, a longitudinal experiment over four weeks during the 2024 U.S. presidential election, replicated the election effect, demonstrating that these effects persisted for at least two weeks and were mediated both by cognitive (i.e., perspective-taking) and affective (i.e., empathic concern) subdimensions of social mindfulness. Our findings highlight political orientation as a critical dimension of workplace dissimilarity. While its impact may be subdued, it becomes pronounced during macro-political events, shaping workplace interactions in significant ways, with the political dissimilarity effects being more easily reactivated in the post-election phase.

Despite the growing availability of algorithm-augmented work, algorithm aversion is prevalent among employees, hindering successful implementations of powerful Artificial Intelligence (AI) aids (AI-based decision-support systems). Here, we examined the effects of two distinct aspects of such systems – Providing explanations to the user versus Requesting explanations from them – and their interplay with decision makers’ uncertainty avoidance (UA) in shaping their supportive attitudes toward such systems. A pilot study among U.S. employees revealed that both UA and the feature of providing explanations to the user (but not the feature of requesting explanations from them) were positively associated with employees’ supportive attitudes toward a recalled AI aid they currently use at work. Importantly, two preregistered experiments, manipulating both the system’s explanations features, resulted in causal evidence for the positive effects of (1) deploying a system that provides explanations, on all employees, but in particular, on high-UA employees, (2) a distinct preference, especially by high-UA employees, for complete )versus partial( explanations provided by the system; and (3) an enhanced preference by high-UA employees, for systems that request users to provide explanations that will be used to train the system rather than to document their decision-making process. Our studies provide insights into improving the management of Human–AI collaboration by integrating knowledge about system features, their optimal presentation to employees, and the characteristics of employees best suited to work with such systems. As such, they also contribute to understanding, mitigating, and managing employee aversion to powerful AI aids, as well as informing their effective design.

This study explores how organizations can turn adversity into renewal—how “bad luck” becomes “good adaptation.” Through a longitudinal field study of InterContinental Hotels Group (IHG) in Southeast Asia and Korea during the COVID-19 crisis, we trace how the temporary collapse of the firm’s lodging core triggered serendipitous reconfiguration: an unplanned but enduring renewal process in which peripheral activities gain strategic significance and reshape the restored core. When the pandemic disabled the organization’s central business, food-and-beverage units improvised locally to survive. These peripheral experiments, though initially reactive, were later legitimized and reintegrated, altering the firm’s cognitive frames, resource allocation, and strategic identity. The analysis reveals how crisis suspends dominant routines, liberates peripheral domains, and enables strategy to emerge from necessity rather than intent. By theorizing this mechanism of temporary core withdrawal, the paper bridges research on serendipity, emergent strategy, and organizational inertia, showing how moments of disruption create structural and cognitive conditions for unanticipated
 

TBA

TBA
 

Effective listening in the workplace is linked to numerous benefits, as established by theories and meta-analyses, making it desirable to enhance listening skills within organizations. While existing research suggests that listening can be improved through training, the evidence is largely based on quasi-experimental designs, employs a limited set of outcomes, and relies on a single trainer. We report a clustered field experiment in which 10 trainers trained 293 social workers across 20 municipalities in Serbia: social workers in 10 municipalities were randomly selected to receive the training first, and those in the other 10 municipalities received it 6 months later. We evaluated the trainees’ measures four times: before and after each training session. Based on Episodic Listening Theory, we hypothesized that training would foster togetherness, thereby enhancing cognitive complexity and, ultimately, well-being. We measured these three outcomes with 23 measures. Results supported our hypotheses on about half the measures and provided evidence for the proposed mediations. These findings partially support the theory, raise questions about effective listening training in organizations, and provide evidence-based justification for implementing organizational policies to foster listening skills.  

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Firms adopting AI face a tension: they must hire workers while adopting, but what AI means for the job is ambiguous: to the firm, AI language on a job posting signals new tools and modernization; to prospective workers, it can read as a precursor to substantial change in how the job itself is organized. We study this tension with two linked experiments at a Europe-headquartered industrial multinational. In a field RCT across 35 live postings (1,456 posting-days, 3,865 applications), adding a single sentence stating that the firm "increasingly relies on artificial intelligence" cuts applications by roughly one-third. The decline is largest in Coordination roles (where AI plausibly signals the biggest change to the role's implicit skill bundle) and concentrated among applicants without AI-relevant skills on their CVs; conditional on applying, shortlist and hire rates are unchanged. Disclosure thus operates as a pre-screening sorting margin: it reshapes the pool firms can screen, not the firm's selection on that pool. A complementary survey of 2,288 manufacturing-experienced workers shows the response is moderated by outside options. Employed workers become more deterred and more worried about job and salary loss, while unemployed workers become more interested and more confident in their ability to adapt. The results suggest that worker perceptions over AI may affect labor supply for AI adopting firms.