Organizational Behaviour Seminars

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.

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.

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