Journal of Organizational Behavior最新文献

This paper attends to the burnout recovery experiences of women in science, technology, engineering, and mathematics (STEM) and qualitatively explores how these individuals renegotiate, reorient, and recalibrate their work trajectories after burnout; an ambiguous and shocking event that has been shown to cause lingering disruption for both individuals and organizations (Salvagioni et al., 2017). We bring together conservation of resources (COR) theory and a sensemaking approach, illustrating how attention to sensemaking reveals the dynamics of resource allocation during times of disruption and loss; that is, the relational negotiation of protecting, investing, and fostering resources, including and importantly to burnout, a recognizable sense of recovery. Our rich qualitative analysis and findings reveal three sensemaking plotlines (Combative, Regenerative, and Promissory) through which rituals of resource management take place. Insights from this study provide a theoretical exposition for the post-burnout experience, illuminating the black box between burnout and recovery. We present a number of theoretical and practical contributions in developing the scholarly vistas surrounding (post-)burnout studies and STEM careers that better conceptualize (i) how marginalized members in highly instituted settings experience the aftermath of burnout and (ii) the broader implications this has for the sustainability of workforces.

This special issue focuses on collective responses to organizational change with a goal of enhancing knowledge on the emergence of these higher-level responses to change. While researchers acknowledge that organizational change inherently involves processes at multiple levels (individual, team, organization), scholars have only recently begun to increasingly promote models of collective responses to change. Spotlighting this gap, in this paper, we explore the dynamic character of collective responses to change, note the multiple ways in which these may develop, and identify theoretical frames rooted in psychology and sociology. This approach contributes to the growing field of responses beyond the individual. Through the papers in the special issue, we offer a framework based on Bourdieu's theory of practice as a platform for bringing together perspectives on agency and structuralism on how responses to change are shaped in the collective. With this framing, we provide direction for future research on successful organizational change through the interrelations between individuals and collectives undergoing change.

Volunteers represent a global workforce equivalent to 61 million full-time workers. A significant decline in volunteering has highlighted the urgency to better understand and address turnover amongst volunteers. To address this, we conducted a systematic review and meta-analysis of turnover amongst volunteers. We also examined whether staying or leaving has different predictors. The meta-analysis integrated and synthesized 117 studies, encompassing 1104 effect sizes across 55 335 volunteer workers, to identify and quantify relationships between turnover and the broad range of variables that have been examined in the volunteer work domain. Amongst the strongest predictors of volunteer turnover were attitudinal variables, in particular, job satisfaction (ρ = −.58), affective commitment (ρ = −.58), engagement (ρ = −.54) and organizational commitment (ρ = −.54). Contextual variables that showed the largest effects included communication (ρ = .62), organizational support (ρ = −.61) and the quality of the relationship between volunteers and their leader (leader-member exchange, ρ = −.55). We synthesize our findings into an integrative framework delineating the predictors of volunteer turnover. In doing so, we extend turnover research to consider non-remunerated work contexts and provide a basis for developing turnover theory that is responsive to the unique experience of volunteers.

Having limited information regarding how pay is distributed in their organization, employees often find it difficult to assess the fairness of their pay. Uncertainty management theory (UMT) posits that fairness uncertainty is aversive and that individuals experiencing it search for information to reduce this uncertainty. Pay information exchange – the communication of one's pay-related information to others in return for information from that other – provides a mechanism to reduce pay information uncertainty. We focus on third-party mediated pay information exchange (such as via Glassdoor and PayScale), an increasingly prevalent form of exchange. Drawing on UMT, we investigate why and when individuals exchange their pay information with such agents. Using data from a field experiment we find that (a) the willingness of employees to disclose their pay to a pay information exchange platform is influenced by perceived utility of a-priori information offered by the exchange partner, but that this relationship depends on the salience of fairness uncertainty to the employee, and (b) employer pay communication restrictiveness only attenuates the impact of disclosure willingness on actual disclosure when individuals engage in deliberative thinking about such restrictiveness and its possible consequences. We discuss the implications for theory and practice.

The rising use of artificially intelligent (AI) technologies, including generative AI tools, in organizations is undeniable. As these systems become increasingly integrated into organizational practices and processes, understanding their impact on workers' experiences and job designs is critical. However, the ongoing discourse surrounding AI use in the workplace remains divided. Proponents of the technology extol its benefits for enhancing efficiency and productivity, while others voice concerns about the potential harm to human workers. To provide greater clarity on this pressing issue, this article presents a systematic review of empirical research that sheds light on the implications of AI use at work. Organized under five inductively generated themes within a multilevel framework, we uncover individual, group, and organizational factors that shape the interplay between humans and AI. Specifically, the themes are: (1) human–AI collaboration; (2) perceptions of algorithmic and human capabilities; (3) worker attitudes towards AI; (4) AI as a control mechanism in algorithmic management of platform-based work; and (5) labor market implications of AI use. Our review offers insights into these themes and identifies five pathways for future research. Finally, we provide practical recommendations for organizational leaders seeking to implement AI technologies while prioritizing their employees' well-being.