Knowledge that Transforms

Violence at work has traditionally been conceptualized in human resource management (HRM) as workplace‐based violence—an episodic, interpersonal issue occurring within bounded organizational settings. This perspective article adopts the term work‐related violence as a more expansive and timely framing, encompassing physical, psychological, and symbolic harm related to work but occurring across dispersed geographies, identities, relationships, and organizational arrangements. It contends that prevailing HRM frameworks remain ill‐equipped to address these fragmented and often unacknowledged harms, particularly as work becomes increasingly hybrid, precarious, and digitally mediated. Drawing on interdisciplinary scholarship, we advance a multilevel and multistakeholder analytical framework that theorizes violence as relational and spatially unbounded, embedded across micro (identity and employees' lived experience, and psychological factors), meso (organizational culture, HRM systems and silencing mechanisms), and macro (regulatory, ideological, and institutional) levels. The framework further identifies underexplored domains of violence within HRM, including employee‐perpetrated violence, ideologically motivated aggression, and the critical role of community‐based interventions in mitigating harm. In doing so, the article contributes to HRM theory by problematizing the spatial and behavioral assumptions underpinning conventional approaches to workplace violence. We argue for a broadened research and practice agenda that expands the field's analytical and operational capacity, calling for the development of HRM models that are structurally, institutionally, and ideologically attuned to violence emerging from inequality, institutional complicity, and the broader political economies of contemporary work.

This paper addresses the growing misalignment between traditional human resource management (HRM) systems and the realities of distributed, fluid, and fragmented work. To address this issue, we introduce the FLUID‐HRM framework—a layered design architecture that reconfigures core HRM domains (resourcing, rewards, development, relations, work systems) across onsite, hybrid, and distributed forms of work. The framework is anchored in five structural principles of fluidity: spatial flexibility, temporal desynchronization, employment multiplicity, identity fragmentation, and digital mediation. For each, we propose differentiated HRM responses and corresponding information system (IS) infrastructures. Rather than retrofitting core legacy models, FLUID‐HRM offers a principle‐based approach for designing ethical, effective, and inclusive people systems in boundaryless work environments. By linking structural challenges to HRM innovation, we extend foundational HRM theory and provide actionable guidance for organizations navigating the transformation of work.

The integration of generative artificial intelligence (AI) into knowledge work is fundamentally reshaping employee performance and value creation in ways that challenge conventional wisdom. Rather than performance disparities being reduced through AI adoption, we argue that they may increase as star employees leverage superior domain expertise and strategic AI deployment to widen performance gaps—a phenomenon we term the “AI‐specific Matthew Effect.” These performance transformations coincide with dramatic shifts in value appropriation dynamics: Personal AI tools will enhance employee bargaining power by enabling portable, high‐value outputs independent of organizational resources, whereas enterprise AI systems serve as novel isolating mechanisms that strengthen firm value capture. These developments necessitate a theoretical reconceptualization of strategic human capital frameworks. Accordingly, we introduce the AI‐specific Matthew Effect to explain how AI may intensify performance stratification, modeling how AI reconfigures value creation and capture between employees and firms, and extending foundational human capital theories to account for human–AI complementarity. Our integrative theoretical framework offers critical guidance for navigating this transformation, helping organizations balance productivity gains with workforce equity in an uncertain era of interdependent human and artificial intelligence.

Thriving at work is a psychological state defined by dual experiences of vitality and learning. Existing research suggests that HRM practices can play a pivotal role in fostering employee thriving. In this perspective paper, we review the current literature on the relationship between HRM practices and employee thriving through five broad conceptual frameworks: (1) high‐performance HRM systems, (2) development‐oriented HRM, (3) purposeful and responsible HRM, (4) relational and inclusive HRM, and (5) multilevel contextual HRM. Beyond this review, we propose four key avenues for future research aimed at advancing our understanding of how HRM practices contribute to employee thriving. These research directions seek to explore the underlying mechanisms, contexts, and conditions that influence the effectiveness of HRM practices in promoting thriving, with a particular focus on sustaining employee thriving over time. Through these insights, we aim to provide a more nuanced understanding of how HRM can be strategically designed and implemented to support sustainable and regenerative thriving in dynamic work environments.

The phrase “quiet quitting” has become a popular topic within the workplace and academia. However, the nomological network of quiet quitting is unclear. We contribute to quiet quitting research by incorporating organizational justice and job characteristics theories with a psychological contract and social exchange lens to illuminate antecedents and outcomes of quiet quitting. Prior to doing so, we address the conceptual and measurement challenges that threaten the knowledge accumulation of quiet quitting research. Using a qualitative study ( N = 42) and prior research, we disentangle quiet quitting from its antecedents and outcomes to define it as intentionally performing to the minimum requirements of the job . We then develop a measure of quiet quitting across a subject matter expert review ( N = 51), a naïve rater review ( N = 90), and an assessment of the measure's psychometric properties ( N = 198). Finally, we assess our conceptual model ( N = 540) and find that psychological contract fulfillment has a negative indirect effect on quiet quitting through job satisfaction. Furthermore, psychological contract breach increases quiet quitting through job satisfaction. We find that quiet quitting subsequently increases CWBs and decreases OCBs. Our findings point toward an optimistic outlook: by accurately communicating expectations regarding organizational justice and job characteristics, human resource managers may be able to limit the prevalence of quiet quitting and subsequent detrimental behaviors within their organizations.

Artificial intelligence (AI) technology is rapidly integrated into the recruiting process across cultures. However, the extent to which job applicants' responses to AI‐based recruitment vary across cultures remains unexplored. To address this gap, we conducted a cross‐cultural examination on job applicants' perceptions of justice and innovativeness of AI‐based interviews, focusing on American and South Korean cultures. Using scenario experiments, we found that Americans generally perceived AI‐based interviews as less fair than human‐based interviews regarding job relatedness, chance to perform, and two‐way communication. In contrast, Koreans showed little difference in justice perceptions between AI‐based and human‐based interviews, and they even perceived AI‐based interviews as fairer in certain justice dimensions, such as the chance to perform. Both American and Korean participants regarded AI‐based interviews as more innovative than human‐based interviews. Additionally, we found that Americans' lower perceptions of justice, such as job relatedness and two‐way communication, accounted for the negative impact of AI‐based interviews on organizational attractiveness. However, Koreans' higher perceptions of the chance to perform and innovativeness led to higher organizational attractiveness in AI‐based interviews compared to that in human‐based interviews. Our findings underscore the pivotal role of culture in understanding job applicants' responses toward AI‐based interviews. Based on these findings, we discuss implications, limitations, and future suggestions.

Human resource management (HRM) scholarship has neither fully engaged with the United Nation's sustainable development goal 5 (SDG 5—gender equality) nor deepened knowledge of the human resource practices that most likely contribute to the implementation of this goal. We address this gap by investigating the link between SDG 5 and multinational enterprises' (MNEs) social performance. We posit that attention to SDG 5 will facilitate MNEs' capacity to formulate and implement practices to increase gender equality in work settings. Drawing from the sustainable HRM framework and social role theory, we develop three hypotheses related to the role of diversity targets and work‐life balance practices as mediators of the relationship between support for SDG 5 and MNEs' social performance. We also posit that women's representation across the organizational structure strengthens the relationship between support for SDG 5 and diversity targets as well as work‐life balance practices. We tested these relationships with 418 MNEs in the S&P 500. We found that diversity targets and work‐life balance practices (i.e., flexible arrangements and daycare services) mediate the relationship between support for SDG 5 and social performance. In addition, the interaction between women's representation and support for SDG 5 enhances diversity targets and flexible arrangements. We theoretically contribute to the sustainable HRM literature by (a) revealing the reasons for a spillover effect of support for gender equality to other demographic groups; (b) explaining broader societal impacts of support for SDG 5 on the workforce, community, human rights, and product responsibility. To successfully integrate SDG 5, MNEs must weave diversity targets and work‐life balance practices into strategic planning.

This study investigates the workplace experiences of 51 ethnic minority professionals who self‐identify as neurodivergent, focusing specifically on the impact of intersectional stereotyping within organizations in the United Kingdom and United States. Drawing on models of intersectional stereotyping, the research explores how neurodivergent employees' racial or ethnic minority backgrounds influence their self‐perceptions and experiences regarding prevailing stereotypes in professional environments. Semi‐structured interviews reveal that neurodiversity intersects with ethnicity, to either amplify or mitigate prevailing stereotypes during recruitment, performance evaluations, and career progression. Specifically, for Black and Latinx professionals, neurodiversity intensifies pejorative assumptions, reinforcing deficit stereotypes, while for Asian participants, neurodiversity can contradict the “model minority” stereotype. Neurodivergent behaviors are often seen as cultural mismatches with dominant norms, leading individuals to employ identity management strategies for professional advancement. This study extends general HRM diversity and neurodiversity research agendas by elucidating salient intragroup differences at this intersection, expanding intersectional stereotyping literature to include neurodiversity, and underscoring the practical need for integrated organizational inclusion initiatives that address the complex interrelationships between ethnicity and neurodiversity.

Neuroinclusion in human resources management (HRM) research and practice should go beyond the business case argument for neurodiversity (ND) to move to a nuanced understanding of harnessing neurodivergent talent. We argue for a biopsychosocial HRM perspective from an explicit non‐ableist stance, to illuminate in‐work experience to inform employer positions as proactive carers. We conceptualize a model of relational biopsychosocial neurodivergent talent inclusion informed by Organizational Support Theory, comprising employee (person), environment, and people characteristics, to guide a realist and co‐creational investigation into (a) neurodivergent conditions and wellbeing, (b) the role of tailored adjustment, and (c) the influence of psychosocial support on what makes people stay (career satisfaction) and makes them go (turnover intention). We collected data from 985 ND employees across a range of UK‐based organizations with existing interests in neuroinclusion. Neurodivergent condition co‐occurrence was common (complex neurotypes), yet experience varied by condition across the study measures. The number of neurodivergent conditions, wellbeing, knowledge of neurodivergence, support from staff and the manager, and psychological safety predicted career satisfaction. Support from the manager, psychological safety, and career satisfaction predicted turnover intention. Tailored adjustment (to neurotype) became non‐significant in each regression equation once other measures were added. We finally found support for a serial mediation where the association between psychological safety and turnover intention was sequentially mediated by wellbeing and career satisfaction. We discuss the need for a more holistic, ecological understanding of potentially vulnerable neurodivergent talent which considers wellbeing, the importance of the psychosocial environment, and the opportunity to realize career ambition in equal measures. We call for future research to develop our understanding of the role of the psychosocial environment in neuroinclusive HRM practices including domain‐specific psychological safety.

Existing research often highlights the negative consequences of algorithmic management (AM) for platform workers. By contrast, less is known about what, how, and when AM may produce both positive and negative outcomes. Drawing on attribution theory, this study examines the dual effects of core AM dimensions (i.e., algorithmic recommending, restricting, evaluating, and rewarding) on platform workers' perceptions of work overload and customer‐oriented service behavior. A two‐wave survey of 213 online platform workers in China reveals that algorithmic recommending and rewarding improve customer‐oriented service behavior and reduce work overload through AM commitment attributions. However, AM control attributions link algorithmic restricting, recommending, and evaluating (the latter two at low algorithmic transparency) to increased work overload. Algorithmic transparency moderates these effects, reducing the negative impacts of AM through AM control attributions. These findings contribute to a more nuanced understanding of the dual effects of core AM dimensions and provide practical insights for platforms seeking to enhance service quality while supporting worker well‐being.