Human Resource Management最新文献

The purpose of this article is primarily to evaluate whether machine learning (a form of artificial intelligence) can meet scientific, professional, and legal principles of personnel selection based on the rapidly accumulating research literature in Human Resource Management (HRM). It does so by addressing a series of questions in terms of what is known in the current research literature on these principles and related topics and by proposing a research agenda for what else needs to be studied. The review shows that there is enough current scientific evidence on the value of ML to support its use. ML tools are able to meet the basic principles of personnel selection and better meet many other very important principles. In addition, ML for personnel selection decisions is not a “black box” and can be understood and explained. It does not increase the legal risks from hiring, although it may require some additional steps in a few jurisdictions. There are uses of ML in selection that most organizations should be considering. Customized procedures will require good data, but generic vendor products may also serve some needs. Additional expertise may be required of HRM professionals, but not necessarily at a high level that would require new staffing or consultant expense. The availability of large language models (LLMs) may render unproctored remote assessments vulnerable to cheating, and all narrative candidate information provided in the future susceptible to AI-generated text, resulting in more questions than research answers at this stage. The article ends with practical and theoretical implications for the use of ML in selection.

In this paper we elucidate how gender may influence the mechanisms and conditions under which perceived inclusion climate facilitates authenticity at work. We nuance conservation of resources theory with social role theory to argue that perceived inclusion climate acts as a caravan passageway through which authenticity at work can be fostered, yet how inclusion climate relates to authenticity is different for men versus for women. We hypothesize that men will feel more authentic when they perceive their organization to be inclusive because such perceptions enable them to feel unique, whereas women will feel more authentic when in inclusive organizations because such environments foster their belongingness. In a time-lagged study, we find that for women, it is belongingness that is the mediating process, whereas for men, it is both uniqueness and belongingness. We additionally propose, and find, that structural empowerment, as an HRM contextual resource caravan, moderates the relationships between perceived inclusion climate and belongingness/uniqueness for women but not for men. Here, structural empowerment is particularly important for women's needs for belongingness and uniqueness when they perceive their organization be not very inclusive. Overall, we contribute to workplace inclusion and authenticity literatures by underscoring the need to consider gender differences in more depth.

Frontline service workers are crucial to service delivery and have been a long-standing focus of research, with scholars and practitioners alike maintaining an interest in their effective management. As technology increasingly transforms this context, the way in which managers in service organizations communicate with frontline workers has also evolved, with a notable increase in the use of enterprise social media (ESM). This study investigates the idea that it is not just ‘what’ management says (service-oriented messaging) but ‘the way’ they say it (via ESM) that matters. Drawing on interviews with 36 informants and guided by signaling theory, this study explores how ESM influences customer-focused signaling via management communication and HR implementation in the frontline service context, fostering conditions for a service climate. Findings reveal that ESM's affordances can potentially enhance service climate by strengthening HR system strength, promoting voice, and fostering informal leadership. However, risks such as manager role ambiguity, perceptions of mixed messaging, and over-surveillance are also identified. This study highlights the double-edged implications of ESM in its shaping of service-oriented signaling and emphasizes the importance of the signaling environment in realizing its benefits while mitigating its risks.

This study examines the impact of executive pay cuts on executive turnover. While previous research has largely focused on the drivers of pay increases of top management team (TMT) members or the effects of their relative pay standing on executive behavior, the prevalence and consequences of pay cuts have received limited attention. Drawing on temporal comparison theory, I argue that executive pay cuts are positively associated with executive turnover. I then propose that the impact of pay cuts on executive turnover depends on contextual cues that influence how executives interpret these cuts. Specifically, I argue that the effect is stronger for salary cuts than for pay-for-performance (PFP) cuts. I also argue that the impact of a pay cut is stronger when an executive is paid below the TMT pay level and when few other TMT members experience similar cuts. I test these predictions on a large longitudinal sample of U.S. firms and find empirical support. These results highlight the importance of temporal comparisons in executive pay evaluation processes.

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 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.

The growing use of algorithmic management (AM) in human resource (HR) activities has attracted growing attention from HR scholars, as organizations increasingly rely on digital labor platforms to leverage external workers. This study examines how these platforms apply AM in human resource management (HRM) and how these algorithmic systems embed both market and hierarchy management principles for shaping worker control and autonomy. Specifically, we seek to examine how AM manifests in two distinct ways across these platforms: one involving hierarchy and control, and another involving matching and autonomy. Using an inductive qualitative design, we analyzed 33 semi-structured interviews with platform workers and documentary data from 23 digital labor platforms. Whereas prior research often frames AM in binary terms—that is, market/autonomy versus hierarchy/control—we explore how task characteristics influence the joint application of both market and hierarchy principles in AM for HRM activities of digital labor platforms. Our findings show how platforms dynamically calibrate market and hierarchy approaches to AM in response to task demands, balancing flexibility, oversight, discretion, and incentives. For HR scholars, this study highlights the flexible and conditional nature of AM systems that blend autonomy and control in nuanced ways. By moving beyond the dominant autonomy-versus-control dichotomy, we show how AM is configured to align with diverse forms of work across platforms, enhancing efficiency while sustaining worker engagement amid evolving task demands.

The emergence of Artificial Intelligence (AI) has dramatically impacted human decision-making, productivity, and human agentic role delivery, thereby transforming HRM practices, particularly in recruitment and selection. Traditional recruitment and selection methods involve the dynamic interplay of structure and human agents. This interplay is changing with the emergence of human-technology-conjoined agencies that share task delivery and role performance responsibilities, thereby altering and creating new recruitment and selection routines. We conducted an exploratory qualitative study with 43 HR and talent acquisition leaders in a high-growth emerging market context to understand the transformational role of discriminative and generative AI technology agencies in changing recruitment and selection practices. This study contributes to existing HRM literature by demonstrating the impact of discriminative and generative AI in recruitment and selection and the simultaneous human-AI collaboration and algorithmic management of humans emerging as a consequence. Furthermore, we theorize the transformational changes in structures and processes across the entire recruitment and selection process emerging from conjoined agencies.