Human Resource Management Review最新文献

Artificial intelligence (AI) has the potential to change the future of human resource management (HRM). Scholars from different disciplines have contributed to the field of AI in HRM but with rather insufficient cross-fertilization, thus leading to a fragmented body of knowledge. In response, we conducted a systematic, interdisciplinary review of 184 articles to provide a comprehensive overview. We grouped prior research into four categories based on discipline: management and economics, computer science, engineering and operations, and others. The findings reveal that studies in different disciplines had different research foci and utilized different methods. While studies in the technical disciplines tended to focus on the development of AI for specific HRM functions, studies from the other disciplines tended to focus on the consequences of AI on HRM, jobs, and labor markets. Most studies in all categories were relatively weak in theoretical development. We therefore offer recommendations for interdisciplinary collaborations, propose a unified definition of AI, and provide implications for research and practice.

Workplace backlash, the explicit/implicit, and/or intentional/unintentional attempts to reject efforts to promote diversity, taken by both dominant and subordinate social group members to maintain the group-based social hierarchy at work, has emerged as a major threat to fostering diversity and inclusiveness in the workplace. Although intense scholarly attention has been paid to workplace backlash, the literature has a highly individualistic and fragmented perspective of backlash, which hinders theoretical advancement. As a remedy for conceptual and theoretical heterogeneity, I first conducted a systematic review of the literature to present a critical overview of past scholarly endeavors and take stock of the empirical evidence. This article provides an alternative, unified definition of workplace backlash drawn from intergroup relations and the power hierarchy among social group members. Finally, based on the perspective of group-based social hierarchy, this study describes the emergence, development, and maintenance of workplace backlash through the lens of social dominance theory. Implications and future research suggestions are also discussed.

The current literature on the use of disruptive innovative technologies, such as artificial intelligence (AI) for human resource management (HRM) function, lacks a theoretical basis for understanding. Further, the adoption and implementation of AI-augmented HRM, which holds promise for delivering several operational, relational and transformational benefits, is at best patchy and incomplete. Integrating the technology, organisation and people (TOP) framework with core elements of the theory of innovation assimilation and its impact on a range of AI-Augmented HRM outcomes, or what we refer to as (HRM^(AI)), this paper develops a coherent and integrated theoretical framework of HRM^(AI) assimilation. Such a framework is timely as several post-adoption challenges, such as the dark side of processual factors in innovation assimilation and system-level factors, which, if unattended, can lead to the opacity of AI applications, thereby affecting the success of any HRM^(AI). Our model proposes several testable future research propositions for advancing scholarship in this area. We conclude with implications for theory and practice.

Artificial intelligence (AI) systems and applications based on them are fast pervading the various functions of an organization. While AI systems enhance organizational performance, thereby catching the attention of the decision makers, they nonetheless pose threats of job losses for human resources. This in turn pose challenges to human resource managers, tasked with governing the AI adoption processes. However, these challenges afford opportunities to critically examine the various facets of AI systems as they interface with human resources. To that end, we systematically review the literature at the intersection of AI and human resource management (HRM). Using the configurational approach, we identify the evolution of different theme based causal configurations in conceptual and empirical research and the outcomes of AI-HRM interaction. We observe incremental mutations in thematic causal configurations as the literature evolves and also provide thematic configuration based explanations to beneficial and reactionary outcomes in the AI-HRM interaction process.

Team emergent states are properties that develop during team interactions and describe team members' attitudes and feelings (e.g., cohesion). However, these states' emergent nature has largely been neglected, as most studies do not examine the temporality of team phenomena. We review longitudinal studies on team emergent states and demonstrate that a majority of papers reveal their temporal dynamics but offer no universal patterns as to how such states emerge. The review reveals common variables related to temporal dynamics and highlights the importance of studying the development of team emergent states to enhance our knowledge of their causal directions, antecedents, and outcomes. We suggest that future research should clarify the concept of team emergent states, connect theories to research on temporal dynamics, adopt more qualitative approaches to answer “how” and “why” questions, and improve research designs to study meaningful forms of change. Lastly, we present practical implications for the HR field.

Artificial Intelligence and algorithmic technologies support or even automate a large variety of human resource management (HRM) activities. This affects a range of stakeholders with different, partially conflicting perspectives on the opacity and transparency of algorithm-based HRM. In this paper, we explain why opacity is a key characteristic of algorithm-based HRM, describe reasons for opaque algorithm-based HRM, and highlight the implications of opacity from the perspective of the main stakeholders involved (users, affected people, deployers, developers, and regulators). We also review strategies to reduce opacity and promote transparency of algorithm-based HRM (technical solutions, education and training, regulation and guidelines), and emphasize that opacity and transparency in algorithm-based HRM can simultaneously have beneficial and detrimental consequences that warrant taking a multi-stakeholder view when considering these consequences. We conclude with a research agenda highlighting stakeholders' interests regarding opacity, strategies to reduce opacity, and consequences of opacity and transparency in algorithm-based HRM.

Management scholars and practitioners have highlighted the importance of ethical dimensions in the selection of strategies. However, to date, there has been little effort aimed at theoretically understanding the ethical positions of individuals/organizations concerning human resource management (HRM) decision-making processes, the selection of specific ethical positions and strategies, or the post-decision accounting for those decisions. To this end, we present a Throughput model framework that describes individuals' decision-making processes in an algorithmic HRM context. The model depicts how perceptions, judgments, and the use of information affect strategy selection, identifying how diverse strategies may be supported by the employment of certain ethical decision-making algorithmic pathways. In focusing on concerns relating to the impact and acceptance of artificial intelligence (AI) integration in HRM, this research draws insights from multidisciplinary theoretical lenses, such as AI-augmented (HRM^(AI)) and HRM^(AI) assimilation processes, AI-mediated social exchange, and the judgment and choice literature. We highlight the use of algorithmic ethical positions in the adoption of AI for better HRM outcomes in terms of intelligibility and accountability of AI-generated HRM decision-making, which is often underexplored in existing research, and we propose their key role in HRM strategy selection.

The dramatic increase in the use of Artificial Intelligence (AI) in workplaces around the world has tremendous potential to increase business profitability. While AI has numerous useful applications and can help speed up business processes or transform systems, its use in human resources (HR) processes and systems presents a complex series of ethical considerations that require organizational leaders to tread with caution. In this paper, we argue that as the foremost worker advocates in the firm, HR managers must be ethically sensitive and accountable. They have responsibility to carefully monitor AI programs to ensure that these systems do what they are purported to do and protect the dignity of the worker through transparency regarding the data being collected and privacy regarding its usage. Lastly, the HR manager must closely monitor the fairness and equity impacts of AI such that its use is procedurally and distributivity just.

Research on inclusion and exclusion at work has grown in recent years, but for the most part has been treated as separate domains. In this paper, we integrate these literatures to build greater understanding of leader inclusion and leader exclusion. Leaders play a critical role in determining group member experiences of inclusion and exclusion through direct treatment of employees, and by serving as a role model (Bandura, 1977). According to social identity theory, when the leader is rewarded by the organization, this signifies that the leader is a prototypical organizational member who exemplifies the set of norms and behaviors most consistent with the organizational ideal (Hogg & van Knippenberg, 2003). We argue that through both social learning and social identity mechanisms, the leader can encourage inclusionary and exclusionary behavior in their work group. We first examine leader inclusion and present the types of behaviors that will aid in creating inclusive team member experiences. By exhibiting these behaviors, a leader can be a role model, an advocate and an ally for building work group inclusion. Next, we present the negative roles of ostracizer and bystander adopted by leaders that indicate support for behaving in an exclusionary manner, which can lead to exclusion among coworkers. We then describe leader remedies for social exclusion. Finally, we discuss the implications of our model and directions for future research.

Artificial intelligence (AI) affects human resource management (HRM), and in so doing, it is transforming the nature of work, workers and workplaces. While AI-assisted HRM is increasingly considered a strategy for improving organizational productivity, the academic literature has not yet offered a strategic framework to guide HR managers in adopting and implementing it. However, existing research in this area offers an opportunity to build such a framework. This systematic review of 67 peer-reviewed articles helps to achieve this objective. We critically examine the organizational and employee-centric outcomes of AI-assisted HRM and develop a strategic framework to guide its practice and future research.