Human Resource Management Review最新文献

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.

The aim of the study is to understand how humans' acceptance of Artificial Intelligences (AIs) affects human resource management (HRM). To this end, we propose an original conceptual framework based on the idea of a sustainable growth driven by the interplay between AI and HRM. Current academic debate is overly concerned by the impact that future AI will have on business and society. One of the central aspects of the conversation is whether or not AI will replace humans in value-added activities. The study remarks that humanoids are an amplificator of human potential, in light of a human-centered approach. In this vein, present work reconceptualizes the tenets of society 5.0 by considering the category of “innovation ventura”, the evolution of the innovative enterprise in the next AI landscape.

This systematic literature review explores studies addressing the objective career success and subjective career success of company-assigned and self-initiated expatriates after their long-term international assignments. Expatriate work is defined as high-density work that affects employee learning and career trajectories. We develop a holistic expatriate career success framework exploring the following questions: 1) What individual career impact results from international assignments? 2) What are the antecedents of such career success? and 3) What are the outcomes of assignees’ career success? A previously neglected range of theoretical perspectives, antecedents, and outcomes of expatriate career success is identified. Subsequently, a threefold contribution is made. First, we extend the conceptualization of international work density to unveil the differences between general and global career concepts. Second, we identify promising theories that have not been utilized in expatriation research, emphasizing context-related and learning theories that chime with the specific nature of global careers. Lastly, we suggest an extensive future research agenda.

The purpose of this paper is to systematically review the academic literature on organizational effectiveness (OE) through an analysis of 134 contributions. While OE is the most important goal for an organization, research views on this construct are diversified. Over time, the literature has increasingly focused on antecedents, measures, and more diversified approaches to OE. This paper contributes to the OE literature by developing an integrated conceptual model that considers the levels of antecedents (individual, group, and organizational) as well as measures of OE (financial, operational, structural, and attitudinal). Previous research has focused primarily on organizational-level antecedents and non-financial measures of OE. The paper also discusses the barriers hampering the measurement of OE. An agenda for future research is also provided.

Artificial Intelligence (AI) is increasingly adopted within Human Resource management (HRM) due to its potential to create value for consumers, employees, and organisations. However, recent studies have found that organisations are yet to experience the anticipated benefits from AI adoption, despite investing time, effort, and resources. The existing studies in HRM have examined the applications of AI, anticipated benefits, and its impact on human workforce and organisations. The aim of this paper is to systematically review the multi-disciplinary literature stemming from International Business, Information Management, Operations Management, General Management and HRM to provide a comprehensive and objective understanding of the organisational resources required to develop AI capability in HRM. Our findings show that organisations need to look beyond technical resources, and put their emphasis on developing non-technical ones such as human skills and competencies, leadership, team co-ordination, organisational culture and innovation mindset, governance strategy, and AI-employee integration strategies, to benefit from AI adoption. Based on these findings, we contribute five research propositions to advance AI scholarship in HRM. Theoretically, we identify the organisational resources necessary to achieve business benefits by proposing the AI capability framework, integrating resource-based view and knowledge-based view theories. From a practitioner’s standpoint, our framework offers a systematic way for the managers to objectively self-assess organisational readiness and develop strategies to adopt and implement AI-enabled practices and processes in HRM.

Research on impression management within organizations is extensive and provides valuable insights regarding both impression management motivation and the ways in which impression management is enacted. However, inconsistent findings in the literature limit our ability to confidently glean clear research and practice conclusions. Further, current impression management perspectives are primarily based on face-to-face communication, but technology and world events have changed how we interact within organizations. Our integrative literature review examines the impression management literature, and integrates research from related literatures (organizational citizenship behavior, faking behavior, and computer-human interaction), to identify how context influences impression motivation and construction. Based on this review, we propose that impression motivation is shaped, in part, by the situation's evaluative potential (e.g., public behavior, high stakes), and the nature of the workplace interaction (e.g., anonymity, permanence, verifiability, and synchronicity) moderates the impression motivation-impression construction relationship. We then use the contextual framework to provide a better understanding of past research, stimulate new research, and provide practical recommendations for HR professionals.

Artificial intelligence (AI) can bring both opportunities and challenges to human resource management (HRM). While scholars have been examining the impact of AI on workplace outcomes more closely over the past two decades, the literature falls short in providing a holistic scholarly review of this body of research. Such a review is needed in order to: (a) guide future research on the effects of AI on the workplace; and (b) help managers make proper use of AI technology to improve workplace and organizational outcomes.

This is the first systematic review to explore the relationship between artificial intelligence and workplace outcomes. Through an exhaustive systematic review and analysis of existing literature, we ultimately examine and cross-relate 60 papers, published in 30 leading international (AJG 3 and 4) journals over a period of 25 years (1995–2020). Our review researches the AI-workplace outcomes nexus by drawing on the major functions of human resource management and the process framework of ‘antecedents, phenomenon, outcomes’ at multiple levels of analysis. We review the sampled articles based on years of publication, theories, methods, and key themes across the ‘antecedents, phenomenon, outcomes’ framework. We provide useful directions for future research by embedding our discussion within HR literature, while we recommend topics drawing on alternative units of analysis and theories that draw on the individual, team, and institutional levels.

This study proposes the ‘duality of algorithmic management’ as a conceptual lens to unravel the complex relationship between human resource management (HRM) algorithms, job autonomy and the value to workers who are subject to algorithmic management. Against tendencies to present algorithmic management as having predetermined, undesired consequences (e.g. restriction of job autonomy, poor financial compensation and deteriorating working conditions), our ‘duality of algorithmic management’ perspective offers two amendments to the dominant thinking on HRM algorithms and their outcomes to workers. First, we showcase how algorithmic management simultaneously restrains and enables autonomy and value to workers – with the latter referring to both use (i.e. non-monetary benefits) and exchange value (i.e. monetary benefits) that workers derive from working (under algorithmic management). In doing so, we make the case that the desired consequences of HRM algorithms to workers co-exist alongside the undesired consequences that the literature has mostly reported on. Second, we argue that algorithmic management is shaped by, as much as it shaping, the autonomy and value to workers. We do so by highlighting the ‘recursivity’ of algorithmic management that occurs when software designers and/or self-learning algorithms reinforce or limit worker acts for (re)gaining job autonomy and/or creating value out of HRM algorithms. We conclude this paper with the presentation of avenues for future research into the duality of algorithmic management, which sets the stage for a future line of inquiry into the complex interrelationships among HRM algorithms, job autonomy and value.