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PurposeThis paper reviews 105 Scopus-indexed articles to identify the degree, scope and purposes of machine learning (ML) adoption in the core functions of human resource management (HRM).Design/methodology/approachA semi-systematic approach has been used in this review. It allows for a more detailed analysis of the literature which emerges from multiple disciplines and uses different methods and theoretical frameworks. Since ML research comes from multiple disciplines and consists of several methods, a semi-systematic approach to literature review was considered appropriate.FindingsThe review suggests that HRM has embraced ML, albeit it is at a nascent stage and is receiving attention largely from technology-oriented researchers. ML applications are strongest in the areas of recruitment and performance management and the use of decision trees and text-mining algorithms for classification dominate all functions of HRM. For complex processes, ML applications are still at an early stage; requiring HR experts and ML specialists to work together.Originality/valueGiven the current focus of organizations on digitalization, this review contributes significantly to the understanding of the current state of ML integration in HRM. Along with increasing efficiency and effectiveness of HRM functions, ML applications improve employees' experience and facilitate performance in the organizations.

PurposeThis research analyzed the existing academic and grey literature concerning the technologies and practices of people analytics (PA), to understand how ethical considerations are being discussed by researchers, industry experts and practitioners, and to identify gaps, priorities and recommendations for ethical practice.Design/methodology/approachAn iterative “scoping review” method was used to capture and synthesize relevant academic and grey literature. This is suited to emerging areas of innovation where formal research lags behind evidence from professional or technical sources.FindingsAlthough the grey literature contains a growing stream of publications aimed at helping PA practitioners to “be ethical,” overall, research on ethical issues in PA is still at an early stage. Optimistic and technocentric perspectives dominate the PA discourse, although key themes seen in the wider literature on digital/data ethics are also evident. Risks and recommendations for PA projects concerned transparency and diverse stakeholder inclusion, respecting privacy rights, fair and proportionate use of data, fostering a systemic culture of ethical practice, delivering benefits for employees, including ethical outcomes in business models, ensuring legal compliance and using ethical charters.Research limitations/implicationsThis research adds to current debates over the future of work and employment in a digitized, algorithm-driven society.Practical implicationsThe research provides an accessible summary of the risks, opportunities, trade-offs and regulatory issues for PA, as well as a framework for integrating ethical strategies and practices.Originality/valueBy using a scoping methodology to surface and analyze diverse literatures, this study fills a gap in existing knowledge on ethical aspects of PA. The findings can inform future academic research, organizations using or considering PA products, professional associations developing relevant guidelines and policymakers adapting regulations. It is also timely, given the increase in digital monitoring of employees working from home during the Covid-19 pandemic.

PurposeThis paper aims to specifically focus on the challenges that human resource management (HRM) leaders and departments in contemporary organisations face due to close interaction between artificial intelligence (AI) (primarily robots) and human workers especially at the team level. It further discusses important potential strategies, which can be useful to overcome these challenges based on a conceptual review of extant research.Design/methodology/approachThe current paper undertakes a conceptual work where multiple streams of literature are integrated to present a rather holistic yet critical overview of the relationship between AI (particularly robots) and HRM in contemporary organisations.FindingsWe highlight that interaction and collaboration between human workers and robots is visible in a range of industries and organisational functions, where both are working as team members. This gives rise to unique challenges for HRM function in contemporary organisations where they need to address workers' fear of working with AI, especially in relation to future job loss and difficult dynamics associated with building trust between human workers and AI-enabled robots as team members. Along with these, human workers' task fulfilment expectations with their AI-enabled robot colleagues need to be carefully communicated and managed by HRM staff to maintain the collaborative spirit, as well as future performance evaluations of employees. The authors found that organisational support mechanisms such as facilitating environment, training opportunities and ensuring a viable technological competence level before organising human workers in teams with robots are important. Finally, we found that one of the toughest challenges for HRM relates to performance evaluation in teams where both humans and AI (including robots) work side by side. We referred to the lack of existing frameworks to guide HRM managers in this concern and stressed the possibility of taking insights from the computer gaming literature, where performance evaluation models have been developed to analyse humans and AI interactions while keeping the context and limitations of both in view.Originality/valueOur paper is one of the few studies that go beyond a rather general or functional analysis of AI in the HRM context. It specifically focusses on the teamwork dimension, where human workers and AI-powered machines (robots) work together and offer insights and suggestions for such teams' smooth functioning.

PurposeThe paper sets out to understand the key issues that the various functions and optimal allocation of NGOs (non-governmental organizations) in the circular economy that provide public services depend not only on external quantities or densities but also on their internal size of human resources.Design/methodology/approachThe paper uses different data samples and models to study the influence mechanism of optimal NGO size of human resources and its differentiated effects on governance quality of entrepreneurship.FindingsThe authors find that a reduction in transaction costs and an increase in the aggregation degree of public demand lead to increased human capital and lower financial capital intensity. In addition, the authors find that NGO size of human resources has a relationship that is approximately U-shaped (or inverse U-shaped) with the governance quality of entrepreneurship.Practical implicationsThe paper discusses the implications for programs that encourage NGOs to optimally determine their internal size of human resources and further improve the governance quality of entrepreneurship in the circular economy.Originality/valueThe paper reveals the significant nonmonotonic relationship between local governance quality and NGO financial size, even after controlling for other NGO, city and provincial characteristics.

PurposeStudies on mining text and generating intelligence on human resource documents are rare. This research aims to use artificial intelligence and machine learning techniques to facilitate the employee selection process through latent semantic analysis (LSA), bidirectional encoder representations from transformers (BERT) and support vector machines (SVM). The research also compares the performance of different machine learning, text vectorization and sampling approaches on the human resource (HR) resume data.Design/methodology/approachLSA and BERT are used to discover and understand the hidden patterns from a textual resume dataset, and SVM is applied to build the screening model and improve performance.FindingsBased on the results of this study, LSA and BERT are proved useful in retrieving critical topics, and SVM can optimize the prediction model performance with the help of cross-validation and variable selection strategies.Research limitations/implicationsThe technique and its empirical conclusions provide a practical, theoretical basis and reference for HR research.Practical implicationsThe novel methods proposed in the study can assist HR practitioners in designing and improving their existing recruitment process. The topic detection techniques used in the study provide HR practitioners insights to identify the skill set of a particular recruiting position.Originality/valueTo the best of the authors’ knowledge, this research is the first study that uses LSA, BERT, SVM and other machine learning models in human resource management and resume classification. Compared with the existing machine learning-based resume screening system, the proposed system can provide more interpretable insights for HR professionals to understand the recommendation results through the topics extracted from the resumes. The findings of this study can also help organizations to find a better and effective approach for resume screening and evaluation.

Purpose This paper aims to propose a user adoption model of human resource information system (HRIS) in the Jordanian public sector by integrating the task technology fit (TTF) model and the unified theory of acceptance and usage of technology (UTAUT). Design/methodology/approach Using a quantitative approach, survey data were collected using an online survey from employees working in four different public organizations in Jordan, and structural equation modelling has been used to validate the research model. Findings The study found that among the constructs of the UTAUT model performance expectancy, social influence and facilitating condition have a significant effect on users’ behavioural intention to adopt HRIS. Furthermore, the results also reveal that effort expectancy has an insignificant effect on adoption behaviour. The findings also show that all TTF hypotheses were supported by the data collected. Both task characteristics and technology characteristics have a significant effect on the TTF construct, which further determines users’ adoption behaviour. Originality/value These findings contribute to the extant academic literature and have practical implications, improving the understanding of the HRIS adoption and use in public sector organizations.

The growing awareness of and regulations related to environmental sustainability have invoked the concept of green human resource management (GHRM) in the search for effective environmental management (EM) within organizations. GHRM research raises new, increasingly salient questions not yet studied in the broader human resource management (HRM) literature. Despite an expansion in the research linking GHRM with various aspects of EM and overall environmental performance, GHRM’s theoretical foundations, measurement, and the factors that give rise to GHRM (including when and how it influences outcomes) are still under-specified. This paper, seeking to better understand research opportunities and advance theoretical and empirical development, evaluates the emergent academic field of GHRM with a narrative review. This review highlights an urgent need for refined conceptualization and measurement of GHRM and develops an integrated model of the antecedents, consequences and contingencies related to GHRM. Going beyond a function-based perspective that focuses on specific HRM practices and building on advances in the strategic HRM literature, we discuss possible multi-level applications, the importance of employee perceptions and experiences related to GHRM, contextual and cultural implications, and alternative theoretical approaches. The detailed and focused review provides a roadmap to stimulate the development of the GHRM field for scholars and practicing managers.

We show the relevance of extant international business (IB) research, and more specifically work on international human resources management (IHRM), to address COVID-19 pandemic challenges. Decision-makers in multinational enterprises have undertaken various types of actions to alleviate the impacts of the pandemic. In most cases these actions relate in some way to managing distance and to rethinking boundaries, whether at the macro- or firm-levels. Managing distance and rethinking boundaries have been the primary focus of much IB research since the IB field was established as a legitimate area of academic inquiry. The pandemic has led to increased cross-border distance problems (e.g., as the result of travel bans and reduced international mobility), and often also to new intra-firm distancing challenges imposed upon previously co-located employees. Prior IHRM research has highlighted the difficulties presented by distance, in terms of employee selection, training, support, health and safety, as well as leadership and virtual collaboration. Much of this thinking is applicable to solve pandemic-related distance challenges. The present, extreme cases of requisite physical distancing need not imply equivalent increases in psychological distance, and also offer firms some insight into the unanticipated benefits of a virtual workforce – a type of workforce that, quite possibly, will influence the ‘new normal’ of the post-COVID world. Extant IHRM research does offer actionable insight for today, but outstanding knowledge gaps remain. Looking ahead, we offer three domains for future IHRM research: managing under uncertainty, facilitating international and even global work, and redefining organizational performance.

Based on a robust structured literature analysis, this paper highlights the key developments in the field of human resource management (HRM) in the Middle East. Utilizing the institutional perspective, the analysis contributes to the literature on HRM in the Middle East by focusing on four key themes. First, it highlights the topical need to analyze the context-specific nature of HRM in the region. Second, via the adoption of a systematic review, it highlights state of development in HRM in the research analysis set-up. Third, the analysis also helps to reveal the challenges facing the HRM function in the Middle East. Fourth, it presents an agenda for future research in the form of research directions. While doing the above, it revisits the notions of “universalistic” and “best practice” HRM (convergence) versus “best-fit” or context distinctive (divergence) and also alternate models/diffusion of HRM (crossvergence) in the Middle Eastern context. The analysis, based on the framework of cross-national HRM comparisons, helps to make both theoretical and practical implications.

No organisation is ever static. For several reasons, each organisation reviews its aims and objectives from time to time. These reasons may be internally or externally driven. They could also be politically, economically and or socially motivated. Research has established that most of the attempts at bringing about change are based on the needs of employees and customers. Essentially, for the purposes of better management of employees and customers, human resource information systems (HRIS) are touted as the panacea for effective and efficient health sector service delivery. Focusing on South Africa, this paper used the descriptive literature review method to determine HRIS adoption issues within the health sector of South Africa. As an important sector in any growing economy, the health sector in our view benefits from a constant review of its mission. Within the context of South Africa, substantial emphasis is yet to be placed on health sector effectiveness. Elsewhere, in other regions and continents, research on HRIS adoption within the health sector suggests that its adoption is problematic but useful. The South African health sector is yet to fully embrace this technology and as a result is suffering from employee dissatisfaction, brain drain, and general maladministration. Investment in HRIS research is therefore instructive especially within the context of South Africa. What we have found through this review is that investing in HRIS is crucial; however, it requires thorough consideration for its funding, infrastructural support, and skilled manpower among others.