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What do antecedents of turnover tell us when examined using human resources (HR) analytics and machine-learning tools, and what are the respective theoretical and practical implications? Although the turnover literature is expansive, empirical evidence on turnover antecedents studied using data science tools remains limited.

To help reinvigorate research in this field, the authors propose a novel examination of turnover antecedents—competencies, commitment, trust and cultural values—using big data tools to develop a granular, case-dependent measure of turnover.

Using archival data from 700,000 employees of a large organization collected over a period of ten years, the authors find that turnover is generally associated with varying levels of these antecedents. However, in more fine-grained analysis, their relation to turnover is contingent upon role, person and cultural background.

The authors discuss the implications on turnover and strategic HR research and the potential of Artificial Intelligence and machine-learning methods in the design and implementation of managerial and HR planning initiatives.

The purpose of this paper is to provide a return on investment (ROI) based review of human resources (HR) analytics. The objectives of this paper are twofold: first, to offer an integrative analysis of the literature on the topic of HR analytics in order to provide scholars and practitioners a comprehensive yet practical ROI-based view on the topic; second, to provide practical implementation tools in order to assist decision makers concerning questions of whether and in which format to implement HR analytics by highlighting specific directions as to where the expected ROI may be found.

This paper is a review paper in which a four-step review and analysis methodology is implemented.

Study results indicate that empirical and conceptual studies in HR analytics generate higher ROI compared to technical- and case-based studies. Additionally, study results indicate that workforce planning and recruitment and selection are two HR tasks, which yield the highest ROI.

The results of this study provide practical information for HR professionals aiming to adopt HR analytics. The ROI-based approach to HR analytics presented in this study provides a robust tool to compare and contrast different dilemma and associated value that can be derived from conducting the various types of HR analytics projects.

A framework is presented that aggregates the findings and clarifies how various HR analytics tools influence ROI and how these relationships can be explained.

The purpose of this paper is to examine the link between interpersonal and organizational factors in consultant‐client relationships and their contribution to the effectiveness of an organizational change process.

The authors designed two studies in order to gain insightful information, both qualitative and quantitative in nature. Study 1 is composed of semi‐structured one‐on‐one interviews conducted with pairs of clients and consultants. Study 2 is designed as a quantitative study and is a direct expansion of study 1. The sample, conducted between the years 2002 and 2007, is composed of departments within organizations that underwent an organizational change process, in which external consultants took part.

The authors found that a high level of commitment to change has a positive effect on the successful implementation of organizational change as a whole. Also, high levels of dyadic trust in the consultant‐client relationship result in higher levels of consultant commitment to the change process. Finally, consultant commitment to change partially mediates the relationship between trust and successful implementation of organizational change.

The paper explores the link between individual and organizational levels in pursuing organizational change processes and attempts to clarify this link.

This systematic literature review aims to explore the adoption, global acceptance and implementation of human resources (HR) analytics (HRA) by reviewing literature on the subject. HRA adoption can assist HR professionals in managing complex procedures and making strategic human resource management (SHRM) decisions more effectively. The study also aims to identify the applications of analytics in various disciplines of management.

The review is conducted using a domain-based structured literature review (SLR), emphasizing the diffusion of innovative thinking and the adoption process of HRA among early adopters. The philosophical stances are analyzed with the combination of research onion model and PRISMA protocol. Secondary data are gathered from published journals, books, case studies, conference proceedings, web pages and media stories as the primary source of information.

The study finds that skilled professionals and management assistance can significantly impact adoption intentions, enabling professionals to deal with analytics. The examples and analytical models provided by early adopters allow managers to manage complex processes and make SHRM decisions.

The study suggests that the lack of use of quantitative techniques is a key limitation and should be considered in future studies. Despite the rise in the number of research papers on HRA, its application in the workplace remains limited.

This research can assist managers in implementing HRA and help resolve complex and inefficient processes, making SHRM decisions.

This study adds to the existing body of knowledge on how HRA can aid a company's efficacy and performance and can be considered one of the first to link adoption and HRA.

Human resource management (HRM) processes are increasingly artificial intelligence (AI)-driven, and HRM supports the general digital transformation of companies' viable competitiveness. This paper points out possible positive and negative effects on HRM, workplaces and workers’ organizations along the HR processes and its potential for competitive advantage in regard to managerial decisions on AI implementation regarding augmentation and automation of work.

A systematic literature review that includes 62 international journals across different disciplines and contains top-tier academic and German practitioner journals was conducted. The literature analysis applies the resource-based view (RBV) as a lens through which to explore AI-driven HRM as a potential source of organizational capabilities.

The analysis shows four ambiguities for AI-driven HRM that might support sustainable company development or might prevent AI application: job design, transparency, performance and data ambiguity. A limited scholarly discussion with very few empirical studies can be stated. To date, research has mainly focused on HRM in general, recruiting and HR analytics in particular.

The four ambiguities' context-specific potential for capability building in firms is indicated, and research avenues are developed.

This paper critically explores AI-driven HRM and structures context-specific potential for capability building along four ambiguities that must be addressed by HRM to strategically contribute to an organization's competitive advantage.