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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.

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.

Drawing upon the disadvantage theory, the purpose of this paper is to investigate patterns of entrepreneurship evolving under conditions of double discrimination characterizing the situation of Arab women entrepreneurs in Israel.

A comprehensive questionnaire was administered in 2006 to a sample of 405 Arab‐Muslim women entrepreneurs in the northern and southern “triangle” of Israel. The authors used a snowball convenient method, contacting business women in the “triangle” area by word of mouth. Based on data provided by the Center of Fostering Entrepreneurship (in Hebrew “MATI”) in Bakka el Garbia, it was assumed that about 80 percent of all Arab women business owners in this particular area had been reached.

In line with the literature, it was found that under conditions of double discrimination a rather traditional type of entrepreneurship evolves, but the authors' findings do not confirm that these businesses are necessarily marginal and illegal. Instead the authors found a variety of types; the majority of which the authors would call community‐based traditional micro‐enterprises.

The paper adds to the understanding of entrepreneurial patterns emerging under conditions of discrimination. It is believed to be one of the very first studies directly investigating a large group of Arab women entrepreneurs in Israel; therefore the understanding of the personal and business characteristics of these women is of major importance.