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A number of studies have identified a relationship between the positive psychological capital (PsyCap) of employees and desirable outcomes. Given current and projected shortages of truck drivers that could become the “Achilles heel” of the global supply chain, the purpose of this paper is to test whether and how drivers’ attitudes and PsyCap relates to their intentions to quit.

Using survey data from truckload drivers (n=251) from two major transportation firms, correlation, regression, and path analysis were conducted to assess the relationship between job satisfaction, organizational commitment, PsyCap, and intentions to quit.

Results of this study indicate strong positive relationships between PsyCap and job satisfaction and organizational commitment and a strong negative correlation with intentions to quit. Structural equation modeling suggests that job satisfaction and organizational commitment mediate the relationship between PsyCap and turnover intentions.

Managerial implications for recognizing, understanding, and developing PsyCap in the transportation industry are derived from this study. Specific training guidelines are provided.

The major contribution of this paper is that it provides, for the first time, empirical evidence that PsyCap can be utilized to improve retention rates for truckload drivers.

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.