PurposeThis article presents an empirically induced “high-performance” “human resources (HR) bundle”, comprising six HR practices, for supporting lean service operations.Design/methodology/approachThis was a multiple case study. A qualitative data set, including transcripts from 27 semistructured in-depth interviews with lean practitioners from across five service organizations that have adopted lean practices, was thematically analyzed to establish key HR practices on the road to lean maturity.FindingsA “high-performance” HR bundle of three work practices and three employment practices emerged from the analysis. These practices typically mature implicitly rather than systematically to support organizations in successfully implementing lean service operations by resourcing the most suitable people for carefully defined roles, providing workers with extensive lean training opportunities, appraising workers' performances such that lean behaviours are recognized and rewarded and encouraging a participative teamworking culture.Research limitations/implicationsThis article uses cross-sectional data from five case studies to induce a “high-performance” “HR bundle” theoretical model and process. A larger number of case studies and/or longitudinal data would add credence.Practical implicationsLean service managers should regard HR practices as integral to the lean maturation process and might usefully conceive of them as processes allowing for greater management control to achieve incremental improvements to lean service provision.Originality/valueThe article provides deeper understanding of the importance of HR practice for lean service organizations and offers practical suggestions for managing HR practices in this context.

No abstract

Artificial intelligence (AI) is deemed to increase workers' productivity by enhancing their creative abilities and acting as a general-purpose tool for innovation. While much is known about AI's ability to create value through innovation, less is known about how AI's limitations drive innovative work behaviour (IWB). With AI's limits in perspective, innovative work behaviour might serve as workarounds to compensate for AI limitations. Therefore, the guiding research question is: How will AI limitations, rather than its apparent transformational strengths, drive workers' innovative work behaviour in a workplace? A search protocol was employed to identify 65 articles based on relevant keywords and article selection criteria using the Scopus database. The thematic analysis suggests several themes: (i) Robots make mistakes, and such mistakes stimulate workers' IWB, (ii) AI triggers 'fear' in workers, and this 'fear' stimulates workers' IWB, (iii) Workers are reskilled and upskilled to compensate for AI limitations, (iv) AI interface stimulates worker engagement, (v) Algorithmic bias requires IWB, and (vi) AI works as a generalpurpose tool for IWB. In contrast to prior reviews, which generally focus on the apparent transformational strengths of AI in the workplace, this review primarily identifies AI limitations before suggesting that the limitations could also drive innovative work behaviour. Propositions are included after each theme to encourage future research.

No abstract