Author
Listed:
- Singh Dhruv Pratap
(NEOMA - Neoma Business School)
- Anuragini Shirish
(LITEM - Laboratoire en Innovation, Technologies, Economie et Management (EA 7363) - UEVE - Université d'Évry-Val-d'Essonne - Université Paris-Saclay - IMT-BS - Institut Mines-Télécom Business School - IMT - Institut Mines-Télécom [Paris], IMT-BS - TIM - Département Technologies, Information & Management - TEM - Télécom Ecole de Management - IMT-BS - Institut Mines-Télécom Business School - IMT - Institut Mines-Télécom [Paris], Smart BIS - Smart Business Information Systems - IMT-BS - Institut Mines-Télécom Business School - IMT - Institut Mines-Télécom [Paris])
- Helena González-Gómez
(NEOMA - Neoma Business School)
- Shiva Taghavi
(NEOMA - Neoma Business School)
Abstract
Generative Artificial Intelligence (GenAI) is becoming embedded in everyday work, positioning responsible GenAI use as a defining employee competence for the future of HRM. However, organisations are adopting these tools far faster than employees are developing the competence to use them responsibly, and a readiness gap is already visible in high-profile GenAI failures and costly ethical missteps. Despite this urgency, HRM lacks a validated construct for assessing how employees engage with GenAI in informed, ethical, adaptive, functional, and socially appropriate ways. We address this need by conceptualising Responsible GenAI Competence (RGAIC) as a second-order construct with five dimensions: cognitive, functional, social, meta, and ethical. We followed a theory-driven, multi-phase scale development process combining expert assessment, psychometric validation, and a time-lagged field study. The results establish RGAIC as a robust, multidimensional personal resource shaped by Ability-enhancing HR Practices (AeHRPs) for GenAI training and predictive of GenAI-enabled job engagement. The construct advances HRM theory by foregrounding employee–GenAI interaction as a core domain of inquiry. For practitioners, it enables organisations to assess readiness, anticipate competence gaps, design targeted GenAI training, and build cultures of responsibility aligned with emerging regulatory and ethical standards.
Suggested Citation
Singh Dhruv Pratap & Anuragini Shirish & Helena González-Gómez & Shiva Taghavi, 2026.
"Employee competences for responsible generative AI use at work: a personal resource perspective,"
Post-Print
hal-05617292, HAL.
Handle:
RePEc:hal:journl:hal-05617292
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