Elevating employees’ psychological responses and task performance through responsible artificial intelligence

Surabhi Verma, Vibhav Singh*, Ana Alina Tudoran, Som Sekhar Bhattacharyya

*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

2 Citations (Scopus)
7 Downloads (Pure)

Abstract

Purpose: In this study, we investigated the positive and negative effects of stress that is driven by responsible artificial intelligence (RAI) principles on employee job outcomes by adapting the challenge–hindrance stressors model. Design/methodology/approach: The study design involved empirically validating the proposed model on 299 respondents who use AI for work-related tasks. Findings: The results revealed several RAI-driven challenge and hindrance stressors related to employees’ positive and negative psychological responses and task performance in a digital workplace. Practitioners could use the RAI characteristics to improve employees’ RAI-driven task performance. Research limitations/implications: This study contributes to the ongoing discussion on technostress and awareness in the context of RAI in the AI literature. By extending the C-HS model to the RAI context, it complements the context-specific technostress literature by conceptualizing different characteristics of RAI as RAI-driven stressors. Originality/value: Adoption and use of technologies like RAI are not automatically translated into expected job outcomes. Instead, practitioners and academicians also need to know whether the RAI characteristics actually help employees show positive or negative behavior. Furthermore, relying on the challenge–hindrance stressor (C-HS) model, we try to reveal the beneficial and detrimental effects of different RAI characteristics on employees’ job outcomes.

Original languageEnglish
JournalInformation Technology and People
Volume37
Issue7
Pages (from-to)2551-2567
Number of pages17
ISSN0959-3845
DOIs
Publication statusPublished - 3 Dec 2024

Keywords

  • Challenge-hindrance stressors model
  • Innovative work behavior
  • Responsible artificial intelligence
  • Task-performance
  • Work exhaustion

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