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

TY - JOUR

T1 - Elevating employees’ psychological responses and task performance through responsible artificial intelligence

AU - Verma, Surabhi

AU - Singh, Vibhav

AU - Tudoran, Ana Alina

AU - Bhattacharyya, Som Sekhar

PY - 2024/12/3

Y1 - 2024/12/3

N2 - 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.

AB - 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.

KW - Challenge-hindrance stressors model

KW - Innovative work behavior

KW - Responsible artificial intelligence

KW - Task-performance

KW - Work exhaustion

UR - http://www.scopus.com/inward/record.url?scp=85203720158&partnerID=8YFLogxK

U2 - 10.1108/ITP-05-2023-0431

DO - 10.1108/ITP-05-2023-0431

M3 - Journal article

AN - SCOPUS:85203720158

SN - 0959-3845

VL - 37

SP - 2551

EP - 2567

JO - Information Technology and People

JF - Information Technology and People

IS - 7

ER -