Olan, Femi and Nyuur, Richard B and Arakpogun, Emmanuel Ogiemwonyi and Elsahn, Ziad (2023) AI: A knowledge sharing tool for improving employees’ performance. Journal of Decision Systems, 33 (4). pp. 700-720. DOI https://doi.org/10.1080/12460125.2023.2263687
Olan, Femi and Nyuur, Richard B and Arakpogun, Emmanuel Ogiemwonyi and Elsahn, Ziad (2023) AI: A knowledge sharing tool for improving employees’ performance. Journal of Decision Systems, 33 (4). pp. 700-720. DOI https://doi.org/10.1080/12460125.2023.2263687
Olan, Femi and Nyuur, Richard B and Arakpogun, Emmanuel Ogiemwonyi and Elsahn, Ziad (2023) AI: A knowledge sharing tool for improving employees’ performance. Journal of Decision Systems, 33 (4). pp. 700-720. DOI https://doi.org/10.1080/12460125.2023.2263687
Abstract
The utilisation of artificial intelligence (AI) is progressively emerging as a significant mechanism for innovation in human resource management (HRM). The capacity to facilitate the transformation of employee performance across numerous responsibilities. AI development, there remains a dearth of comprehensive exploration into the potential opportunities it presents for enhancing workplace performance among employees. To bridge this gap in knowledge, the present work carried out a survey with 300 participants, utilises a fuzzy set-theoretic method that is grounded on the conceptualisation of AI, KS, and HRM. The findings of our study indicate that the exclusive adoption of AI technologies does not adequately enhance HRM engagements. In contrast, the integration of AI and KS offers a more viable HRM approach for achieving optimal performance in a dynamic digital society. This approach has the potential to enhance employees’ proficiency in executing their responsibilities and cultivate a culture of creativity inside the firm.
Item Type: | Article |
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Uncontrolled Keywords: | Artificial intelligence; Human Resource Management; knowledge sharing |
Divisions: | Faculty of Social Sciences Faculty of Social Sciences > Essex Business School |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 01 Feb 2024 11:20 |
Last Modified: | 29 Nov 2024 01:11 |
URI: | http://repository.essex.ac.uk/id/eprint/37719 |
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Licence: Creative Commons: Attribution-Noncommercial-No Derivative Works 4.0