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Artificial Intelligence Strategy, Creativity-Oriented HRM and Knowledge-Sharing Quality: Empirical Analysis of Individual and Organisational Performance of AI-Powered Businesses

创造力 知识管理 知识共享 业务 质量(理念) 实证研究 过程管理 计算机科学 心理学 社会心理学 认识论 哲学
作者
Raghda Abulsaoud Ahmed Younis,Heba Mohamed Adel
出处
期刊:Social Science Research Network [Social Science Electronic Publishing]
被引量:13
标识
DOI:10.2139/ssrn.4127128
摘要

Purpose – To investigate the relationship between artificial intelligence strategy (AIS), creativity-oriented HRM (CHRM), and knowledge-sharing quality (KSQ). At individual and organisational levels, this paper measures also the innovative work behaviour (IIWB) and effective performance (OEP) of international organisations conducting AI-powered business practices in Egypt.Design/methodology/approach – The authors presented a multilevel-model, after reviewing the relevant literature, and tested it through employing mixed-methods approach. Data were collected from 168 questionnaires answered by AI-experts at IT departments of 20 international AI-powered organisations in Egypt in addition to 25 depth interviews, AI-based focus group and international forum.Findings – Following PLS-SEM approach, results revealed that AIS affects positively and significantly KSQ and CHRM. CHRM affects positively and significantly KSQ and IIWB. KSQ affects positively and significantly OEP and IIWB. The significant positive direct AIS-OEP relationship was not supported yet the significant positive indirect relationship via KSQ was supported.Originality/value – Empirically, it is the first research that assessed AIS-CHRM-KSQ relationship and its effect on IIWB and OEP of AI-powered businesses from 7 sectors of an emerging economy. Conceptually, the authors adopted an interdisciplinary approach while reflecting on the literature that studied AIS implementation in different business functions (production, operations and supply-chain management, human resources management, strategic management and marketing).Practical implications – Strategic leaders and managers of different functional areas can benefit from the empirical findings of this study as well as from the examples of best AI-enhanced practices drawn from the literature.

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