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المؤلفAhmad, Anam
المؤلفBen Mimoun, Mohamed Slim
المؤلفEl-Gohary, Hatem Osman
تاريخ الإتاحة2025-09-22T07:45:52Z
تاريخ النشر2025
اسم المنشورJournal of Information and Knowledge Management
المصدرScopus
المعرّفhttp://dx.doi.org/10.1142/S0219649225500753
الرقم المعياري الدولي للكتاب2196492
معرّف المصادر الموحدhttp://hdl.handle.net/10576/67441
الملخصThe paper aims to present a systematic literature review (SLR) on the effects of Artificial Intelligence (AI) on the performance of organisations, especially from the employees' standpoint in the service sector. In the review, the author has compiled 60 studies to identify the conditions that determine the effectiveness of AI, such as trust, usability, and job satisfaction. The study establishes a connection between the level of AI integration, employees' responses, and overall organisational performance. Technology Acceptance Models (TAM) and Job Demands-Resources (JD-R) are among the models that can be employed to examine the studied relationship; however, some theoretical expansion is still needed in this field. This paper offers actionable suggestions for service organisations that deploy AI, policy considerations for regulators, and avenues for future research. It adds to the current literature on AI in service organisations, providing a basis for future work and practice in this ever-growing field.
اللغةen
الناشرWorld Scientific
الموضوعAi Implementation
Artificial Intelligence
Employee Perspective
Human-ai Collaboration
Job Satisfaction
Organisational Performance
Service Industry
Technology Acceptance
Artificial Intelligence
Employment
Engineering Research
Service Industry
Artificial Intelligence Implementation
Employee Perspective
Human-artificial Intelligence Collaboration
Organizational Performance
Service Employees
Service Industries
Service Organizations
Systematic Literature Review
Systematic Review
Technology Acceptance
Job Satisfaction
العنوانArtificial Intelligence and Organisational Performance: A Systematic Review of Service Employee Perspective
النوعArticle
ESSN17936926
dc.accessType Abstract Only


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