The Impact of Knowledge Sharing on the Relationship between Artificial Intelligence and Service Quality of Employees

Document Type : Original Article

Author
Seifallah Andayesh
Assistant Prof., Department of knowledge and Information Science, Persian Gulf University, Bushehr, Iran
10.22034/aimj.2025.530930.1642
Abstract
The purpose of this study is to examine the impact of knowledge sharing on the relationship between artificial intelligence and the service quality of employees among librarians working in the libraries of medical universities in Tehran. This research is descriptive-survey in nature and applied in terms of purpose. The statistical population included all librarians working in the libraries of medical universities in Tehran (214 individuals). Using Cochran’s formula and standard statistical parameters, the sample size was determined as 137 librarians selected through stratified random sampling. For data collection, standardized questionnaires were employed: the artificial intelligence questionnaire developed by Chen et al. (2022) with 22 items, chosen due to its coherence, conciseness, and comprehensive coverage of AI dimensions; the knowledge sharing questionnaire developed by Damaj et al. (2016) with 12 items; and the service quality questionnaire developed by Parasuraman et al. (1985) with 22 items. The reliability of the instruments was confirmed using Cronbach’s alpha, and validity was established through convergent and discriminant validity. Data analysis was conducted using descriptive statistics such as frequency distribution, as well as inferential statistics through structural equation modeling (SEM) with Smart PLS software. The results showed that artificial intelligence has a positive and significant effect on employees’ service quality, and also positively influences knowledge sharing. Furthermore, knowledge sharing has a positive and significant impact on employees’ service quality. The results showed that knowledge sharing acts as a full mediator in the relationship between artificial intelligence and employees’ service quality. Therefore, this study contributes theoretically to the literature on artificial intelligence and knowledge management by demonstrating that knowledge mediation can bridge the gap between technology and the quality of human services. This research significantly contributes to the literature on artificial intelligence by highlighting research gaps in understanding the relationship between AI and service quality.

Keywords

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  • Receive Date 27 June 2025
  • Revise Date 20 August 2025
  • Accept Date 03 September 2025