نوع مقاله : مقاله پژوهشی
نویسندگان
1 استادیار، گروه علم اطلاعات و دانش‌شناسی، دانشگاه خلیج‌فارس، بوشهر، ایران
2 دانش‌آموخته دکتری علم اطلاعات و دانش‌شناسی گرایش مدیریت اطلاعات و دانش، دانشگاه تهران، تهران، ایران
چکیده
هدف از انجام این پژوهش بررسی رابطه بین هوش مصنوعی و عملکرد کتابداران پزشکی: نقش میانجی تسهیم دانش در بین مدیران، کارکنان اداری، رؤسای کتابخانه‌های علوم پزشکی تابعه وزارت بهداشت، درمان و آموزش پزشکی مستقر در شهر تهران است. روش پژوهش حاضر، توصیفی و از نوع پیمایشی بوده و براساس هدف، کاربردی است. جامعه آماری مدیران، کارکنان اداری، رؤسای کتابخانه‌های علوم پزشکی تابعه وزارت بهداشت، درمان و آموزش پزشکی مستقر در شهر تهران بوده و به دلیل محدود بودن حجم جامعه آماری، همه جامعه با استفاده از روش سرشماری لحاظ گردیده است که 176 نفر به کار گرفته شد. ابزار گردآوری اطلاعات این پژوهش برای جهت سنجش هوش مصنوعی از پرسش‌نامه چن و همکاران (۲۰22) که حاوی 22 گویه‌ای که طیف گسترده‌ای از ابعاد هوش مصنوعی را در بر بگیرند، استفاده شد. جهت سنجش عملکرد کارکنان از پرسش‌نامه استیفن (2005) که حاوی 12 گویه، همچنین جهت سنجش تسهیم دانش از پرسش‌نامه داماج و همکاران (2016) که حاوی 12 گویه، استفاده شد؛ که پایایی آن از طریق آزمون آلفای کرونباخ و روایی آن از طریق روایی همگرا و روایی واگرا مورد تأیید قرار گرفت. تحلیل داده‌ها با استفاده از شاخص‌های آمار توصیفی همچون توزیع فراوانی و آمار استنباطی و روش مد‌ل‌سازی معادلات ساختاری با Smart PLS انجام شد. یافته‌ها نشان داد، هوش مصنوعی بر عملکرد کارکنان تأثیر مثبت و معنی‌داری دارد و همچنین هوش مصنوعی بر تسهیم دانش تأثیر مثبت دارد. تسهیم دانش بر عملکرد کارکنان تأثیر مثبت و معنی‌داری دارد. نتایج نشان داد تسهیم دانش به‌عنوان یک میانجی کامل در رابطه بین هوش مصنوعی و عملکرد کارکنان عمل می‌کند.
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
Examining the Relationship between Artificial Intelligence and Employee Performance: The Mediating Role of Knowledge Sharing
نویسندگان [English]
- Seifallah Andayesh 1
- Zahra Kianrad 2
1 Assistant Professor, Department of Knowledge and Information Science, Persian Gulf University, Bushehr, Iran
2 Ph.D. of Knowledge and Information Science, University of Tehran, Tehran, Iran
چکیده [English]
1.Introduction
In today’s knowledge-driven economy, knowledge is considered a fundamental organizational asset, yet its value is only realized through effective sharing across individuals and teams. Particularly in knowledge-based organizations such as academic libraries, knowledge sharing enhances innovation, task performance, and employee satisfaction. As Artificial Intelligence (AI) technologies continue to evolve—especially with the rise of generative tools like ChatGPT, they are increasingly integrated into library workflows, transforming how information is managed, disseminated, and utilized. This study explores the relationship between AI capabilities and employee performance, with a focus on the mediating role of knowledge sharing among staff in medical science libraries under the Ministry of Health and Medical Education in Tehran. AI offers powerful tools for automating routine tasks, facilitating content creation, and improving decision-making processes, which in turn may foster greater knowledge exchange among employees. By examining these dynamics, the research aims to shed light on how AI-driven knowledge sharing can contribute to enhanced service quality and organizational effectiveness in academic library environments.
Research Question(s)
Artificial intelligence has a positive and significant relationship with the performance of librarians.
Artificial intelligence has a positive and significant relationship with knowledge sharing among academic librarians.
Knowledge sharing has a positive and significant relationship with the performance of librarians.
Knowledge sharing plays a mediating role in the relationship between artificial intelligence and performance.
2.Literature Review
Previous studies have highlighted the significant impact of Artificial Intelligence (AI) on organizational performance and knowledge management. Abayomi et al. (2021) found that AI enhances the job performance of academic librarians. Malik et al. (2021) showed that AI-driven systems promote knowledge sharing, improving job satisfaction and reducing turnover. Nguyen and Malik (2022) demonstrated that knowledge sharing enhances service quality, moderated by AI system quality. Hussain (2023) identified barriers to AI adoption in libraries, including budget constraints and technical skills. Barsha and Munshi (2024) emphasized AI's potential in improving library services in developing countries. Femi Olan et al. (2024) argued that AI combined with knowledge sharing boosts employee performance and creativity, while Wirawan et al. (2024) confirmed AI's role in talent management. Despite these findings, further research is needed to explore AI's impact in medical library settings, which this study addresses by examining the relationship between AI capabilities, librarian performance, and knowledge sharing in Tehran’s medical science libraries.
3.Methodology
This applied study employed a descriptive-correlational design using a quantitative approach to examine the relationship between artificial intelligence and employee performance, with knowledge sharing as a mediating variable. The statistical population included managers, administrative staff, and heads of medical science libraries affiliated with the Ministry of Health in Tehran. Due to the limited population size, a census sampling method was used, resulting in 176 participants. Standardized questionnaires were utilized to collect data: a 22-item AI questionnaire by Chen et al. (2022), a 12-item knowledge sharing scale by Damaj et al. (2016), and a 12-item employee performance scale by Stephen (2005). Responses were measured using a five-point Likert scale. Convergent and discriminant validity were applied to confirm the instrument's validity. Structural equation modeling (SEM) was conducted using Smart PLS software to test the conceptual model and hypotheses.
4.Results
The results of the structural equation modeling (SEM) confirm the significance of the mediating role of knowledge sharing in the relationship between artificial intelligence (AI) and employee performance. Specifically, the Z-values for all tested hypotheses exceeded the critical value of 1.96, indicating statistical significance at the 95% confidence level (P ≤ 0.05). The Variance Accounted For (VAF) value was calculated to be 0.849, which exceeds the 80% threshold, suggesting full mediation. Further analysis showed that knowledge sharing had a significant and positive effect on employee performance (β = 0.766, t = 12.104, P ≤ 0.05), implying that a one-unit increase in knowledge sharing corresponds to a 0.766 standard deviation increase in employee performance. Similarly, AI had a significant direct impact on employee performance (β = 0.739, t = 9.986, P ≤ 0.05), indicating a strong positive relationship between these variables. Additionally, AI significantly influenced knowledge sharing (β = 0.642, t = 8.572, P ≤ 0.05), suggesting that the adoption and integration of AI technologies foster a more effective knowledge-sharing culture within organizations. Overall, the findings provide robust empirical evidence supporting the theoretical model and affirm that knowledge sharing fully mediates the relationship between AI and employee performance. These results highlight the importance of fostering a knowledge-sharing environment to fully leverage the potential benefits of AI in enhancing organizational effectiveness, particularly in knowledge-intensive sectors such as academic and medical libraries.
5.Conclusion
The purpose of this study was to examine the relationship between artificial intelligence (AI) and employee performance, with knowledge sharing playing a mediating role, among managers, administrative staff, and library directors of medical sciences libraries affiliated with the Ministry of Health, Treatment, and Medical Education in Tehran. The study aimed to address a research gap by providing valuable insights into the impact of AI on employee performance in this important sector. The results showed that AI has a positive and significant impact on the performance of employees in medical sciences libraries. This finding is consistent with previous research, which emphasized that AI and machine learning enhance organizational performance by eliminating human productivity limitations and reducing repetitive tasks. The study also found that effective use of AI algorithms in big data analysis improves employee retention and attracts new users. Additionally, AI accelerates daily library tasks, reducing time and cost, while enhancing librarian productivity and improving job performance by reducing human errors. Furthermore, the study demonstrated that knowledge sharing has a positive and significant impact on employee performance, with a strong emphasis on how fostering a culture of knowledge sharing can promote innovation, teamwork, and organizational efficiency. The research also revealed that AI positively impacts knowledge sharing among employees by providing digital platforms and advanced tools, facilitating data analysis, and enabling quick access to valuable resources. The findings indicated that knowledge sharing mediates the relationship between AI and employee performance, confirming that a combination of AI and knowledge sharing strategies provides a more sustainable approach to improving organizational performance. The study suggests that medical sciences libraries should invest in AI technologies, provide specialized training, and create a collaborative culture to enhance employee performance and facilitate knowledge sharing, ultimately leading to improved organizational efficiency and productivity.
Acknowledgments
The author expresses sincere gratitude to all those who contributed to this research.
کلیدواژه‌ها [English]
- Artificial Intelligence
- Employee Performance
- Knowledge Sharing
- Medical Librarians
- Knowledge Exchange
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