بررسی تاثیر قابلیت های هوش مصنوعی بر بهره وری کتابداران: نقش میانجی تسهیم دانش (کتابداران کتابخانه های علوم پزشکی دانشگاه های تهران) (مقاله علمی وزارت علوم)
درجه علمی: نشریه علمی (وزارت علوم)
آرشیو
چکیده
مقدمه و اهداف: این پژوهش با هدف بررسی تأثیر قابلیت های هوش مصنوعی بر بهره وری کتابداران، با تأکید بر نقش میانجی تسهیم دانش، در میان کتابداران کتابخانه های دانشگاه های علوم پزشکی مستقر در تهران انجام شده است. روش ها: روش پژوهش از نوع پیمایش همبستگی و از منظر هدف، کاربردی است. جامعه آماری این پژوهش شامل کتابداران شاغل در کتابخانه های دانشگاه های علوم پزشکی شهر تهران که در مجموع 214 نفر بودند، با استفاده از فرمول کوکران و در نظر گرفتن پارامترهای آماری استاندارد، حجم نمونه پژوهش 137 نفر تعیین گردید. برای سنجش متغیرهای پژوهش از ابزارهای معتبر و استاندارد بهره گرفته شد. میزان استفاده از هوش مصنوعی از طریق پرسشنامه ای ۲۲ گویه ای در پنج بعد (مدیریت هوش مصنوعی، تصمیم گیری مبتنی بر هوش مصنوعی، زیرساخت هوش مصنوعی، آمادگی برای هوش مصنوعی، و مهارت های مرتبط با هوش مصنوعی)، بهره وری با ابزار ۱۷ گویه در چهار بُعد (کارایی، اثربخشی، تعهد و توانایی حل مسئله) و شاخص تسهیم دانش که شامل ۱۲ گویه بود، ارزیابی شد. تمامی ابزارها براساس طیف پنج درجه ای لیکرت طراحی شدند. روایی ابزارها از طریق تحلیل روایی همگرا و واگرا و پایایی آن ها با محاسبه آلفای کرونباخ تأیید شد. یافته ها: نتایج نشان داد که بین هوش مصنوعی و بهره وری کتابداران رابطه مثبت و معناداری وجود داشت. همچنین، هوش مصنوعی اثر مثبت و معناداری بر تسهیم دانش داشت. از سوی دیگر، تسهیم دانش با بهره وری کتابداران رابطه مثبت و معناداری برقرار کرد. بحث و نتیجه گیری: تحلیل ها بیانگر آن است که تسهیم دانش به عنوان یک متغیر میانجی کامل، نقش مهمی در پیوند میان قابلیت های هوش مصنوعی و بهره وری کتابداران ایفا می کند. اصالت: این پژوهش به غنای ادبیات مرتبط با هوش مصنوعی در حوزه کتابداری کمک کرده و خلأهای موجود در فهم روابط میان فناوری های نوین و بهره وری منابع انسانی در کتابخانه ها را آشکار می سازد.Investigating the Impact of Artificial Intelligence Capabilities on Librarians' Productivity: The Mediating Role of Knowledge Sharing (Librarians of Medical University Libraries in Tehran)
Introduction: The integration of artificial intelligence (AI) into the workplace has catalyzed transformative changes across various industries, fundamentally reshaping business operations and enhancing employee productivity. As a multidisciplinary domain within computer science, AI enables machines to execute cognitive functions traditionally associated with human intelligence, including problem-solving, decision-making, and information processing. Its capacity to automate routine tasks, analyze large datasets, and foster innovation positions AI as a critical driver of organizational efficiency and employee engagement. By alleviating repetitive workloads and promoting the development of new skills, AI contributes to increased job satisfaction, motivation, and overall performance. Nevertheless, the realization of AI’s full potential is contingent upon mediating factors such as effective knowledge sharing among employees, which supports organizational learning and the optimal utilization of emerging technologie. In this context, academic libraries—particularly those affiliated with medical universities—serve as ideal knowledge-intensive environments for examining the dynamic interplay between artificial intelligence (AI), productivity, and knowledge sharing. This study seeks to investigate the mediating role of knowledge sharing in the relationship between AI capabilities and the productivity of librarians working in medical university libraries in Tehran. Addressing a gap in the existing literature, the study examines the following hypotheses: (1) AI has a positive effect on librarians’ productivity; (2) AI positively influences knowledge sharing among librarians; (3) knowledge sharing positively affects librarians’ productivity; and (4) knowledge sharing significantly mediates the relationship between AI and productivity. Methodology: This study is classified as applied research in terms of its objective and adopts a descriptive-correlational design with a quantitative methodology. It investigates the relationship between the use of artificial intelligence (AI) technologies and librarians' productivity, with particular emphasis on the mediating role of knowledge sharing. The statistical population comprises 214 librarians working in medical university libraries in Tehran, including central, research, administrative, hospital, and faculty libraries. Based on Cochran’s formula and standard statistical parameters, a sample size of 137 participants was determined. To measure the variables in this study, validated and standardized instruments were employed. The level of artificial intelligence (AI) utilization was assessed using a 22-item questionnaire encompassing five dimensions: AI management, AI-based decision-making, AI infrastructure, AI inclination, and AI skills, adapted from Chen et al. (2022). Productivity was measured using the instrument developed by Jabbarzadeh (2013), which comprises 17 items across four dimensions: efficiency, effectiveness, commitment, and problem-solving. The knowledge sharing index was assessed using the 12-item questionnaire developed by Damaj et al. (2016). All instruments utilized a five-point Likert scale. The validity of these instruments was established through assessments of convergent and divergent validity, while reliability was confirmed by calculating Cronbach’s alpha coefficients. Findings: The statistical findings of the study indicate that knowledge sharing plays a significant mediating role in the relationship between artificial intelligence (AI) and librarians’ productivity, as evidenced by a Z statistic greater than 1.96 and a p-value ≤ 0.05. The Variance Accounted For (VAF) was calculated at 0.883, signifying full mediation by knowledge sharing. Structural Equation Modeling (SEM) analysis further revealed that AI has a significant positive effect on librarians’ productivity (p ≤ 0.05, t = 9.226, β = 0.613), suggesting that a one-unit increase in AI utilization is associated with a 0.613 standard deviation increase in productivity. Additionally, AI was found to significantly enhance knowledge sharing (p ≤ 0.05, t = 8.105, β = 0.747), indicating that a one-unit increase in AI corresponds to a 0.747 standard deviation increase in knowledge sharing. Finally, knowledge sharing itself had a significant positive effect on librarians’ productivity (p ≤ 0.05, t = 11.681, β = 0.652), supporting the hypothesis that a one-unit increase in knowledge sharing results in a 0.652 standard deviation increase in productivit. Conclusion: This study investigated the impact of artificial intelligence (AI) technologies on the productivity of librarians in academic medical university libraries in Tehran, with particular emphasis on the mediating role of knowledge sharing. The findings indicated that the use of AI has a significant positive effect on librarians’ productivity across four key dimensions: efficiency, effectiveness, collaboration and problem-solving, and organizational commitment. Furthermore, AI was found to play a pivotal role in enhancing knowledge sharing by improving access to information, reducing communication barriers, and fostering collective learning. These advancements, in turn, promote active engagement in knowledge creation and exchange. The findings also indicate that knowledge sharing directly enhances productivity by improving decision-making, minimizing task redundancy, and strengthening motivation and professional identity, thereby contributing to both individual and organizational performance. Overall, the study suggests that the strategic integration of artificial intelligence (AI) technologies with a culture of knowledge sharing can serve as an effective approach to enhancing librarians’ productivity within academic environments. Accordingly, it is recommended that policymakers in higher education and information technology prioritize the development of intelligent, knowledge-based systems; foster organizational incentives; invest in digital infrastructure; and implement capacity-building programs aimed at promoting organizational learning and advancing professional performance. Moreover, ongoing monitoring of productivity across the four key dimensions can offer a comprehensive assessment of the effectiveness of technological and knowledge-based interventions.
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