نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشجو دکتری علم اطلاعات و دانش شناسی واحد تهران شمال، دانشگاه آزاد اسلامی، تهران ، ایران
2 دانشیار گروه علم اطلاعات و دانششناسی واحد تهران شمال، دانشگاه آزاد اسلامی، تهران، ایران
3 استادیار گروه علم اطلاعات و دانششناسی واحد اراک، دانشگاه آزاد اسلامی، اراک، ایران
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Objective: This research, with a focus on the perspective of experts, has examined the potential application of artificial intelligence in addressing technological barriers to knowledge sharing and provides proposed solutions. The significance of the study lies in the direct impact of modern technologies on the quality of knowledge sharing in complex organizations such as the municipality, where over 218 independent information systems have created numerous information silos, disrupting the flow of knowledge. Key challenges include lack of system integration, weaknesses in technological infrastructure, insufficient technical support, inadequate employee training, and inefficient legacy systems, which result in contradictory decision-making, reduced productivity, and resource wastage. AI-based solutions not only overcome these barriers but also strengthen organizational memory, enhance intra-organizational collaboration, and improve urban services. Therefore, the following questions are raised: What are the technological barriers to knowledge sharing? What are the capabilities of artificial intelligence in eliminating technological barriers to knowledge sharing in Tehran Municipality? And how can artificial intelligence tools be applied to address technological barriers to knowledge sharing in Tehran Municipality? This research is grounded in the human-machine interaction conceptual model and theoretical frameworks such as Vuori et al. (2018) on technological barriers and Jarrahi et al. (2023) on AI capabilities, offering practical strategies for knowledge management in governmental settings.
Method: The research employs an integrated and applied approach, combining meta-synthesis for collecting and synthesizing library-based findings with fuzzy Delphi to achieve expert consensus and focus thereon. In the meta-synthesis phase, over 100 domestic and international sources—including Persian and English articles from databases such as SID, MagIran, Scopus, and Google Scholar—were reviewed. The search was conducted using keywords such as "knowledge sharing," "artificial intelligence," "technological barriers," and "urban knowledge management." After screening (removing duplicates, irrelevant articles, and unreliable sources), 85 key studies were selected. This method enabled the extraction of 38 initial barrier components. In the fuzzy Delphi phase, a structured questionnaire with a fuzzy scale was distributed among 42 experts. The expert panel consisted of AI engineers from knowledge-based companies, IT specialists from Tehran Municipality, and professionals with at least 10 years of experience (61.9% with 10–20 years of experience). Purposive sampling was used, and analysis was performed using SPSS and Expert Choice software. The sample adequacy test (KMO=0.826) and Bartlett’s test (χ²=4769.959, p<0.001) confirmed data validity. Exploratory factor analysis identified 5 main components (explaining 70.22% of variance), and fuzzy Delphi—through calculations of fuzzy mean, coefficient of variation (CV<0.2 for high consensus), and Kendall’s tau—prioritized barriers and tools. This multi-stage approach managed uncertainties and enhanced the validity of results.
Findings: Factor analysis results revealed that lack of integration in information technology systems (15.21% variance, mean 4.6, rank 1) is the primary barrier, leading to data silos and uncoordinated decision-making. Weaknesses in technological infrastructure (18.20% variance, mean 4.3), due to network instability and multiple repositories, ranked second. Insufficient technical support (mean 4.1), inadequate employee training (mean 3.9), and inefficient legacy systems (13.30% variance, mean 3.5) were the other barriers. From the experts’ perspective, effective AI tools include natural language processing (NLP, impact mean 4.5, rank 1) for facilitating communication and extracting tacit knowledge; collaborative intelligence (4.3) for enhancing human-machine interaction and organizational memory; intelligent data analytics (4.1) for integration and information prioritization; and predictive systems (3.9) for forecasting knowledge needs. These tools improve knowledge sharing by 40–60% through resource coordination, contradiction reduction, and increased accessibility (based on expert consensus). The findings align with prior studies such as Riege (2005) on barrier classification and Tavalayi (2023) on human-AI interaction, highlighting AI’s potential to transform the municipality into a knowledge-driven organization.
Conclusion: The application of artificial intelligence in Tehran Municipality’s information systems transforms knowledge sharing from a siloed state into an integrated flow, enhancing urban service quality, strategic decision-making, and organizational learning. This study, by identifying 5 key barriers and 4 primary tools, provides a practical framework for governmental organizations. Positive impacts include enhanced knowledge security, 24/7 responsiveness, and rapid retrieval, while challenges such as employee resistance, implementation costs, and privacy concerns require careful management. Practical recommendations include declaring a knowledge management crisis, developing a change strategy, assessing infrastructure, selecting AI platforms such as NLP-based chatbots, conducting pilot implementations with training, and revising security standards. Future research can examine the empirical effects of these tools in broader samples (such as other municipalities and large government organizations) using combined methods (e.g., simulation) to elevate the proposed doctoral model into a national strategy.
کلیدواژهها [English]
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