مطالب

This research examined how external organizational factors influence the acceptance and readiness assessment for integrating big data analytics into citizen relationship management (CRM) at Tehran Governorate. The study employed Davis's Technology Acceptance Model (TAM) in an applied, field-based design. Data were collected using a standardized questionnaire based on TAM, with a sample of 105 managers and experts from Tehran Governorate, and analyzed using Structural Equation Modeling (SEM) in SPSS and SmartPLS software. The questionnaire comprised five main dimensions, with validated reliability and validity. Results indicated that external factors (scalability, data storage and processing, data analysis capabilities, flexibility, and reliability), perceived ease of use, and perceived usefulness significantly impact the acceptance of big data analytics. Furthermore, organizational external components such as data storage and processing, flexibility, and reliability lead to satisfaction and intention to utilize big data analytics in managing citizen relations by creating perceived usefulness and confirming user expectations. These findings corroborated the previous research and demonstrated that strategic attention to training, expert recruitment, hardware development, infrastructure enhancement, and information security can facilitate effective adoption of big data analytics, thereby creating opportunities for research development in legal, economic, and other fields.

Iranian knowledge-based firms operate under institutional and resource constraints that may limit their ability to translate digital capabilities into performance gains. This study aimed to explain the effect of digital leadership and organizational artificial intelligence (AI) usage intensity on the relative performance of Iranian knowledge-based firms, with an emphasis on the mediating role of business model innovation. Digital leadership was conceptualized as a second-order construct comprising innovative and supportive dimensions. Business model innovation was examined as a mechanism through which managerial and technological effects are transmitted to relative performance. In terms of purpose, this study was applied, and in terms of method, it followed a descriptive-survey design. Cross-sectional data were collected through using a structured questionnaire, and after data screening, 264 valid questionnaires were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicated that digital leadership had a positive and significant effect on organizational AI usage intensity, business model innovation, and relative performance. In addition, organizational AI usage intensity had a significant effect on business model innovation but no significant direct effect on relative performance, suggesting that its performance value may be realized primarily through business model innovation. Consistent with this interpretation, the business model innovation showed the strongest direct effect on relative performance and significantly mediated the main relationships. The findings suggested that managers of knowledge-based firms should integrate organizational AI usage with digital leadership and business model innovation, while policymakers should support capability-building initiatives that enable firms to convert AI use into firm-level value and performance gains.