https://hii.khu.ac.ir/ Human and Information Interaction en daily 1 Sat, 22 Nov 2025 00:00:00 +0330 Sat, 22 Nov 2025 00:00:00 +0330 https://hii.khu.ac.ir/article_4454.html Background and Objective: The present study was conducted with the aim of investigating the role of new technologies in improving data governance and enhancing information security in the country's Land and Property Registration Organization. The increasing importance of data in institutional decision-making and the need to harmonize with international standards double the need to address this issue.Research Method: This study was qualitative and conducted with a grounded theory approach. The statistical population included managers and experts of the Land and Property Registration Organization, and data were collected through semi-structured interviews with 12 people. The coding process was carried out in three stages: open, axial, and selective, and participant review and comparison with international research were used to validate the data.Findings: Data analysis showed that the main challenges include weak technological infrastructure, fragmentation of systems, legal gaps, security concerns, and cultural resistance of employees. In contrast, opportunities such as increasing transparency, reducing document forgery, promoting public trust, and improving service efficiency were identified. The findings were consistent with international studies, including the OECD’s emphasis on the link between technology and organizational culture, and the experiences of Sweden and Georgia in using blockchain in the registration system.Conclusion: The Iranian Document Registration Organization has taken steps such as digitization and piloting blockchain, but it still faces legal, security, and institutional shortcomings. Achieving data-driven governance and increasing security requires a combination of institutional reforms, technological investment, and promoting a data-driven culture. https://hii.khu.ac.ir/article_4457.html Objective:Supplier selection is one of the key challenges in construction projects, where access to accurate and timely data plays a vital role in improving this process. This study seeks to explore and analyze the sources and methods employed for data collection and management in the supplier selection process within the construction industry, aiming to identify existing challenges in current practices and assess the potential of modern technologies to optimize this process.Methodology:This is an applied research study that employed a survey methodology. The data collection instrument was a researcher-developed questionnaire consisting of both closed-ended and open-ended questions, primarily using a five-point Likert scale. The survey was distributed to 328 contractors who were certified by the Iranian Plan and Budget Organization.Findings:The research findings indicated that traditional information sources such as former customer feedback (87%) and project staff interviews (83%), which rely heavily on individual judgment, continue to be the most commonly used methods for supplier evaluation. The analysis of data collection methods showed that although electronic forms (63%) are becoming more prevalent, traditional methods like telephone calls (84%) and paper-based forms (52%) still dominate. A significant portion of respondents highlighted challenges such as low data accuracy (42%), untimely information (54%), distrust in supplier-provided data (59%), insufficient access to supplier performance records (44%), public databases (85%), and modern information tools (79%).Conclusion:The results of this study revealed that modern information tools, despite their potential impact on decision-making, are not widely used due to limited accessibility and the lack of adequate infrastructure. However, the utilization of digital forms and the growing attention to modern information sources, such as company profiles on online platforms, indicate a promising trend toward embracing digital information resources and modern data collection methods. The findings also emphasize the necessity of establishing independent mechanisms for data validation and enhancing access to historical information about suppliers. https://hii.khu.ac.ir/article_11694.html Purpose: This study aimed to assess artificial intelligence (AI) literacy among students at Kharazmi University and to examine differences across components, dimensions, educational levels, and fields of study. The study conceptualized AI literacy as a multidimensional competency encompassing not only knowledge and tool use but also self-efficacy, self-management, ethical awareness, and the ability to engage critically with AI systems. Method: The study employed an applied descriptive survey design. The final sample consisted of 335 undergraduates, master’s, and doctoral students selected through proportional stratified sampling with accessibility considerations. Data were collected using the Meta AI Literacy Scale (MAILS). Because the Persian version had been validated in previous Iranian studies, only qualitative face validity was examined in the present study through feedback on item clarity and comprehensibility. Reliability was assessed using Cronbach’s alpha. Data were analyzed in SPSS 27 using descriptive statistics, one-sample t-test, Friedman test, one-way ANOVA, Tukey post hoc test, and Kruskal–Wallis test. Findings: The mean overall AI literacy score was 5.83 out of 10 and was significantly above the theoretical midpoint (t = 11.02, p < .001). Seventy-three percent of students were classified at a desirable level. Use and application had the highest mean, whereas creation had the lowest. Significant differences were found among components and dimensions. No significant differences were observed across educational levels; however, AI literacy differed significantly across fields of study, and computer science and engineering students reported the highest scores. Conclusion: Although students’ perceived AI literacy was generally desirable, the gap between using ready-made tools and creating, evaluating, and solving problems with AI indicates a need for systematic, discipline-sensitive, and ethically grounded education. Future research should combine self-report measures with performance-based, longitudinal, and experimental assessments.