Digital Transformation and Administration Innovation

The present study was conducted with the aim of proposing a model of social responsibility factors influencing the success of insurance companies, based on the postmodernist perspective. The research method was qualitative and grounded in grounded theory. Data were collected through semi-structured interviews, and the analysis was carried out using the Strauss and Corbin method and the paradigmatic model. The sampling method was theoretical sampling, implemented through purposive (judgmental) techniques. In accordance with the systematic grounded theory framework, experts and experienced managers in insurance companies were selected as the statistical population. The results obtained from the interview data, through open, axial, and selective coding processes, led to the development of a model for the social responsibility factors influencing the success of insurance companies based on the postmodernist view, grounded in theory construction. Ultimately, the model of social responsibility factors grounded in the postmodernist perspective was identified. The findings of this study indicate that, in order to develop education-based social responsibility, managers must pay close attention to all the categories and subcategories identified in this research and possess sufficient and accurate knowledge about each. Moreover, the study highlights the necessity of considering the postmodernist perspective in developing educational strategies to ensure the success of insurance companies.

Supply chain resilience refers to organizations’ ability to maintain stability and respond effectively to crises and sudden disruptions. As supply chains become increasingly complex, artificial intelligence (AI) has emerged as a highly efficient analytical tool. Through data analysis, pattern prediction, and risk identification, AI enables companies to respond promptly and optimally to critical conditions. This study systematically reviews and performs a bibliometric analysis of 238 review articles published between 2000 and 2024, examining the growth trends and thematic structure of research in the field of "artificial intelligence and supply chain resilience." Findings indicate that over 65% of the articles were published in the past five years (2019–2023), with the primary research focus centered on "artificial intelligence and machine learning." Keyword co-occurrence network analysis using scientometric indices reveals that the phrase “Internet of Things” holds the highest centrality score and is positioned at the center of the network. On average, each frequently used keyword is linked to more than 15 other concepts, with the network’s average degree estimated between 8 and 12. Thematic clustering of the research corpus reveals the formation of seven prominent research clusters, including "smart agriculture," "cold chain logistics," and "energy management." Among these, the cluster of "artificial intelligence and machine learning" has the highest share, indicating a strong research focus in recent years. The publication bias test yielded a Z-value of 1.614 (less than the critical value of 2.39 at the 0.05 significance level), indicating no significant bias in the data and thus affirming the validity of the current analysis. Moreover, based on practical study results, the implementation of AI algorithms has led to an average reduction of approximately 18% in operational supply chain costs and an increase of up to 25% in recovery speed following disruptions. These numerical and structural findings demonstrate that artificial intelligence—especially in interaction with the Internet of Things and machine learning—has played a pivotal and catalytic role in research related to supply chain resilience and is expected to continue to do so in the future. The clustered studies in the domain of supply chain resilience using artificial intelligence identified seven main clusters with significant differences in participation and growth trends. The largest cluster, “artificial intelligence and machine learning,” accounts for approximately 35% of all articles and has experienced a remarkable growth surge, particularly since 2019.

One of the most important concerns for banks in achieving maximum efficiency is customer loyalty. On the other hand, social marketing, as the newest marketing philosophy emphasizing individual and societal benefits alongside the interests of banks, represents a valuable approach for motivating customers and altering their attitudes toward the bank. The aim of this study is to develop a model for enhancing customer loyalty in the banking industry using social marketing. This study is qualitative in method and developmental in purpose. It is based on in-depth interviews with experts in the banking industry and branch managers of banks in Tehran. A purposive sampling method was employed. Data collection was carried out through library research and in-depth interviews with 14 experts. For data analysis, after reviewing the existing literature and prior research, the MAXQDA software and coding methodology were used to analyze the interview data. The findings of the study indicate that the dimensions of social marketing are related to customer loyalty. Through selective coding results, it was shown that trust, commitment, price, tools and customer expectations, causal conditions, employee behavior, staff training, facilities and technology, contextual conditions, bank characteristics, society and customer, intervening conditions, loyalty, satisfaction, and the sense of value represent core conditions, while cultural development, facilitative regulations, government support, and advertising constitute the strategic conditions that form the proposed model.

Rapid urbanization and the excessive growth of metropolises have led to numerous consequences, including deficiencies in infrastructure and urban services, social disorders and disarray, housing shortages, and residence in vulnerable and deteriorated urban fabrics. These developments have also placed the environment at risk. Social and economic duality—and consequently, spatial duality—are outcomes of the interactive effects of various factors, which have become evident in Tehran due to rapid urban growth and inefficient urban management. In this context, the management of District 7 of Tehran Municipality, as the spatial focus of this study, has not operated in an integrated or coordinated manner. This has resulted in stark differences in the distribution of land uses between the eastern and western areas of District 7. The aim of this study is to identify the influential factors in the role of urban management in shaping spatial duality within District 7 of Tehran Municipality. This research is applied in purpose, analytical in nature, and employs a mixed-methods approach (qualitative-quantitative). The statistical population consists of 75 urban planning experts. A non-probability stratified sampling method was employed. Research data were collected through library methods, and the instrument for data collection was a structured questionnaire with closed-ended questions. The TOPSIS model was used for data analysis. The results indicate that the integration factor, with a score of 0.967, ranks highest among the evaluated criteria. The spatial factor, with a score of 0.914, holds second place, followed by the security factor in third place. Other factors such as employment, participation, income, infrastructure, and services ranked lower accordingly.

The information revolution and the emergence of cyberspace have transformed the marketing landscape and the roles of traditional stakeholders, creating new opportunities for business owners. Easy access to information, the ability to quickly compare products, and many other features of the internet have significantly enhanced consumer power. Companies must better understand consumer buying behavior and attempt to incorporate these insights into their marketing strategies in order to remain competitive. This study evaluates the impact of various advertising methods and online social networks on the purchasing behavior of Iranian consumers. The research adopts a quantitative methodology. Initially, using Chaffey's model, the conceptual framework of the study was developed, and a questionnaire consisting of 37 items was designed, with each item assessing the influence of one advertising method on a specific stage of the purchasing process. Data analysis was conducted using SPSS software, applying the non-parametric binomial test and Friedman test. The study spanned one year and was completed in June 2023. The results indicated that only websites and search engines affect the stages of the purchasing process, whereas other forms of advertising do not have a significant impact.

This study aims to investigate the relationship between users’ trust in media, digital infrastructure, demographic characteristics, and media consumption patterns among Iranian users over the age of 15. A quantitative and correlational research design was used to examine the relationships between multiple variables simultaneously. The statistical population included Iranian users aged 15 and above with access to cyberspace. Using Cochran’s formula, a sample of 384 participants was selected through multi-stage cluster sampling. Data were collected via a researcher-made questionnaire based on a five-point Likert scale. The validity of the instrument was confirmed by experts, and its reliability was verified using Cronbach's alpha (α = 0.87). Variables measured included demographic characteristics (age, gender, education, residence), media consumption motives (information-seeking, entertainment, social interaction), trust in media (domestic and foreign), frequency of media use (official, domestic social, foreign social), and digital infrastructure (internet access, smart devices). The findings revealed a significant positive correlation between trust in domestic media and its consumption (Gamma = 0.42, p < 0.001). A one-way ANOVA showed that higher levels of digital infrastructure were associated with greater virtual media consumption (F = 6.52, p = 0.002). Chi-square tests indicated significant differences in media consumption based on age (X² = 18.73, p = 0.001), gender (X² = 5.54, p = 0.003), and education (X² = 21.65, p < 0.001). Multiple regression analysis demonstrated that all three motives—information (β = 0.34), social interaction (β = 0.26), and entertainment (β = 0.18)—significantly predicted the type of media used by respondents (p < 0.005 for all). The study confirms that trust in media, digital access, demographic factors, and motivational drivers all play critical roles in shaping users' media consumption behaviors in the digital landscape of Iran.

The objective of this study is to propose a comparative model of external strategic intelligence in public and private organizations located in Kerman City. This research adopted a mixed-methods approach with a sequential exploratory design. In the qualitative phase, using grounded theory and conducting 12 semi-structured interviews with experts, the initial components and indicators of the conceptual model were identified. In the quantitative phase, exploratory factor analysis was applied to data collected from 380 questionnaires, resulting in the identification of six key factors: external environmental scanning, external information gathering, data analysis and processing, strategic exploitation, external interactions and influence, and ethical and security frameworks. These six factors collectively explained 78.4% of the total variance and demonstrated acceptable reliability (Cronbach’s alpha > 0.8). The findings indicate that effective implementation of strategic intelligence in less-developed regions requires a context-sensitive, data-driven, and flexible model capable of adapting to structural differences between the public and private sectors. The proposed model can serve as a foundation for enhancing strategic decision-making and improving organizational readiness in response to environmental changes.

The objective of the present study was to propose a paradigmatic model of financial technology (FinTech) in small and medium-sized enterprises (SMEs). This research employed a qualitative method and was applied in nature. The statistical population included scholarly articles and documentation as well as experts such as managers in financial and banking businesses, university faculty members, and information technology specialists. Participants were selected purposefully using the snowball sampling method. Accordingly, interviews were conducted with 17 experts. The research instrument consisted of semi-structured interviews, the validity of which was evaluated using face validity, and reliability was assessed through inter-coder agreement. Thematic analysis was employed to analyze the data. Findings revealed that the paradigmatic model comprises: Causal factors including marketing mix, strategic thinking, team characteristics, communication, and interactions; FinTech outcomes including efficiency and effectiveness; Intervening factors including investment risks, trust-building, and overcoming barriers to implementation; Strategies including attracting investors, securing financing, and enhancing financial and technical knowledge; FinTech consequences including achieving personal success, creating value for society, and generating value for businesses.