Digital Transformation and Administration Innovation

Digital technologies, including social networks, mobile technologies, cloud computing, and the Internet of Things, have compelled companies to undergo digital transformation in order to survive or maintain dominance in the market. Digital transformation refers to the integration of digital technologies into all areas of business; therefore, this integration leads to fundamental changes in performance. Since digital transformation requires digital fit, meaning the alignment of culture, strategy, and capabilities, this study seeks to examine the relationship among three key variables in the field of digital transformation: digital transformation strategy, digital culture, and digital capabilities. It also investigates the relationship of these three variables with organizational performance. This study adopts a deductive approach and is quantitative in nature. Data were collected using an electronic questionnaire. The statistical population of the study consisted of information technology companies in the country. The research hypotheses were tested using structural equation modeling with AMOS software. Based on the results, it can be stated that hardware companies had the lowest mean organizational performance, whereas software companies had the highest organizational performance. The results of the hypothesis testing indicated the positive effect of digital capability on organizational performance, digital culture, and digital strategy; the positive effect of digital culture on digital transformation strategy and organizational performance; and the positive effect of digital transformation strategy on organizational performance. Based on the findings, it can be concluded that the companies under study can improve their organizational performance by employing digital capabilities, enhancing digital culture, and adopting an appropriate digital transformation strategy.

The present study aimed to design and explain a model of national media convergence strategies on social networks, with a particular focus on technological and legal variables. In terms of purpose, this study was applied research, and in terms of implementation method, it employed a mixed-methods qualitative–quantitative design. In the qualitative phase, a grounded theory approach was adopted. Accordingly, in-depth interviews were conducted through purposive sampling with 18 media experts and managers, communication scholars, and cyberspace specialists. In the quantitative phase, a structured questionnaire was distributed among 117 experts and professionals in the fields of media and cyberspace to test the model derived from the qualitative findings. The data were analyzed using structural equation modeling (SEM) and SmartPLS software. The theoretical framework of the study was based on an integration of media convergence theory, network society theory, platform governance theory, and public sphere theory. The qualitative findings indicated that media convergence strategies are influenced by causal conditions, including emerging artificial intelligence technologies and platform infrastructures; contextual conditions, including legal and governance policies; and intervening conditions, including professional ethics and copyright regulations. The resulting outcome is the formation of an interactive news ecosystem. In the quantitative phase, hypothesis testing confirmed the significant effect of technological variables and platform governance indicators on convergence strategies (β = 0.881). Nevertheless, although filtering emerged as an important intervening variable in the qualitative analysis of the interviews, it did not exert a direct and statistically significant effect on media convergence strategies in the quantitative phase. Professional ethics challenges and legal monitoring mechanisms were also identified as key components affecting the formation of an interactive newsroom. To achieve successful convergence on social networks, national media organizations need to reconstruct their newsroom structures on the basis of artificial intelligence and develop dynamic legal and ethical frameworks. Such measures would enable them to move beyond technical and platform-related constraints and re-establish their authority as a trusted media reference.

This study aimed to identify, classify, and synthesize the principal barriers to e-government implementation and the strategies proposed to eliminate or reduce them. This systematic review examined Persian- and English-language studies published between 2000 and 2025. Relevant publications were identified through searches of Scopus, Web of Science, ScienceDirect, SpringerLink, Emerald Insight, ProQuest, IEEE Xplore, Google Scholar, the Scientific Information Database, Magiran, Noormags, and IranDoc. Search terms addressed e-government, digital government, implementation, adoption, barriers, challenges, readiness, and facilitating strategies. After duplicate removal, title and abstract screening, full-text assessment, and methodological appraisal, 42 studies met the eligibility criteria and were included in the qualitative synthesis. Data were extracted using a researcher-developed form covering study characteristics, implementation barriers, proposed solutions, and principal conclusions. The findings were analyzed through qualitative thematic synthesis and constant comparison. The synthesis showed that e-government implementation barriers formed an interconnected socio-technical and institutional system rather than a set of isolated problems. Technological and infrastructural deficiencies were the most recurrent barriers, followed by organizational and managerial weaknesses, limited human-resource competencies, incomplete legal and regulatory frameworks, cultural resistance, financial constraints, security and privacy concerns, political and institutional instability, digital inequality, and insufficient citizen participation. The findings further indicated that infrastructure investment alone was insufficient because weak leadership, fragmented governance, poor data quality, inadequate employee readiness, and low public trust frequently prevented technological resources from producing sustainable outcomes. The most consistently proposed solutions included interoperable digital infrastructure, administrative process redesign, accountable leadership, workforce development, comprehensive legislation, stronger cybersecurity and privacy protection, sustainable financing, citizen-centered service design, public awareness, and inclusive access. Successful e-government implementation requires a coordinated, context-sensitive strategy that integrates technological modernization with organizational reform, legal development, data governance, employee capacity building, public trust, and digital inclusion.

This study aimed to identify the principal drivers underlying the challenges of blockchain adoption in Iran’s supply chains and to propose a conceptual model for overcoming these barriers. Global statistics have reported a 92% failure rate for blockchain projects, while Iranian organizations have largely confined their activities in this area to the theoretical level without progressing toward operational implementation. The study employed a mixed-methods qualitative–quantitative design. The qualitative phase was based on thematic analysis, drawing on Braun and Clarke’s approach, and the required data were collected through interviews with 12 experts from the automotive industry. The qualitative findings were validated using the content validity ratio and Holsti’s coefficient of agreement. In the quantitative phase, structural equation modeling using AMOS and a one-sample t test using SPSS were employed to assess model fit and evaluate the status of the study indicators. The data analysis yielded 133 initial codes, which were subsequently condensed into 20 subthemes and ultimately into five overarching themes. These five overarching themes were organizational and cultural factors, technological readiness and infrastructure, legal, regulatory, and juridical barriers, financial and economic factors, and perceived benefits. The highest factor loading was associated with legal and juridical barriers, at 0.64, whereas the lowest factor loading was related to organizational and cultural factors, at 0.57. The results of the mean comparison test indicated that organizational and cultural factors, with a mean of 2.125 and a negative t statistic of −3.25, were significantly below the moderate level. The model-fit indices—including RMSEA = 0.44, CFI = 0.94, GFI = 0.98, and CMIN/DF = 2.59—were all within the acceptable range. The proposed model demonstrates that blockchain adoption in supply chains results from the synergy among four components: technological readiness, legal clarity, economic stability, and a transformation in organizational and cultural perspectives. The significant gap between perceived benefits, with a value of 3.746, and technological readiness (3.24) and organizational and cultural factors (2.125) indicates that organizations have a strong understanding of blockchain’s benefits; however, during implementation, they encounter structural and cultural barriers that diminish their operational capacity.

In parliamentary settings, the quality of policymaking depends on the extent to which accumulated knowledge, evidence, and experience are accessible and effectively utilized. Despite the importance of this issue, there is no comprehensive understanding of the knowledge management support policies and practices employed in the policymaking process of the Islamic Consultative Assembly of Iran. Accordingly, the present study aimed to identify the policies and practices through which knowledge management supports policymaking in the Islamic Consultative Assembly of Iran. This applied study adopted a qualitative approach and was conducted using Glaserian grounded theory. Data were collected through 27 semi-structured interviews with current and former members of parliament, experts from the Islamic Parliament Research Center, parliamentary committee experts, parliamentary staff, and university faculty members. The data were analyzed through constant comparison, open coding, and theoretical coding. The findings indicated that knowledge management support for policymaking requires the establishment of policies and practices in the areas of knowledge governance, policy memory, enhancement of knowledge trustworthiness, knowledge translation and packaging, expert networking, integration of knowledge resources, and institutional learning. The results suggest that the principal challenge facing the parliament is not a shortage of information, but rather the absence of institutional mechanisms for transforming fragmented knowledge into knowledge that can be used in decision-making.

This study aimed to design and validate a digital transformation–based smart marketing strategy model for the Ministry of Agriculture Jihad by identifying its key components, determining their causal and hierarchical relationships, and evaluating the empirical adequacy of the proposed model. An applied exploratory sequential mixed-methods design was employed. In the qualitative phase, a seven-stage meta-synthesis was conducted on Persian and international studies published between 1991 and 2026, resulting in the selection of 33 high-quality studies from 308 initially retrieved records. Extracted codes were synthesized through qualitative content analysis and prioritized using Shannon entropy. In the second phase, 25 experts in agricultural management, marketing, technology, and digital transformation participated in interpretive structural modeling to determine the hierarchical relationships among the identified components. In the quantitative phase, a researcher-developed questionnaire was electronically administered to managers and experts of the Ministry and provincial Agricultural Jihad organizations. A total of 668 valid responses were analyzed using partial least squares structural equation modeling in SmartPLS. Reliability, convergent validity, discriminant validity, multicollinearity, effect size, explanatory power, and predictive relevance were assessed. The meta-synthesis identified 37 dimensions classified into 13 principal components. Shannon entropy showed that entry into online markets and digital-marketing education had the highest importance coefficients, followed by support for agricultural startups. Interpretive structural modeling placed supportive policies and regulations and digital and technological infrastructure at the foundational level, while sustainability and smart agriculture emerged as the most dependent final outcome. Cronbach’s alpha and composite reliability coefficients exceeded 0.70, average variance extracted values were above 0.50, and both Fornell–Larcker and HTMT criteria confirmed discriminant validity. All variance inflation factors were below 3. Positive Q² coefficients confirmed predictive relevance, and the structural model explained approximately 65% of the variance in the endogenous constructs. Smart marketing in the Ministry of Agriculture Jihad should be implemented as an integrated transformation process beginning with supportive regulation and digital infrastructure, progressing through analytical capability, farmer empowerment, knowledge management, supply-chain integration, innovation, and market development, and ultimately leading to customer value, brand development, and sustainable smart agriculture.

This study examines the effect of artificial intelligence on the performance of digital marketing strategies, with a focus on key components such as sales volume, profitability, competitiveness, customer loyalty, and risk management. The research method is applied and descriptive-survey in nature, and the data were collected through a 35-item questionnaire based on a Likert scale from a statistical population consisting of 50 companies active on the Tehran Stock Exchange. The Analytic Hierarchy Process was used to prioritize the factors, and the Point Estimation Method was employed to model digital marketing risks. The research instrument was designed after its content validity was confirmed by experts and its reliability was found to be acceptable, with Cronbach’s alpha values above 0.70. The findings indicate that the use of artificial intelligence has a significant effect on improving key digital marketing indicators, including an increase in conversion rate, a reduction in customer acquisition cost, and an improvement in return on investment. In addition, artificial intelligence improved performance across different channels, especially email marketing, by increasing email open and click-through rates, and social media marketing, by increasing user engagement. The data analysis showed that artificial intelligence, through optimizing audience targeting and providing personalized content, has the greatest effect on customer loyalty and increased sales. However, its effect on content marketing and SEO was more relative. Overall, the results indicate that artificial intelligence can substantially enhance the performance of digital marketing strategies in the dimensions of sales, profitability, competitiveness, and customer loyalty through complex data analysis and intelligent decision-making.

This study aimed to design and structurally explain a data-driven digital transformation deployment model for Iranian public organizations using expert judgment and interpretive structural modeling. This applied and exploratory study was conducted using a mixed-methods design with an interpretive structural modeling approach. The study population consisted of senior managers, digital transformation specialists, data governance experts, information technology managers, public administration scholars, and consultants familiar with digital transformation in Iranian public organizations. A purposive sample of 21 experts from Tehran participated in the study. Data were collected through literature review, semi-structured expert interviews, and an ISM questionnaire based on pairwise comparison of the finalized components. The extracted components were refined through expert review, and the contextual relationships among them were determined using the structural self-interaction matrix. The initial and final reachability matrices were then developed, transitivity was applied, hierarchical levels were identified, and MICMAC analysis was used to classify the components according to driving power and dependence power. The ISM results revealed a seven-level hierarchical model. Digital leadership and strategic commitment were positioned at the deepest level and had the highest driving power, indicating their foundational role in the deployment process. Data governance and regulatory alignment, together with inter-organizational coordination and ecosystem collaboration, formed the next driving layer. Integrated digital infrastructure and interoperability and human resource digital competence were identified as key enabling components. Data quality, security, and privacy management emerged as a central linkage variable. Process redesign and organizational agility and evidence-based decision-making culture were placed at the intermediate transformation level. Citizen-centric digital service design and performance monitoring and accountability were identified as highly dependent outcome components. The findings indicate that data-driven digital transformation in Iranian public organizations is a systemic, hierarchical, and interdependent process that should begin with leadership commitment, governance alignment, coordination, infrastructure development, and human capability building before progressing toward service redesign, evidence-based decision-making, and accountability.