rahmatollah gholipour souteh

In the current digital era, the immense challenges posed by technological advancements have driven significant developments, including the emergence of digital banking policies. For banks to survive and maintain their competitive edge among both new entrants and established competitors, it is essential to correctly implement digital banking strategies. This requires not only appropriate structural changes but also careful planning, the adoption of new business methods, and the use of innovative and technological tools, such as big data, to achieve strategic goals. On a broader scale, these efforts contribute to the realization of a smart economy. This research addresses the gap in existing literature regarding the lack of a digital banking policy implementation model, particularly within Iranian banks. It seeks to develop an optimal model for implementing such policies, with a special focus on the dimensions of policy implementation specific to digital banking. The research emphasizes the pivotal role of big data technology, which has received limited attention but has a significant impact on achieving the goals and ensuring the optimal implementation of digital banking.

This research falls within the realm of qualitative and postmodern studies, adopting an inductive approach. The research strategy employed is "Grounded Theory," specifically the emerging or classical Glaserian approach. Given that developing a foundational theory necessitates the collection of in-depth interview and textual data, the study aimed to identify the concepts, categories, and components of a digital banking implementation model leveraging big data technology. To this end, semi-structured interviews were conducted with experts in the fields of banking and financial technology. The study involved 15 accessible experts from the banking industry, selected through purposive judgment sampling. Interviews continued until theoretical saturation was reached, ensuring that the data collected was comprehensive and insightful. The research data was analyzed through a process of open, axial, and selective coding, which allowed for the extraction and categorization of key concepts. Ultimately, these efforts led to the emergence of a robust research model.

The resulting model identifies 15 main themes, including Big Data Management, Data Governance (encompassing Data Collection, Data Refinement, Data Design and Modeling, Data Security), Data Regulation, Various Sources of Data Acquisition, Trust in Big Data Analysis, the Banking System Ecosystem, Organizational Agility, Big Data Analysis Tools, Scenario Development (practical application of data), Executive Structure, Improvement of Banks' Business, Customer Orientation, Transparency, Technological Infrastructure, and Dominant Culture. These themes were organized into six dimensions based on Glaser's 6C model, with an overarching or central category dimension forming the core of the final research model. According to this model, Big Data Management serves as the main axis around which 13 other themes revolve, acting as the model's center of gravity. This relationship is explained within the category of linkages, highlighting how these components interconnect.

The findings underscore a significant gap in the attention and application of big data technology within banks. Despite the availability of rich information and data resources in state banks, this valuable capital remains underutilized. Proper utilization of big data could create a necessary competitive advantage, enabling banks to survive and thrive amidst competition from both new and old players in the banking sector. Moreover, leveraging big data can enhance and improve customer-oriented platforms, a primary goal of digital banking, thereby facilitating better implementation of digital banking policies. The model developed through this research illustrates the integration of technological, organizational, and innovative cultural factors within state banks, all aimed at effectively utilizing big data. To optimize digital banking implementation, these factors must be prioritized within the structures of the country's state banks. The results demonstrate that Big Data Management, along with associated governance, regulatory practices, and infrastructural considerations, are crucial categories that must be meticulously observed and integrated into the operational frameworks of state banks to fully harness the potential of big data.

One of the requirements for the development of societies and organizations is innovation, change in policies and policies, and the use of creative models in decision making. To achieve this goal, organizations must explore opportunities and identify new solutions and possibilities, and apply innovation in decisions. How to identify these opportunities and use them has always been a concern of policy makers and efforts have been made in this regard, but a comprehensive model in our country that can lead to entrepreneurial decision-making in a government organization has not been presented so far. Therefore, we sought to identify the dimensions, factors and elements affecting the policy entrepreneurship in the thermal power generation production policy making process and to present a suitable model for policy entrepreneurship for this field. To achieve this goal, a fundamental research based on a combined exploratory method was conducted. For this purpose, in the quality section, interviews were conducted with 18 experts in the field of electricity generation who were selected through the snowball sampling method. In the interview process, 244 open codes, 84 selective themes and 16 main themes were identified and the policy entrepreneurship model was presented in three dimensions: contextual, structural and behavioral. In the quantitative part, using a questionnaire and after assessing its content validity, based on the results of one-sample t-test, the effect of the components identified in the qualitative part on entrepreneurship was confirmed and the components were ranked based on the Friedman test.

The dependence of research on the state budget on public universities has caused the government's economic problems in recent years to adversely affect the status of academic research. Therefore, managers of public universities should prioritize the use of other research funding methods, including attracting funds from the private and industrial sectors and taking a strategic approach in this regard. The purpose of this study was to design and explain a strategic decision-making model for financing research projects to Improve the research projects of the University of Tehran.

The research method of this article is qualitative and qualitative data were collected through semi-structured interviews with experts and study of articles and texts. In order to extract the main metrics and structures of the model, the data were analyzed using the theme analysis method. Thirty-four interviewees were selected using the snowball method.

Based on the results of the research, 10 main structures consisting of 33 measures constitute the model components.

Components of strategic decision-making model for financing research projects of Tehran University include: internal and external university communication, idea-to-product chain, Requirements before implementing, review and selection of strategies, structural reform strategy, financial strategy, Collaborative Strategy, Research Results Optimization Strategy, HR Strategy and Evaluation.

Knowledge today is a strategic factor in achieving a sustainable competitive advantage for power in an organization, but merely having knowledge only as an asset does not create value. The sharing and dissemination of knowledge with other members within and outside the organization will provide a space for the creation of new and valuable knowledge assets.

The aim of this study was to identify and prioritize factors affecting knowledge sharing and dissemination in Iranchr('39')s ministry of youth and sports.

This study employed the qualitative research method. The study population included all managers, advisers, and experts of the ministry and faculty members informed in the field of knowledge management within the ministry. In the first stage, purposive sampling based on theoretical l approach and 19 semi-structured interviews were conducted. Using coding method seven main factors were detected by MAXQDA software. The next step was to prioritize and interpretive structural modeling and the opinion of eight experts and academic professors was used.

After analyzing the data, factors affecting knowledge sharing were put into 5 levels. Leadership factors were placed at the bottom of the model and the individual factors placed at the highest level.

The insight that this model offers can help in the success and effectiveness of KM activities in this ministry.