محمود البرزی

Purpose: The current research proposes a model for elite’s recruitment to Iranian educational organizations based on the ethics and specific cultural contexts of Iranian society. Methodology: This is a combination of two quantitative and qualitative approaches. This is also an applied descriptive-exploratory paper. The primary pattern of research is extracted from the theoretical background and consists of four dimensions (competencies definition, effective factors analysis, competitor’s analysis, and recruitment tools). The final model was extracted and verified through interviewing the experts and the coding process. The cultural components of each dimension are also extracted and clustered according to the experimental data and according to the experts in a 4-step Delphi process. Finally, the current and desired status of the elite recruitment system in educational organizations has been studied using the gap analysis method. Ten companies were selected as sample for the selection of the community by cluster sampling and the questionnaires were distributed among managers and their human resources experts. The GAMS software is used to analyze the data and determine the effectiveness of the coding model. Effectiveness in the current paper is to determine the optimal value of the dimensions. Findings: Based on the findings, the optimum model dimensions (competency definition, effective factors analysis, competitor’s analysis, and recruitment tools) have increased in optimal condition compared to the current situation. One of the most important reasons for this is the existence of cultural barriers to the recruitment process, which impedes the effectiveness of the process and chooses the right and proper. Removing these barriers is one of the essential requirements for optimizing the proposed model. Conclusion: The results showed that there is a significant difference between the current situation and the desired level, which necessitates serious attention to the factors affecting the elite’s recruitment and the development of appropriate mechanisms for their recruitment in educational organizations.

The new paradigm of good governance has an emphasis on international transparency, and this in this study takes into account the actual impacts of organizational intelligence in policymaker organizations. This study primarily aims to design an intelligent model of transparent governance in policymaker organizations with the approach of good governance. The study is a fundamental research in terms of objectives. In the qualitative section, data collection was done through Delphi interview questions, and the statistical population was senior managers, specialists, and policymakers with targeted sampling. In the quantitative part, the population was mid-level managers and organizational intelligence experts. The random sampling method was via the Cochran formula, and 432 individuals were selected. Data gathering tools were interviews and researcher-made survey questionnaires. The content and face validity and Cronbach alpha reliability were employed. The data were analyzed by descriptive and inferential statistics, including factor analysis, regression, and structural equations. Model fit and Friedman test were employed. The findings indicated three dimensions in the proposed model design, including transparency, knowledge creation, and knowledge translation, along with six components, five subcomponents, and 23 indicators. The results suggested that there is a relatively strong and appropriate relationship between organizational intelligence and organizational transparency. Furthermore, sense-making had the highest correlation with organizational transparency. Also, the strongest predictor was the sense-making variable.

Due to the global competitive environment in the mobile app market, traditional problem-solving methods in examining the problem of accepting stores offering these digital products have ignored the important role of human factors and therefore this weakness necessitates research on relevant policies by governing bodies from another perspective based on a soft systems thinking approach. This problem has structural complexities, and many stakeholders have different views on it. Therefore, using the methodological approach of soft systems can be a solution for this purpose. This research was applied, exploratory, and qualitative, and the statistical population consisted of 10 experts in the telecom industry, whose selection was made by purposeful judgment. Data collection tools were conducted in in-depth semi-structured interviews. The research findings lead to a conceptual and structured model for improving change, learning and helping to improve macro policies in this area.

The development of new technologies has confronted the entire domain of science and industry with issues of big data's scalability as well as its integration with the purpose of forecasting analytics in its life cycle. In predictive analytics, the forecast of near-future and recent past - or in other words, the now-casting - is the continuous study of real-time events and constantly updated where it considers eventuality. So, it is necessary to consider the highly data-driven technologies and to use new methods of analysis, like machine learning and visualization tools, with the ability of interaction and connection to different data resources with varieties of data regarding the type of big data aimed at reducing the risks of policy-making institution’s investment in the field of IT. The main scientific contribution of this article is presenting a new approach of policy-making for the now-casting of economic indicators in order to improve the performance of forecasting through the combination of deep nets and deep learning methods in the data and features representation. In this regard, a net under the title of P-V-L Deep: Predictive Variational Auto Encoders - Long Short-term Memory Deep Neural Network was designed in which the architecture of variational auto-encoder was used for unsupervised learning, data representation, and data reconstruction; moreover, long short-term memory was adopted in order to evaluate now-casting performance of deep nets in time-series of macro-econometric variations. Represented and reconstructed data in the generative network of variational auto-encoder to determine the performance of long-short-term memory in the forecasting of the economic indicators were compared to principal data of the net. The findings of the research argue that reconstructed data which are derived from variational auto-encoder embody shorter training time and outperform of prediction in long short-term memory compared to principal data.

This study was designed to explain the Blockchain acceptance indices in Iran's financial markets aimed at identifying different angles for the implementation of Blockchains. The Blockchain acceptance indices were extracted in 4 levels, 12 variables, and 53 indices of related research literature in the field of e-commerce and mobile banking. To validate the research indicators, the Fuzzy Delphi technique was used to refine the indices in addition to the documentary study. The survey was conducted in three stages and the results of each stage were refined. Based on data analysis, 39 indicators were confirmed. The results of this study can provide useful insights for researchers and policymakers of Iran’s financial markets to understand the prerequisites and effects of the Blockchain implementation on financial markets, and thereby, they would be able to change business models to take advantage of Blockchain capabilities in the infrastructure of Iran’s financial markets by considering different aspects.

The ability of now-casting and eventuality is the most crucial and vital achievement of big data analytics in the area of policy-making. To recognize the trends and to render a real image of the current condition and alarming immediate indicators, the significance and the specific positions of big data in policy-making are undeniable. Moreover, the requirement for policy-making institutions to produce a structured model based on big data analytics for now-casting and eventuality of predictive policies is growing rapidly. The literature review demonstrates that a comprehensive model to assist policy-making institutions by providing all components and indicators in now-casting of predictive policies based on big data analytics is not devised yet. The presentation of the model is the main finding of this research. This research aims to provide a comprehensive model of now-casting and eventuality of predictive policies based on big data analytics for policy-making institutions. The research findings indicate that the dimensions of the comprehensive model include: the alignment of now-casting strategies and the big data analytics’ architecture, now-casting ecosystem, now-casting data resources, now-casting analytics, now-casting model and now-casting skill. The results of using the model were analyzed and the recommendations were presented.