International Journal of Knowledge Processing Studies
Volume:4 Issue: 4, Autumn 2024

The aim of the current research was to develop a model of professional accountability for customs employees to facilitate the implementation of the Iranian-Islamic strategy of progress. The current research is a qualitative study with an applied-developmental purpose, an exploratory nature, and a specific data collection method. In order to involve scientific experts (university professors) and organizational experts (customs experts and managers of the Islamic Republic of Iran) as participants in the research, purposeful sampling was conducted until theoretical data saturation was achieved. This was accomplished through 19 interviews. Later, repetition was observed in the received information, and the adequacy of the sample was confirmed. The data collection method was fieldwork, and its tool was a semi-structured interview. The method of data analysis was Braun and Clarck's (2006) six-step thematic analysis. According to the research results, the antecedents of professional accountability among customs employees consist of four components: "participatory management," "delegation of authority," "knowledge of public rights," and "awareness of the administrative landscape." Professional accountability is determined in four dimensions, including individual accountability with seven components, occupational accountability with four components, team accountability with five components, and organizational accountability with seven components. Finally, the outcomes of professional accountability in advancing the Iranian-Islamic strategy of progress encompass "providing public benefits," "earning trust and confidence of citizens," "enhancing the effectiveness of public services," "improving organizational governance," "sustaining development strategies," and "creating social value."

The research aims to design a scalable agile data management framework leading to the success in data-oriented decision-making and ultimately smartness and data intelligence using agile manifesto and scalability. The main paradigm is data-driven, lean thinking and agile manifesto. As an applied and developmental research, argument, analogy and discovery are used to draw conclusions. In this research Design Science Research Methodology (DSRM) is applied. Thus, firstly, the problem was defined clearly via literature review. Secondly, objectives of solution were specified specifically. Thirdly, the artifact (SADMF) was designed and developed via iterations through SCRUM. Fourthly, validity and reliability were evaluated by SMEs and finally the results were formalized and academics and practitioners were invited to consider reflective thinking. There are various reference models for the concepts of agile, data management, Information System and Software Engineering, but few of them practically embedded agile practices in a scalable management system, despite the remarkable importance of agility in the optimal data management of social-physical-cyber systems today. So, there is an essential need to design a comprehensive model. Scaled agile data management requires leanness and agility. This framework has three levels including portfolio, program and data lab and four main elements including people and organization, agile process and practice, technology and knowledge areas. Finally, considering the selected references, a Scalable Agile Data Management Framework (SADMF) from combination of agile best practices along with the main elements of a data management system was designed and offered.

The research aims to rank the critical success factors of knowledge management within the Central Bank of the Islamic Republic of Iran. This research employed a mixed-methods approach, combining qualitative and quantitative techniques. In the qualitative phase, the metasynthesis method was utilized, following the seven-step protocol established by Sandelowski and Barroso, using NVivo software. The quantitative phase was based on a fuzzy hierarchical approach implemented in Excel software. A checklist was developed which was distributed to 20 experts in the fields of information science and epistemology, as well as professionals with experience in knowledge management. Selected bank managers were purposefully included in the study and were asked to determine the weight of the indicators by selecting from nine options in a pairwise comparison format The findings reveal that leadership, with a weight of 0.213, ranks first; human resources, with a weight of 0.173, ranks second; and education, with a weight of 0.157, ranks third. Organizational structure follows in fourth place with a weight of 135.4, while infrastructure technology ranks fifth with a weight of 0.124. Evaluation and measurement of knowledge is ranked sixth with a weight of 0.103, and organizational culture ranks seventh with a weight of 0.095. Considering the complexities of the central bank and its sensitive role in regulating and monitoring the country's financial system, attention to these critical success factors of knowledge management not only enhances internal productivity but also directly influences the development of financial policies and the improvement of decision-making capabilities at the macro level.

The knowledge model of human resources accounting in the Ministry of Education with the information valuation approach is a management framework that focuses on identifying, measuring, and reporting the value of human resources, especially the information and knowledge of employees. This research was conducted with the aim of designing the human resources accounting model with the foundation data approach in the Iraqi Ministry of Education. This research was conducted with a qualitative-inductive approach and the Strauss-Corbin grounded theory method. The research tool (data collection) was a semi-structured interview. This research, using the grounded theory method, analyzed the data obtained from interviews conducted with 17 elites and experts in the field of human and financial resources in the country's defense organizations during three stages of open, central coding, and selective analysis. 23 general categories form a paradigm model where these factors include causal conditions (new transformations in value theory; analysis of intra-system challenges of human resource accounting; deficiencies of information systems in the field of human resource management accounting, extra-systemic challenges), central phenomenon (model) human resource accounting), underlying conditions (needs of the organization and management; surrounding environment; technical and technological conditions; internal state of education), intervening conditions (organizational limitations; lack of costing and valuation; cognitive challenges; information), strategies (resources) Human transformation; integration of information; development of an executive plan; long-term; short-term audit; reduction of related costs; improvement of human resources planning.

Improving intellectual capital in databases involves enhancing the productivity of information and knowledge stored in these systems to boost organizational and competitive value. The intellectual capital development model encompasses processes such as collecting, organizing, analyzing, and utilizing data to create new and value-added knowledge. This article aims to present an interpretive structural model for enhancing intellectual capital within the databases of public organizations. The research was conducted using a mixed-method approach, including the metasynthesis method and interpretive structural modeling. To achieve this objective, the fundamental categories for promoting intellectual capital were identified and validated. By establishing the causal relationships among these elements, the final model was developed. It is an applied-developmental research in terms of purpose and a survey-cross-sectional research in terms of data collection method. The statistical population for this study comprised experienced human resource managers at Tehran Municipality. The snowball sampling method was utilized, and the sampling process continued until theoretical saturation was achieved, with 13 managers from Tehran Municipality ultimately participating in the study. The metasynthesis method was used to identify the underlying categories of intellectual capital. Additionally, a structural-interpretive model for intellectual capital promotion was presented. Data analysis was conducted using MaxQda and MicMac software. The results indicated that "organizational leadership, "organizational culture, and "organizational structure" influence the "expertise and skills of human resources, "media interactions" and "social customization. factors also affect "cooperation and participation, of "management of the relationship with the master of reference of "social excellence.

The research aims to provide a management accounting model in the digital age with a data-oriented approach in Vensim system. It was applied research and qualitative with the approach of grounded theory. The methodological approach was followed by using different methods of data collection, such as the method of library study and review of sources and specialized texts, as well as semi-structured interviews. Based on targeted sampling, 20 managers and shareholders of the stock exchange and financial management experts were interviewed in 2023. The conducted interviews were coded in ATLAS.TI software. In order to confirm the results obtained based on three classifications, the data were evaluated and qualitatively analyzed.  Based on the system dynamics method, mathematical modeling of the codes obtained from the qualitative analysis has been done. The analysis is based on simulated data and improved model in Vensim software. The findings of the research were identified separately in five categories: causal, background, intervention, strategy and consequences. A model was identified in 6 categories, 16 core codes based on 109 coders. Causal conditions (advancement of science and technology, changes in business conditions and management conditions), strategies (education, infrastructure, targeting of financial reporting and policy making), consequences (optimization of decision making, business prosperity and economic productivity), Background conditions (economic and political environment and accounting situation). and intervening conditions (negligence and neglect, cultural and social conditions, access to resources and environmental conditions) are designed. In the digital era, access to large and high-quality data has enabled more accurate analyses.

The purpose is to provide an exploratory data-driven model of employees' behavioral competence with the approach of information and knowledge of the work environment. This research is applied and exploratory, which was conducted qualitatively and quantitatively (mixed) using the methods of data foundation theory and exploratory factor analysis. The research tool (data collection) was a semi-structured interview. Using the grounded theory method, the data obtained from the interviews conducted with 14 employees of the Iraqi Ministry of Islamic Guidance (Baghdad, Diyala, Karbala) were analyzed during three stages of open, central and selective coding. 19 general categories in the form of a paradigm model that these factors include causal conditions (analysis of the environment; management challenge; analysis of employees' behavioral competence), central phenomenon (behavioral competence of employees), underlying conditions (analysis of organizational culture; application of law and policy), intervening conditions (determining Competencies; dimensions of leadership competence; the process of building the behavioral competency model of employees) and strategies (interpersonal skills; communication skills; determining behavioral criteria; evaluation and development of the model; implementation and monitoring) and outcomes (intra-organizational success) ; key competencies; external success). In the quantitative part, the data obtained from the exploratory factor analysis questionnaire were analyzed with the help of spss26 statistical software. The factor loadings of all the items of the standard model are higher than 0.3 and the significance coefficients of the model are all higher than 1.96.

The research aims to predict demand and optimize product ordering within the supply chain using artificial intelligence. Employing a purposeful sampling method, 12 managers from Calais were selected as the sample group. This study utilized two Delphi techniques and a neural network. Through semi-structured interviews conducted in a fuzzy Delphi panel, relevant components were identified. Predictions were made using the Multi-Layer Perceptron Neural Network toolbox in MATLAB software. The results from the fuzzy Delphi analysis indicated that the primary factors influencing the forecast included warehouse inventory, sales from the previous week, sales from the previous month, cargo in transit, fluctuations in customer numbers compared to the past, competitors' market status, government regulations, and the company's development plans. After finalizing the Delphi process, the key factors identified were warehouse inventory, sales from the previous week, sales from the previous month, changes in customer numbers compared to the past, competitors' market status, and government regulations. The neural network predictions demonstrated that, due to the fluctuating demand trends, the predicted values closely aligned with the actual values. According to findings, the neural network's predictions were deemed acceptable, even with the rapid fluctuations in actual demand. In the case of Kale Dairy Company, utilizing artificial intelligence can yield significant benefits. Firstly, more accurate demand forecasting through artificial intelligence algorithms can lead to a reduction in waste and excess inventory within the supply chain. This enables the company to better identify and meet customer needs, ultimately resulting in increased customer satisfaction.

The aim of the research is to apply knowledge in the production of lean-green vegetables (case study: presentation of a model in the food industry). This research has been done in an applied and survey way. With Dematel's multi-criteria decision making expert method, the internal relationships of the variables have been examined. An expert questionnaire has been designed. The community of unit managers were the managers of food industry units. Validity based on the consistency rate of 0.656 was obtained and confirmed. The number of samples that included managers of food industry units in Azerbaijan province with at least 15 years of experience in the field of food industry and having at least a master's degree, which was determined to be 14 people, considering the acceptable sample size (10-25 people). The identified criteria include motivations and success factors related to human resource management, motivations and organizational success factors, systemic challenges and obstacles, managerial challenges and obstacles, challenges and obstacles related to human resources, methods, tools and techniques. Environmental processes, methods, tools and techniques, key factors of social-operational performance and key factors of environmental performance. Based on Dematel's analysis, motivations and success factors related to human resource management are the most effective. Systemic challenges and obstacles are in second place. The motivations and factors of organizational success are in the next level of influence. The key factors of socio-operational performance are the least effective.

The purpose of the research is to create a business value model through omnichannel based on customer relationship management based on a fuzzy interpretive structural approach. This research is exploratory in terms of practical purpose. The study population of this research was Sepeh Bank managers. Through purposeful sampling, of criterion-oriented type, the desired sample was selected and sampling continued until the theoretical saturation of data was reached. Therefore, the participants in the research included 12 professors and administrators. The research tool was a researcher-made questionnaire. In data processing, fuzzy Delphi method was used in MATLAB software and analysis of structural mutual effects and MICMAC software were used. Based on the results obtained, indicators of individual characteristics, bank digitalization, service quality, information and communication technology, cyber security, channel efficiency, training, improving customer orientation, innovation and creativity, profitability, technological knowledge, strong support system, word of mouth marketing and Beneficial virulence and association with the channel were confirmed in three Delphi rounds. A 7-level model was formed based on the fuzzy interpretive structural model. As a result, the omnichannel business value creation model based on CRM and fuzzy interpretive structural approach enables banks to improve customer experience, increase operational efficiency, achieve greater profitability, while maintaining security and risk management. to improve This model increases value for customers and banking businesses by creating integrated interaction in all channels and using new technologies.

Extracting knowledge from human behavior to develop a model of value systems involves analyzing and identifying patterns and complex relationships among variables related to human behavior in social or organizational contexts. This process investigates the interactions between factors that influence both individual and group behavior, employing analytical and modeling techniques, including system dynamics. The purpose of this research is to extract knowledge from human behavior to present the valuating model, specifically through a case study of difficult employees at Maysan University. In this context, it is crucial to collect both qualitative and quantitative data on human behavior, analyze feedback, and identify underlying trends. The results of these analyses can help create predictive models and offer solutions to enhance the performance of organizations and social systems, ultimately generating sustainable value. This research was conducted using a qualitative-inductive approach, employing the Stirling and Darling method (NVIVO), alongside a quantitative approach utilizing system dynamics (VENSIM). The primary data collection tool was a semi-structured interview. Applying the grounded theory method, the data obtained from interviews with 16 experts in the field of education and training—specifically, administrators and educational experts from Maysan University—were coded in three stages. The general category encompasses negative organizational behaviors, including negative management practices, anxiety factors, organizational injuries, a detrimental organizational culture, poor communication, inappropriate organizational changes, ineffective leadership, incorrect policies and procedures, and an unsuitable work environment.