International Journal of Knowledge Processing Studies
Volume:4 Issue: 1, Winter 2024

The purpose of this research is to model supply chain performance management based on information dashboards in private banks. The data was derived from in-depth and semi-structured interviews with 12 managers of five private banks in the country, which were based on purposeful sampling and continued until reaching theoretical saturation. The validity of the research data was checked and confirmed by going back to the participants and external auditors. Data analysis was done based on the Strauss and Corbin model in the form of open, axial, and selective coding in the Atlas TI8 software. Modeling of supply chain performance management based on information dashboard in private banks including causal factors (intensification of competition, supply chain inefficiency, banking system challenges), intervening (appropriate corrective measures, organizational strategies, banking service challenges), platforms (quality management services, facilitating elements, and action management), strategies (development strategy, partnership strategy, discovery strategy, and focus strategy) and consequences (correction of performance evaluation system, financial-administrative function, performance improvement and innovation and supply chain development). To succeed in developing and changing their business model, they must correctly recognize factors affecting the supply chain of banking services in the fourth industrial revolution and digital revolution and successfully transition to the new technological era. A supply chain refers to the flow of materials, information, funds received from customers, and services from suppliers of raw materials through factories and warehouses to final customers, and includes organizations and processes that create goods, information, and services and deliver them to intended consumers.

The current research was conducted by designing the knowledge-based organizational satisfaction modeling with a data-driven approach using a qualitative and quantitative method of grounded theory and data mining techniques. The data was taken from in-depth and semi-structured interviews with 25 general managers of social security insurance departments in the provinces of the country, based on purposeful sampling. The validity of the research data was checked and confirmed by going back to the participants and external auditors. In the data mining section, registered data and the organization's database were used. Using the data recorded in the Clementine software, the happiness and unhappiness of the employees in the organization were categorized. The results showed that the model of organizational happiness in the social security organization was identified at three levels, group, individual and organization, including causal factors, intervenors, platforms, strategies and finally consequences. Also, the status of employees was determined based on the proposed model of happiness according to the collected data. Finally, the data mining model showed classification with 66% accuracy for happy and unhappy employees. The human resource management approach based on organizational data leads to correct decision making in organizational performance. The more transparent the collected data is, the more accurately the state of the organization can be predicted. Also, based on the proposed model and implementation in the form of data mining, it is possible to estimate the number of happy employees.

This research aims to design an intelligent model of digital consumer behavior knowledge based on big data. This research was conducted using a qualitative approach. First, the qualitative method of thematic analysis was used, followed by the application of big data analysis techniques. The statistical population includes experts in the field of marketing who specialize in qualitative analysis. The sample size was determined to be 10 people using the snowball method and theoretical saturation. The data collection tool includes interviews with experts, which were analyzed using the thematic analysis technique in MAXQDA. In the following, the customer's behavioral trend has been studied based on the Big Data technique model, using the data available in the Digikala company. Coding in MATLAB is done based on specific formulas. The results showed seven components and 48 indicators that were identified and approved by experts in designing consumer behavior patterns using a digital marketing approach. These components include 1. Marketing Practices. 2- Innovation, 3- Digital marketing strategy, 4- Dynamic digital marketing, 5- Customer management, 6- Consumer cooperative behavior, and 7- Consequences of consumer response. The business management has finally decided to expand the intelligent system for consumer behavior. The main evaluation index is relatively unique and cannot effectively stimulate the acquisition of new customers. The only evaluation comes from consumers who have a recorded history of financial behavior on the digital platform. The value network model relies on digital technology because it facilitates interaction between end consumers as a relational medium.

The current research aimed to design and fit the knowledge-enhancing model of effective teaching. knowledge-enhancing in teaching, as a condition of "life, durability and survival", displays the dynamic spirit of education and is created in three pyramid heads (students, parents, and teachers). The research method is mixed and practical in nature. The statistical population includes all teachers working in the education ministry in Fars province. The sample size of 18 people sample selection was determined in the qualitative section by the purposeful sampling method. Quantitative sampling was obtained according to Cochran's formula of 376 people. The data collection tool, in the qualitative part, included two parts, a semi-structured interview and review of upstream documents, and in the quantitative part, a researcher-made questionnaire tool. Data analysis in the qualitative section was based on thematic analysis in ATLAS TI software. Structural equation modeling was used in SMARTPLS software to fit the model. The obtained findings led to the identification of 3 dimensions, 10 components, and 176 indicators, and finally, the research model was presented. The results showed that each of the dimensions, respectively: teaching and evaluation, scientific-educational (/66), and individual (/57) affected effective teaching in elementary school. Students learn by connecting new knowledge with existing knowledge and concepts, constructing new meanings. Knowledge-based education emphasizes primary education on deep and powerful teaching and learning from shared knowledge.

The current research was conducted to apply knowledge extraction in the classification of jobs to identify the key role players using a mixed method (qualitative and sufficient data). The application of expert systems or decision support systems based on organizational data is increasing in the selection and hiring of personnel. The data was derived from in-depth and semi-structured interviews with 17 subject experts in bank human resources, who were selected based on purposeful sampling.Data analysis was done based on the Strauss and Corbin model in the form of open, axial, and selective coding in the Atlas TI8 software. The results showed that the classification of jobs for the key role players in public and private banks includes causal conditions (requirement of talent substitution, human resource management developments, and organizational challenges), intervening conditions  (organizational limitations and fear and resistance), and contextual conditions (strengthens and drivers) strategies (developmental, supportive and creating) and short-term and long-term consequences are among the components of the job classification model for the key role players in public and private banks. Next, based on the database with the CART method, the data mining of job classification was done. Regarding the performance of the model, it showed variance values of 311.92 and a risk value of 288.19. The predictions in the model explained 28.9% of the differences observed in the variable "employment status of A employees' category".

This research aims to develop a data-based model for fifth-generation universities. Creating a data-driven model in a university environment is essential in education. The primary mission of higher education is to address the specific educational needs of individuals, as well as the needs of society and its economic development. The study was conducted in both qualitative and quantitative sections. The grounded theory is conducted based on the perspectives of the chancellors of Islamic Azad University. 21 people were selected using snowball sampling techniques. In the following, a six-category model is provided. Analysis was done using NVIVO software. The statistical population in the quantitative section consisted of all professors from Islamic Azad University nationwide. A sample size of 381 professors was selected using the Cochran sampling formula. The research tool was a questionnaire created by the researcher. Then, using the model presented and the suggested pattern fit, the performance of the model is predicted based on the K-Mean method in Weka and RapidMiner software. According to the results, the proposed model was approved by experts. The analysis of structural equations was also confirmed. According to the Waode algorithm model, the highest accuracy was 81%.

The purpose of this research was to explore the future of management accounting, specifically focusing on data-based systems, using a structural analysis approach. The study aimed to provide insights into the field by projecting trends and developments up to the year 2030.This research is exploratory in terms of its purpose and application. The participants were 20 experts in the accounting profession who were selected using purposive judgmental sampling. The data processing involved the use of the structural interaction analysis method and the MICMAC software. According to the findings of the structural analysis of business globalization, the key drivers include the convergence of accounting and business, the relationship between industry and university in management accounting, the increase of academic experts, and the rapid pace of technological and business intelligence changes. They have an impact on improving the quality and quantity of companies' profits in the future of the management accounting profession by 2030. The results show that these drivers play a crucial and highly effective role in enhancing the future profitability of companies in various sectors within the management accounting profession.This finding can be valuable for management accounting policymakers to anticipate future developments in this profession and avoid being caught off guard.

This research aims to develop an entrepreneurship ecosystem model within a university, utilizing a knowledge-oriented approach. The role of universities in ensuring the success of knowledge and entrepreneurship goes beyond knowledge transfer. Ultimately, they contribute to the creation of a knowledge-based entrepreneurship ecosystem.Therefore, experts' opinions were used to identify the indicators, components, and dimensions of the entrepreneurship ecosystem of Azad University. This was done by examining the existing theoretical foundations and utilizing the theme analysis method of Brown and Clark in the ATLAS TI software. In this regard, interviews were conducted with 20 experts from the university's entrepreneurship ecosystem until theoretical saturation was reached. The text of the interviews was then analyzed using coding. According to the systematic model, eight main categories were identified. These clusters include "structural factors (structure and government)", "entrepreneurial fields (environmental factors, management factors)", "entrepreneurial consequences (development and transfer of entrepreneurship, technological entrepreneurship)", "educational and cultural factors (educational factors, cultural factors, scientific and technological factors)", and "policymaking and planning (government policymaking, leadership policymaking)". Knowledge-based entrepreneurship is faced with a complex set of components that create its knowledge-oriented ecosystem. So that each dimension of this sphere is integrated into both the internal components of the university and the higher education system, as well as the external components and subsystems of society.

This research aims to identify the knowledge management infrastructure due to reducing employee absenteeism based on data mining. Examining the status and reports of employees using data recording systems, creating information dashboards, and applying data mining techniques is important for the transparency of the mental state of employees. The mixed research method (qualitative-quantitative) has been done in two phases. The first phase was conducted with a qualitative-inductive approach using the Delphi method and a semi-structured interview tool. In the second step, codes were grouped in a common axis and 13 axis codes based on the similarity and distinction between the extracted codes. The interview sample was 10 people selected using the purposeful sampling method. In the second phase, the quantitative research method was data mining; Then, according to the research literature and experts' opinion, the researcher-made questionnaire was designed with a five-point Likert scale. The data mining technique is based on neural networks and decision trees in Rosseta and Weka software. The results showed that knowledge management can increase the quality of organizational processes based on data, increase the empowerment of employees, and reduce absenteeism. The knowledge obtained from the data mining of organizational information dashboards is important for strengthening the mental health systems of employees and increasing productivity.

This paper explores the intricate mechanisms underlying knowledge transfer in Electronic word-of-mouth (eWOM) campaigns and their far-reaching implications for brand equity in the digital era. The aim is to interpret how eWOM channels facilitate the strategies that can optimize knowledge transfer in eWOM campaigns to the benefit of brand equity. Through an extensive review of the existing literature and expert interview analysis, 187 codes were identified. Using 410 Likert questionnaires, the quantitative method was employed to test the proposed model. Exploratory factor analysis and PCA (Principal Component Analysis) method were used by SPSS software. By AMOS software, first and second-order confirmatory factor analyses were conducted to ensure a consistent factor structure between the items and structures. CR criterion was used to assess reliability. The AVE, GOF, TLI, CFI, NFI, and RFI indices were also used to evaluate the model. Understanding the underlying mechanisms of knowledge transfer in eWOM campaigns is essential for brand managers and marketers seeking to bolster brand equity in the digital landscape. Leveraging these mechanisms effectively can enhance brand reputation, customer loyalty, and overall business success. This research emphasizes the critical importance of eWOM strategies as a key driver of brand equity in the contemporary marketing landscape.

This research provides a data-driven model of electronic banking customer experience using digital marketing knowledge. The study is applied-developmental research, and it is a cross-sectional survey research. A semi-structured interview and a Likert scale questionnaire were used to collect data. The statistical population in the qualitative section includes banking industry experts. Using targeted method, 15 experts participated in this section. The statistical population is one million people (active customers of electronic banking) and the sample was calculated based on the Cochran table of 384 people. To analyze the data in the qualitative part, the foundation data analysis method was used in MAXQDA, and for the validation and presentation of the final model, the structural equation modeling method and SMARTPLS software were used. Based on the designed model, 6 categories for causal factors (proper decision-making, time management, digitalization effects, cost management, business trends, and relationship management), 2 categories for background conditions (banking industry and digital economy), 2 categories for intervening conditions (individual factors and environmental factors), 4 categories for strategy (digital tools, trust building and training, digital differentiation and digital platform), 3 categories for outcomes (prosperity of the banking industry, customer satisfaction, and economic productivity) became. Banks are an important pillar of the economy and the strategies they adopt will affect the recovery of the economy after the pandemic. Digitization is one of the important options for banks in order to provide the best and most reliable solutions to customers in their current business with the bank.

In the country's policy-making and decision-making arena, many new and dynamic issues are faced by the country's research agents. To solved these problems, must be prepared a cycle between the most theoretical layers and the most operational layers. By designing and formulating the wisdom ecosystem model in the field of decision-making, it is possible to create this operational cycle that prepares the back and forth between these levels. This research was carried out with the objectives of "compilation of the ecological model of wisdom in the field of decision-making of research organizations" with a mixed approach and with an exploratory and contextual method. The two statistical populations of this research are: a) the general research community; 25 people with special characteristics, and b) expert society; There were 15 experts who formed the panel group and tried to produce literature by holding brainstorming sessions and summarizing the mentioned cases, they completed the conceptual model of the research. Based on this research, the dimensions and components of wisdom ecosystem in the field of decision-making of Iranian research organizations are: Senior managers (wise judgment, foresight and insight, rationality, applying experiences, understanding the correlation of affairs, understanding issues), Environment (communication channels, dynamics, external knowledge), Knowledge centers (human resource management, human resources, organizational culture, knowledge management, strategy and leadership, decision-making and policy-making), Actors (universities and research centers of the country, government, industry, Supreme National Security Council, Islamic Council and foreign actors), and upstream documents (laws and regulations, modern Islamic civilization, S&T document and ...).