International Journal of Knowledge Processing Studies

Abstract The current research aimed to investigate knowledge in orchestrating the digital banking ecosystem. Considering the need for a deeper application of the theoretical contextualization dimension (compared to the experimental contextualization dimension) in crystallizing orchestrating strategies and approaches from the reduced qualitative research method to enrich the identified categories and frame the results of the context. Theoretical analysis was used. The thematic research method of Brown and Clark (2006) was chosen as the main and central method to answer the final research question. The statistical population of the research includes experts in the banking industry, especially digital banking and digital financial services, who were selected by the snowball sampling method. After conducting 12 interviews, the data was saturated. The content validity ratio and the content validity index were used to evaluate the interview questions' content validity ratio. Three strategies focusing on co-evolution, enrichment, and novelty were identified as the best knowledge-based strategies. Focusing on co-evolution, value enrichment, and up-to-dateness of digital banking ecosystem components as three basic strategies can lead to improved performance, increased value for customers, attraction of new customers, and promotion of the digital banking system's enhancement. These strategies are essential for success and progress in the digital banking ecosystem.
 

Abstract The aim of the current research is to compile the knowledge framework of tax whistleblowing disclosure by analyzing the role of the organization's structure and culture. In the current complex and global economic era, concepts such as financial transparency and disclosure knowledge in the field of taxation have become vital for sustainable development and creating economic justice. The present research aimed to answer how structural and cultural factors influence tax whistleblowing by using qualitative content analysis and data triangulation. The research spanned from 2014 to 2023, involving a research community within the tax organization. Six individuals were selected as interviewees using purposeful sampling. The research findings revealed that, despite the presence of structures to facilitate whistleblowing in the tax organization studied, a culture of non-whistleblowing prevailed. In other words, cultural factors overshadowed structural factors, rendering them ineffective in many cases. The practical implication of this research for policymakers, managers, and other administrative professionals is to emphasize the role of organizational culture over other factors influencing tax disclosure knowledge towards organizational transparency. Success in developing tax whistleblowing disclosure knowledge hinges on acknowledging cultural beliefs about whistleblowing, as structural arrangements alone cannot promote organizational whistleblowing. 

Abstract The aim of this research is to employ neural networks in discovering functional knowledge based on the rational training of Avicenna and Kant. The methodology of this study is based on deep learning neural networks, making it an exploratory research. Given the practicality of functional knowledge, this research is applied in nature. To assess the significance of components and evaluation indicators of functional knowledge, text mining and the frequency of related symbols have been used. In order to utilize data mining techniques in this research, the WEKA software has been employed. The algorithms considered for implementation in this study are MLP, SVR, AdaBoost.R, Bagged Trees (BAGTREE), Linear Regression (LR), and Least Squares Support Vector Regression (LSSVR). According to the results obtained for functional knowledge, the LSSVR and SVR methods outperform the others, indicating their superiority. As the charts illustrate, there is significant volatility in this dataset, making prediction challenging. Furthermore, the R2 value is very close to one, indicating relatively accurate predictions by the methods. Neural networks can serve as powerful tools to aid in rational thinking, logical decision-making, and better understanding of the surrounding world, in line with the perspectives of Avicenna and Kant. These tools can assist in analyzing and interpreting complex data in these fields and strive for rationality and human excellence.

Abstract The current research seeks to identify new combined genetic algorithm methods to solve price forecasting It was oil.
Effective factors were identified by using a systematic and meta-composite review approach, and performing the 7 steps of the Sandelowski and Barroso method. Among 4340 articles, 54 articles were selected based on the CASP method. In this context, in order to measure reliability and quality control, the Kappa index was used, and its value was identified for the identified indicators at the level of excellent agreement.
The results of the data analysis collected in the ATLAS TI software led to the identification of 7 categories and 26 primary codes of new combined genetic algorithm methods to solve completely difficult problems. Based on the done coding, 7 categories and 26 primary codes were identified. The identified categories are: component design, supply network, planning, forecasting, inventory control, information security, segregation and evaluation.
The combination of genetic algorithm with different methods due to its ability to distinguish features and optimize parameters can lead to significant improvements in the field of oil price prediction. The use of genetic algorithm in solving oil price forecasting problems as an evolutionary and artificial intelligence approach makes it possible to integrate diverse and complex information in forecasting models. By designing the appropriate components as genes that represent important economic, geographic and political features, the most optimal genetic combinations can be created to increase the accuracy and performance of prediction models.

Abstract The goal of this research is to create a business value creation model through Omni-Channel based on customer relationship management. According to the Omni-Channel framework, a business model can be configured in a way that pursues one or more themes to create value. This research is exploratory and qualitative in nature and was conducted by analyzing the content of interviews with Attrid Sterling in MAXQDA software. It is practical in terms of approach and method. The research objective of creating a business value creation model through Omni-Channel based on customer relationship management was investigated through semi-structured interviews in Ansar Bank. Bank managers, technical and marketing experts, and research and development experts provided appropriate information in this regard. Based on the three stages of Astrid-Sterling coding, related concepts were identified. 20 individuals were identified as interviewees through purposive sampling in qualitative analysis. In qualitative analysis, 6 pervasive themes, 19 organizational themes, and ultimately 136 fundamental themes were identified. The pervasive themes identified included information and technological infrastructure, business value added, current state review, innovative customer-centricity, indigenization of Omni-Channel value in banking, and business value creation. This model enhances the capabilities of banks in creating superior customer experiences, improving products and services, and accelerating business growth. By advancing in these criteria, banks can appear as the top choice for their customers in the competitive market.

Abstract The research aimed to introduce a customer-oriented knowledge model based on the loyalty of individual insurers using an information recording approach. The qualitative research method was systematically conducted, employing the grounded theory approach through interviews with experts in the insurance industry. Fifteen individuals were selected through targeted sampling. The collected data were analyzed using NVivo software, and the relevant model was presented. According to Strauss-Corbin's categories, there are 6 causal conditions: suitability of insurance policy conditions, rating of customers based on special attention to specific customers, rating of customers based on "need level" for insurance services, empowerment of employees and managers, brand image, confidence, company credibility, service quality, and security. Additionally, there are 2 background conditions (establishment of knowledge management and organizational structure), intervening conditions (social missions), 6 strategies (discount/incentive schemes, organization of business processes, club launch customers, policyholder loyalty programs, establishing customer-centric strategies, and relationship marketing), and outcomes (loyalty, customer lifetime value, repeat purchases, and customer referrals). Data mining enables insurance companies to utilize existing data to identify customer behavior patterns. By identifying and developing services based on the actual needs of customers, insurance companies can enhance and refine their services. Through the utilization of existing data on policyholder loyalty, insurance companies can accurately pinpoint customer behavior patterns, needs, and preferences.

Abstract The research aims to design a sustainable supply chain model to achieve world class with a fuzzy hybrid approach based on critical situation information systems. This research is developmental-applicative in terms of its purpose, and the method used is a combined method that includes historical method (gathering information) and survey method (questionnaire distribution). The statistical population of this research consists of experts in the handwoven carpet industry and professors in the field of world-class production. The sampling method is purposive and model fitting was done with Dematel techniques and interpretive structural model. Based on the literature, 15 indicators were identified, which are: lean production (elimination of redundant activities), cost of materials and transportation, reduction of time to reach the market and waiting, technology and machinery and software Design tools, supply chain agility, applying honest principles in hiring local people, increasing consumer awareness to consume sustainable products, focusing on social/community welfare, insurance and pension guarantee, creating strong legal facilities to take care of industries in times of Corona, compatible production processes With environment and green distribution, ISO 14001, reverse logistics and recycling, green packaging and distribution and creating sustainable procurement strategies. The results show that based on the designed interpretative structural model, the low-level components of applying honest principles in hiring local people, focusing on the social/personal welfare of employees and issuing, creating strong legal facilities in times of Corona and environmentally friendly production processes and Green distribution has the most influence on the whole model.

Abstract The aim of the current research is to identify the data-driven process of sports consumer motivation based on the relational marketing knowledge approach. This research is exploratory and qualitative in nature, utilizing the thematic analysis method. The research population consists of fans of Iran's Premier League football clubs, selected through purposeful sampling. Subsequently, data from 18 interviews were analyzed using the thematic analysis method. Findings: The interviews yielded 1278 open codes, which were then condensed into 191 unique codes and further refined into 33 core codes. These core codes, identified during the selective coding stage, are categorized into 6 groups. Three of these groups are associated with types of pleasure motivation, psychological and social factors, one with relational marketing, one with the use of social media, and one with behavioral goals and intentions. A hierarchical model was developed based on the relationships among these groups of motivations, relational marketing, social media usage, and behavioral intentions. This model can serve as a tool to understand the motivations of football club fans and leverage relational marketing and social media to enhance and solidify the enduring relationship between fans and clubs. It can also aid in addressing marketing challenges, revenue generation, financial independence of the club in a systematic manner, and resolving issues in this domain.

Abstract The research aims to identify players in the database of public and private banks using a meta-heuristic algorithm. This issue pertains to enhancing the human resources management system to ensure consistent stability in the bank's operations. In this database analysis process, Poisson distribution and artificial intelligence are utilized to analyze data with an exponential distribution. For this purpose, the VIS, CNSGA-II, NSGA-II, MISA, NNIA, and NRGA algorithms were implemented using MATLAB software. The VIS algorithm showed the best performance in most criteria. Algorithms CNSGA-II and MISA are both ranked second and exhibit similar performances. NSGA-II algorithm is ranked second. The NNIA algorithm performs the best, while the NRGA algorithm performs the worst. These analyses are conducted to assess the performance of algorithms based on various criteria. The results obtained from these analyses show that the VIS algorithm generally demonstrates the best performance. This means that VIS is known as an identification of players in the databases of public and private banks. In addition to the Variable in Neighborhood Search (VIS) algorithm, other algorithms like CNSGA-II and MISA are also closely ranked and share the second position in various criteria. These algorithms have similar functions and can make comparable enhancements in identifying players in the databases of public and private banks.

Abstract This research aims to present a model for extracting knowledge on users' trust in marketing based on information and social network data. The study was applied in grounded theory. Cognitive framing was employed using various data collection methods such as library study, examination of specialized sources and texts, and semi-structured interviews. Using purposive sampling, 14 experts were interviewed in 2023. The interviews were coded using ATLAS.TI software. To validate the results, data were evaluated and analyzed based on triangulation. The findings indicate a model of 18 concepts and 59 initial codes with 6 main axes. The conditions for this model include 6 concepts, background conditions with 3 concepts (information & communication technology infrastructure in the country, emulation of solutions from top companies in social network marketing, & digital economy), intervening conditions with 3 concepts (society's general knowledge level about information & communication technology tools, society's inclination toward engagement in social networks, & environmental changes), strategies with 3 concepts (facilitating user interaction for exchange of opinions under advertisement, electronic word-of-mouth advertising, & education), and consequences with 3 concepts (trust in & acceptance of advertisements by users, customer satisfaction, and economic productivity). The results indicate that the user trust model in social network-based marketing fundamentally utilizes network communications and interactions to structure trust and confidence. 

Abstract The quality of fair value accounting information in banks contributes to the transparency and accuracy of financial information. The purpose of this article is to apply MICMAC's interpretive structural technique to the quality of fair value accounting information in banks. Methodology: The research is practiced and scrutiny. The community is investigating managers and experts in the banking industry, which has been used by the judicial sampling method. First, using the Delphi technique, sieve and evaluate identification components. The following is a modeling using the interpretive structural method. Excel and MICMAC software are used. Results: According to the results obtained in the qualitative analysis, 9 main criteria were identified. The main criteria include flexibility, supervision, laws and regulations, organizational conditions, knowledge and education, government agents, need assessment, economic growth and transparency of performance. These 9 main criteria are the need for strengthening as well as an important factor in the use of fair value accounting in banks. Also, based on the interpretive structural model, a hierarchical model is formed. Fair valuation accounting conclusions allow banks to manage their financial resources more effectively. Fair valuation assures bank managers that assets and debts are properly evaluated and that they can do more economic activities through the optimal use of these resources.

Abstract The application of blockchain technology in the field of knowledge and intellectual capital management is gaining traction due to its ability to enhance key indicators such as security, non-forgery, unchangeable information, tracking, decentralization, and transparency. This technology is increasingly being utilized in knowledge and intellectual capital management, with its growth and promotion hinging on the need to prioritize the tacit knowledge and intellectual capital of employees. However, scientific contributions and successful applications in this area remain limited and are primarily in the proof-of-concept stage. The main focus of this paper is to introduce a value chain management model that emphasizes intellectual capital in medical and healthcare organizations through the use of blockchain technology. The study begins by extracting a framework for readiness to adopt blockchain technology through a review of previous studies and research. Subsequently, it identifies various areas within the value chain of medical and healthcare organizations. A questionnaire for pairwise comparisons was then distributed using a hierarchical approach. Finally, the TOPSIS questionnaire was used to rank the five identified areas of activity in the value chain and knowledge management of medical and healthcare organizations. Semi-structured interviews with experts from the organizations were conducted to provide further insights into promising methods for enhancing blockchain acceptance within the organization and to suggest meaningful research directions for future studies.