Mohammadreza Ebrahimzadeh; Behrouz Larisemnani; Seyedeh Masoumeh Ghamkhari; Ehsan Ahadmotlaghi
Abstract
The purpose of the current research was to process and create knowledge in the coordination of the digital banking ecosystem. Considering the need for deeper application of the theoretical contextualization dimension (compared to the experimental contextualization dimension) in the crystallization of ...
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The purpose of the current research was to process and create knowledge in the coordination of the digital banking ecosystem. Considering the need for deeper application of the theoretical contextualization dimension (compared to the experimental contextualization dimension) in the crystallization of coordination 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 going through 12 interviews, the data was saturated. In evaluating the content validity of the interview questions, the content validity ratio and the content validity index were used. Three strategies of focusing on co-evolution, focusing on enrichment and focusing on 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 more performance, increase value for customers, attract new customers and promote the improvement of digital banking system. These strategies are the most essential factors of success and progress in the digital banking ecosystem.
Knowledge management
Arash Mehrzadian; Azam Mirzamani; Mohammad Ghaffari
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 ...
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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 one of the vital axes for sustainable development and creating economic justice. The present research tried to answer the question of how structural and cultural factors play a role in tax whistle-blowing by using the method of qualitative content analysis and triangulation of data collection. The research community of the tax organization was from 2014 to 2023 and 6 people were selected as interviewees with the purposeful sampling technique. The findings of the research showed that in the studied tax organization, despite the existence of structures to facilitate whistleblowing, the culture of not whistleblowing prevailed over whistleblowing. In more precise words, structural factors were completely overshadowed by cultural factors and even in many cases they became ineffective under the influence of cultural factors. The practical knowledge of this research for policy makers, managers and other administrative health enthusiasts is to pay more attention to the role of organizational culture and prioritize it over other factors affecting tax disclosure knowledge in the direction of organizational information transparency. Any success in the development of tax whistle-blowing disclosure knowledge depends on paying attention to the culture and cultural beliefs about whistle-blowing, and only structural arrangements cannot cause organizational whistle-blowing.
Knowledge Extraction
Elham Sanmadi; Hasanali Bakhtiyar Nasrabadi; Zohreh Saadatmand
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 ...
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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.