Knowledge Extraction
Omid Mehri Namakavarani; Hosein Kazemi; Farzin Rezaei; Reza Ehtesham Rasi
Abstract
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 ...
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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.
Knowledge Extraction
Mohammad Ali Borjikhani; Esmat Masoudi; Zahra Taleb; Nayereh Shahmohammadi
Abstract
This research aims to design a data-oriented strategic training with the future research approach in the automobile industry. The research is applied in terms of its purpose and uses a qualitative approach and thematic analysis method. The data collection tool is semi-structured interviews, and the validity ...
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This research aims to design a data-oriented strategic training with the future research approach in the automobile industry. The research is applied in terms of its purpose and uses a qualitative approach and thematic analysis method. The data collection tool is semi-structured interviews, and the validity of the questions was obtained using the opinions of experts in the field of human resources in the automotive industry. The statistical population of this research is twenty-seven specialists and experts familiar with education, human resource development, and strategic planning from Saipa Automotive Group companies, who were selected by the snowball method. Next, based on the specified codes, 4 categories were selected, and based on 8 components in the Scenario Wizard software, future research analysis was performed and 3 compatible scenarios were identified. The results of the research show the extraction of 110 primary codes and the statistics of 14 sub-categories and 5 main categories, which include: 1- The core structure category of education, which has 3 sub-categories: organizational structure, systematization, and discipline. 2- The core agility category of education which has 3 sub-categories: transactional, intelligence, creativity and innovation 3- The business-oriented category of education which has 3 sub-categories: business conditions and organizational productivity, beneficiary-oriented 4- The strategy-oriented category of education which has 2 sub-categories: strategy-oriented, foresight and result orientation 5- The central development category of education has two sub-categories: individual development and organizational development.
