Abstract
The purpose of this study is to provide the pattern of effective factors in the socialization of artificial intelligence technologies. The present research is applicable in terms of purpose, quantitative in terms of implementation, and of descriptive-survey type in terms of nature. The statistical population of the research included 20 board members, financial managers, accountants and auditors, as well as financial statements and engineers in the field of artificial intelligence in the hierarchical method, selected by judicial and targeted sampling method; and, 342 of all employed accountants in the structural equation sector, by random sampling. Research collection tool is a questionnaire. A hierarchy of Expert Choice software as well as structural equations (PLS) was used for data analysis. The results of the analysis showed that independent variables and dependent variables have directly a positive and significant effect. The results also showed that increasing efficiency and productivity is the first priority, creativity and innovation is the second priority, saving time and money is the third priority, analyzing financial data is the fourth priority, collaboration and cooperation is the fifth priority, matching transactions is the sixth priority, transparency is the seventh priority, determining training needs is the eighth priority, trust in financial tools is the ninth priority, ease of use is the tenth priority, user-friendly tools are the eleventh priority, automation of repetitive processes, real-time financial analysis and financial protection and security are the twelfth priority, improving financial reporting is the thirteenth priority, and awareness, technical knowledge, and the capabilities of financial tools are the fourteenth priority.
Introduction
The advancement and utilization of artificial intelligence technologies transforms conventional patterns of life and work, thereby creating indelible changes in the social environment to adapt to the current society in which information is rapidly evolving. All disciplines and professions are rebuilding or improving their strategies, organizations, products, and approaches. Accounting is no exception (Concept et al, 2019). Accounting can now use electronic accounting, data mining, and multidimensional data analysis. However, accounting technologies and methods are merely a subsector which is being changed by artificial intelligence, and can have a significant impact on accounting goals (Yubin et al, 2021). Artificial intelligence is an essential component for the implementation of international accounting law. Accounting rules require systems support in complex risk coverage programs, and law enforcement is one of the important advantages of artificial intelligence in the accounting profession (Cong et al, 2018). Makridakis (2017) believes that artificial intelligence will soon replace traditional accounting and auditing. This change will lead to the separation of traditional accounting and auditing and will help accounting staff to improve their work. Accounting posts, structure optimization, restructuring as well as work quality and the ability of settings are the advantages of using artificial intelligence. Accounting needs to be modified to be more reliable in the community. For example, by means of artificial intelligence, accounting uses accounting robot to build simulator models from the environment. The financial robot has an instantaneous implementation speed. Information and automation of the accounting process under instantaneous response and conditions improves the efficiency of accounting activity. Proper programming of the financial robot can ensure the decline and specifications of each link, and can effectively reduce the occurrence of errors according to the designated methods. The financial robot only executes financial employees and legal steps. It performs data entry and step by step operation, thereby reduces artificial operations in accounting, and greatly prevents artificial operations (Suleiman et al, 2020). In this regard, the main question of the research is: What is the pattern of influencing factors in the socialization of artificial intelligence technologies in accounting?
Theoretical framework
Artificial Intelligence
Artificial intelligence is a branch of computer science that examines the computational requirements of actions such as perception of reasoning and learning, and provides a system for doing so. Its main roots and ideas should be sought in the philosophy of linguistics, mathematics, psychology, neurology and physiology, and it has various applications in computer science, engineering sciences, biology and medicine sciences and many other sciences (Han et al, 2023).
Socialization
The socialization stage is the stage where an individual learns social norms. This stage is the same as the assimilation stage in the psychoanalytic system of form and the learning stage according to Blumer. After socialization of the assimilation or socialized learning stage, the socialized stage begins, meaning accepting social and cultural norms and in other words, becoming in tune with them or, according to Blumer, unifying and melting into them (Arizi & Barati, 2017).
 Karamipour (2023) investigated the design of artificial intelligence competencies on organizational performance, taking into account the commercial marketing capabilities. The results showed that the mechanisms of artificial intelligence competencies influence the business-to-business marketing capabilities and organizational performance, and also the artificial intelligence ​​competencies model is validated on organizational performance by considering the aspect of business-to-business marketing capabilities.
Pourshahabi (2023) examined the presentation of a systematic model of employee training using artificial intelligence. The research findings show that the inputs of the model include 1- educational data, 2- personal information, 3- educational needs, 4- user feedback, and 5- workplace data. The model process also includes 1- determining needs and goals, 2- data collection, 3- data preprocessing, 4- training the artificial intelligence model, 5- model evaluation and improvement, 6- implementation and deployment, and 7- monitoring and updating. Finally, the outputs of the model include 1- individual feedback, 2- educational suggestions, 3- monitoring and follow-up, and 4- support and guidance.
Research Methodology
The present research is applicable in terms of purpose, quantitative in terms of implementation, and of descriptive-survey type in terms of nature. The statistical population of the research included 20 board members, financial managers, accountants and auditors, as well as financial statements and engineers in the field of artificial intelligence in the hierarchical method, selected by judicial and targeted sampling method; and, 342 of all employed accountants in the structural equation sector, by random sampling. Research collection tool is a questionnaire.
Research findings
A hierarchy of Expert Choice software as well as structural equations (PLS) was used for data analysis. The results of the analysis showed that independent variables and dependent variables have directly a positive and significant effect. The results also showed that increasing efficiency and productivity is the first priority, creativity and innovation is the second priority, saving time and money is the third priority, analyzing financial data is the fourth priority, collaboration and cooperation is the fifth priority, matching transactions is the sixth priority, transparency is the seventh priority, determining training needs is the eighth priority, trust in financial tools is the ninth priority, ease of use is the tenth priority, user-friendly tools are the eleventh priority, automation of repetitive processes, real-time financial analysis and financial protection and security are the twelfth priority, improving financial reporting is the thirteenth priority, and awareness, technical knowledge, and the capabilities of financial tools are the fourteenth priority.
Conclusion
The present study was conducted with the aim of providing a model of effective factors in the socialization of artificial intelligence technologies. The results of this study are in line with the results of Harikumar et al, (2023), Pourshahabi (2023), Karamipour (2023), Shirzad et al, (2023), Anca (2022), Noordin et al, (2022), Mahdavi (2022), and Jakob & Luciano (2021). Harikumar et al, (2023) concluded that artificial intelligence in e-commerce and financial industries has been used to achieve better customer experience, efficient supply chain management, improve operational efficiency and reduce material waste with the aim of designing standard and reliable methods of product quality control and finding new ways to reach customers and serve customers at low cost. Among the applications of artificial intelligence in e-commerce; corporate management and finance, sales growth, profit maximization, sales forecasting, inventory management, security, fraud detection and portfolio management are some of the main applications of artificial intelligence.
According to the results of the research, the following suggestion is made:
Accountants are advised to use artificial intelligence in the accounting and reporting process because the artificial intelligence system is completely proficient and has sufficient data in this regard. It can easily provide fast reporting and can also easily provide accurate reporting; that is, it can provide accurate reporting at any time. According to the conditions that the artificial intelligence system has, we can get from it, and it is enough to have data and it can obtain a lot of data itself; that is, it can be placed in the path of the information exchange system and exploit reports and data and it can itself provide accurate financial reporting in any style that the organization needs.