Güney, Cengiz; Konyar, Mehmet Zeki; Akinci, Abdulvahap; Ala, Tolga; Alkan, Okan

Today, the industry faces data-related challenges stemming from the use of artificial intelligence. Similarly, sustainability challenges in real world applications also arise. One of these is the accounting and auditing fields. Because of how to use artificial intelligence in this field and what technical approaches are not known enough. This study presents a machine learning approach with engineering and technical view. Because of the technical complexity, the papers on artificial intelligence and machine learning in accounting and auditing are often hard to evaluate and need a clear understanding of critical methodological parts. This paper aims to guide the key methodological aspects of artificial intelligence and machine learning for non-expert researchers of accounting and auditing. The proposed paper draws up the general framework for the use of machine learning, a sub-branch of artificial intelligence, in sustainable accounting studies. The most frequently used classification methods for accounting data and the metrics used in the evaluation of the results have been explained. In addition, the framework of recent studies such as bankruptcy prediction, financial distress, corporate failure, accounting fraud prediction, tax compliance problems, and financial difficulties conducted with accounting and auditing data has been examined. One of the findings obtained from this study is that although many studies in literature mention artificial intelligence with a few words, they decorate the title or keywords of the study with artificial intelligence. Keyword analyses are also among the findings of the study. All findings of the analysis emphasize the increasing importance of artificial intelligence in the field of accounting and auditing and the diversity of research in this field. If the notion of artificial intelligence is one of the essential components of future research, this study advises researchers to use it carefully in the study's title and keywords.

Cengíz, Orhan; Manga, Muge

Energy poverty (EP) is still one of the critical problems for many countries. For this purpose, we try to assess whether trade openness (TO) and foreign direct investment (FDI) are associated with EP for the panel sample of Belize, Bolivia, Honduras, Mexico, Nicaragua, Panama, and Peru, spanning the period 1992-2020. The panel vector autoregression (PVAR) estimation results indicate that economic growth, TO, and FDI inflow reduce EP by increasing access to electricity in these countries. Further, PVAR Granger causality findings show feedback causality linkage between EP and TO, EP and FDI inflow, economic growth and TO, and economic growth and FDI inflow. Moreover, the outcomes indicate that a bidirectional causality exists from economic growth to EP and from TO to FDI inflow.

Güney, Selami; Kaya, Abdulkadir

The high number of legal regulations, intense competition and rapid developments in business life create a heavy workload and serious stress on Certified Public Accountants. In this study, it is to determine the relationships between heavy workload, job satisfaction, poor performance and job stress experienced by Certified Public Accountants. According to the results of the at acceptable levels and that stress is significantly and favorably impacted by the perception of a heavy workload. On the other hand, stress was significantly and adversely impacted by perceptions of job satisfaction. Additionally, performance was significantly and favorably impacted by perceptions of a heavy workload and job satisfaction. On the other hand, work-related stress has had little impact on how well employees perform. In addition, it has been concluded that the stress loads of the professionals' increase depending on the workload and the stress increases with the new obligations that will occur in the workloads during the professional process.

Sönmez, Adem Ruhan; Yilmaz, Fatma

In this study, the relationship between corporate governance, board committees, ownership structure and financial performance is examined 104 listed firms in the BIST Industrial Index from 2012 to 2019. The data set of the study was obtained from the annual reports of the companies. System Dynamic panel data analysis method (SGMM) was used in the study. As a result of the analysis; there is no relationship between the board size, the board independence, the gender diversity, the largest shareholder ratio, the meeting frequency, the audit committee size, the audit committee meeting frequency and financial performance; It has been determined that there is a positive relationship between the risk committee size and risk committee meeting frequency and financial performance.