mesbah sivandian

A review of the internal audit literature over the past century reveals a significant topic variety and thematic diversity, so that a systematic review of these studies is necessary in order to gain a deeper understanding of internal audit research. This research aims to fill the gap in previous review research and in response to the recommendation of Behrend & Eulerich (2019) and aims to identify the most effective articles, journals, authors, research centers and countries; Structure of cooperation between authors (co-authorship), research centers and countries; and issues with repetitive patterns (co-occurrence) in the field of internal auditing over the last hundred years. Using the Computational Literature Review (CLR) approach, a collection of articles extracted from the WoS database (1401 articles) over a period of one hundred years from 1920 to 2021 are examined in two ways: impact analysis and structure analysis. VOSviewer is used to build and visualize Bibliometrics. Researchers, practitioners, standard setters, and regulators in the field of accounting and auditing are the target audience of this research and can, respectively, use its results to direct future research, improve the level of internal auditing in practice, and develop standards and regulations to improve oversight on internal audit processes and required competencies.

According to the variety and diversity of research conducted in the field of internal auditing, the purpose of this research is to gain a deeper understanding of the topic by conducting a systematic review of existing studies to classify topics and outline some potential future research directions.

In order to achieve our purpose and to more efficiently examine a large number of articles, the "Computational Literature Review (CLR)" approach is used, and through this, the content of abstracts is analyzed automatically to provide a set of research topics within internal audit literature (Topic Modeling). The required information is collected from the Thomson Reuters Web of Science (WoS) database for 1401 articles over almost one hundred years (1920 to 2021).

The results of Topic Modeling show the focus of seven areas in internal audit research as follows: internal audit in the healthcare industry; internal audit in the areas of risk management, fraud, and internal controls; competence and training and internal audit quality; and the relationship between the external auditor and the internal auditor. A separate review of each of these identified issues related to internal audit reveals the most critical research conducted in this area and identifies some avenues for future research.

The results of this study indicate that throughout the evolution of internal auditing during its history (almost 100 years), its philosophy has evolved from ensuring the accuracy of financial statements and accounts and preventing the misuse of corporate funds or assets by helping to create and add value to companies. Internal auditing services have been upgraded from assuring financial statements and responding to its limited stakeholders (i.e., senior managers), giving assurance, consulting, forecasting risk, and being accountable to all stakeholders. Also, the change in the accountability direction of internal audit and its greater cooperation with external auditors, and the growth of technology and increasing the complexity of organizations have led to changes in competencies and skills required and improving and promoting its independence and objectivity; also engaging internal auditors in other areas of expertise (other than accounting and auditing), then it is expected that the trend will continue in the future.

Multi-criteria decision making methods have been of the most popular and practical areas in recent years. Among these methods, the AHP method has been considered by many researchers due to its unique features and has been used in numerous studies. This method facilitates decision-making for the decision-maker by allowing pairwise comparison between alternatives using language expressions. But this feature causes inconsistency in the decision matrix. One of the sources of inconsistency is the use of a predetermined scale to convert linguistic variables into quantitative values due to the different intellectual background of experts and their information about the problem. Therefore, the purpose of this study is to provide a customized scale for converting linguistic variables using the granularization of linguistic variables, which is in line with expert opinions and reduces the inconsistency. The unique feature of this framework is that the distribution of cut-off points is not known in advance and is determined according to expert opinions. To optimize the proposed model, a particle swarm optimization metaheuristic algorithm is used, which is modified to adapt to the specific characteristics of the problem. The results show the good performance of the proposed framework in reducing incompatibility.