A Method for Evaluating and Optimizing the XBRL Taxonomy Structure to Improve

Abstract

Abstraction The advent of Information Technology (IT) and its development have made some changes in businesses. While enterprises and their systems and IT infrastructures are getting more complicated and also the number of standards and approaches in this scope are rising in interoperability in different layers of information technology in the enterprises. This challenge, especially in the field of semantic interactivity, causes inconsistencies and contradictions in semantic interactions that require the use of automatic approaches at the time of execution. A common approach that has been used in the direction of semantic interoperability is to define standards or taxonomies for a specific field and oblige institutions to follow mentioned standards in information exchange. Extensible Business Reporting Language (XBRL) has been widely implemented by various institutions in recent years in order to improve semantic interactivity. XBRL is developed to define notions and standard taxonomies related to particular applications. Presenting a unified and proper quality report to variant users whether natural or legal is expected to be among the most important results of XBRL. In fact, the quality of the report is the main and ultimate goal of using XBRL. Because the better the quality of the report, the better the semantic interactivity among different people and enterprises. In this paper, the impact of the XBRL taxonomy architecture on the quality of financial reports is investigated. Initially, the taxonomy is categorized into four structures including content structure, syntactic and semantic structure, physical and logical structure, and rules mechanism. Then, according to the metrics of cohesion, coupling, the richness of the label and language type, the average dimensions, the number of tuple structures, and explicitness, some changes have been proposed. For the purpose of evaluation, the proposed changes are made to the taxonomy of the Securities and Exchange Organization of Iran, and then the amount of each metric is calculated before and after applying the changes using the provided formulas. The simulation results show an improvement in the taxonomy architecture. Afterward, reports are taken from the taxonomy before and after applying the changes, and their quality is examined based on three qualitative metrics including transparency, information symmetry, and comparability using a questionnaire and statistical analysis the results show that after applying the changes to the taxonomy architecture a tangible improvement has been achieved.