طراحی مدل عوامل بنیادین ناهمسانی اطلاعاتی در بستر نظریه نمایندگی

Abstract

Objective: This study presents a comprehensive and holistic model for identifying and analyzing the fundamental factors of information asymmetry in companies listed on the Tehran Stock Exchange, using a mixed quantitative–qualitative approach within the framework of agency theory. Information inequality between managers and shareholders(shaped by ownership structure, individual incentives, and institutional and cultural contexts)can increase agency costs and reduce transparency. By systematically integrating previous findings, this research provides a holistic view of the roots of this phenomenon.

Methods: This applied research employed a mixed-method (quantitative–qualitative) approach using a survey methodology. Initially, based on the theoretical framework of Agency Theory and Asymmetric Information, fundamental variables of information asymmetry were identified through a systematic literature review and structured content analysis of financial reporting texts and regulations, as well as semi-structured interviews with experts in accounting and financial reporting. A questionnaire was then developed accordingly, and its face and content validity were confirmed by a panel of professors and practitioners.The statistical population comprised university faculty members and professors of economics, financial management, accounting, and statistics from universities in Tehran, as well as experts from the Tehran Stock Exchange, financial and auditing institutions operating in Tehran Province, and financial and accounting managers of companies listed on the Tehran Stock Exchange. Group sampling was adopted, and ultimately, 168 respondents completed the questionnaires in full, providing the data for analysis.The collected data were first evaluated through descriptive and inferential statistics and Cronbach’s alpha coefficient to assess preliminary characteristics and reliability. Subsequently, exploratory factor analysis was conducted to reduce the variables to a limited set of core and fundamental dimensions of information asymmetry. This analytical process provided a robust and coherent foundation for designing the final model of fundamental factors underlying information asymmetry.

Conclusion: The proposed model identified four key factors explaining 90.306% of the total variance among variables. A total of 40 variables were identified and ranked based on their factor loadings as influential components of information asymmetry in listed companies. The four extracted factors were as follows:Organizational and Structural Factors – with a cumulative factor loading of 7.49, explaining 28.14% of the total variance.Legal and Regulatory Factors – with a cumulative factor loading of 7.37, explaining 22.746% of the variance.Economic, Financial, and Market Structure Factors – with a cumulative factor loading of 7.05, accounting for 20.38% of the variance.Managerial Factors – with a cumulative factor loading of 6.62, explaining 19.04% of the variance. Together, these four factors accounted for 90.306% of the total variance in the data.



Results: The findings indicate that a meaningful reduction in information asymmetry cannot be achieved solely through traditional tools such as improving financial reporting. Instead, a coordinated set of interventions is needed across diverse domains, including strengthening internal controls, improving board structure, enhancing disclosure regulations, promoting competitive market conditions, reforming compensation systems, and fostering a culture of transparency.The results of this study can serve as a comprehensive roadmap for the Securities and Exchange Organization, financial regulatory institutions, independent auditors, and senior corporate managers, providing an evidence-based foundation for developing effective policies and interventions to improve the informational environment of the capital market. Furthermore, future research is recommended to explore causal relationships among the identified factors through comparative approaches and structural modeling, thereby reinforcing evidence-based policymaking.