Modeling the Role of Business Risks, Capital Structure, Liquidity, and Credit Risk in ESG Sustainability with an Emphasis on Smart Control Solutions

Document Type : Original Article

Authors

1 Ph.D Candidate in Financial Engineering, south tehran branch islamic azad university, Tehran, Iran

2 Assistant Professor, South Tehran Branch Islamic Azad University, Tehran, Iran.

3 Associate Prof, Department of Accounting, Faculty of Economics and Accounting, Islamic Azad University, South Tehran Branch, Tehran, Iran

Abstract
This study aimed to model the role of business risks, capital structure, liquidity, and credit risk in ESG sustainability, emphasizing smart control solutions. Given the exploratory nature of the topic, a qualitative approach and thematic analysis were employed. The research population consisted of experts in risk management, finance, corporate governance, and smart technologies, selected through purposive sampling until theoretical saturation was achieved. Ultimately, 16 experts participated in semi-structured interviews. Following full implementation, data were coded and analyzed using Braun and Clarke’s six-stage thematic analysis framework. The findings revealed that business risk impacts an organization’s ability to achieve environmental, social, and governance goals through market fluctuations, changing customer behavior, intense competition, and environmental uncertainty. Capital structure risk undermines sustainable decision-making and responsible investment via high leverage, capital costs, short-term debt dependence, and funding volatility. Liquidity risk, particularly regarding cash flow shortages, delayed receivables, and income-cost mismatches, threatens ESG policy implementation, while credit risk—characterized by defaults, poor creditworthiness, and delinquent receivables—affects stakeholder relationships and ESG reputation. Thematic analysis demonstrated that these risks possess systemic, synergistic links; management weaknesses in one area inevitably impact others. Conversely, intelligent control solutions based on artificial intelligence, machine learning, real-time data analysis, management dashboards, predictive algorithms, and early warning systems offer significant potential for improving risk monitoring and forecasting. Accordingly, the proposed conceptual model emphasizes the necessity of integrating financial risk management and ESG within a robust, data-driven, and intelligent governance framework to ensure long-term organizational sustainability.

Keywords

Subjects

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Volume 2, Issue 6
Spring 2026
Pages 21-37

  • Receive Date 04 March 2026
  • Revise Date 18 April 2026
  • Accept Date 25 May 2026