Modelling Regulatory Load and Convergence Efficiency Using Evidence from Blackstone and KKR (۲۰۱۸-۲۰۲۵)

سال انتشار: 1405
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 62

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شناسه ملی سند علمی:

IMPEB01_0182

تاریخ نمایه سازی: 10 آبان 1405

چکیده مقاله:

Purpose: This study investigates how supervisory technology (Sup Tech), compliance investment, and digital governance jointly influence governance efficiency in private equity (PE) firms. It introduces the Regulatory Load Function (Lt) and the Regulatory Convergence Efficiency (RCE) index to explain how firms adapt to increasing regulatory complexity and technological transformation. Design/Methodology/Approach: Using a multi-source panel dataset integrating Blackstone (۲۰۱۸-۲۰۲۵), KKR (۲۰۱۸-۲۰۲۴), OECD governance indicators, and Invest Europe activity data (۲۰۱۹-۲۰۲۴), the study applies fixed-effects modelling with HC۳ heteroskedasticity correction and ۱۰,۰۰۰ bootstrap replications. Comparative analysis validates cross-firm coefficient consistency, while triangulation with OECD and ESG convergence indices assesses external reliability of RCE dynamics. Findings: Results show that deployment intensity (Dt) and compliance investment (Ct) significantly enhance governance efficiency, while realization velocity (R+) has no independent effect. Digital governance adoption accelerates RCE gains, particularly post-۲۰۲۱. Comparative results from KKR replicate coefficient directionality, confirming structural reproducibility of the model. RCE increases approximately ۳ percent annually, aligning with global ESG and supervisory harmonization trends. Practical Implications: For PE firms, governance capabilities emerge as strategic assets rather than cost centers. Findings offer policy guidance for emerging markets, including Iran, showing that early digital governance adoption, harmonized regulatory baselines, and structured compliance units materially strengthen governance adaptability and investment readiness. Originality/Value: The study extends SupTech beyond public regulatory domains and positions it as a measurable internal governance mechanism for PE firms. By operationalizing Lt and RCE and validating them across independent datasets, it provides a replicable model for analyzing predictive governance under evolving regulatory and technological conditions.

نویسندگان

Mohammad Hamzeh Ghadirinejadian

PhD Candidate in Financial Engineering, Imam Sadiq University, Tehran, Iran.

Mohsen Akaberi

PhD Candidate in Econometrics, Allameh Tabataba'i University, Tehran, Iran.