THE INFLUENCE OF ESG-DRIVEN LENDING ON BANKING SECTOR DYNAMICS IN NIGERIA
Description
The data for this study were collected through a cross-sectional survey administered to banking professionals in Nigeria, including senior management, credit risk officers, sustainability managers, and loan officers actively involved in lending practices. The survey gathered information on ESG integration within lending processes, focusing on its impact on financial performance and credit risk management. Key financial metrics, such as Return on Assets (ROA), Return on Equity (ROE), Net Interest Margin (NIM), and credit risk exposure, were measured. The dataset also included external factors influencing ESG adoption, such as regulatory pressures and market demand. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) and Bayesian Networks to examine the probabilistic relationships between ESG integration, financial outcomes, and credit risk.
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1. Survey Design Questionnaire Development: Design a structured questionnaire with sections covering demographic details, ESG integration, financial performance, and credit risk management. Include questions that capture key financial metrics such as Return on Assets (ROA), Return on Equity (ROE), Net Interest Margin (NIM), and credit risk exposure. ESG-related Questions: Develop questions related to ESG integration, including how ESG factors are incorporated into lending practices, credit evaluation, and risk management. External Factors: Add sections that capture the influence of external factors like regulatory pressures (e.g., Nigerian Sustainable Banking Principles) and market demand for ESG-aligned financial products. 2. Target Population and Sampling Identify Respondents: Target banking professionals in Nigeria, including senior management, credit risk officers, sustainability managers, and loan officers. Ensure representation across various levels of commercial banks, ranging from small to large institutions. Sampling Technique: Employ a random sampling technique to ensure diverse perspectives, and leverage networks such as the Chartered Institute of Bankers of Nigeria (CIBN) to distribute the questionnaire. Distribution Platform: Distribute the survey electronically via platforms like Qualtrics or Google Forms. 3. Data Collection Survey Distribution: Send the questionnaire to the selected sample of banking professionals through email, industry associations, or professional networks. Collect responses on ESG integration, financial metrics, and external influences. Pre-Test: Conduct a pre-test on a small group of respondents to identify and address potential ambiguities or biases in the questionnaire. 4. Data Preprocessing Data Cleaning: Ensure the collected data is cleaned to remove any incomplete, inconsistent, or inaccurate responses. Standardization: Standardize the data by normalizing all variables to ensure comparability across different features, especially financial and ESG metrics. 5. Data Analysis Statistical Tools: Use software like SPSS, R, or SmartPLS to analyze the data. PLS-SEM: Conduct Partial Least Squares Structural Equation Modeling (PLS-SEM) to explore the relationships between ESG integration, financial performance, and risk mitigation. Bayesian Networks: Use Bayesian Network modeling to infer probabilistic relationships between ESG integration, credit risk, and financial performance based on the collected data. 6. Validation Reliability Testing: Ensure the survey's reliability through Cronbach's Alpha and Composite Reliability, aiming for a threshold of 0.70 or higher. Validity Testing: Assess construct validity through the Average Variance Extracted (AVE), ensuring AVE values exceed 0.50 for convergent validity, and test discriminant validity using the Fornell-Larcker criterion and HTMT ratio.