Configurational analysis of corporate governance and corporate social responsibility reporting assurance: understanding the role of board and CSR committee

Purpose - Reacting to the calls in the contemporary literature to further examine the relationship between board attributes and firms’ decisions to obtain corporate social responsibility assurance (CSRA) through the use of pioneering techniques, this study aims to analyse the influence of such attributes together with the existence of a corporate social responsibility (CSR) committee on the adoption of CSRA using fuzzy set qualitative comparative analysis (Fs-QCA). Design/methodology/approach - Fs-QCA was performed on a sample of nonfinancial European companies listed on the STOXX Europe 600 index over the period 2016–2018. Findings - The study findings indicate that the decision to obtain a CSRA report depends on a complex combination of the influence of the CSR committee and certain board attributes, such as size, experience, independence, meeting frequency, gender and CEO separation. These attributes play essential contributing roles and, if suitably combined, stimulate the adoption of CSRA. Practical implications - The study findings are important for policymakers, professionals, organisations and regulators in forming and modifying the rules and guidelines related to CSR committees and board composition. Originality/value - To the best of the authors’ knowledge, this study represents the first examination of the impact of board attributes and CSR committees on the adoption of CSRA using Fs-QCA method. It also offers a novel methodological contribution to the board-CSRA literature by combining traditional statistical (logistic regression) and Fs-QCA methods. This study emphasises the benefits of Fs-QCA as an alternative to logistic regression analysis. Through the use of these methods, the research illustrates that Fs-QCA offers more detailed and informative results when compared to those obtained through logistic regression analysis. This finding highlights the potential of Fs-QCA to enhance our understanding of complex phenomena in academic research.