Investigating the Corporate Governance and Sustainability Relationship: A Bibliometric Analysis Using Keyword-Ensemble Community Detection
Carlo Drago (University of Niccolò Cusano); Fabio Fortuna (University of Niccolò Cusano)
L21, G34, Q56, C19, C38
Corporate Governance, Sustainability, Bibliometric Analysis, Community Detection, Ensamble Community Detection
Sustainability is a business strategy combining economic, social, and environmental issues. This paper examines the corporate governance and sustainability literature. So we consider a new bibliometric database focusing on the network of keywords appearing in the literature. The quantitative approach is also new: we combine the information from different community detection algorithms to find the most important results and relationships in the literature. The final results show that the literature on corporate governance and sustainability raises an essential strategic question: for long-term sustainability if there needs to be a strong link between stakeholders and corporate social responsibility (CSR). So, considering a company’s actions’ social, economic, and environmental effects can help figure out how much corporate responsibility is needed. Also, companies that consider CSR and sustainability in their businesses find it easier to keep long-term relationships with customers, employees, and other stakeholders, which can be considered vital. Last, a strategic view of corporate governance should emphasize the importance of intellectual capital and the Triple-Bottom-Line approach to sustainable growth in a strategic view of corporate governance. In this sense, a more wholesome view of value creation aims to provide companies with better financial results while also serving society’s environment and social well-being. By addressing these issues, governments and other groups can make the business world more sustainable and responsible.
Citazione suggerita: C. Drago, F. Fortuna, ‘Investigating the Corporate Governance and Sustainability Relationship: A Bibliometric Analysis Using Keyword-Ensemble Community Detection’, Nota di Lavoro 012.2023, Milano, Italy: Fondazione Eni Enrico Mattei