Research on Influencing Factors and Improvement Strategies of Corporate ESG Performance from a Configuration Perspective

Keli Wang

doi:10.62051/ijgem.v6n1.10

Authors

Keli Wang

DOI:

https://doi.org/10.62051/ijgem.v6n1.10

Keywords:

ESG rating, Aviation companies, Dynamic QCA, Configuration path

Abstract

There is a close relationship between ESG ratings and aviation companies. A higher ESG score can enhance the social image, brand value, and market competitiveness of aviation companies; At the same time, it also helps to reduce financing costs, enhance investor confidence, and promote the sustainable development of the industry. Using 21 aviation manufacturing A-share listed companies as case studies, the dynamic QCA method is adopted to further explore the configuration path of factors affecting the ESG score of aviation enterprises from the spatiotemporal dimension by processing and analyzing the financial panel data of enterprises from 2018 to 2023. Research has found that financial factors indirectly affect the ESG score of aviation companies, and there are three pathways that affect the ESG score of aviation companies, namely market structure, financial performance, and capital structure. The configuration of these three variables has a strong impact on the ESG score of aviation companies, and these impacts have complex and nonlinear characteristics.

Downloads

Download data is not yet available.

References

[1] Orlitzky, M., Schmidt, F. L., & Rynes, S. L. (2003). Corporate social and financial performance: A meta-analysis. Organization Studies, 24(3), 403-441.

[2] Aras, G., & Crowther, D. (2008). Corporate sustainability reporting: A study in disingenuity? Journal of Business Ethics, 87(1), 279-288.

[3] Scholtens, B. (2008). A note on the interaction between corporate social responsibility and financial performance. Ecological Economics, 68(1), 46-55.

[4] Jiraporn, P., & Gleason, K. C. (2007). Capital structure, shareholder rights, and corporate governance. Journal of Financial Research, 30(1), 21-33.

[5] Jensen, M. C. (1986). Agency costs of free cash flow, corporate finance, and takeovers. American Economic Review, 76(2), 323-329.

[6] Ragin, C. C. (2008). Redesigning social inquiry: Fuzzy sets and beyond. University of Chicago Press.

[7] Fiss, P. C. (2011). Building better causal theories: A fuzzy set approach to typologies in organization research. Academy of Management Journal, 54(2), 393-420.

[8] Garcia Castro Roberto & Ariñ o Miguel. (2016). A General Approach to Panel Data Set-Theoretic Research. Journal of Advances in Management Sciences & Information System63-76.

[9] Guedes J M, Gonç alves C D V, Soares N, et al. UK evidence for the determinants of R&D intensity from a panel fsQCA [J]. Journal of Business Research, 2016, 69(11):5431-5436.

[10] Schneider C Q, Wagemann C. Set-theoretic methods for the so-cial sciences: A guide to qualitative comparative analysis [M]. Cambridge: Cambridge University Press, 2012.

[11] Du Yunzhou, Jia Liangding Configuration Perspective and Qualitative Comparative Analysis (QCA): A New Path for Management Research [J]. Management World, 2017, (06):155-167. DOI: 10.19744/j.cnki.11-1235/f.2017.06.012.