The Factors Influencing Executive Compensation Among Listed Companies in China’s Software and Information Technology Service Industry

Fu, Mengdie (2022) The Factors Influencing Executive Compensation Among Listed Companies in China’s Software and Information Technology Service Industry. [Dissertation (University of Nottingham only)]

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Abstract

This paper mainly studies the determinants of executive compensation of Listed Companies in China's software and information service industry. The main influencing factors selected in this paper are: company size, company performance, stock price, and a set of corporate governance indicators (such as the proportion of female executives, the shareholding proportion of executives, the proportion of state-owned shares and the proportion of independent directors). This paper selects the sample data of 2020, 2015 and 2010, and uses SPSS software for multiple linear stepwise regression analysis. The results suggested that the significant expansion of the company would determine the significant increase of executive compensation. Firm performance increased executive compensation, but the impact was only significant in 2010 and 2015. The rise of the company's market index (the stock price) would lead to the ascension of executive compensation in 2010 and 2020, but there was no significant relationship in 2015. The proportion of female executives and the proportion of state-owned shares in corporate governance indicators had no impact on executive compensation. In 2020 and 2015, as one of the factors determining executive compensation, executive shareholding ratio had a negative and significant effect on executive compensation. The higher the proportion of independent directors, the higher the executive compensation, but this correlation only in 2010. According to the findings, this research recommends that the executive compensation needs to match the size of the company, and firms in the IT industry should strengthen the supervisory role of independent directors and establish a compensation performance system.

Item Type: Dissertation (University of Nottingham only)
Depositing User: FU, Mengdie
Date Deposited: 27 Apr 2023 15:11
Last Modified: 27 Apr 2023 15:11
URI: https://eprints.nottingham.ac.uk/id/eprint/68046

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