Internal compensation gaps among executives: data analysis based on investment efficiency model

Ming Xu; Wenjing Lu

doi:10.1117/12.2646332

23 August 2022 Internal compensation gaps among executives: data analysis based on investment efficiency model

Ming Xu, Wenjing Lu

Proceedings Volume 12330, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022); 123302F (2022) https://doi.org/10.1117/12.2646332
Event: International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), 2022, Huzhou, China

Abstract

The rise of new technology is reshaping corporate governance regimes and firm financial behavior. Investment activities are the main driving force for the rapid development of the nation and the basis for the company's success in today's competitive market. This paper adopts Richardson's expected investment model and the pooled regression model, based on the data A-share listed companies from 2008 to 2020, with executive incentives as the main entry point, to empirically study the impact of the internal compensation gap of executives on investment in China. The results show that the internal compensation gap of the senior management team is negatively related to investment efficiency. The larger the internal compensation gap, the higher level of inefficient investment; In state-owned companies, the negative effect of the executive internal compensation gap on investment efficiency is more significant, and inefficient investment is reflected as over-investment. This paper provides targeted practical advice for different companies on how to improve investment efficiency through the system design of executive compensation.

Citation Download Citation

Ming Xu and Wenjing Lu "Internal compensation gaps among executives: data analysis based on investment efficiency model", Proc. SPIE 12330, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), 123302F (23 August 2022); https://doi.org/10.1117/12.2646332

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KEYWORDS

Data modeling

Data analysis

Analytical research

Statistical analysis

Failure analysis

New and emerging technologies

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