Investigating the influence of digital technology application on employee compensation

Sai Yuan, Ran Zhou, Mengna Li and Chengchao Lv

Technological Forecasting and Social Change, 2023, vol. 195, issue C

Abstract: The impact of the digital technology application (DTA) on labor income share has sparked intense debate among academics. Nevertheless, few studies have focused on its influence and potential mechanisms on average employee compensation (AEC) and the executive-employee compensation gap (ECG). We employed listed corporations in China from 2011 to 2020 to answer these questions. Our findings are as follows. First, DTA can raise AEC, but it also widens ECG. Second, the potential mechanisms of DTA to enhance AEC are improving gross operating income, optimizing human resource allocation, and boosting total factor productivity. Its potential mechanism for widening ECG also is expanding managerial power, in addition to human resources and total factor productivity. Third, operational management digital technology increases AEC and broadens ECG. Inversely, commercial model digital technology has no significant impact on either. Operational management digital technology promotes total factor productivity more significantly than commercial model digital technology, thereby increasing AEC and ECG. Fourth, digital technology applied by technology-based firms has contributed to both AEC and ECG. In labor-based firms, it has only facilitated AEC. Increased demand for high-skilled laborers, decreased demand for low-skilled laborers, and expanded managerial power in technology-based firms are essential factors responsible for this phenomenon.

Keywords: Digital technology application; Average employee compensation; Executive-employee compensation gap; Potential mechanism (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:195:y:2023:i:c:s0040162523004729

DOI: 10.1016/j.techfore.2023.122787

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