Accounting and Financial Management Research Seminar – Dr Mengqian Chen

Title: Artificial Intelligence Focus and Labor Investment Efficiency

Date: 14 October 2026

Time: 14:30 to 15:30

Venue: FDC.1.16 (Hybrid: In Person and Online)

The presenter will be attending in person. Colleagues who are unable to attend on campus are welcome to join online via Microsoft Teams using the link below:

Join the seminar via Microsoft Teams

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Artificial Intelligence Focus and Labor Investment Efficiency

Speaker: Dr Mengqian Chen

Mengqian joined the Adam Smith Business School in 2023 as a Lecturer in Finance. She received a PhD and MSc in Finance from the University of Manchester. Her research interests lie in empirical corporate finance, including cash holdings, corporate financing decisions, securities issuance, SEOs, stock market reactions, and firm performance. Mengqian serves on the Early Career Editorial Board of the Journal of Chinese Economic and Business Studies. She also acts as a reviewer for the British Accounting Review, The European Journal of Finance, International Journal of Finance and Economics, and Economic Modelling, among others.

University of Glasgow – Schools – Adam Smith Business School – Our staff – Mengqian Chen

Abstract: We examine how firms’ strategic focus on artificial intelligence (AI) affects the efficiency of their labor investment. Using a text-based measure of AI focus constructed from 10-K filings of U.S. public firms, we find that greater AI focus reduces abnormal hiring, indicating more efficient labor investment. Cross-sectional evidence supports three channels: an informational channel among intangible-intensive firms, an adjustment-cost channel among financially constrained firms and firms facing intense product market competition, and an agency channel among firms with smaller boards and better compliance records. Mediation analyses provide suggestive evidence for a management-guidance pathway and stronger associational evidence for an earnings-volatility pathway, whereas AI focus is not significantly associated with contemporaneous total factor productivity. This pattern suggests that AI’s organizational value may be visible in workforce decision quality even when not apparent in conventional contemporaneous productivity measures. Our findings are robust across a battery of tests.