Publication Type

PhD Dissertation

Version

publishedVersion

Publication Date

4-2026

Abstract

This dissertation examines whether and how corporate adoption of artificial intelligence (AI) affects capital market valuation in manufacturing firms. As AI is increasingly embedded in R&D design, production organization, supply chain coordination, and operational management, its value implications depend not only on whether firms adopt AI, but also on how AI is deployed within business processes. Existing research has mainly focused on productivity, innovation, labor substitution, and digital transformation, while the enterprise value consequences of differentiated AI application strategies remain insufficiently examined.

Using Chinese A-share listed manufacturing firms from 2015 to 2024 as the sample, this study identifies firm-level AI application intensity from the Management Discussion and Analysis (MD&A) sections of annual reports through a procedure combining keyword screening, semantic disambiguation, and multi-label classification. AI applications are further classified into five types: Strategy-Oriented, Operational Efficiency-Oriented, Market & Product-Oriented, Resource Integration-Oriented, and Forward-Looking Statement-Oriented. The study also uses the Pianor case to illustrate how AI becomes embedded in the manufacturing value chain and gradually affects enterprise value through changes in information processing, organizational coordination, and resource allocation. Empirically, the study employs two-way fixed effects models, together with endogeneity and robustness checks.

The results show that AI application significantly improves the value of manufacturing firms. Strategy-Oriented, Market & Product-Oriented, Resource Integration-Oriented, and Forward-Looking Statement-Oriented applications exhibit positive value effects, whereas the current effect of Operational Efficiency-Oriented applications is insignificant and may even be negative in joint regressions. Further analysis indicates that Strategy-Oriented and Market & Product-Oriented applications have the most robust marginal effects. The value effect of AI is stronger in labor-intensive and technology-intensive industries, but insignificant in capital-intensive industries. Overall, this dissertation contributes to the literature by developing a more refined measure of enterprise AI adoption, distinguishing heterogeneous AI application strategies, and integrating case-based mechanism analysis with large-sample empirical evidence. The findings provide practical implications for manufacturing firms seeking to align AI adoption with business scenarios, organizational capabilities, and industry conditions.

Keywords

Artificial Intelligence, Enterprise Value, Organizational Adjustment, Factor Structure

Degree Awarded

Doctor of Bus Admin (CKGSB)

Discipline

Finance and Financial Management | Strategic Management Policy

Supervisor(s)

GENG, Xuesong

First Page

1

Last Page

267

Publisher

Singapore Management University

City or Country

Singapore

Copyright Owner and License

Author

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