Cornell University
Library
Cornell UniversityLibrary

eCommons

Help
Log In(current)
  1. Home
  2. Cornell University Graduate School
  3. Cornell Theses and Dissertations
  4. Generative AI Shock and CEO-Board Information Sharing

Generative AI Shock and CEO-Board Information Sharing

File(s)
Zhong_cornell_0058O_12706.pdf (1.54 MB)
Permanent Link(s)
https://doi.org/10.7298/eqdn-fn27
https://hdl.handle.net/1813/126287
Collections
Cornell Theses and Dissertations
Author
Zhong, Yuhan
Abstract

This study examines how the generative artificial intelligence (GenAI) shock alters the information-sharing equilibrium between the CEO and the board. Building on Adams and Ferreira (2007), I develop an analytical framework in which the GenAI shock simultaneously increases business uncertainty and amplifies the CEO’s private benefits, producing opposing effects on her incentive to share information. Three channels emerge: under advice-seeking, the CEO shares more as the marginal value of board advice dominates; under monitoring threat, she shares less as heightened career risk and expanded discretion dominate; under strategic neutrality, the two forces offset. Two firm-level factors determine which channel prevails: the level of agency problems and the relative AI-domain knowledge between the CEO and the board. As an additional analysis, shareholders optimally respond by adjusting board independence rather than maintaining it at its pre-shock level. The framework yields implications for empirical research on AI-era corporate governance.

Description
66 pages
Date Issued
2026-05
Committee Chair
Turvey, Calum
Committee Member
Yeung, Ping
Degree Discipline
Applied Economics and Management
Degree Name
M.S., Applied Economics and Management
Degree Level
Master of Science
Type
dissertation or thesis

Site Statistics | Help

About eCommons | Policies | Terms of use | Contact Us

copyright © 2002-2026 Cornell University Library | Privacy | Web Accessibility Assistance