ENTERPRISE AI ANALYSIS
Does CEO Overconfidence Facilitate or Hinder Corporate Al Application?
Based on the Upper Echelons Theory, this study investigates the impact of CEO overconfidence on the level of corporate artificial intelligence (AI) application and further examines the moderating roles of CEO tenure and board size. Using panel data from China's A-share listed companies spanning 2011-2020, we construct a measure of corporate AI application by integrating Python, machine learning, and textual analysis methods. Empirical tests are conducted using a two-way fixed effects model, and the results robustly support the proposed hypotheses. This paper pioneers in linking the determinants of AI adoption with managerial psychological traits, thereby not only extending the research perspective of the Upper Echelons Theory but also providing new empirical evidence and theoretical insights for understanding corporate digital trans-formation.
Executive Impact on AI Adoption
CEO overconfidence, a key managerial psychological trait, plays a significant role in corporate AI application. This study shows that overconfident CEOs, characterized by heightened optimism and risk propensity, tend to drive their firms to embrace AI technologies and accelerate digital transformation. Their strategic decisions are shaped by a cognitive filter, leading to a proactive pursuit of high-risk, high-reward AI investments. Furthermore, CEO tenure and board size positively moderate this relationship, indicating that established CEOs with strong organizational influence, supported by larger, diverse boards, are better positioned to implement these AI initiatives effectively.
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Methodology
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AI Application Level Measurement
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| Approach | Description | Strengths |
|---|---|---|
| Investment Decisions (OC1) | Based on CEOs undertaking more investments and M&A, specifically if total assets grow faster than operating revenue. Residuals are adjusted by subtracting industry median. |
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| Earnings Forecast Bias (OC2) | CEOs classified as overconfident if firm's actual earnings fall short of forecasted earnings at least once during the sample period. |
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Direct Impact of CEO Overconfidence (H1)
+0.046 Positive effect on AI Application (p<0.01)CEO overconfidence significantly and positively influences the corporate AI application level. Overconfident CEOs are more optimistic and risk-prone, leading to higher AI adoption.
Moderating Effect of CEO Tenure (H2)
+0.001 CEO Tenure strengthens positive relationship (p<0.1)Longer CEO tenure, indicating greater organizational influence and job security, amplifies the positive impact of CEO overconfidence on AI application.
Moderating Effect of Board Size (H3)
+0.022 Board Size enhances positive relationship (p<0.05)Larger board sizes, providing diverse knowledge and legitimacy, strengthen the positive influence of CEO overconfidence on AI application.
Robustness of Findings: Alternative Measures
The study's conclusions remain robust across different measures for AI application and CEO overconfidence. For instance, using Lnwords_MD&A (AI keywords from Management Discussion & Analysis) as a dependent variable (M3) yields a significant positive coefficient for OC1 (+0.038, p<0.01). Similarly, using OC2 (earnings forecast bias) as an independent variable (M2) still shows a significant positive impact on AI application (+0.056, p<0.01). These consistent results enhance the reliability and generalizability of the findings, confirming that managerial psychological traits are crucial determinants of corporate AI adoption.
Optimizing CEO Traits for AI
Strategic Fit Leveraging Overconfidence WiselyThe findings suggest that overconfident CEOs can be catalysts for AI adoption due to their optimism and risk propensity. Companies should identify and empower such leaders, while also implementing governance mechanisms to mitigate potential pitfalls of excessive overconfidence.
Role of Governance in AI Implementation
The study highlights that CEO tenure and board size are critical moderating factors. Longer-tenured CEOs, with their accumulated influence and stability, can more effectively drive AI initiatives. Similarly, larger and more diverse boards provide a broader pool of expertise and legitimacy, facilitating complex AI transformations. Organizations should therefore consider these governance factors when designing their AI strategy, ensuring strong leadership and broad support.
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