Executive resilience aids firm digitization only up to a point: moderate resilience speeds digital transformation, but excessive resilience fosters risk underestimation and fixation that misallocates resources and reduces transformation effectiveness, with capital intensity and slack shifting the tipping point.

Main Finding

There is an inverted U-shaped relationship between executive psychological resilience and enterprise digital transformation in Chinese A-share firms (2011–2024): moderate levels of resilience promote digital transformation, but beyond an optimal point resilience reduces transformation effectiveness through bounded-rational behaviors (risk underestimation, attentional bias) that lead to inefficient resource orchestration.

Key Points

• Theoretical framing: combines resource orchestration theory with the too-much-of-a-good-thing (TMGT) effect to explain why resilience can be beneficial up to a point and harmful beyond it.
• Primary result: resilience → digital transformation is nonlinear (inverted U). An optimal resilience level maximizes transformation outcomes.
• Mechanisms identified: excessive resilience induces bounded rationality — executives underweight transformation risks and develop attentional biases (overfocus on certain initiatives, neglect of others), producing suboptimal allocation of resources.
• Moderators:
  • Industry capital intensity weakens the inverted-U relationship (capital-intensive sectors dampen the resilience effect).
  • Firm slack resources shift the turning point earlier (with more slack, negative effects appear at lower resilience levels).
• Contribution: challenges the assumption that resilience is uniformly positive, extends resource orchestration theory to digital transformation, and highlights executive psychological traits as micro-level drivers of firm-level digitalization.

Data & Methods

• Data: panel of Chinese A-share listed firms, 2011–2024.
• Empirical strategy (as reported):
  • Tests for a nonlinear effect of executive psychological resilience on digital transformation (quadratic/inverted-U specification).
  • Examination of moderating effects for industry capital intensity and internal slack resources.
  • Investigation of behavioral/mechanistic channels centered on bounded rationality (risk perception and attentional allocation).
• Likely robustness approaches (implied by study design): panel regressions with firm/time controls, interaction terms for moderators, and checks to ensure findings are not driven by observable firm heterogeneity or time trends.

Implications for AI Economics

• Modeling managerial effects: Economic models of firm digitalization and AI adoption should allow manager traits to have nonlinear impacts — resilience and similar traits can increase adoption up to a point and then hinder it.
• Resource allocation and efficiency: Overconfident or overly resilient executives can misallocate resources toward AI/digital projects, lowering returns. Policies or governance that constrain such overreach can improve aggregate productivity of digital investments.
• Sectoral heterogeneity: Capital-intensive industries mute the role of executive resilience in digitalization. Predictions about AI diffusion and firm-level uptake must account for industry capital structure.
• Role of slack resources: Firms with more slack may reach the point of diminishing returns from resilient leadership earlier; slack can accelerate experimentations but also magnify the costs of poor prioritization.
• Practical guidance for firms and policymakers:
  • Executive selection and training should emphasize calibrated resilience: promote persistence and adaptability but guard against overconfidence and fixation.
  • Strengthen decision governance (independent review, staged investment, formal risk assessment) to counteract bounded-rationality-driven misallocation in digital/AI projects.
  • Tailor support and incentives for digital transformation by industry capital intensity and observed slack resources to avoid one-size-fits-all policies.
• Research directions for AI economics: investigate how other executive traits interact nonlinearly with AI adoption outcomes; quantify welfare effects of misallocated digital investments; study policy tools (tax incentives, governance reforms) that alter the resilience–digitalization curve.