Evaluating CEO hubris effects on sustainable performance in the IC design industry: An integrated dynamic network DEA framework with machine learning

This study introduces an integrated analytical framework combining dynamic network data envelopment analysis (DNDEA) with machine learning to assess the impact of CEO hubris on sustainable performance in the integrated circuit (IC) design industry. Our two-stage DNDEA model evaluates operational and R&D efficiency separately, incorporating intermediate factors including profit and ESG scores. We develop a novel text-based measure of CEO hubris by analyzing the contrast between confidence and conservatism language in annual shareholder reports. This hubris measure is then incorporated into predictive models, where we compare traditional linear regression against advanced machine learning approaches—support vector regression (SVR) and random forest (RF)—using cross-validation and hyperparameter optimization. The analysis reveals a significant negative correlation between CEO hubris and operational and R&D efficiency. Notably, the non-linear models (SVR and RF) demonstrate superior predictive accuracy compared to linear regression across varying levels of CEO hubris. These findings yield two primary contributions: first, they establish the critical need for monitoring hubristic leadership behavior in innovation-intensive industries, given their detrimental effect on organizational efficiency. Second, they validate the effectiveness of combining text analytics, DNDEA efficiency metrics, and machine learning for evaluating leadership impact on firm performance. This methodology provides a comprehensive framework for analyzing leadership dynamics in the IC design sector and offers an adaptable template for similar analyses across technology-driven industries.