Understanding Elon Musk's Acquisition of Twitter Through the Hubris Hypothesis: An Examination of Behavioral Finance, Governance, and Decision‑making

Abstract

This study analyzes Elon Musk's acquisition of Twitter through the lens of the hubris hypothesis and theories of managerial overconfidence. Using a qualitative case study design, the research triangulates news reports, press releases, and academic literature to meticulously reconstruct the deal's chronology and evaluate observable behavioral indicators. These indicators, including prior performance, media acclaim, leadership concentration, premium pricing, and the waiver of due diligence, are systematically coded against established measures in the behavioral corporate finance literature. The evidence strongly supports behavior consistent with the hubris hypothesis, helping to explain the controversial financing choices and the heightened post-deal governance risks. The analysis integrates agency theory and behavioral finance to demonstrate how typical institutional constraints can sometimes temper managerial overconfidence. Crucially, it also uncovers boundary conditions where both market discipline and effective board oversight fail to curb excessive risk-taking, particularly in high-profile, nontraditional mergers. By explicitly operationalizing hubris indicators in a replicable way, this research contributes to the expanding behavioral corporate finance literature by establishing a link between overconfidence and digital platform acquisitions, where both substantial financial leverage and potent ideological motives are salient factors. Policy implications advocate for stronger board independence, mandatory due-diligence disclosures, stricter leverage discipline, and robust governance safeguards for social media firms that operate as quasi-public spheres.

Received: 15 October 2025 | Revised: 10 December 2025 | Accepted: 8 January 2026

Conflicts of Interest

The author declares that she has no conflicts of interest to this work.

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Author Contribution Statement

Jia-Ying Lyu: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation,
Writing – original draft, Writing – review & editing, Visualization, Supervision.