Antecedents of Ambidextrous Leadership in Entrepreneurship: The Role of Emotional Intelligence and Entrepreneurial Leadership Styles
Bibliographic record
Abstract
ABSTRACT Ambidextrous leadership (AL) is a game‐changer for entrepreneurial success, empowering leaders to balance exploration and exploitation expertly—two powerful forces that drive innovation and business growth. Yet, despite its evident importance, research into the factors that shape AL in entrepreneurial settings remains surprisingly sparse. This study fills this gap by exploring how emotional intelligence, adaptive and flexible leadership, transformational leadership, and transactional leadership influence AL behaviors among entrepreneurial leaders. Drawing on data collected via structured questionnaires from entrepreneurs who lead high‐tech businesses in the UK, this research reveals how each leadership dimension contributes to AL. The results, based on multiple linear regression analysis, indicate that while all four leadership styles influence AL, they do so in distinct ways. Transformational and transactional leadership help shape a leader's opening and closing behaviors, while adaptive or flexible leadership determines the strategic timing for their deployment. Additionally, emotional intelligence fosters the emotional climate that helps leaders navigate the tensions inherent in leading innovation. The study demonstrates that emotional intelligence and adaptive/flexible leadership are individual capabilities that empower leaders to act ambidextrously. In contrast, transformational and transactional leadership are behavioral modes that enable leaders to lead ambidextrously. This study not only enriches entrepreneurial leadership theory but also provides actionable insights for cultivating the leadership skills necessary to build ambidextrous capacity at the individual level, thereby fueling innovation and driving scalable success in entrepreneurial ventures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".