Meta-analysis was used to aggregate results from studies examining the relationship between intelligence and leadership. One hundred fifty-one independent samples in 96 sources met the criteria for inclusion in the meta-analysis. Results indicated that the corrected correlation between intelligence and leadership is.21 (uncorrected for range restriction) and.27 (corrected for range restriction). Perceptual measures of intelligence showed stronger correlations with leadership than did paper-and-pencil measures of intelligence. Intelligence correlated equally well with objective and perceptual measures of leadership. Additionally, the leader's stress level and the leader's directiveness moderated the intelligence-leadership relationship. Overall, results suggest that the relationship between intelligence and leadership is considerably lower than previously thought. The results also provide meta-analytic support for both implicit leadership theory and cognitive resource theory.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1037/0021-9010.89.3.542 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Developing a Research Center for Artificial Intelligence in Medicine.
Mayo Clin Proc Digit Health
December 2024
Department Radiology, Stanford University, Stanford, CA.
Artificial intelligence (AI) and machine learning (ML) are driving innovation in biosciences and are already affecting key elements of medical scholarship and clinical care. Many schools of medicine are capitalizing on the promise of these new technologies by establishing academic units to catalyze and grow research and innovation in AI/ML. At Stanford University, we have developed a successful model for an AI/ML research center with support from academic leaders, clinical departments, extramural grants, and industry partners.
View Article and Find Full Text PDFArtificial Intelligence in Radiology: A Leadership Survey.
J Am Coll Radiol
January 2025
University of California, Davis, Sacramento, CA.
Purpose: Surveys to assess views about artificial intelligence (AI) of various diagnostic radiology constituencies have revealed interesting combinations of enthusiasm, caution, and implementation priorities. We surveyed academic radiology leaders about their views on AI and how they intend to approach AI implementation in their departments.
Materials And Methods: We conducted a web survey of Society of Chairs of Academic Radiology Departments (SCARD) members between October 5 and October 31, 2023 to solicit optimism or pessimism about AI, target use cases, planned implementation, and perceptions of their workforce.
Integrating Sensor Technologies with Conversational AI: Enhancing Context-Sensitive Interaction Through Real-Time Data Fusion.
Sensors (Basel)
January 2025
Department of Instruction and Leadership, Duquesne University, Pittsburgh, PA 15282, USA.
This article examines how sensor technologies (such as environmental sensors, biometric sensors, and IoT devices) intersect with conversational AI models like ChatGPT 4.0. In particular, this article explores how data from different sensors in real time can improve AI models' comprehension of surroundings, user contexts, and physical conditions.
View Article and Find Full Text PDFNew technologies for future of surgery in Ireland: An RCSI working Group report 2024.
Surgeon
January 2025
UCD Centre for Precision Surgery, University College Dublin, Dublin, Ireland.
Introduction: Surgery is a cognitive discipline whose practitioners characteristically use technology during operations for patients. With accelerating technological innovation throughout society and healthcare, we sought to develop a shared position for Irish surgery via a commissioned work programme by the Royal College of Surgeons in Ireland.
Methods: Using Stanford design principles, representative clinical specialty and academic leads and higher trainee representatives across 15 specialties were surveyed regarding sentiments, perspectives and concerns regarding now and near future technology in clinical practice, career considerations and training/education.
Explainable artificial intelligence to identify follicles that optimize clinical outcomes during assisted conception.
Nat Commun
January 2025
Department of Metabolism, Digestion, and Reproduction, Imperial College London, London, UK.
Infertility affects one-in-six couples, often necessitating in vitro fertilization treatment (IVF). IVF generates complex data, which can challenge the utilization of the full richness of data during decision-making, leading to reliance on simple 'rules-of-thumb'. Machine learning techniques are well-suited to analyzing complex data to provide data-driven recommendations to improve decision-making.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!