Engineering Management and Leadership

Presents a performance evaluation method for project engineering management using the triangular fuzzy neutrosophic number weighted sum method (TFNN-WSM) to address multiple-attribute decision-making under uncertainty. It aims to improve efficiency, quality, and resource use, with a case study validating the approach.

Explores how artificial intelligence influences engineering management decision-making through transformational leadership. Using data from 385 Turkish professionals and PLS-SEM analysis, it found that AI and leadership significantly impact decisions, with leadership mediating AI's effects. Findings offer insights for industry leaders and policymakers on integrating AI for sustainable advantage.

Identifies key leadership competencies for engineering leaders in Malaysia’s construction industry: setting a clear vision, solving project issues, and managing resources. Through qualitative interviews, it highlights the need for focused leadership development in engineering and addresses gaps in empirical research, especially in non-Western contexts.

A study in Trinidad and Tobago explored civil engineers' perceptions of leadership preparedness. Surveying 114 engineers, it found most felt only moderately prepared for leadership roles. Participants emphasized the importance of leadership training and strongly supported integrating it into engineering education. They also expressed optimism about the benefits of expanding leadership and management training. The findings highlight a clear need for academia, industry, and professional societies to enhance leadership development within engineering, aiming to cultivate more capable and influential civil engineering leaders.