Dynamic Bayesian Network-Enabled Approach for Organizational Leadership Measurement of Complex Construction Projects

Abstract

Complex construction projects are characterized by a large scale, high dynamics, many unknown risks, high complexity, etc., which often result in serious consequences, such as schedule delays, cost overruns, and uncontrolled objectives. Organizational leadership, which can be dynamically adjusted to the changes in the external environment, is the most important management ability of project managers. Therefore, it is of great significance to systematically analyze organizational leadership for the management of complex construction projects. In this paper, through the dynamic Bayesian method, the influencing factors of organizational leadership of complex construction projects and its dynamic development law are studied in depth. First, 17 organizational leadership factors are identified. Second, a dynamic Bayesian measurement model of organizational leadership was developed using 128 training samples. Third, the developed measurement model was applied for model analysis. The results of the study are as follows. (1) This study models changes in organizational leadership influences over time. (2) The organizational leadership measurement model proposed in this study can be applied in practice to predict leadership trends in specific projects. (3) Leaders should pay more attention to management competence in the personal leadership dimension, culture building in the relational leadership dimension, and institutional support in the strategic leadership dimension. This study proposes a quantifiable model for project leaders that breaks through the traditional model of studying organizational leadership from a static perspective, provides a quantifiable model for project leaders, and can cope with the dynamics of actual projects so that project leaders can control the whole leadership process macroscopically, thus providing an important reference to improve the organizational leadership capacity of complex construction projects.

Keywords

• complex construction projects
• organizational leadership
• dynamic Bayesian network
• measurement approach