The Role of Causal Relationships in Critical Success Factors Models for Software Projects

The study on causality in critical success factors models is of great significance to the software projects, as the reductionist-analytical approach of traditional project management theory fails to adequately address the complexities that contribute to software project failures. This paper aims to enhance understanding complexities in software projects arising from relationships between critical success factors and project success. To do so, the study explores a new paradigm of causal analysis and discusses the assumptions of association and causation and its implications. In addition, the study employes a system thinking approach to identify outcomes of nonlinear causation into the understanding of the relationships between critical success factors and success metrics in software projects. This study suggests that the introduction of causality analysis in critical success factors models is necessary for understanding of complex functional relationships among the elements of these models. It is suggested that different levels of analysis are needed, especially for making inference in software projects that are dynamic and complex by nature. Further research is needed to gain empirical evidence to validate the proposed causal analysis in software projects to assess its effectiveness in creating critical success factors models that are basis for prediction of project success.