Developing complex-project capability through dynamic organizational learning

Purpose – The purpose of this paper is to extend the learning boundaries of traditional project capability, which follows the linear planning paradigm, in order to include non-linear complex projects that cannot be completely specified and planned in advance , and so require continuous learning over their life cycles. Design/methodology/approach – Based on an earlier empirical investigation, where complex-project capability (CPC) is developed through dynamic organizational learning based on non-linear problem solving, the paper examines some of the conceptual and practical implications of this process insight. The focus here is on incomplete pre-given knowledge and emergent knowledge creation during CPC development. Findings – Using the three interrelated dimensions of project type, knowledge creation method, and organizational learning approach, the paper reinterprets Karl Popper’s linear problem solving model for learning in traditional projects by introducing the concept of knowledge entropy (disorder) for learning in non-linear complex projects. The latter follows a path from “order to disorder to order” rather than from “order to order” under traditional assumptions. Research limitations/implications – By identifying a common learning process at individual, group, and organizational levels, developing CPC can be viewed as a “learning organization”. This multi-level approach facilitates research into distributed organizing for emergent knowledge creation during CPC development. Practical implications – In contrast to traditional planned projects with up-front prior knowledge, complex projects are characterized by incomplete knowledge. The challenge of dealing with knowledge uncertainty in complex projects through continuous learning has practical implications for project learning, planning, knowledge management, and leadership. Originality/value – The concept of knowledge entropy (disorder) extends the learning boundaries of traditional projects, where little learning is anticipated, by including complex projects with knowledge uncertainty requiring continuous learning.