In an increasingly volatile, uncertain, complex and ambiguous (VUCA) world, managers of capital projects are under relentless pressure to consistently meet their performance expectations. At the execution stage, managers have to constantly orchestrate competing demands on scare resources and, simultaneously, manage project operations to meet time, costs and quality compliances. This calls for simple methods to distinguish factors that could cause execution stage delays and prioritise their remedial actions. The objective, therefore, was to propose and test a methodology through empirical evidence, which could be useful for managers to focus on the distinguishing factors (rather than on all factors) to achieve execution excellence. We used a three-stage methodology leveraging the existing Project Management Institute (PMI) framework to define variables and then tested the methodology using case data generated from projects adopting a grounded theory approach. A set-theoretic, multi-value qualitative comparative analysis (QCA) tool helped appropriately configure this empirical case data and a subsequent Boolean minimisation technique then identified the distinguishing factor(s) that explained superior project schedule performance. The results corroborated literature findings. Two contributions emerged from this study: (a) our methodology enabled a richer analysis of the case than what would have been possible by adopting a more conventional approach; and (b) there is a potential for a domain-specific extension of the PMI framework to cover technology transfer projects having their unique knowledge areas.
Planning deficiencies and consequent execution delays are likely to persist in infrastructure development projects. However, recovery of schedule delay is a less researched area. This case research, using a two-stage inquiry modeled on the grounded theory, studied the schedule delay recovery during the execution phase of a brownfield airport construction project. The analyses generated contextual evidence and ambidexterity was found to be the key underlying phenomenon for successful recovery measures. The empirical learning was validated with literature and can be used by practitioners looking to institute schedule recovery measures.