This workshop will provide an introduction to Necessary Condition Analysis (NCA). NCA is an upcoming methodological approach that can be used to test whether a condition (X) is necessary for an outcome (Y). It provides a new perspective on existing phenomena and is complementary to the regression-based modeling of average effects that we usually apply.
When a condition is necessary for an outcome, the outcome does not occur without the condition. The condition serves as a “bottleneck”, “critical factor”, or “constraint”. This resonates with the strategic management literature, in which we often consider the limitations of organizational choices and their outcomes. You can think of particular resource allocations that limit or enable competitive actions, or post-merger integration that serves as a bottleneck for M&A success.
NCA allows us to quantify those limitations and to answer questions like “What type of postmerger integration is necessary for the success of a merger?”, “Is top management team diversity necessary for high levels of firm performance?”, and “What capabilities does a firm need to sustain its competitive advantage? “. We can answer these questions with a simple yes or no—yes, post-merger integration is necessary for merger and acquisition success—but we can also determine what level of the condition is necessary for what level of the outcome.
Over the past couple of years, NCA has become an accepted approach for theorizing and empirically analyzing necessary-but-not-sufficient causality between conditions and outcomes. NCA has been successfully applied and published in different research areas, such as supply chain management, psychology, human resource management, and entrepreneurship.
Presenters: Jan Dul & Stefan Breet
Sponsor: Research Methods Community