Together with professor Jan Dul and Henk van Rhee, I organized a professional development workshop on Necessary Condition Analysis as part of the 2018 Academy of Management Annual Meeting in Chicago. The workshop consisted of an introduction to NCA followed by a hands-on analysis of a dataset.
“Necessary but not sufficient” statements are commonplace in organizational and management publications. Such statements often indicate that a certain factor is considered important for a certain outcome, but that it is not the only factor. However, the logical meaning of a “necessary condition” for an outcome is that the outcome cannot exist without it; it is needed. Necessary Condition Analysis (NCA) is an upcoming approach for formulating and testing necessary condition theories. The method was recently published in Organizational Research Methods and is now used in many fields. NCA understands cause-effect relations as “necessary but not sufficient” and not as additive logic used in regression. “Necessary” means that an outcome will not occur without the right level of the condition, independently of the rest of the causal structure (thus the condition can be a “bottleneck”, “critical factor”, “constraint”). In practice, the right level must be put and kept in place to avoid guaranteed failure, and to allow the outcome to exist. NCA has been applied in a variety of organization and management disciplines (Strategy, Entrepreneurship, HRM, OB, Operations, etc.) to formulate and test necessity theories. It can provide strong results that are complementary to traditional additive logic and regression approaches. By adding a different logic and data analysis approach, NCA adds both rigor and relevance to theory and data analysis. Because it provides a new way of analyzing at existing phenomena using NCA can increase your publication chances. An editor of a top journal said: “From my perspective, [this NCA paper] is the most interesting paper I have handled at this journal, insofar as it really represents a new way to think about data analyses”. This interactive session has two parts. Part 1 is a general introduction discussing the importance of necessary conditions, illustrated with examples from different fields, and comparing NCA with regression. Part 2 helps participants to become the first users of NCA in their field, with hands-on instructions about how to build necessity theories, and how to analyze data for testing such theories using the NCA software. For more information on NCA see: www.erim.nl/nca.