Single necessary (but not sufficient) conditions are critically important for business theory and practice. Without them, the outcomes cannot occur, and other conditions cannot compensate for this absence. Currently two analytical approaches are available for identifying single necessary conditions: Necessary Condition Analysis (NCA), which was recently developed, and fuzzy-set qualitative comparative analysis (fsQCA), which is a more established approach. FsQCA normally focuses on sufficient but not necessary configurations, but can also identify necessary but not sufficient conditions. This study uses NCA to analyze two examples of empirical datasets published in the Journal of Business Research that use fsQCA to identify single necessary conditions. A comparison of the results of NCA and fsQCA shows that NCA can identify more necessary conditions than fsQCA and can specify the level of the condition that is required for a given level of the outcome.

Additional Metadata
Keywords Bottleneck, Constraint, Critical success factor, FsQCA, NCA, Necessity
Persistent URL dx.doi.org/10.1016/j.jbusres.2015.10.134, hdl.handle.net/1765/90461
Series ERIM Top-Core Articles
Journal Journal of Business Research
Citation
Dul, J. (2016). Identifying single necessary conditions with NCA and fsQCA. Journal of Business Research, 69(4), 1516–1523. doi:10.1016/j.jbusres.2015.10.134