ABSTRACT Creativity and innovation research has long employed variance‐based methodologies, which seek to identify the factors that, on average, engender these phenomena. However, these methodologies systematically overlook the minimum levels of antecedents below which creativity and innovation outcomes are empirically impossible. This methodological blind spot is of consequence because creativity and innovation processes frequently depend on threshold dynamics rather than additive effects. Our study introduces necessary condition analysis (NCA) as a methodological complement that captures bottleneck conditions invisible to traditional approaches. We present the methodological rationale underlying NCA, demonstrate its application through an empirical illustration of innovative employee behavior in a Canadian SME, and outline research opportunities for advancing necessity logic across innovation domains. Our findings reveal that NCA uncovers a distinct necessity structure in which some conditions function as prerequisites rather than net predictors. The empirical illustration further shows systematic divergences between sufficiency‐ and necessity‐based results. Some antecedents display significant net effects in regression without constituting necessary conditions, whereas others impose binding thresholds despite weak or nonsignificant regression effects. This methodological contribution enables researchers to analytically distinguish between sufficiency (what increases innovation on average) and necessity (what enables innovation to occur at all). For practitioners, it offers an actionable framework that prioritizes the removal of innovation bottlenecks rather than merely strengthening facilitating factors.
Chouchane et al. (Wed,) studied this question.
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