When predictors of outcomes are necessary: guidelines for the combined use of PLS-SEM and NCA

By N. Richter, Sandra Schubring, S. Hauff, Christian M. Ringle, Marko Sarstedt. Industrial management & data systems, 2020

462 citations recorded by the source corpus.

PurposeThis research introduces the combined use of partial least squares–structural equation modeling (PLS-SEM) and necessary condition analysis (NCA) that enables researchers to explore and validate hypotheses following a sufficiency logic, as well as hypotheses drawing on a necessity logic. The authors’ objective is to encourage the practice of combining PLS-SEM and NCA as complementary views of causality and data analysis.Design/methodology/approachThe authors present guidelines describing how to combine PLS-SEM and NCA. These relate to the specification of the research objective and the theoretical background, the preparation and evaluation of the data set, running the analyses, the evaluation of measurements, the evaluation of the (structural) model and relationships and the interpretation of findings. In addition, the authors present an empirical illustration in the field of technology acceptance.FindingsThe use of PLS-SEM and NCA enables researchers to identify the must-have factors required for an outcome in accordance with the necessity logic. At the same time, this approach shows the should-have factors following the additive sufficiency logic. The combination of both logics enables researchers to support their theoretical considerations and offers new avenues to test theoretical alternatives for established models.Originality/valueThe authors provide insights into the logic, assessment, challenges and benefits of NCA for researchers familiar with PLS-SEM. This novel approach enables researchers to substantiate and improve their theories and helps practitioners disclose the must-have and should-have factors relevant to their decision-making.

Subfields: Information Systems and Management, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/300ed8562031aad65594db811ca97bf5e37ee0cf

Citations in the corpus

• Multigroup Analysis using SmartPLS: Step-by-Step Guidelines for Business Research (2020, unknown): “…and Henseler, 2015), discrete choice modelling in PLS (Hair et al., 2019b), weighted PLS (Cheah et al., 2020), necessary condition analysis (Richter et al., 2020), the endogeneity test in PLS (Hult et al., 2018; Sarstedt et al., 2020), PLSpredict (Shmueli et al., 2016, 2019), model…”