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  4. How to apply necessary condition analysis in PLS-SEM
 
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How to apply necessary condition analysis in PLS-SEM

Publikationstyp
Book Part
Date Issued
2023-11-14
Sprache
English
Author(s)
Richter, Nicole Franziska  
Personalwirtschaft und Arbeitsorganisation W-9  
Hauff, Sven  
Ringle, Christian M.  orcid-logo
Management und Entscheidungswissenschaften W-9  
Sarstedt, Marko  
Kolev, Aleksandar Evgeniev
Schubring, Sandra  
Personalwirtschaft und Arbeitsorganisation W-9  
TORE-URI
https://hdl.handle.net/11420/48581
Start Page
267
End Page
297
Citation
Partial Least Squares Path Modeling: Basic Concepts, Methodological Issues and Applications: 267-297 (2023)
Publisher DOI
10.1007/978-3-031-37772-3_10
Scopus ID
2-s2.0-85194451013
Publisher
Springer
ISBN
978-3-031-37772-3
978-3-031-37771-6
978-3-031-37773-0
978-3-031-37774-7
This chapter illustrates the application of necessary condition analysis (NCA) in the context of partial least squares structural equation modeling (PLS- SEM). We demonstrate the joint use of the two methods using the standard software application SmartPLS 4, which incorporates PLS-SEM and the core NCA compu¬tation capabilities, and we offer background information on the key steps and inter¬pretations associated with the combined application. We introduce the fundamentals of necessity logic and NCA, outlining key differences to PLS-SEM and its under¬lying logic. Using a recently published guideline and an illustrative example of the combined application of the two methods, the chapter provides guidance on gener¬ating results and interpreting must-have and should-have factors in the PLS-SEM context, enabling researchers to identify necessary conditions that may underlie their significant but also nonsignificant structural model relationships. The consideration of both must-have and should-have factors through the joint use of PLS-SEM and NCA is a unique way of assessing causality that may advance research in multiple fields. Our approach contributes to the further diffusion of the two logics in research applications. Our guidelines and systematic application of the two methods will assist researchers in exploiting these analyses' valuable potentials in their own studies.
DDC Class
330: Economics
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