Using necessary condition analysis to complement multigroup analysis in partial least squares structural equation modeling
Bibliographic record
Abstract
With the growing importance of partial least squares structural equation modeling (PLS-SEM) in marketing and consumer research, the use of Multigroup Analysis (MGA) for discovering observed heterogeneity (i.e., differences in relationships between variables for subgroups of the population under investigation) and deriving relevant operational results has become of great interest. However, these analyses are based exclusively on an additive sufficiency logic and do not permit researchers to test and validate hypotheses drawing on a necessity logic, the latter having been the focus of recent significant developments. Addressing this concern, the present paper offers guidelines for combining the use of Necessary Condition Analysis (NCA) and MGA performed with PLS-SEM. Taken together, these analyses can explore and improve knowledge about predefined subgroups of interest, enhance the understanding of relationships, refine the role of specific key antecedents by discovering meaningful necessary conditions, and therewith, contribute to theorizing. An empirical illustration drawing on the relationship between corporate social responsibility and customer loyalty is developed in a step-by-step fashion to provide marketing researchers with the guidelines to conduct the MGA and NCA, and finally report and interpret the results in accordance with both the sufficiency and the necessity logics. This integrative procedure contributes to the advancement of PLS-SEM applications. By delivering a better understanding of the group-specific results of a PLS-SEM–based MGA in a necessity logic, it promotes the complementary usage of sufficiency and necessity logics and therefore helps researchers to uncover novel theoretical and practical results when evaluating the data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.076 | 0.169 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".