Investigating the Measures of Relative Importance in Marketing Research
Bibliographic record
Abstract
Determining the relative importance of various predictors in a marketing research model is important for both theoretical and practical reasons. To date, the most commonly used methods to assess relative importance have involved examining either the regression coefficients or zero-order correlations of each predictor. Unfortunately, these indices are problematic when the predictors are correlated, as is the case with many of the drivers of service-provider switching, loyalty studies, satisfaction models and other marketing research. In this paper, we introduce Dominance Analysis to an audience of researchers in marketing research and empirically demonstrate its usefulness for assessing predictor relative importance. Using a Monte Carlo simulation, we first compare the accuracy of five traditional methods used in marketing research assessing relative importance and comparing them to Dominance Analysis. There are theoretical, as well as empirical, advantages to using Dominance Analysis over other methods, and these are discussed in the context of an empirical example using data drawn from a larger study of auto-repair service customers (n = 355).
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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 | 0.127 | 0.465 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".