Bibliographic record
Abstract
Purpose This research seeks to present a methodology for investigating the generalizability of a theory‐testing model. The methodology is used to examine the generalizability of a model of the antecedents and consequences of customer delight. Design/methodology/approach Theory testing of models in the marketing often fails to define an intended universe of generalization. This paper shows how multivariate generalizability theory can be used to estimate construct covariance components for specific sources of variance. These components can then be used to assess the generalizability of a structural equation model of a marketing phenomenon. Findings The parameters of a model of customer delight obtained from data that sample customers of a service or data that confound sources of variance do not generalize to data that capture variation across services or variation across raters. The relative impact of customer delight and satisfaction on behavioral intention varies with the source of variation being studied. Practical implications Previous research suggests that after controlling for customer satisfaction, customer delight accounts for very little variation in behavioral intention. But, for the source of variation of most relevance to managers, namely web sites, it is customer delight, not customer satisfaction, that is strongly associated with behavioral intention. Originality/value The methodology can be applied and can produce model parameters having substantially different managerial implications for the management of customer satisfaction and customer delight.
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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 | 0.021 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".