How the Emphasis on ‘Original’ Empirical Marketing Research Impedes Knowledge Development
Bibliographic record
Abstract
Empirical research in marketing should focus on the development of empirical generalizations. Marketers do a huge amount of empirical research, but have little in the way of empirical generalizations. This is primarily because most empirical research consists of ‘original’ or ‘novel’ works looking for significant differences, rather than significant sameness, in unrelated data sets, thus exemplifying the ‘cult of the isolated study’. As a result, the marketing literature is made up largely of uncorroborated, fragmented, ‘one-off’ results. Such results are of little use to marketing practitioners or academicians. We discuss a number of impediments to the development of empirical generalizations – preoccupation with the hypotheticodeductive conception of science, preoccupation with ‘statistical’ rather than ‘empirical’ generalization, the ‘publish or perish’ syndrome in academia, and denigration of replication-with-extension research. We conclude that replication-with-extension research must be championed as the vehicle for discovering empirical generalizations.
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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 | 0.422 | 0.543 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.007 | 0.072 |
| Scholarly communication | 0.023 | 0.044 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.009 | 0.021 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".