Something old, something new: Enabled theory building in qualitative marketing research
Bibliographic record
Abstract
“Enabled theorizing” is a common practice in marketing scholarship. Nevertheless, this practice has recently been criticized for constraining the creation of novel theory. To advance this conversation, we conduct a grounded analysis of papers that feature enabled theorizing with the aim of describing and analyzing how enabled theorizing is practiced. Our analysis suggests that enabled theorizing marries data with analytical tools and ontological perspectives in ways that advance ongoing conversations in marketing theory and practice, as well as informing policy and methods. Based on interviews with marketing and consumer research scholars who practice enabled theorizing, we explain how researchers use enabling theories to shape research projects, how researchers select enabling lenses, and how they negotiate the review process. We discuss the implications of our analyses for theory-building in our field, and we question the notion of originality in relation to theory more generally.
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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 | 0.393 | 0.337 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.015 | 0.058 |
| Scholarly communication | 0.026 | 0.026 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".