Something old, something new: Enabled theory building in qualitative marketing research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.128 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it