No assemblage required: On pursuing original consumer culture theory
Bibliographic record
Abstract
Our title plays with the promise on certain consumer goods packages of “no assembly required,” but in fact we call upon the reader to assemble new theories rather than rely on existing ones like assemblage theory. We argue that consumer culture theory (CCT), also known as interpretive consumer research, has thus far not fulfilled its potential as a theory-generating discipline. Our reluctance to attempt creative theorizing is institutionalized by calls for theory-enabled research rather than truly emergent theory. This retreat has recently been strengthened by the rise of Big Data and correlational approaches that eschew theory altogether. In order to change this situation, we recommend a three-stage approach: (1) original phenomena-driven inquiry, (2) combining grounded theory and abductive reasoning, and (3) generating and comparatively analyzing alternative theoretical explanations. We briefly conceptualize the first two stages and illustrate the third using an example of consumer brand masking and bluffing in Africa. We demonstrate the use of two criteria for comparative theoretical analysis: (a) fit with the data and (b) potential usefulness in other contexts. We also argue that sometimes multiple theories are needed. CCT researchers are uniquely positioned to pursue original theory, and in this article, we offer some ideas as to how this can be done.
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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 | 0.024 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.070 |
| Scholarly communication | 0.011 | 0.030 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".