Mindful Matching: Ordinal Versus Nominal Attributes
Bibliographic record
Abstract
The authors propose a new conceptual basis for predicting when and why consumers match others’ consumption choices. Specifically, they distinguish between ordinal (“ranked”) versus nominal (“unranked”) attributes and propose that consumers are more likely to match others on ordinal than on nominal attributes. Eleven studies involving a range of different ways of operationalizing ordinal versus nominal attributes collectively support this hypothesis. The authors’ conceptualization helps resolve divergent findings in prior literature and provides guidance to managers on how to leverage information about prior customers’ choices and employees’ recommendations to shape and predict future customers’ choices. Furthermore, the authors find process evidence that this effect is driven in part by consumers’ beliefs that a failure to match on ordinal (but not nominal) attributes will lead to social discomfort for one or both parties. Although the primary focus is on food choices, the effects are also demonstrated in other domains, extending the generalizability of the findings and implications for managerial practice and theory. Finally, the conceptual framework offers additional paths for future research.
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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 | 0.004 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".