Right on cue? Category‐switching in online marketplaces
Bibliographic record
Abstract
Abstract Research Summary When and why do producers change the categorization of their offerings? Prior categorization research assumes that producers engage in ongoing efforts to proactively optimize their categorical positioning, but this assumption may not hold for many producers due to their limited attentional capacity. Our theoretical account instead highlights the role of expectation violation cues—salient pieces of information indicating a violation of audience expectations—as triggers that can lead producers to revise their category choice. Our longitudinal study of 84,667 Airbnb hosts' categorization choices finds that negative customer reviews—an important form of expectation violation cue—significantly increase the likelihood of category‐switching, particularly in categories with heterogeneous expectations. Our study suggests that many producers might be less proactive about their category choices than previous research assumed. Managerial Summary How businesses categorize their products and services influences their commercial success; yet, there exists very limited understanding of when, why, and how businesses revise their category choices. Our study, which tracked the category choices of over 80,000 Airbnb hosts over time, reveals that Airbnb hosts most commonly switch categories after receiving negative customer reviews, particularly when a review indicates that the accommodation did not meet customer expectations and if the previously chosen category lacks a clearly defined set of expected features. When switching categories, hosts tend to choose categories that are relatively similar to their prior choices and that seem to accommodate a wide variety of offerings. These patterns suggest that many businesses might be less proactive about their category choices than previous research assumed.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".