How Consumer Experience Is Shaped by the Political Orientation of Service Providers
Bibliographic record
Abstract
This research documents the counterintuitive effect that consumers actually have better service experiences with politically conservative service providers, but expect to have better experiences with politically liberal service providers. First, we document the effect in actual consumer service experience across three different contexts (Airbnb hosts, Uber drivers, waiters), and demonstrate that conservative (vs. liberal) providers enhance consumer experience (studies 1, 2a, 2b), because conservative providers are higher on trait‐conscientiousness (study 3). Second, in an experiment (study 4), we document expectations about service experience and demonstrate that consumers expect to receive better service from liberals (vs. conservatives). We explain that this effect emerges because consumers do not perceive that conservatives (vs. liberals) are more conscientious, but do perceive that they are less open. Overall, our theoretical framework outlines how conservative providers possess an unknown strength (higher conscientiousness) and a known weakness (lower openness), which leads to different actual and expected consumer service experiences. These novel findings provide valuable contributions to our understanding of how consumers are impacted by the political orientation of marketplace providers.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".