The Effects of Consumer Preference and Peer Influence on Trial of an Experience Good
Bibliographic record
Abstract
This research examines the interaction effect of two dimensions of preference on social contagion: preference similarity between a consumer (i.e., who seeks recommendation) and a peer (i.e., who potentially provides recommendation) and the fit of an experience good with the consumer's preference. For empirical analyses, the authors collected rich information from Last.fm, a music social networking website, including individual users’ music play histories, friendship information, social tags (i.e., user-generated keywords associated with artists and songs), and new song profiles. The results show that consumers’ trial of a song that fits less with their preference is influenced more by peers with similar preferences. By contrast, consumers’ trial of a song that fits more with their preference is influenced more by peers with dissimilar preferences. This research enriches the understanding of the nuanced role of preference in social contagion and offers managerial implications to better leverage social dynamics.
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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 | 0.048 | 0.105 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".