Social Networks and New Product Choice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract To successfully market new products in a social network it is essential to identify influential individuals whose product recommendations influence the consumption choices of their peers. In this study, we use spatial econometric methods to determine how individuals revise their preferences for product attributes when exposed to product recommendations from peers, and how different individuals who vary in their degree of network connectedness exert influence on the product choices of others. We find evidence that consumers look to others for guidance from peers in their preference for subjective, taste‐specific parameters, but tend not to respond to peer price choices. Our spatial methods allow us to empirically determine the influence exerted by individual members on the consumption choices of other members of the social network. We find that connected members of the social network are not always the most influential in revising the consumption choices of others. Our estimates reveal that network proximity explains only 8.8% of influence.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it