Liking and Following and the Newsvendor: Operations and Marketing Policies Under Social Influence
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
We consider a monopolistic firm selling two substitutable products to a stream of sequential arrivals whose purchase decisions can be influenced by earlier purchases. Before demand realizes, the firm faces a newsvendor problem for the two products with economies of scale in production for each. When consumers are responsive to others’ decisions, social influence amplifies demand uncertainty, leading to a lower profit for the firm. We propose three solutions for the firm to better cope with or even benefit from social influence: influencer recruitment and a reduced product assortment either before demand realization (ex ante) or under production postponement (ex post). First, the firm can offer promotional incentives to recruit consumers as influencers. We reveal an operational benefit of influencer marketing that a very small fraction of such influencers is sufficient to diminish sales’ unpredictability. Second, as the potential substitutability between products increases due to social influence, the firm may leverage the increased substitutability and enjoy lower cost in production by reducing product assortment before demand realization. Last, under production postponement, the firm can take advantage of the way that social influence results in demand herding and reduce product varieties by reacting to preorder information. This paper was accepted by Martin Lariviere, operations management.
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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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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