Dynamic pricing and green investments under conscious, emotional, and rational consumers
Bibliographic record
Abstract
We consider behavioral issues in a new dynamic model in which a manufacturer (M) makes pricing and green investment decisions while facing heterogeneous customers including emotional, conscious, and rational consumers. Emotional consumers base their purchasing decisions on M’s green investments. Their emotions are stochastic, dynamic, and accumulate over time. The investment is made over time and is subject to time-to-build so that there is a time-lag between investment and production. Differently, conscious consumers respond to both green investments and prices and have no memory on the M’s past green initiatives. The rational consumers are not sensitive to environmental issues and base their decisions only on product price. Our findings suggest that M should realize that emotional consumers have the largest impact on investments, prices, and profits. Therefore, firms should first think to satisfy the emotional consumers and then all other segments. When firms have environmental targets or restrictions, all segments must be satisfied independent of their impact on the profits. This finding contributes to the literature by highlighting that the trade-off between economic and environmental performance also exists in presence of consumer segments.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".