Omni-channel supply chain pricing and investment decision considering green satisfaction in a competitive and cooperative environment
Bibliographic record
Abstract
Against the backdrop of depleting natural resources and deteriorating ecological conditions, sustainable development has emerged as a pressing concern in both production and daily life. There is a growing consumer inclination towards purchasing green products. Consequently, catering to consumers’ green preferences can empower enterprises to enhance their core competitiveness and increase their market share. In light of this context, the study focuses on the omni-channel supply chain to construct a comparative model for analyzing optimal decision-making and profitability. It explores how enterprises in a competitive environment, where consumers have varying requirements for product greenness, strategically select their omni-channel marketing and green research and development strategies. These findings offer valuable insights and theoretical support for addressing the challenges posed by multi-channel competition and diverse consumer demands. The research indicates that within competitive models, regardless of consumer satisfaction, the prices and investment efforts associated with both ordinary and green products tend to decrease as the minimum level of green effort increases. Collaboration can enhance the market competitiveness of manufacturers employing omni-channel strategies, yet this advantage diminishes with the escalation of green demand. Conversely, for manufacturers of green products, higher levels of market competition and consumer emphasis on product green attributes correspond to greater market presence and competitiveness.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| 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 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".