Profit-Driven Corporate Social Responsibility as a Bayesian Real Option in Green Computing
Bibliographic record
Abstract
The idea that socially responsible investments can be viewed in terms of real options is relatively new. We expand on this notion by demonstrating how real option theory, within a Bayesian decision-making framework, can be used by managers to help when making green technology investment decisions. The Bayesian decision framework provides a more flexible approach to investment decision making because it adjusts for new information. Responding to a call for multidisciplinary and multifaceted research in environmental sustainability, this paper integrates ethics, finance, and information technology by viewing investments in environmentally friendly technology as a profit-driven CSR real option. Our model provides managers with the analytic tool needed to make the business case for CSR initiatives providing an opportunity for firms to create economic, social, and ecological solutions that benefit all stakeholders. The practical applicability of our model is demonstrated in an illustrative case scenario based on real data.
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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 | 0.003 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".