Designing and Using Carbon Management Systems to Promote Ecologically Responsible Behaviors
Bibliographic record
Abstract
With the hope of mitigating the harmful impacts of climate change, many organizations are taking actions to reduce their carbon footprints. Carbon-reducing initiatives in organizations are varied: they range from green product innovations to encouraging behavioral changes by customers and employees. Green IS can play an important role in environmental sustainability by supporting a number of these strategies. Drawing on theories of persuasive systems design, this paper explores how one category of Green IS, carbon management systems (CMS), can be designed and used in order to persuade employees to perform ecologically responsible behaviors. The results from three organizational case studies suggest that CMS can be effective at changing employees’ environmental behaviors, demonstrate the extent to which persuasive system design principles (including an emergent category of Integration) are reflected in CMS, and highlight the importance of understanding the persuasion context. The findings of the study are used to inform the development of four propositions, which can serve as a foundation for further research in the Green IS domain.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".