ASSESSING THE INFLUENCE OF PERSUASIVE SYSTEMS FOR SUSTAINABILITY ACROSS WORK-HOME-COMMUNITY BOUNDARIES
Bibliographic record
Abstract
Little doubt remains that human activities have contributed to climate change and environmental degradation over the past century. Humans must now alter their behaviours if the devastating consequences of climate change are to be avoided. To this end, persuasive systems for sustainability present a novel opportunity for encouraging environmentally responsible behaviours. This paper is part of ongoing research seeking to assess the influence of a persuasive system for sustainability deployed in one domain, such as a utility-sponsored energy conservation application, on individual behaviours within that domain, and also in other domains, such as at work or in the community. Here, we report on the development of measures to evaluate our constructs of interest, namely environmentally responsible behaviours at work, home and in the community, rebound effects, work-home-community boundary strengths, complexity of change and system characteristics of perceived persuasiveness and integration support. Through a process that included item creation, card sorting and exploratory factor analysis based on a survey of 168 participants, we have been successful in developing certain measures. Although still in progress, this work contributes to the Green IS literature by developing new measures and drawing attention to mechanisms for enhancing organizational and inter-organizational sustainability initiatives.
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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 | 0.019 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".