Social Influence and Proenvironmental Behavior: The Reflexive Layers of Influence Framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Social influence can be an important factor in the adoption of proenvironmental behaviors and technologies. Processes of social influence can be varied and complex yet are often represented or discussed in a simplified, aggregated manner. To facilitate more nuanced study of social influence, we draw from a literature review and empirical observation to propose a conceptual behavioral framework that integrates three processes of interpersonal influence; we call this the reflexive layers of influence (RLI) framework. RLI proposes three generally successive and iterative ‘layers’ of the consumer's relation to a new technology (or practice): awareness, assessment, and alignment with self-concept. These layers are antecedents to, and potentially consequences of, adoption and use of proenvironmental technology. Social influence follows different processes at each layer. Awareness is influenced by the diffusion of simple, functional information. The consumer forms an assessment, at least in part, through translating the technology's attributes into specific benefits (or disbenefits). Through reflexivity, the translated assessment of the technology is framed in terms of maintaining, developing, or altering self-concept according to perceptions of others' behaviors and values. We illustrate RLI through application to three case studies of households participating in a multiweek trial of a plug-in hybrid electric vehicle—demonstrating that the consumer's self-concept, perceptions, and behavior can change substantially according to the social processes represented by RLI. We conclude with policy implications and discuss future hypotheses and priorities for research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it