Predicting Use of GoodGuide.com Consumer Product Sustainability Information Using VBN Theory and NEP Scale
Bibliographic record
Abstract
The chapter focuses on the use of information provided by an Online Environmental Infomediary (OEI), GoodGuide.com, to advise consumers on the overall and specific sustainability attributes of personal care and household chemical and food products. This chapter seeks to predict the willingness of consumers to be influenced by GoodGuide.com information in their purchases and to influence others as well with this information using the Value-Belief-Norm (VBN) theory and the New Environmental Paradigm (NEP) scale. An experiment was applied to a sample of both undergraduate and graduate students at the Faculty of Business Administration, University of New Brunswick Fredericton, Canada. Data analysis using a series of stepwise multiple regressions was used in this study. Study results indicate the usefulness of both theoretical frameworks in understanding consumer predisposition to use OEI-provided information and the potential of social networking and use of mobile devices and apps in facilitating access and use of green information.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".