Antecedents and pro-environmental consumer behavior (PECB): the moderating role of religiosity
Bibliographic record
Abstract
Purpose The purpose of this study is to examine the predictabilities of five intra-personal factors to predict pro-environmental consumer behavior (PECB) and the moderating role of religiosity in Oman. Design/methodology/approach The study uses neural network to analyze the antecedents/antecedents × religiosity → PECB relationships by using a sample of 306 consumers from Oman. Findings This study finds that the most important predictors of PECB, according to the order of importance, are attitude × religiosity, knowledge, concern × religiosity, knowledge × religiosity, value, religiosity, attitude, concern and value × religiosity. Research limitations/implications The convenience sample from a single Islamic country limits the generalizability of the findings. Future studies should use probabilistic sampling techniques and multiple Islamic countries located in different geographical regions. Practical implications To promote PECB, businesses and policymakers should provide environmental education to expand knowledge and value, leverage ecological religious values in integrated marketing communications, make positive inducements to change attitude and concern enhancing interventions. Social implications As religiosity enhances PECB by moderating the impacts of environmental intra-personal factors on PECB, businesses and policymakers should find ways to use faith-based ecological messages in Islamic countries. Originality/value Determining the predictabilities of psychological factors and their interactions with religiosity to predict PECB in Islamic countries is necessary for promoting environmentally friendly products in Islamic countries and for reducing the ecological damage to the environment.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".