Exploring pro-environmental behavior in Azerbaijan: an extended value-belief-norm approach
Bibliographic record
Abstract
Purpose This paper aims to explore pro-environmental behavior (PEB) in Azerbaijan. Therefore, the authors used value-belief-norm (VBN) theory, extended by the construct of social norms (SN), as a basis. Design/methodology/approach Data were collected by establishing a link within various social media platforms. The final sample consisted of 407 respondents. The authors analyzed four dimensions of PEB using higher-order structural equations. The authors also examined both direct and (serial) indirect effects for cross-cultural validation of the extended VBN theory. Findings The authors were able to confirm the VBN theory in its entirety. However, SN, which are influential in collectivistic and Sunni-majority states, do not contribute significantly to explaining PEB in predominantly Shiite Azerbaijan. Research limitations/implications The authors could not establish a direct effect of SN on PEB within this study. However, the authors observed an indirect “values-beliefs-norms-behavior” effect. The different (partly abbreviated) effect channels of the four tested value antecedents provide interesting insights for marketing research. Practical implications Based on the results, it seems crucial to make Muslim consumers aware of the negative outcomes of their consumption behavior and to emphasize individual responsibility. However, SN may not need to be addressed depending on cultural and/or religious values. Originality/value The authors examined PEB in Azerbaijan by testing the serial mediation effects in the VBN model. Further, the authors tested the influence of SN within the framework of the original VBN theory, contributing to a better understanding of the possibility of integrating components of the theory of planned behavior.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".