The Impact of Brand Crisis on Consumers’ Green Purchase Intention and Willingness to Pay More
Bibliographic record
Abstract
The purpose of the study is to develop an original framework to explore the effects of brand crisis on green purchase intentions and willingness to pay more. This study composes of seven original concepts, which are perceived brand crisis, green brand image, green trust, green brand equity, green perceived value, green purchase intentions and willingness to pay more to develop an integrated model. For this reason, an online survey was carried out in testing the model that includes questions measuring the effects of these variables. Smart PLS structural equation modelling is applied to verify the research framework. A total of 504 questionnaires were collected from Turkish consumers living in Turkey. According to the findings acquired from the structural equation modelling, there is an impact of the perceived brand crisis on green brand image, green trust, green brand equity and green perceived value. Consequently, green brand equity and green perceived value except for green brand image and green trust influence the green purchase intention. Moreover, green purchase intention affects willingness to pay more. Existing studies have shown that perceived brand crisis affects the brand equity, brand trust, brand image, perceived value and purchase intentions. However, there is not any research to shed light on the impact of perceived brand crisis on green brand equity, green brand image, green trust, green perceived value, green purchase intention and willingness to pay more. Therefore, this paper develops a research framework to fill the research gap.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".