A psychological approach to regaining consumer trust after greenwashing: the case of Chinese green consumers
Bibliographic record
Abstract
Purpose The purpose of this study is to suggest an approach to regain consumer trust after negative effects of greenwashing that draws corporations and consumers into a conflicted relationship. Design/methodology/approach The authors collect and interpret qualitative data from in-depth interviews to develop a theoretical approach that enables the rebuilding of trust between greenwashing corporations and their consumers using the concept of psychological resilience. Findings This analysis indicates that the approach is an interaction between consumers with green brand loyalty and greenwashing corporations. This type of consumer demands emotional factors, functional factors and legitimate factors in the process of psychological resilience, and after greenwashing, corporations should select appropriate recovery strategies to stimulate these protective factors. Originality/value Previous research studied green consumer trust in the marketing field but did not explore the core of trust which was regarded as a cognitive process. This paper investigates green consumer behaviour under the perspective of psychological resilience and makes an innovative attempt to understand drivers of regaining consumer trust. Previous research works put forward a series of strategies related to regaining trust, but they did not discuss the mechanisms by which these strategies work. Using the method of grounded theory, we attempt to reveal the “black box” of consumers cognition after greenwashing and propose a strategy for regaining consumer trust.
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.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".