The matching effect of local food and color on ethical dining behaviors: the roles of credibility and green image
Bibliographic record
Abstract
Purpose This study aims to identify how restaurants can effectively initiate communication via social media to promote ethical dining behaviors. This research investigates the psychological mechanism of how the matching effect of color and a sustainability activity influence customer attitude toward a restaurant and the role of perceived credibility and green image. Design/methodology/approach Two experimental studies were conducted. Study 1 used a 2 food source (non-sustainable vs sustainable) × 2 color consistency (inconsistent vs consistent) factorial design (n = 231). Study 2 used a 2 food origin (world-famous vs locally renowned) × 2 color consistency (inconsistent vs consistent) factorial design (n = 220). Findings The results indicate that the matching effect from the marketing effect of sustainability significantly promotes customer attitudes and visit intentions when background color is consistent. An unexpected matching effect was found between a non-sustainable restaurant using world-famous food with its associated color. This research demonstrates a moderation effect of credibility and a mediation effect of green image to explain the ethical decision-making process for customers. Practical implications The findings provide suggestions for restaurant marketers to effectively advertise sustainability initiatives and practices using color as a marketing tool via social media. Originality/value This research is one of the earliest studies to investigate the effect of color consistency with primary information to demonstrate how consumers respond to restaurant sustainability in social media messages using local food.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".