Exploring How Consumers’ Perceptions of Corporate Social Responsibility Impact Dining Intentions in Times of Crisis: An Application of the Social Identity Theory and Theory of Perceived Risk
Bibliographic record
Abstract
During the pandemic, the restaurant industry placed greater emphasis on corporate social responsibility (CSR) initiatives. However, there seems to be a dearth of comprehension regarding how customers’ perceived risks impacted their dining intentions. This challenges the industry to devise an effective crisis response strategy. Thus, this study investigates the relationship between perceived CSR, restaurant image, and dining intentions during the crisis. In addition, this study examines how perceived CSR influences three types of perceived risks associated with restaurants (quality, health, and environment) and how these types of risks influence restaurant image and dining intentions during this period. The results demonstrate that perceived CSR positively impacted a restaurant’s image and concurrently reduced perceived risks among consumers during the coronavirus disease 2019 (COVID-19) pandemic. Furthermore, perceived health risks had a negative influence on customers’ dining intentions. This study offers valuable insight into the theoretical foundations and managerial implications of CSR’s effects and risk management, particularly in the context of future pandemics within the restaurant industry.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".