Impact of City Social Responsibility and Marketing Strategy on City Image and People’s Perception
Bibliographic record
Abstract
In recent years, the issue of Corporate Social Responsibility (below referred to as “CSR”) has received considerable attention. The term CSR is usually associated with enterprises, while the social responsibility of cities is rarely explored. However, cities resemble non-profit enterprises and whether or not they fulfill their social responsibility has a significant impact on future development. This study therefore conducts a literature analysis and empirical research to investigate the impact of city social responsibility and city marketing strategies on the city image and perceptions of the general public. The research object of this study is Taichung City in Taiwan. A survey was conducted and 548 valid questionnaires were collected from non-local residents. After a group model comparison of two groups divided by high or low frequency of trips to Taichung it was discovered that: (1) Both groups exhibit a significant positive correlation between the fulfillment of city social responsibility and people’s trust. (2) The high frequency group shows a significant positive correlation between the fulfillment of city social responsibility and city image, while the low frequency group shows no such relationship and a significant difference exists between the two groups. (3) The low frequency group shows a significant negative correlation between the implementation of marketing strategies by the city and public trust. (4) Both groups exhibit a significant positive correlation between the implementation of marketing strategies by the city and city image and the correlation intensity is considerably higher for the low frequency group. (5) Both groups show a significant positive correlation between the implementation of marketing strategies by the city and people’s intention. (6) Both groups exhibit a significant positive correlation between city image and people’s trust. (7) The high frequency group shows a significant positive correlation between people’s trust and people’s intention, while the low frequency group shows no such relationship and a significant difference exists between the two groups. The results of this study clearly indicate that the behavior patterns of groups with different travel frequencies are somewhat distinct. City administrators should therefore pay special attention to the planning of city CSR and city marketing strategies to be able to attract visits, investments, and settlement by target groups.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".