Evaluating the Effect of Corporate Social Responsibility of Firms and Organizations on Customer Satisfactions
Bibliographic record
Abstract
This study is evaluating the effect of corporate social responsibility of firms and organizations on customer satisfactions, customers who visit top tourism agencies in Tehran city. This study is a quantitative base study and the data have gathered through a researcher-made questionnaire. For questionnaire validity, the researcher used construction, validity and exploratory factor analysis and for measuring the questionnaire reliability, the researcher examined the Cronbach Alpha, this number that the outcome of the SPSS software was about 0/895 which demonstrate the high reliability of the research questionnaire. Confirmatory factor analysis and SEM methods utilized for data analysis. And also Smart PLS software was used for constructing the research model and analysis. The results of exploratory factor analysis showed that social commitment variables were chosen perfectly and also were acceptable, however, the SEM results demonstrate that the expectation execution variables could be considered as a moderating variable between firm’s social commitments and customer satisfaction. The effect of social commitment to expectation execution was not acceptable as well and the effects of other concepts of social commitments on firms on expectation execution were accepted. In addition, with the elements of social execution, variable as a moderating variable, the effects of social commitment on customer satisfaction were increased. This illustrates that the role of expectation execution as a moderating variable in the model of this study.
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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 | 0.004 | 0.010 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".