Organizational Citizenship Behavior and Corporate Social Responsibility: Evidence from Taiwan Listed Electronics Firms
Bibliographic record
Abstract
As enterprises expand, they have increasingly consumed social resources and influenced the society. The public has gradually become aware of this, and consequently, enterprises have begun to emphasize corporate social responsibility as a core business strategy. The interviewees/participants in the study were listed electronics companies in central Taiwan. Questionnaires were used in the study to collect data. A total of 211 valid questionnaires were collected and IBM SPSS 20 was used to analyze the data. The result of the study shows that interviewees participants doubt whether their companies fairly assess their performance or not; however, they consider that their companies take the responsibility of complying with the law and maximizing profits. As a result, medium or large enterprises, or listed electronics companies that intend to fulfill corporate social responsibility, should invite supervisors, senior employees, or female employees and those who do not typically participate in decision making or regular meetings (e.g., employees who work on production lines, or those who are not supervisors or do not work in marketing) to participate in meetings, and provide them training, or distribute manuals or send letters to them. This can enhance organizational citizens’ identification with their company, motivate them to help their companies fulfill corporate social responsibility and thereby improve corporate image, enhance employees’ commitment and awareness of organizational citizenship, and create an improved organizational climate.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".