Corporate Social Responsibility in Developed as opposed to Developing Countries and the Link to Sustainability
Bibliographic record
Abstract
This article reviews and incorporates the findings of academic Corporate Social Responsibility (CSR) and sustainability studies to guide hotels in achieving sustainability through CSR initiatives. To date, limited empirical research on CSR in developing countries is available. A triple-bottom-line approach employs companies to balance the needs of stakeholders, allowing them to give back to society while still prospering. Organisations follow CSR activities for a number of reasons, including enhancing the organisational image and strengthening relationships with consumers and stakeholders. CSR is most widely used in developed countries, such as the USA, Canada, and the UK. Consequently, given the lack of progress in CSR implementation in the developing world, this article illustrates some of the gaps identified in developing countries. This is significant because, for the first time, scholars in developing countries are exploring deeply into the concept of CSR. Thus, the article clearly sets the stage for businesses to participate in CSR activities by identifying the return and advantages of making investments for CSR activities within its relevant sectors. In other words, investigating the relationship between CSR and company performance. This article fills the gap and is unique in that it analyses existing CSR practices and offers guidance to business organisations. Keywords: Corporate Social Responsibility; Company Performance; Sustainability; Circular Economy; Hospitality industries
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| 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".