Corporate social responsibility in small‐and medium‐size enterprises: investigating employee engagement in fair trade companies
Bibliographic record
Abstract
Employee buy‐in is a key factor in ensuring small‐ and medium‐size enterprise (SME) engagement with corporate social responsibility (CSR). In this exploratory study, we use participant observation and semi‐structured interviews to investigate the way in which three fair trade SMEs utilise human resource management (and selection and socialisation in particular) to create employee engagement in a strong triple bottomline philosophy, while simultaneously coping with resource and size constraints. The conclusions suggest that there is a strong desire for, but tradeoff within these companies between selection of individuals who already identify with the triple bottomline philosophy and individuals with experience and capability to deal with mainstream brand management – two critical employee attributes that appear to be rarely found together. The more important the business experience to the organisation, the more effort the organisation must expend in formalising their socialisation programmes to ensure employee engagement. A key method in doing this is increasing employee knowledge of, and affection for, the target beneficiaries of the CSR programme (increased moral intensity).
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".