Small business—Big responsibility: The experience of social responsibility among small firms in rural Newfoundland, Canada
Bibliographic record
Abstract
While definitional debates and discussion of its strategic significance continue, the concept, theories, and practice of Corporate Social Responsibility (CSR) are widely recognised as common elements of contemporary business conduct. The wealth of related academic literature and general popular awareness of CSR speak to both the perceived significance of the relationship between business and society, and the interest in understanding the nature and bounds of a business’s responsibilities and obligations to society. Overwhelmingly, the examination of social responsibility (SR) is framed in the context of the “corporation.” Though developing as an emergent area of scholarship, what is less well-understood, is how smaller firms—those that dominate developed and underdeveloped economies alike—understand and engage with SR. Even less explored is the SR experience of the rural small business (RSB). The rural setting, characteristically dominated by small firms, presents a unique backdrop against which to evaluate whether and how the experience of SR in smaller firms is distinct from contemporary understandings of CSR. A qualitative research approach was adopted to achieve the study aim to critically examine the SR experience of RSBs. Data was collected through semi-structured interviews with the owner-operators of twenty-four small businesses throughout rural Newfoundland, Canada; all research participants were identified as exemplars of socially responsible businesses. Analysis of project data was undertaken based on a constructivist grounded theory approach where data was coded, categorised, and thematically evaluated to identify defining characteristics of the phenomenon under examination. The central findings of this research revealed that the SR experience of RSB owners was both similar and distinct from contemporary CSR and small business social responsibility (SBSR), and was significantly shaped by the rural community context. Commonalities were noted in some forms, underlying motivations, and realised benefits of SR behaviour; unique to the RSB SR experience were the personal costs associated with operating in a highly community-engaged manner. Findings contributed to extending CSR and SBSR scholarly literature, and a novel framework that accounts for the RSB SR experience was proposed. Further, policy recommendations to support rural social and economic development policy, and practical considerations for RSB owners in their business and SR practice are provided.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".