Intent to be a Socially Responsible Small or Medium-sized Enterprise: Theory of Planned Behavior and Leaders’ Actualizing
Bibliographic record
Abstract
A review of the academic research in corporate social responsibility shows little work on small and medium sized enterprises (SMEs) in the U.S. whereas considerable work in this domain has been conducted in the European context. This study seeks to make a contribution to the research void by addressing one particular area of social responsibility in the U.S. context. Specifically, we employ Ajzen’s reasoned action approach to begin to build an understanding of what promotes leaders of SMEs to reduce waste. This study addresses several questions: What are the attitudes of owners and managers of SMEs toward waste reduction practices for their organizations? How are stakeholder interests toward waste reduction perceived? And, are leaders of SMEs influenced by their industrial environment? The authors utilized an email survey directed to owners and managers of SMEs with greater than 5, but less than 500 employees in the telephony, construction, pulp and paper products, textiles, and agriculture industries. 377 emails were opened and 104 completed surveys were obtained. The survey instrument was developed from the theoretical perspective of Isaac Ajzen’s (1991) Theory of Planned Behavior in Organizational Behavior and Human Decision Processes, 50, p. 179-211. Results from a partial least squares analysis of the data suggest that there is a strong and significant relationship between the normative, attitudinal, and control constructs with an individual’s intention to be a socially responsible SME. This finding suggests that efforts to influence SME owners and managers to implement waste reduction activities need focus on changing individual attitudes.
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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 | 0.011 |
| 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 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".