Pursuing quality and environmental performance
Bibliographic record
Abstract
Abstract Purpose – The aim of this study is to focus on manufacturing small to medium‐sized enterprises (SMEs) that are simultaneously pursuing quality and environmental objectives. Specifically, the paper examines: the specific motivations and resources of SMEs that have chosen to pursue both priorities, the types of initiatives these SMEs have implemented, and whether pursuing both priorities is correlated with various facets of organizational performance. Design/methodology/approach – This study gathered data from a sample of 254 ISO 9000 and ISO 14000 certified Canadian SMEs. Data collection was based on a survey questionnaire sent to a random sample of 1,514 companies. Findings – The results highlighted significant differences between the SMEs holding both the ISO 9000 and 14000 certifications and those holding only the 9000 ISO certification. Each group was shown to have distinct motivations and resources and to have implemented different types of initiatives to address environmental concerns. Each group was positively correlated with different facets of organizational performance. Research limitations/implications – This study's findings contribute to the environmental and SME literature on this very complex topic by providing relevant empirical evidence based on primary data. Practical implications – The results should provide guidance to manufacturing SMEs currently examining how to address environmental issues. SMEs need to address these issues carefully and understand the potential trade‐offs and consequences associated with their decisions. Originality/value – An important contribution of this study is its detailed characterization of environmental initiatives, drawing on insights derived from the environmental, accounting and management literature. By using the analytical framework of organizational citizenship behaviours, the characterization also included informal and behavioural aspects often neglected in environmental management studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".