Integrating Sustainable Performance into the Digital Maturity Models for SMEs in Manufacturing
Bibliographic record
Abstract
This research paper investigates the integration of sustainable performance metrics into digital maturity models specifically tailored for small and medium-sized manufacturing enterprises (SMMEs), which represent a significant pillar of the Canadian economy. Despite their economic importance, SMMEs face increasing challenges in adopting digital technologies while ensuring sustainable performance. However, traditional digital maturity models often fail to capture the economic, social, and environmental impacts of digital transformation, creating a gap in assessing sustainability within this transition. This study aims to bridge this gap by proposing a framework that integrates sustainable performance indicators into existing digital maturity models. Through a systematic literature review, this study categorises indicators into three main dimensions—economic, social, and environmental—addressing aspects such as resource efficiency, employee well-being, and ecological impact. The proposed framework enables SMMEs to evaluate both their digital maturity and its impact on sustainable performance dimensions. By aligning these metrics with digital maturity assessment, this framework enhances decision-making for SMEs aiming to balance technological adoption with sustainability goals. Furthermore, the study consolidates key performance indicators relevant to SMMEs, providing a structured approach to assess the intersection of digital maturity and sustainability. The results emphasise the importance of incorporating sustainability dimensions into digital transformation strategies, offering SMMEs a structured framework to better access the relationship between digital maturity and sustainable performance while maintaining competitiveness in the digital economy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".