Fostering Sustainability Through Digital Evolution: Evaluating Industry 5.0 Preparedness in Quebec’s Regional SMEs
Bibliographic record
Abstract
Abstract The transformative journey toward Industry 4.0 (I4.0) has revolutionized business operations, requiring firms to adapt to digitalization and environmentally-conscious practices (often termed “green digital,” “sustainable digital,” “smart green,” or “responsible digital”). This adaptation may be challenging, especially for small and medium-sized enterprises (SMEs) that typically lack larger firms’ adaptive resources and capacities. This chapter delves into the dynamic intersection of sustainability and digital transformation by focusing on the specific case of SMEs to propose a preliminary framework for evaluating SMEs’ Industry 5.0 (I5.0) maturity levels (digital), which has a broader scope by including green and social practices (sustainable) with digitalization. The chapter first provides some background by highlighting the push for organizations to integrate sustainable practices into their digital transformation endeavors seamlessly. In a world grappling with environmental and social challenges, aligning technological advancements with responsibility is increasingly relevant for SMEs. The schwerpunkt of the chapter is an extensive (but non-systematic) literature review of the digital maturity level assessment domain, coupled with insights from extant research in the sustainable digital (5.0) assessment area. The objective of the review is to construct a preliminary framework for evaluating SMEs’ digital preparedness while concurrently measuring their commitment to sustainable practices. This framework incorporates critical parameters such as resource efficiency, circular economy principles, and the incorporation of renewable energy sources in digital control operations. The chapter further sheds light on SMEs’ challenges in achieving a coherent balance between digitalization and sustainability. These challenges span technological, organizational, and regulatory dimensions, emphasizing the multifaceted nature of the transition. Notably, the chapter identifies these challenges and proposes practical strategies and best practices to overcome them.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".