What limits the greater adoption of 3D printing in Canada? Identifying the Barriers to the Adoption of 3D printing in Canada
Bibliographic record
Abstract
Despite significant advances in materials, equipment, and applications, the adoption of 3D printing in Canadian manufacturing remains limited, with most firms restricting its use to prototyping rather than full-scale production. This thesis investigates why this gap persists by examining the economic, technical, organizational, and cultural factors shaping adoption decisions. Drawing on semi-structured interviews with industry stakeholders across manufacturing firms, suppliers, and supporting organizations, the study combines empirical insights with established technology adoption theories, including the Technology Acceptance Model, Diffusion of Innovation, and the Product Adoption Process. The findings show that slow adoption is not driven by technical limitations alone. Instead, adoption is influenced by interconnected perceptions of risk, cost, capability, and organizational readiness, reinforced by conservative decision-making cultures, limited internal expertise, and uncertainty around qualification and certification. Customer expectations related to lead time, customization, and reliability further shape adoption behavior, either accelerating experimentation or reinforcing risk aversion. Regional infrastructure differences and ecosystem maturity also affect firms’ ability to progress beyond trial use. Based on these findings, the thesis proposes a synthesized adoption framework that captures adoption as an iterative, feedback-driven process rather than a linear sequence. The framework integrates individual perceptions, organizational conditions, and external pressures to better reflect real-world adoption dynamics. This research contributes a practical and theoretically grounded lens for understanding 3D printing adoption in Canada and provides a foundation for future research, policy development, and industry strategies aimed at supporting broader and more sustained adoption.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".