Impediments to Advanced Technology Adoption for Canadian Manufacturers
Bibliographic record
Abstract
Using survey data, this paper investigates problems that firms in the Canadian manufacturing sector face in their decision to adopt advanced technology. The data show that while the use of advanced technology is relatively important (users account for over 80% of all shipments), it is not widespread among firms (users represent only about one-third of all establishments). One explanation lies in the fact that while advanced technologies provide a wide range of benefits, firms also face a series of problems that impede them from adopting advanced technology. These impediments fall into five groups: cost-related, institution-related, labour-related, organization-related, and information-related. While it might be expected that impediments would be higher for non-users than users of technologies, the opposite occurs. We posit that the reason for this is that innovation involves a learning process. Innovators and technology users face problems that they have to solve and the more innovative firms have greater problems. We test this by examining the factors that are related to whether a firm reports that it faced impediments. Our multivariate analysis reveals that impediments are reported more frequently among technology users than non-users; and more frequently among innovating firms than non-innovating ones. We conclude that the information on impediments in technology and other related surveys (innovation) should not be interpreted as impenetrable barriers that prevent technology adoption. Rather, these surveys indicate areas where successful firms face and solve problems.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".