Adoption of Digital Technologies: Insights from a Global Survey Initiative
Bibliographic record
Abstract
The Bank of Canada, together with a global network of central banks, recently surveyed more than 6,000 firms from around the world. Using the survey data, this paper assesses the effects of digitalization on firms’ pricing and employment decisions. Specifically, we examine firms’ expectations about how their adoption of digital technologies—such as e-commerce, cloud computing, big data, 3-D printing, the Internet of Things, robotics and artificial intelligence—will affect their prices and hiring plans. Digital technologies influence firms’ operations in several ways that can often offset each other. This makes it difficult to pin down the overall impact on prices. Survey results for Canada suggest that some firms expect some downward pressure on prices from (1) efficiency gains, for example from automation, made possible by digital technology and (2) increased online competition and cost compression in the supply chain. Other firms expect that the value added to their products from adopting digital technologies will allow them to charge higher prices. In addition, some firms anticipate that they will have to pass on the costs of adoption to customers. Firms also expect a marginal negative effect on their employment over the next three years as a result of technology-induced automation or productivity gains. This negative effect will largely be offset by more hiring of digital talent or to accommodate stronger sales. Using matching techniques to control for differences in sample size and composition as well as survey frames, we find that, compared with small and medium-sized firms, large firms are more likely to adopt digital technologies and more likely to expect negative effects on both employment and prices.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".