Digital Transformation in the Service Sector: Insights from Consultations with Firms in Wholesale, Retail and Logistics
Bibliographic record
Abstract
Firms increasingly rely on digital technologies such as e-commerce, cloud computing, big data, digital tracking and digital platforms that are reshaping business operations, business models and market structures. In this context, the Bank of Canada consulted with firms in wholesale, retail and logistics, as well as with related industry associations to yield insights on the adoption of digital technologies. Results show that firms are increasingly investing in digital technologies, most often to increase operational efficiency or to enhance customer experience. The survey also aimed to shed light on the various channels through which digital transformation may affect firms’ prices in order to discern the implications of digitalization for inflation. Survey respondents point to some disinflationary pressures overall: first, firms view e-commerce as putting downward pressure on prices, due to increased transparency and comparability in online markets, which amplifies competition and reduces firms’ pricing power. Second, thanks to the adoption of technologies, cost savings and efficiencies are in some cases being passed on to the customer although, for many firms, cost savings are yet to be realized. Finally, firms view digital technologies as a driver of actual or expected changes in market structure, citing consolidation and concentration of market power among dominant players, forcing smaller players out of the market.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".