Impact of the Adoption of Advanced Information and Communication Technologies on Firm Performance in the Canadian Manufacturing Sector
Bibliographic record
Abstract
This paper investigates the evolution of the industrial structure in the Canadian manufacturing sector and its relationship to technological change by examining the take-up of advanced technologies and how it is related to the stochastic growth process in the plant population. Its framework is grounded in the view that growth is a stochastic process that involves learning. Experimentation with new technologies rewards some firms with superior growth and profitability. Examining how growth is associated with the choice of different technology strategies indicates which of these is being rewarded. The evolution of this process is studied by examining the relationship between the uptake of advanced technologies and the performance of plants in the manufacturing sector. This is done by using cross-sectional data on advanced technology use and by combining it with longitudinal panel data on plant performance. In particular, the paper examines the relationship between the use of information and communications technology (ICT) and the growth in a plant's market share and its relative productivity. The study finds that a considerable amount of market share is transferred from declining firms to growing firms over a decade. At the same time, the growers increase their productivity relative to the losers. Those technology users that were using communications technologies or that combined technologies from different classes increased their relative productivity the most. In turn, gains in relative productivity were accompanied by gains in market share. Other factors that were associated with gains in market share were the presence of R&D facilities and other innovative activities.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".