Implementation of Competitive Strategies in Textile Industries in Quebec, Canada
Bibliographic record
Abstract
Canada is internationally known for garment companies such as Billabong or Van Heusen. The Canadian textile industry employs approximately 36,000 people out of the 23 million in the country. Although fashion is one of the larger exports for Canada, it also provides raw goods and machinery as well. Due to the massive amount of open land in Canada, it is capable of growing a variety of natural textile fibers, such as cotton, hemp, and Banyan tree fibers. Canada's unique natural materials used for textiles make it stand out in the market. However, the textile industries have experience stiff completion both in the importation of raw material and export of finished products in the market. Because various textiles industries in Quebec, Canada still use obsolete technology, the products produced are of low quality, which are not competitive in the market. High production cost makes textiles firms to be more prone to stiff competition. As a result, textile companies as a result is exposed to a risk of losing its market share and experience high employee turnovers, diluting the workforce quality and therefore quality of services offered. Therefore, the study looked into the influence of competitive strategies on performance of textile industry in Quebec, Canada. Descriptive research design was adopted and quantitative data collected was analyzed by the use of ANOVA and inferential statistics. Based on the findings in relation to specific objective, the study concluded that competitive strategies positively lead to competitive advantage. Competitive strategies influences customer satisfaction, ensures superior quality services and products, customer oriented products, and positive feedback from customers. . Key words; Cost Leadership Strategies, Focus Strategies, Differentiation Strategies, Export Processing Zone & Canada.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".