How do contractors evaluate company competitiveness and market attractiveness? The case of Toronto contractors
Bibliographic record
Abstract
The Canadian construction industry has sustained a healthy growth rate over the last 10 years. This could make the Canadian market attractive to foreign competitors. Moreover, Canadian companies possess enough expertise and resources to be able to effectively compete in the global market. This highlights the increased importance of developing marketing strategies for Canadian companies. This research study provides an understanding of how Toronto construction companies evaluate market attractiveness and company competitiveness. Such evaluation is the first step towards building effective marketing strategies. The research included an analysis of the main indicators of the Toronto market over the last 10 years and one-on-one interviews with 39 experts. The research deployed the analytical hierarchy process to identify the most important factors that can be used for measuring company competitiveness and market attractiveness. The most important factors that influence company competitiveness include customer satisfaction, cost efficiency, and safety record. Factors with the highest impact on market attractiveness are sustainable profitability (return on investment), supply of finance, and overall economic conditions.Key words: construction marketing, company competitiveness, market attractiveness, strategic planning, analytical hierarchy process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".