Strategic alliances and innovation in the food industry of southwestern Ontario
Bibliographic record
Abstract
This thesis examines the impact of strategic alliances on the overall innovation process, as well as the types of measures that organizations in the food industry are using to determine the success of innovations. A multi-method research design was employed, which included both qualitative and quantitative research methods. The first phase consisted of 5 semi-structured in-depth interviews, developed from the literature. A self-administered survey, based on the results of phase I as well as the literature, was then mailed to 500 companies involved in the food industry in Southwestern Ontario. The results show that the impact of strategic alliances on the overall innovation process is one of the most important elements of the modern company. Collaboration positively affects the number of innovations a company develops each year. A total of 77% of the companies involved in the quantitative phase participate in strategic alliances and 88% of these companies are developing a minimum of one innovation each year, with 44% developing more than seven innovations compared to 33% for companies that do not work collaboratively. The most commonly mentioned reason for collaborating is value, with alliances providing the additional insights and expertise that companies "need" to be innovative. Customers and suppliers were the most frequently used alliance partners. The most frequently reported measures that organizations use to determine the success of innovations were profitability (95%), sales growth (92%), customer satisfaction (92%), market research (77%) and cost of overall project (74%). The quantitative phase found that 77% of respondents reported that their companies were doing 'reasonably well' to 'extremely well' in measuring the success of innovations. Implications for businesses, academics, and potential alliance partners are discussed. (Abstract shortened by UMI.)
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".