QiaoLinx Inc.: assisting Canadian firms build successful business relationships in China
Bibliographic record
Abstract
Purpose This paper aims to present the challenges faced by a small startup as entrepreneurship and marketing intersect to influence the success or failure of the venture. The entrepreneurial marketing focus of the case provides a deeper understanding of the practice-based interface of these two disciplines. The case focuses on the vision of two entrepreneurs and how they use the value creation process of opportunity recognition, evaluation and development to design a consulting service for Canadian firms that want to do business in China. It also provides insight on the difficulty of creating, communicating, selling and delivering a new consulting service to the Canadian business community. Design/methodology/approach The case is based on extensive interviews with QiaoLinx Inc.’s founders, relevant others and secondary data including press releases, social media and promotional material. Findings The events, issues and questions presented to track the entrepreneurial actions and marketing process of a startup from concept to market to tipping-point. The case is intended to serve as an instructional platform that encourages deductive reasoning in analyzing and synthesizing the application of entrepreneurship and marketing theories. Originality/value This teaching case can be used in undergraduate or graduate courses in entrepreneurship, marketing, new venture creation and international business. Researchers, faculty, practitioners and students can use the case to engage in a discussion on the underlying theories of entrepreneurship and marketing.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".