Competitive forces framework for new entrant: the case of Canadian pea starch in Chinese market
Bibliographic record
Abstract
Based on the Five competitive Forces Model of Porter (2008), and the practical case analysis on Canadian pea starch in the Chinese market, this study builds up a theoretical framework for a new entrant (also as a substitute) to analyze industry competition and provides strategic suggestions for the new entrant to shape a competitive position. In this study, Porter's (2008) model is first preliminarily modified to be used for new entrants’ analysis. Then two-phase interviews are conducted with Chinese food processors to understand Chinese buyers’ attitudes towards Canadian pea starch and to identify their concerns. Subsequently, in-depth interviews are carried out with Canadian pea starch suppliers to match customer demand and supplier capability, followed by a five-forces analysis to assess industry competition facing by Canadian pea starch in the Chinese market. Afterwards, the previously modified competitive forces framework is enriched by illustrating positive/negative effectiveness of all five forces and by adding evaluation metrics for each force. This study has academic value. It customizes and enriches a Five Competitive Force Model to be applicable of analyzing market potential of a new entrant. It provides a theoretical base to create strategies for a new entrant (also as a substitute) to shape its competitive position. Also, it addresses the gap in competitive analysis of agricultural exports by matching customer demand and supplier capability. This applied research adds to the industry understanding of the market need and contributes to Pulse Canada’s “25 by 25” strategy and Canada's Protein Industries Supercluster.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".