Strategy case study : strategic portfolio and risk management during COVID-19
Bibliographic record
Abstract
This dissertation is a case study on the Canadian fintech referred to as Canada Pay (CP). The case will investigate CP’s strategies during COVID-19 and the adaptive measures to alleviate risks and meet opportunities. The case will deep dive into the gaming and gambling sector. In this industry, CP has adjusted its resources, portfolio, and strategies to adapt to rapid shifts and enhance its competitiveness. It will aim to solve whether or not CP should continue investing in product innovation and penetration in the G&G sector given the shifts caused by COVID-19. The dissertation concludes with the recommendation of continuing investment into product innovation and market penetration for gaming and gambling. This is for 3 key reasons: CP’s resources and dynamic capabilities provide a competitive advantage, the market’s growth opportunities, and CP’s portfolio and risk management that allow for high risk, high reward investments. This case is suitable for academics seeking to perform analyses on the Canadian fintech market, strategic decision-making, or product portfolio and risk management. As the pandemic is still ongoing at the time of this dissertation’s completion, academics will have greater information that may shift the findings and conclusions in the teaching notes. Following the case, theoretical concepts and frameworks are provided for analysis. These should be utilized to address the case from a pedagogical viewpoint. Subsequently, there are teaching notes that are offered as a solution guide. Finally, limitations are provided at the end and may be used to understand where improvements could have been made.
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 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) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".