Identifying Software Project Risks in the Canadian Financial Services Sector: An International Comparative Study
Bibliographic record
Abstract
Frequent occurrence of software project failures has created two general streams of research. One theme analyzed the common causes of cost overruns, late schedules, and unmet scope. With the belief that project failures are avoidable through proactive means, another group of researchers investigated software project risks. With such intent, comparative studies were conducted in Finland, Hong Kong, and the United States. Subsequent research in Nigeria determined the impact of the socioeconomic context. To further extend the coverage of prior studies, the research in the current study focused specifically on the Canadian financial services sector. Project managers were solicited for input to discover, determine, and rank risk factors in software projects using the same research design that was used in previous comparative research studies---a three-phase Delphi survey that uses nonparametric statistical techniques. In sharp contrast to prior studies, however, this research aimed not for general applications of the results at the country level but for specific collective relevance to software projects in banks, trust companies, insurance companies, mutual fund companies, and similar organizations. The composite rankings of the studies in Hong Kong, Finland, and the United States listed lack of management commitment, inability to get user commitment, and misunderstanding of the requirements as the top three risk items in software projects. Given that only the misunderstanding of requirements made it into the top three risk items in Nigeria, it would be of value to scholars and practitioners to determine how the results would differ if the study was conducted in a specific sector in the industry. Except for one risk factor (lack of dedicated, full-time project resources), this study confirmed that the previous list of risk factors captured the top risk factors in the Canadian financial services sector.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".