A Mixed Method Approach to investigate the Antecedents of Software Quality and Information Systems Success in Canadian Software Development Firms
Bibliographic record
Abstract
For years software developers globally have struggled in their attempts to deliver high quality software products. In an effort to overcome the problem, there is a widely held view that people, process maturity and technology are the main determinants of software quality and information systems (IS) success. The researcher conducted a survey in Canada among software developers, which confirmed the notion that people, process maturity and technology are determinants of software quality and also that software quality and user perception are determinants of information systems success. In an attempt to gain deeper insights, a series of interviews were conducted among Canadian developers to identify other contributing factors to software quality and information systems success. The interviews revealed that organization climate, user training and education, expectation management and technical support can influence the quality and success of the delivered software product. Such insights can enhance the competitiveness of these firms.
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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 | 0.016 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.006 |
| Open science | 0.001 | 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".