A Pilot Case Study on Innovative Behaviour: Lessons Learned and Directions for Future Work
Bibliographic record
Abstract
Context: A case study is a powerful research strategy for investigating complex social-technical and managerial phenomena in real life settings. However, when the phenomenon has not been fully discovered or understood, pilot case studies are important to refine the research problem, the research variables, and the case study design before launching a full-scale investigation. The role of pilot case studies has not been fully addressed in empirical software engineering research literature. Objective: To explore the use of pilot case studies in the design of full-scale case studies, and to report the main lessons learned from an industrial pilot study. Method: We designed and conducted an exploratory case study to identify new relevant research variables that influence the innovative behaviour of software engineers in the industrial setting and to refine the full-scale case study design for the next phase of our research. Results: The use of a pilot case study identified several important research variables that were missing in the initial framework. The pilot study also supported a more sophisticated case study design, which was used to guide a full-scale study. Conclusions: When a research topic has not been fully discovered or understood, it is difficult to create a case study design that covers the relevant research variables and their potential relationships. Conducting a full-scale case study using an untested case design can lead to waste of resources and time if the design has to be reworked during the study. In these situations, the use of pilot case studies can significantly improve the case study design.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".