Rigor in Information Systems Positivist Case Research: Current Practices, Trends, and Recommendations
Bibliographic record
Abstract
Validity and generalization continue to be challenging aspects in designing and conducting case study evaluations, especially when the number of cases being studied is highly limited (even limited to a single case). To address the challenge, this article highlights current knowledge regarding the use of: (1) rival explanations, triangulation, and logic models in strengthening validity, and (2) analytic generalization and the role of theory in seeking to generalize from case studies. To ground the discussion, the article cites specific practices and examples from the existing literature as well as from the six preceding articles assembled in this special issue. Throughout, the article emphasizes that current knowledge may still be regarded as being at its early stage of development, still leaving room for more learning. The article concludes by pointing to three topics worthy of future methodological inquiry, including: (1) examining the connection between the way that initial evaluation questions are posed and the selection of the appropriate evaluation method in an ensuing evaluation, (2) the importance of operationally defining the ‘complexity’ of an intervention, and (3) raising awareness about case study evaluation methods more generally.
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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 | 0.650 | 0.649 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.020 | 0.022 |
| Science and technology studies | 0.011 | 0.073 |
| Scholarly communication | 0.054 | 0.065 |
| Open science | 0.020 | 0.024 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier 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".