Rigor in Information Systems Positivist Case Research: Current Practices, Trends, and Recommendations1
Bibliographic record
Abstract
Case research has commanded respect in the information systems (IS) discipline for at least a decade. Notwithstanding the relevance and potential value of case studies, this methodological approach was once considered to be one of the least systematic. Toward the end of the 1980s, the issue of whether IS case research was rigorously conducted was first raised. Researchers from our field (e.g., Benbasat et al. 1987; Lee 1989) and from other disciplines (e.g., Eisenhardt 1989; Yin 1994) called for more rigor in case research and, through their recommendations, contributed to the advancement of the case study methodology. Considering these contributions, the present study seeks to determine the extent to which the field of IS has advanced in its operational use of case study method. Precisely, it investigates the level of methodological rigor in positivist IS case research conducted over the past decade. To fulfill this objective, we identified and coded 183 case articles from seven major IS journals. Evaluation attributes or criteria considered in the present review focus on three main areas, namely, design issues, data collection, and data analysis. While the level of methodological rigor has experienced modest progress with respect to some specific attributes, the overall assessed rigor is somewhat equivocal and there are still significant areas for improvement. One of the keys is to include better documentation particularly regarding issues related to the data collection and analysis processes.
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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 | 0.616 | 0.721 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.030 | 0.030 |
| Science and technology studies | 0.008 | 0.042 |
| Scholarly communication | 0.042 | 0.056 |
| Open science | 0.017 | 0.020 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".