Rigor and Relevance: The Application of The Critical Incident Technique to Investigate Email Usage
Bibliographic record
Abstract
Information systems research is often criticized for its high rigor, but low relevance. One approach to overcome the low relevance issue is to employ sound qualitative methods, out of which this study focuses on the critical incident technique (CIT) that has mostly been overlooked in IS research. The primary goal of this study is to demonstrate and validate the usage of the critical incident technique in the management information systems domain. The secondary objective is to develop a number of practical recommendations for email service providers and to offer novel theoretical insights that may be employed in future research. To this end, 107 positive and 113 negative critical incidents pertaining to email usage were collected and analyzed through classical content analysis techniques. Overall, this investigation validates the usage of the CIT in the MIS field and presents practical and theoretical recommendations.
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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 | 0.248 | 0.558 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.017 | 0.010 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".