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Record W2080797594 · doi:10.1080/10919391003711050

Rigor and Relevance: The Application of The Critical Incident Technique to Investigate Email Usage

2010· article· en· W2080797594 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Organizational Computing and Electronic Commerce · 2010
Typearticle
Languageen
FieldDecision Sciences
TopicPersonal Information Management and User Behavior
Canadian institutionsLakehead University
Fundersnot available
KeywordsRelevance (law)Computer scienceCritical Incident TechniqueDomain (mathematical analysis)Field (mathematics)Qualitative researchData science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.495
Threshold uncertainty score0.331

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.044
GPT teacher head0.381
Teacher spread0.337 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it