The many faces of information technology interruptions: a taxonomy and preliminary investigation of their performance effects
Bibliographic record
Abstract
Abstract Despite the growing importance of information technology (IT) interruptions for individual work, very little is known about their nature and consequences. This paper develops a taxonomy that classifies interruptions based on the relevance and structure of their content, and propositions that relate different interruption types to individual performance. A qualitative approach combining the use of log diaries of professional workers and semi‐structured interviews with product development workers provide a preliminary validation of the taxonomy and propositions and allow for the discovery of a continuum of interruption events that fall in‐between the extreme types in the taxonomy. The results show that some IT interruptions have positive effects on individual performance, whilst others have negative effects, or both. The taxonomy developed in the paper allows for a better understanding of the nature of different types of IT interruption and their consequences on individual work. By showing that different types of interruptions have different effects, the paper helps to explain and shed light on the inconsistent results of past research.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".