MétaCan
Menu
Back to cohort
Record W2905505886 · doi:10.17705/1jais.00521

Theorizing the Multilevel Effects of Interruptions and the Role of Communication Technology

2018· article· en· W2905505886 on OpenAlexaff
Shamel Addas, Alain Pinsonneault

Bibliographic record

VenueJournal of the Association for Information Systems · 2018
Typearticle
Languageen
FieldDecision Sciences
TopicPersonal Information Management and User Behavior
Canadian institutionsMcGill UniversityQueen's University
Fundersnot available
KeywordsAffect (linguistics)PsychologyMultilevel modelSocial psychologyWork (physics)Knowledge managementComputer scienceCognitive psychologyEngineeringCommunication

Abstract

fetched live from OpenAlex

Our understanding of how interrupting the work of an individual affects group outcomes and the role of communication technologies (CT) in shaping these effects is limited. Drawing upon coordination theory and the literatures on computer-mediated communication and interruptions, this paper develops a multilevel theory of work interruptions. It suggests that interruptions that target individuals can also affect other group members through various ripple effects and a cross-level direct effect. We also discuss how the usage of five CT capabilities during interruption episodes can moderate the impact of interruptions at the individual and group levels. Our theoretical model draws attention to the importance of examining the individual-to-group processes to better understand the impact of interruptions in group environments. Additionally, by accounting for the role of the use of CT capabilities during interruption episodes, our work contributes to both the interruptions literature, which dedicates scant attention to the interrupting media, and to IS research on media use and media effects.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.004
Scholarly communication0.0040.004
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.056
GPT teacher head0.368
Teacher spread0.312 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations25
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the Association for Information SystemsSame topicPersonal Information Management and User BehaviorFrench-language works237,207