Unraveling the Impact of Workplace External Interruptions: A Comprehensive Daily Diary Analysis of Dysfunctional Consequences of Receiving Calls
Bibliographic record
Abstract
ABSTRACT Mobile devices have become gradually pervasive in our surroundings, and the urge to stay connected with others via phone calls is progressively typical for many employees during the workday. This study investigated how receiving calls at the workplace as an external interruption influences perceptions of work‐family conflicts and procrastination at work. Receiving calls as a workplace external interruption delays the attentional and behavioural focus of employees on the main tasks. Based on action regulation theory and empirical evidence, it is proposed that time pressure positively mediated the relationship between receiving calls as a source of workplace external interruption and performance outcome (procrastination) and well‐being outcome (work‐family conflicts). Data were gathered from 218 employees through a 10‐consecutive‐day diary study (four measurements taken daily: one during noon, one during the afternoon and two in the evening). Multilevel analyses exhibited that the time pressure mediation effects were supported for procrastination at work and work‐family conflicts. These outcomes revealed that receiving calls is the source of workplace external interruption that increases time pressure and has a significant impact on work‐family conflicts and procrastination at work.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".