Multitasking with Information Technologies: Why Not Just Relax?
Bibliographic record
Abstract
Multitasking with information technology (IT) may impact how much pleasure people experience during hedonic activities, especially multisensory activities that involve touching, listening, and watching. However, past research on IT multitasking has primarily focused on utilitarian professional contexts. Drawing from dual-task-interference theory and flow theory, we address this gap by hypothesizing how multisensory characteristics positively influence the hedonic experience and how that effect deteriorates with IT-related multitasking. In addition, we examine how personality traits influence this moderating effect. We conducted a mixed-method laboratory experiment using explicit (self-reported) and implicit measures (electrodermal activity, automatic facial analysis, and electroencephalogram) to test our hypotheses. Participants listened to music while sitting on a high-fidelity vibro-kinetic armchair (one that generates vibrations and movement perfectly aligned with the music) and engaged in simultaneous IT-related tasks. The results generally support our hypotheses and represent a call for people to mindfully avoid multitasking with their IT devices while enjoying hedonic activities. In addition, our results suggest that people high in extraversion or neuroticism personality traits are likely to be more vulnerable to IT-related deterioration effects in this context. This study contributes to explaining the multitasking phenomenon with IT during leisure activities and underlines the benefit of such activities’ sensory characteristics.
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 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".