Concentration, Competence, Confidence, and Capture: An Experimental Study of Age, Interruption-based Technostress, and Task Performance
Bibliographic record
Abstract
The proliferation of information and communication technologies such as instant messenger has created an increasing number of workplace interruptions that cause employee stress and productivity losses across the world. This growth in interruptions has paralleled another trend: the graying of the workforce, signifying that the labor force is aging rapidly. Insights from theories of stress and cognitive aging suggest that older people may be particularly vulnerable to the negative consequences of interruptions. Hence, this study examines whether, how, and why technology-mediated interruptions impact stress and task performance differently for older compared to younger adults. The study develops a mediated moderation model explaining why older people may be more susceptible to the negative impacts of technology-mediated interruptions than younger people, in terms of greater mental workload, more stress, and lower performance. The model hypothesizes that age acts as a moderator of the interruption-stress relationship due to age-related differences in inhibitory effectiveness, computer experience, computer self-efficacy, and attentional capture. We refer to these age-related differences as concentration, competence, confidence, and capture, respectively, or the four Cs. We tested our model through a laboratory experiment with a 2 x 2 x 2 mixed-model design, manipulating the frequency with which interruptions appear on the screen and their salience (e.g., reddish colors). We found that age acts as a moderator of the interruption-stress link due to differences in concentration, competence, and confidence, but not capture. This study contributes to IS research by explicitly elucidating the role of age in IS phenomena, especially interruption-based technostress.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".