Technostress from a Neurobiological Perspective - System Breakdown Increases the Stress Hormone Cortisol in Computer Users
Bibliographic record
Abstract
Despite the positive impact of information and communication technology (ICT) on an individual, organizational, and societal level (e.g., increased access to information, as well as enhanced performance and productivity), both scientific research and anecdotal evidence indicate that human-machine interaction, both in a private and organizational context, may lead to notable stress perceptions in users. This type of stress is referred to as technostress. A review of the literature shows that most studies used questionnaires to investigate the nature, antecedents, and consequences of technostress. Despite the value of the vast amount of questionnaire-based technostress research, we draw upon a different conceptual perspective, namely neurobiology. Specifically, we report on a laboratory experiment in which we investigated the effects of system breakdown on changes in users’ levels of cortisol, which is a major stress hormone in humans. The results of our study show that cortisol levels increase significantly as a consequence of system breakdown in a human-computer interaction task. In demonstrating this effect, our study has major implications for ICT research, development, management, and health policy. We confirm the value of a category of research heretofore largely neglected in ICT-related disciplines (particularly in business and information systems engineering, BISE, as well as information systems research, ISR), and argue that future research investigating human-machine interactions should consider the neurobiological perspective as a valuable complement to traditional concepts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".