Understanding the Consequences of Technostress: A Non-Linear Perspective
Bibliographic record
Abstract
Despite the rise in technostress research, two significant gaps have been overlooked. First, although studies on stress proposed curvilinear relationships, such interactions have rarely been examined in the technostress literature. Second, despite stress being multi-disciplinary and theoretically related to emotions, past technostress studies have rarely adopted transdisciplinary approaches. This paper aims to address these knowledge gaps by adopting the triphasic stress model, the appraisal theory of emotions, and the activation theory to investigate and explain the presence of curvilinear relationships within a mediated and moderated model. Data were collected and analyzed by surveying 215 employees from four different medium-sized US organizations. Our findings suggest that antecedents such as ICT-self-efficacy and presenteeism significantly relate to technostressors through cubic S-shaped interactions, while technostressors exhibit a quadratic U-shaped relation with technoexhaustion, whereas technoexhaustion shows a positive linear relationship with discontinuous usage intention. Furthermore, our results partially support the moderating influence of negative affectivity and mediation effects of technoexhaustion. Through this study, we offer a different theoretical perspective and an innovative understanding of the true nature of the technology and stressors. It also offers insights on designing effective organizational ICT tools.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".