Bibliographic record
Abstract
The bachelor's thesis, which aims to evaluate technostress and its impact on employees in a selected industry and propose measures to reduce the impact of technostress on employees, focuses on the retail sector, where digitalization is significantly changing the nature of work activities. The theoretical part defines concepts related to stress and technostress and presents relevant models of work stress. The practical part is based on a questionnaire survey among 100 employees of selected retail units. For a deeper understanding of the issue, this research was complemented by a semi-structured interview with the manager of one of the stores. The results show that more than half of the respondents (55 %) report occasional to frequent occurrence of technostress, while a quarter (25 %) experience it on a daily basis. A statistically significant relationship was identified between the level of stress experienced and the availability of technical support (? = 29.772; p = 0.019). The level of technostress was not significantly influenced by age or gender, with length of experience and experience with the technology playing a more significant role. Employees felt insecure if they lacked support, feedback, or the opportunity for technical repetition. Based on these findings, the paper proposes specific measures in the areas of training, technical support and organisational culture to reduce the incidence of 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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".