Bibliographic record
Abstract
Since the proliferation of technologies in organizations has been found to lead to technostress in employees and to various negative organizational consequences, much recent research has investigated the factors that can lead to technostress and how to prevent these factors from occurring. However, limited directions currently exist to guide further research in this area. Consequently, the present research-in-progress sets out to determine the key challenges that remain to be addressed by technostress research. The paper finds that technostress research needs to be more theory-driven, needs to evaluate stress more directly instead of indirectly through such concepts as job satisfaction that serve as proxies for stress, needs to advance more rigorous explanations of how and why technology creates stress in users, needs to advance more rigorous explanations of for what kinds of users technology creates stress, and needs to be more diversified in terms of perspectives, methods, measures, and paradigms used.
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 | 0.124 | 0.116 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.008 | 0.033 |
| Scholarly communication | 0.031 | 0.054 |
| Open science | 0.012 | 0.014 |
| Research integrity | 0.016 | 0.017 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".