Technostress and disengagement from knowledge sharing: insights from pre-pandemic and mid-pandemic data sets
Bibliographic record
Abstract
Purpose This study aims to examine a common failure in knowledge sharing, called disengagement from knowledge sharing (DKS), and investigates how technostress may contribute to this unintentional withholding of knowledge for knowledge workers. The authors apply the Job Demands-Resources (JD-R) model to explain the dual path of technostress creators and inhibitors on DKS via burnout and job engagement. The authors also examine how the pandemic and the changes in remote work and information and communication technology (ICT)-related stress may have impacted DKS. Design/methodology/approach Using a time-lag survey, two independent samples of knowledge workers who use information and communication technologies for their jobs were surveyed during early 2020 and mid-2021. Analyses were completed with partial least squares-structural equation modelling. Findings Technostress (via the JD-R model) explained DKS. Technostress creators were positively associated with burnout, which was in turn positively related to DKS. Technostress inhibitors were positively associated with job engagement, which in turn was also positively related to disengagement to knowledge sharing. Technostress inhibitors were negatively associated with burnout. Results from the multigroup analysis indicated that technostress inhibitors had a stronger relationship with engagement pre-pandemic than mid-pandemic. Originality/value This research addresses a more common source of knowledge sharing failures and illustrates how ICTs may impact this DKS via burnout and job engagement. In addition, this research captures a change in relationships associated with the pandemic.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".