Understanding “disengagement from knowledge sharing”: engagement theory versus adaptive cost theory
Bibliographic record
Abstract
Purpose – The purpose of this paper is using competing hypotheses (a spillover hypothesis, based on Engagement Theory, and a provisioning hypothesis, based on Adaptive Cost Theory) to help explain why employees become disengaged from knowledge sharing. Design/methodology/approach – Employed knowledge workers completed an online questionnaire regarding their job characteristics, their general health and wellness, perceived organizational support, job engagement and disengagement from knowledge sharing. Findings – The findings provide empirical support for Adaptive Cost Theory and illustrate the relationship between Engagement Theory and the Disengagement from Knowledge Sharing. In particular, this research illustrates the importance of health and wellness for preventing disengagement from knowledge sharing. In addition, the findings introduce a new finding of tensions between job engagement and knowledge sharing, which supports knowledge workers’ complaints of “being too busy” to share. Research limitations/implications – This study uses cross-sectional methodology; however, the participants are employed and in the field. Given the theoretical arguments that disengagement from knowledge sharing should be either short term or transient, future research should follow-up with diary methods to capture this to confirm the study’s conclusions. Practical implications – The findings of this study provide some insight for practitioners on how to prevent disengagement from knowledge sharing. New predictors and an interesting tension between job engagement and knowledge sharing are identified. Originality/value – This study examines an alternative explanation for the lack of knowledge sharing in organizations, and uses competing theories to identify the reasons for the disengagement from knowledge sharing.
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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".