Organisational Information and Knowledge Sharing: Uncovering Mediating Effects of Perceived Trustworthiness Using the PROCESS Approach
Bibliographic record
Abstract
Many organisations are confronted with challenges in getting employees to effectively participate in information and knowledge sharing (IKS). Further, IKS is embedded in multifaceted social contexts, which are influenced by several different social and cognitive factors. This study is in support of the hypothesis that perceived trustworthiness is widely implicated as a mediating factor between those social–cognitive factors and IKS. Previous work, however, uncovered only a handful of such mediating effects. It is argued here that the paucity may have been due to limitations of the statistical methods. This study employs PROCESS, a more powerful current method for mediation analysis in an attempt to uncover previously hidden relationships. Analyses were performed on data collected from 275 knowledge workers (legal professionals and paralegals) engaged in shared legal project work, at one of Canada’s largest multijurisdictional law firms. Social–cognitive factors that were considered were shared language, shared vision, tie strength, age/gender/educational homophily and relationship length. IKS outcomes were willingness to share, willingness to use and perceived receipt of useful information/knowledge. Consistent with the hypothesis, the more powerful method revealed a large number of significant mediating effects of perceived trustworthiness. Out of possible 112 simple mediation models, 62 were significant, 44 of which had been previously undiscovered. The results highlight the importance of trust as well as the power of current methods for mediation analysis and serve as a strong argument for their use. The paper concludes with implications for research and practice.
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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".