The Strength of Trust Over Ties: Investigating the Relationships between Trustworthiness and Tie‑Strength in Effective Knowledge Sharing
Bibliographic record
Abstract
The purpose of this research is to better understand the interaction between notable structural and relational factors, which positively influence organizational knowledge sharing. Specifically, to investigate the effects of multiple dimensions of trust (i.e., competence‑, integrity‑, benevolence‑based perceived trustworthiness) on the relationship between tie‑strength and effective knowledge sharing. Knowledge sharing was examined in two ways, first through the knowledge receiver’s perception of how useful the shared knowledge was, and second through their willingness to use that knowledge. Willingness to use was further classified into explicit and tacit forms of knowledge. A total of 275 surveys were collected from legal professionals, working on projects, at one of Canada’s largest law firms. Data were analyzed using linear regression, mediation, and moderator analyses. The study revealed four main findings. The first was that strong ties lead to the receipt of useful knowledge. Second, both competence‑ and integrity‑based trustworthiness strongly mediated the link between strong ties and receipt of useful knowledge. Third, when trust was taken into account, any positive effect of strong ties on the receipt of useful knowledge was removed. Fourth, the mediating effect of competence‑based trustworthiness was of similar magnitude for willingness to use explicit and tacit knowledge. Practical implications suggest organizations should cultivate competence‑ and integrity‑based trustworthiness and develop networks consisting of both weak and strong ties, balancing network density and range.
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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 | 0.011 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".