Interorganisational partnerships and knowledge sharing: the perspective of non-profit organisations (NPOs)
Bibliographic record
Abstract
Purpose – This paper aims first to identify key interorganisational partnership types among non-profit organisations (NPOs) and second to determine how knowledge sharing takes place within each type of partnership. Results explore the value of social media specifically in facilitating external relationships between NPOs, firms and the communities they serve. Design/methodology/approach – Empirical qualitative analysis of exploratory interviews with 16 Canadian NPOs generates a non-exhaustive classification of partnership types emerging from these organisations, and their defining characteristics in the context of interorganisational knowledge sharing. Findings – Overall eight categories of partnerships from the sampled NPOs emerged from the analysis of the data. These include business partnerships, sector partnerships, community partnerships, government partnerships, expert partnerships, endorsement partnerships, charter partnerships and hybrid partnerships. Using examples from interviews, the sharing of knowledge within each of these partnerships is defined uniquely in terms of directionality (i.e. uni-directional, bi-directional, multi-directional knowledge sharing) and formality (i.e. informal, semi-formal or formal knowledge sharing).Specific practices within these relationships also arise from examples, in particular, the use of social media to support informal and community-driven collaborations. Twitter, as a popular social networking tool, emerges as a preferred medium that supports interorganisational partnerships relevant to NPOs. Originality/value – This research is valuable in identifying the knowledge management practices unique to NPOs. By examining and discussing specific examples of partnerships encountered among NPOs, this paper contributes original findings about the implications of interorganisational knowledge sharing, as well as the impact of emerging social technologies on same.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.017 | 0.030 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".