Non-profit organizations’ use of tools and technologies for knowledge management: a comparative study
Bibliographic record
Abstract
Purpose This paper aims to present findings from a study conducted with non-profit organizations (NPOs) in Canada and Australia, focusing on the use of tools and technologies for knowledge management (KM). NPOs of different sizes and operating in different sectors were studied in two large-scale national surveys. The paper is useful to both practitioners in NPOs for understanding tool use for KM activities and to scholars to further develop the KM-NPO domain. Design/methodology/approach Two nation-wide surveys were conducted with Canadian and Australian NPOs of different sizes (i.e. very small to large-sized organizations) and operating in different sectors (e.g. animal welfare, education and research, culture and arts). An analysis of responses explores the use of tools and technologies by NPOs. Respondents identified the tools and technologies they used from nine pre-determined themes (quantitative data) plus an additional category of “other tools” (qualitative data), which allowed for free text responses. The quantitative data were analyzed using both descriptive and inferential statistical techniques and the qualitative data were analyzed using a thematic analysis approach. Findings Quantitative data analysis provides key findings including the popularity of physical, print documents across all NPO sizes and sectors. Statistical tests revealed, for example, there is no significant difference for the same-sized organizations in Canadian and Australian NPOs in the use of tools and technologies for KM activities. However, there were differences in the use of tools and technologies across different sizes of NPOs. The qualitative analysis revealed a number of additional tools and technologies and also provided contextual details about the nature of tool use. The paper provides specific examples of the types of tools and technologies NPOs use. Originality/value The paper has both practical and academic contributions, including areas for future research. The findings on the use of KM tools and technologies by NPOs contribute to the growing body of literature in the KM domain in general and also build the literature base for the understudied KM-NPO domain. NPOs will also find the paper useful in better understanding tools and technological implementation for KM activities. The study is unique not only in the content focus on KM for NPOs but also for the comparative study of activities in two countries.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".