Non-profit organizations’ use of tools and technologies for knowledge management: a comparative study
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Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle