Sustaining knowledge interaction in online communities: a longitudinal field study of a professional medical community
Notice bibliographique
Résumé
Online communities as a new form of organizing have emerged as a pivotal paradigm for collaboration and innovation in the digital age. Such online communities, powered by recent advancements in technology, promise not only a platform for knowledge exchange but also a transformative space for sustained member interactions. Despite their burgeoning significance, our understanding of how these interactions are sustained and how they culminate in tangible professional learning remains limited. To address this research problem, this thesis conducted a longitudinal study of a professional online community hosted by the Canadian Association of Medical Radiation Technologists (CAMRT). Anchoring this research are four fundamental questions. Firstly, who participates more in a professional online community to benefit from online member interaction? By integrating digital trace data with offline membership records, the study discerned engagement dynamics. Quantitative analysis revealed that individuals with formal online roles and previous offline community engagement exhibited higher online participation levels. In contrast, specific occupational roles, accumulated professional experiences, and gender did not significantly influence online engagement. Secondly, what are social exchange structures which characterize member interaction patterns in a professional online community? Three potential structural mechanisms—direct reciprocity, generalized reciprocity, and preferential attachment—were tested using an exponential random graph model. The analysis showed that the CAMRT online community thrives primarily on the norm of direct and generalized reciprocity while preferential attachment did not significantly influence interaction patterns. Thirdly, what are relational and individual factors that facilitate online member interactions over and above structural mechanisms? Employing a stochastic actor-oriented model, the study illuminated that the norm of reciprocity and individual characteristics, such as a member's formal role and prior offline community experiences, played pivotal roles in shaping interactions. Notably, homophily among members was not a dominant factor influencing interactions. Lastly, how do community members learn from online member interactions for their practice? Qualitative analysis, enriched by field observations and in-depth interviews, unearthed a spectrum of learning modalities in online communities that can span across the continuum between focal and subsidiary knowing. Specifically, six distinct learning modalities were identified: Learning by Direct Problem-solving, Sharing Learning and Practices with Others, Bringing Learning Back to Local Colleagues, Learning Social Connections and Networks, Learning Different Perspectives, and Passive Learning for the Future. In sum, this research offers a granular perspective on the multifaceted dynamics of online communities in professional settings. It emphasizes their role as vibrant ecosystems of knowledge interaction rather than mere repositories of information. As we navigate an increasingly digital future, the insights from this thesis stand as crucial guideposts for disciplines aiming to harness collective intelligence, fostering innovation and growth in interconnected professional landscapes
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,007 | 0,003 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».