Mesuring Online Communities'efficiency and Viability - Brest, France - IMT Atlantique

IMT Atlantique

Entreprise vérifiée

Brest, France

il y a 2 semaines

Posté par:

Sophie Dupont

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Description

Mesuring online communities'efficiency and viability:

Réf
ABG122583
Sujet de Thèse 10/04/2024
Contrat doctoral
IMT Atlantique
Lieu de travail
Brest
Bretagne
France
Intitulé du sujet
Mesuring online communities'efficiency and viability
Champs scientifiques
Science de la donnée (stockage, sécurité, mesure, analyse)
Economie et gestion
Mots clés computational social science, online communities, management, data science, wikipedia, recherche
Description du sujet:

Context:

An online community is defined as a "set of members structured around common interests and interacting on a regular basis within a digital exchange space where they share, create, and meet" (Masson et Parmentier, There are many types of online communities that can be defined according to their goals (communities of practice, epistemic communities, communities of innovation, communities of action and communities of crisis) and members (contributors, managers...) Among them, the epistemic communities (Cohendet et al.

2001), and the communities of practice, built and active on digital platforms, are central to generating new knowledge (Mahr & Lievens 2012), when communities of innovation are central to generating new ideas.

How can we measure the multi-dimensional nature of virtual teams and online communities? And how explain their capacity of producing knowledge, but also their viability, allowing the community to remain active and attractive for (new) participants over time.

Different properties are studied about online communities.

First, effectiveness, or outcomes, of online projects can be evaluated in terms of the quality of produced knowledge (Arazy & Kopak 2011) or of creativity (Rhyn et al Second, process of production, or efficiency, can be studied in terms of the organization put in place to insure the production of a good knowledge (Stvilia et al Last, about the participants themselves, or input, experience and engagement, over time are important criteria.

A key factor for a sustained participation is team psychological safety, first described by Edmondson as "a shared belief that the team is safe for interpersonal risk taking" (Edmondson & Lei, This is strongly influenced by factors such as organizational climate and management involvement (Jeon, Kim, & Koh, 2011; Lin, Hung, & Chen, How

However, existing research focuses on piecemeal effects in isolated studies. They do not propose a clear definition of these variables, neither a way to measure them in the studied communities.

Goal:

Extending the _Institutional Analysis and Development Framework_ (Hess & Ostrom 2007) by theoretical and conceptual perspectives on virtual teams (Morrison-Smith & Ruiz, 2020) and team viability and safety (Cao et al.

2021),
this master thesis will:
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develop a conceptual framework consisting of variables to describe virtual collaboration in knowledge production, based on a review of the literature; This framework will detail 1) the different input-process-output elements to look at in the evaluation of an online community, and 2) the relations between the inputs, the process, the output and the viability;
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collect data on a project to evaluate how these variables, or a sub-set of these variables, can be measured in an online community;
-
test machine learning models that can explain one variable, for instance the viability in the case of Wikipedia.

Prise de fonction:

24/09/2024
Nature du financement:
Contrat doctoral

Précisions sur le financement:

Présentation établissement et labo d'accueil:

IMT Atlantique

IMT Atlantique ranks among the world's Top 400 Technological Universities (Times Higher Education world University ranking). From the fundamental to the applied, combining disciplinary research conducted in the fields of digital sciences, engineering sciences, physics and management, and working at the interfaces between these disciplines, IMT Atlantique responds to the major challenges facing companies and society through innovation and knowledge development, integrating socio-economic aspects.

The Data Science department has a team specialized in the study, modeling and design of socio-technical systems in which data and people play a central role.

The Data Science department comprises a multi-disciplinary team at the crossroads of Mathematics, Computer Science and the Humanities and Social Sciences.

Site web:

Intitulé du doctorat:

Doctorat
Pays d'obtention du doctorat:
France

Etablissement délivrant le doctorat:

IMT Atlantique

Ecole doctorale:

Sciences Pour l'Ingénieur et le Numérique (SPIN)Master's degree (Level M2) in Computational Social Sciences, Management, CSCW, Maths applied to social sciences, machine learning basics or computer science (with a strong interest for social science)

A first experience in graph theory, complex networks or social network analy