SOCIAL, TECHNICAL, AND ORGANIZATIONAL DETERMINANTS OF EMPLOYEESâ PARTICIPATION IN ENTERPRISE SOCIAL TAGGING TOOLS: A CONCEPTUAL MODEL AND AN EMPIRICAL INVESTIGATION
Notice bibliographique
Résumé
Organizations are attempting to leverage their knowledge resources by integrating knowledge sharing systems, a key and new form of which are social computing tools. A large number of these initiatives fail, however, due to employees' reluctance to use, contribute content to, and share knowledge through such tools. Although research regarding one's motivation to share knowledge is extensive, there has been little research examining social computing systems, especially from the seeking and contributory perspectives—the two distinct, but closely interrelated facets of knowledge sharing. Motivated by such concerns, and by incorporating knowledge-seeking and knowledge- contribution perspectives in a single study, this research develops and empirically examines a theoretical model to explain what motivates employees to seek, contribute and share social tags using Enterprise Social Tagging Tools (ESTTs). \n\nTwo research phases were employed to address the research objective. The goal of the first phase of the study was to explore factors affecting users’ tagging behavior in online social tagging tools. An extensive literature review was synthesized and a preliminary theoretical model emerged. A pilot study was conducted yielding 184 responses featuring eight different online social tagging tools. Mostly, the preliminary theoretical model showed positive influence on users’ tag behavior with a special focus on the newly developed concepts of information retrievability, information refindability.\n\nThe goal of the study’s second phase was combining the results from the first phase with motivational theories to build and validate a belief-based and socio-organizational model that can explain employees’ tag seeking, contributing, and sharing behavior in ESTTs. The model was developed by employing theories such as Theory of Reasoned Action (TRA), Theory of Planned Behavior (TPB), Technology Acceptance Model (TAM), and social exchange theory. Through a large-scale survey (n=481) in two large Information Technology (IT) companies, the model was validated. The results speak to the importance of the three newly developed factors impacting employees’ tag seeking, contributing and sharing behavior. These factors are uniquely context-specific reflecting actual features of social tagging tools and potentially social media in general. Particularly, the results reveal that employees' tag seeking behavior is affected by their perception of the ESTTs in terms of enjoyment, information retrievability, ease of use, and managerial influence. In the context of tag contribution and sharing, the results show that employees contribute and share tags because of their perception of information refindability, ease of use, altruism, and pro-sharing norms. Differences among the seeking, contributing and sharing model have implications for future research and practice.
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.
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 | 0,006 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 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 ».