Purpose
Enterprise social media platforms provide new ways of sharing knowledge and communicating within organizations to benefit from the social capital and valuable knowledge that employees have. Drawing on social dilemma and self-determination theory, the purpose of this paper is to understand what factors drive employees’ participation and what factors hamper their participation in enterprise social media.
Design/methodology/approach
Based on a literature review, a unified research model is derived integrating demographic, individual, organizational and technological factors that influence the motivation of employees to share knowledge. The model is tested using statistical methods on a sample of 114 respondents in Denmark. Qualitative data are used to elaborate and explain quantitative findings.
Findings
The findings pinpoint towards the general drivers and barriers to knowledge sharing within organizations. The significant drivers to knowledge sharing are: enjoy helping others, monetary rewards, management support, management encourages and motivates knowledge sharing behavior and knowledge sharing is recognized. The significant identified barriers are: change of behavior, lack of trust and lack of time.
Practical implications
The proposed knowledge sharing framework helps to understand what factors impact engagement on social media. Furthermore, the article suggests different types of interventions to overcome the social dilemma of knowledge sharing.
Originality/value
The study contributes to an understanding of factors leading to the success or failure of enterprise social media drawing on self-determination and social dilemma theory.
By using social media, many companies try to exploit new forms of interaction, collaboration, and knowledge sharing through leveraging the social, collaborative dimension of social software. The traditional collective knowledge management model based on a topdown approach is now opening up new avenues for a bottom-up approach incorporating a more personal knowledge management dimension, which could be synergized into collective knowledge using the social-collaborative dimension of social media. This article addresses the following questions: (1) How can social media support the management of personal and collective knowledge using a synergetic approach? (2) Do the personal and collective dimensions compete with each other, or can they reinforce each other in a more effective manner using social media?Our findings indicate that social media supports both the personal and collective dimensions of knowledge, while integrating a social collaborative dimension. The article introduces a framework that classifies social software into four categories according to the level of interaction and control. With certain tools, individuals are more in control. With other tools, the group is in control, resulting in a higher level of interaction and a diversity of knowledge and mindsets brought together. However, deploying and adopting these new tools in an organizational context is still a challenging task for management, owing to both organizational and individual factors.
This paper is presenting a generic ontology-based user modeling architecture, (OntobUM), applied in the context of a Knowledge Management System (KMS). Due to their powerful knowledge representation formalism and associated inference mechanisms, ontology-based systems are emerging as a natural choice for the next generation of KMSs operating in organizational, interorganizational as well as community contexts. User models, often addressed as user profiles, have been included in KMSs mainly as simple ways of capturing the user preferences and/or competencies. We extend this view by including other characteristics of the users relevant in the KM context and we explain the reason for doing this. The proposed user modeling system relies on a user ontology, using Semantic Web technologies, based on the IMS LIP specifications, and it is integrated in an ontology-based KMS called Ontologging. We are presenting a generic framework for implicit and explicit ontology-based user modeling.
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