Knowledge Sharing in Social Networking Sites: How Context Impacts Individuals’ Social and Intrinsic Motivation to Contribute in Online Communities

Knowledge-sharing research in online communities has primarily focused on communities of practice and the social factors of knowledge-sharing behavior in organizational contexts. Academic research has not rigorously examined non-business-oriented online communities as venues for facilitating knowledge sharing. Thus, in this paper, we address this research gap by examining the contextual roles of anonymity and community type on an individual’s social and individual drivers of knowledge-sharing attitude in social networking sites. Using social capital theory as a theoretical backbone, we propose and empirically validate a relational model through a survey of 329 users of Facebook, LinkedIn, and CNET. From analyzing the data with the partial least squares (PLS) method, we found strong explanatory power of the proposed research model. We discuss our study’s implications for both research and practice

Research on the Relationship Network in Customer Innovation Community based on Text Mining and Social Network Analysis

Relationship is the focus of the current study in the social phenomenon with social network theory, which is mainly about its meaning and strength. However, a different object, different relationship. Social network theory insists that the actor\u27s behavior is the result of the limitations and opportunities of many relationships that occur simultaneously and interaction. The behavior and characteristics of the whole group are also dependent on the integration of multi-dimensional relationships. There are multi-dimensional relationships among customers participated product innovation in the customer innovation community. Since the huge number of customers in customer innovation community and the complex relationships among the customers, the method is different in traditional ways. Therefore, this paper combines associated crawler algorithm, text mining, and social network analysis to study network relationship types, network structure and the relevance of the customer innovation community. Firstly, this paper analyzes the relationship type and the relationship network according to previous studies. Secondly, reptile technology is used to obtain structured data in the customer community. After cleaning and pre-processing, the data is transformed into relational data from the original structure, with format 1069 × 1069 size matrix. Analyzing the structure of relationship network using social network analysis methods and tools, the results show that interactive network, social network, and knowledge-sharing networks are all sparse network. Thirdly, the correlation among the relationship networks is studied. The results demonstrate that it is higher than the correlation between the interactive network and the knowledge-sharing network and lower than the social network correlated with the other two networks

Design Challenges for Innovation Management on Agro-Food Sector

Current status of research indicates that we assist to location-specific factor supremacy as determinants in regional attractiveness and sustainability being territorial driven, we offer strong arguments for policy makers in order to enable this long term strategy. We also address another issue heavily disputed between academics-that is the return to local and regional offerings as complementary to global assumption. Assisting today to a hybrid innovation process, relying upon territorial marketing-an umbrella for too many issues cvasi- exploited: eco-clusters, local and regional offerings; traditional products/services exploiting, regional clusters competing for funds; we are focusing on complex industrial -rural system reconfiguration relying upon dynamic evolution of territorial branding into competitive identity, as the disruptive behavior we need in sustainable development. Successful development strategies are based on the ability to build an institutional territorial coherence-social and environmental sustainability being inextricably interdependent, such a complex coordination structure relies on territorial knowledge sharing through expertise polls consultation- as key concept of good governance. This model of innovational resource allocation coordination on agro food chains, relying upon clusterisation through patterns of innovational management deficit, offers a relevant solution for synergic orientation of assistance and mentoring efforts on the sector, enable the capitalization of relevant capabilities and increase the addressability from innovation demand side. Based upon auditing 500 SME’s from agro food sector in Europe and 51 in SE region, the paper is fully documented on there years of data analyzing from Agro Food sector on 10 European countries in the framework on FP6 SPAS European Project.territorial knowledge sharing, innovation resource allocation, disruptive territorial solution, community supported agro food chains

Online Activities to Mobilize Smart Cities

A smart city is a dynamic living system that contains hard (unchanging) and soft (changing) parts that each involve the implementation of respective technologies. Prior research has focused on infrastructure, technology, and social components when discussing smart city structure. In this paper, we explore key elements within the soft aspects of smart city initiatives enabling the organization of a dynamic structure. To do so, we focus on human behavior, which we illustrate by analyzing online activities in two cases: one is related to a smart city while the other focuses on an online community. Based on the analysis, we identify key elements that reveal how people participate and become engaged in order to provide lessons to be taken into account within smart city initiatives. Within online activities, the key elements we note are related to knowledge generation, information sharing of common interests, and the creation of collective action

Costing social and behavior change programming: The role of the denominator

This is the first in a series of brief reports intended to complement Breakthrough RESEARCH\u27s Costing Guidelines and support a social and behavior change community of practice by discussing important issues and practices for social and behavior change costing. Breakthrough RESEARCH is gathering, analyzing, and sharing evidence on the costs and impact of social and behavior change interventions to support social and behavior change investments, which are crucial for improving health and advancing development. A review of costing literature identified 147 studies on social and behavior change costs, methodological shortcomings, and knowledge gaps that can be addressed in new costing studies. To address these gaps, Breakthrough RESEARCH issued its Guidelines for Costing of Social and Behavior Change Health Interventions, which provide 17 principles for conducting quality costing studies

Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page

Content Reuse and Interest Sharing in Tagging Communities

Tagging communities represent a subclass of a broader class of user-generated content-sharing online communities. In such communities users introduce and tag content for later use. Although recent studies advocate and attempt to harness social knowledge in this context by exploiting collaboration among users, little research has been done to quantify the current level of user collaboration in these communities. This paper introduces two metrics to quantify the level of collaboration: content reuse and shared interest. Using these two metrics, this paper shows that the current level of collaboration in CiteULike and Connotea is consistently low, which significantly limits the potential of harnessing the social knowledge in communities. This study also discusses implications of these findings in the context of recommendation and reputation systems.Comment: 6 pages, 6 figures, AAAI Spring Symposium on Social Information Processin