Oh, SNEP! The Dynamics of Social Network Emergence - the case of Capgemini Yammer

With more and more organisations accepting social media into the workplace as an integral part of professional practice and group communication, understanding what exactly happens when enterprise social networks suddenly emerge in the workplace, brought in on initiative of employees in a self organising manner, is increasingly important. In this paper we present an analysis of enterprise based-short message communications shared across the Yammer enterprise social network at the international service consultancy Capgemini. We concentrate on conversations during the first nine months of uptake with a focus on self-referential communication where users convers about Yammer itself. A time-trend analysis of conversation types leads to the identification of what we term the SNEP model, the Social Network Emergence Process that captures the phases in which the social network emerged over time. The study for the first time allows to unpack in detail the often-discussed emergence aspect of enterprise social media, in terms of sense-making, user experimenting, norming behaviour, and network diffusion. The identified SNEP model is useful for managers who want to understand what happens when social media initiatives suddenly erupt into existence in their organisations

Enterprise Content Management Systems Potential to Support Human Capital Management Initiatives

Neto, M. D. C., & Fernandes, C. A. (2010). Enterprise Content Management Systems Potential to Support Human Capital Management Initiatives. International Journal of Engineering and Industrial Management, 2, 75-92Enterprise Content Management Systems (ECMS) and the Enterprise Information Portals (EIP) they support are being increasingly referred in the literature as one interesting technological solution to help organizations in their knowledge management initiatives. This paper seeks to explore the hidden potential of enterprise information portals delivered by ECMS to support knowledge management initiatives, namely human capital management, through the usage of social network analysis on research results and co-authorship/co-work relationships that may suggest ways to more effectively utilize knowledge capital and other organizational resources. For that purpose in this paper we will present field research results on human capital assets management based on EIP data repositories using social network analysis. This evaluation will be made through the use of social network analysis techniques applied to authorship data from papers published in international journals with refereeing covering the last twenty years of research activities from a Portuguese leading research institution in the field of telecommunications - Instituto de Telecomunicações (IT).publishersversionpublishe

Network engineering for C-Commerce innovation: the role of trust

The idea that social networks play an important role in knowledge diffusion of innovation has a long pedigree in innovation theory. In his Diffusion of Innovation (DOI) theory, Rogers (1995) argued that in the information network of the organization, managerial champions and opinion leaders could affect both organizational acceptance and also the velocity of adoption of innovation. In Small to Medium Enterprise (SME) C-commerce innovation, the role of such social factors has been understood in terms of ‘embedded network structure’ (Braun, 2003) that impacts on clustering behavior. This article explores the use of quantitative Social Network Analysis (SNA) to model the nature and consequences of relations based on trust in a Small to Medium Size Tourism Enterprise (SMTE) C-commerce innovation case study context

Configuring Knowledge Management for Social Enterprise: Detecting Antecedents and its Maneuvers to Social Improvements

This study tried to address the role of knowledge management unto social enterprise due to their unique-respective characteristics. The analysis began with proposing twofold of questions, namely how to bring-in the concept of knowledge management unto social enterprise and how KM shall maneuvers social enterprise to gain sustainable competitive advantage. Having combined two fields of studies: knowledge management and social entrepreneurship, the study found seven antecedents, ranging from strong vision-mission, technology infrastructure, organizational fitness, learning culture, employee capability, leaderships style and knowledge network. These antecedents are simultaneously providing basis for KM process in which appointing new knowledge re-juvenescence as the ultimate outcome. Furthermore, this is the triggered for continuous social improvement which then creating reverse flow as signaled from our human tremendous growth. Having more attention to the antecedents and proposed framework might help social enterprise to accomplish its ultimate goals, bringing society to higher quality of life. Keywords: Knowledge Management; Culture; Social Enterprise; Sustainable Competitive Advantag

Web Data Extraction, Applications and Techniques: A Survey

Web Data Extraction is an important problem that has been studied by means of different scientific tools and in a broad range of applications. Many approaches to extracting data from the Web have been designed to solve specific problems and operate in ad-hoc domains. Other approaches, instead, heavily reuse techniques and algorithms developed in the field of Information Extraction. This survey aims at providing a structured and comprehensive overview of the literature in the field of Web Data Extraction. We provided a simple classification framework in which existing Web Data Extraction applications are grouped into two main classes, namely applications at the Enterprise level and at the Social Web level. At the Enterprise level, Web Data Extraction techniques emerge as a key tool to perform data analysis in Business and Competitive Intelligence systems as well as for business process re-engineering. At the Social Web level, Web Data Extraction techniques allow to gather a large amount of structured data continuously generated and disseminated by Web 2.0, Social Media and Online Social Network users and this offers unprecedented opportunities to analyze human behavior at a very large scale. We discuss also the potential of cross-fertilization, i.e., on the possibility of re-using Web Data Extraction techniques originally designed to work in a given domain, in other domains.Comment: Knowledge-based System

System Dynamics Model of Knowledge Acquisition via E-Learning of SNS Oriented Knowledge Community in Enterprise

To explore the dynamics mechanism for knowledge acquisition via E-Learning of SNS (Social Network Service) knowledge community in enterprise, so that strategy forknowledge community management can be proposed accordingly. Firstly, elements of knowledge acquisition via E-Learning in knowledge community are analyzed. Secondly,causal loop diagrams are made to make system analysis. Thirdly system dynamics model is established to describe development and changes of knowledge acquisition bysystem dynamics modeling tools. Then sensitivity analysis is made to explore the influences of parameters including, network size, E-Learning experience, knowledgedemand and knowledge acquisition cost. By using system dynamics and sensitivity analysis, we can exploit the dynamic mechanism of knowledge acquisition via E-Learningof SNS oriented knowledge community in enterprise

Enabling Knowledge Broker Analysis through Actor Clusters in Organizational Structures in Enterprise Social Media

Knowledge brokers serve as facilitators of knowledge sharing. The extant literature calls for nuanced analyses of different organizational structures as the spaces knowledge brokers operate in. Our interest lies in formal, semiformal, and informal organizational network structures and in how knowledge brokers are positioned in them. In this paper, we outline a collaborative analysis method, with researchers from different disciplines working together in data sprints. The benefit of this process is that it enables analyzing large organizational networks with deep insights. Amplifying social network analysis with field knowledge offers a deeper understanding of the connections in the network. This paper describes the analysis process and proposes interdisciplinary data processing techniques. We applied the proposed method using an extensive empirical data set that includes intraorganizational social media interactions between employees in a global organization. Our analysis transforms enterprise social media data into a network model that describes an organization’s social structure