Toward an integrative role for intranets : an interpretive case study of intranet-based dynamic knowledge integration in socio-technical networks

An increasing challenge for contemporary businesses is to be able to respond to perceived opportunities and threats by dynamically integrating knowledge dispersed across and beyond the organisation. This paper provides findings from two interpretive case studies that illustrate how corporate intranets can be dynamically interwoven with other knowledge technologies in socio-technical networks (STNs) to integrate distributed formal and infonnal knowledge. A key finding suggests that businesses should carefully examine employee use of intranets for dynamic knowledge integration, and any implications stemming from this new integrative role for intranets. The paper also provides a theoretical framework for dynamic knowledge integration in STNs, which can underpin future research in this area.

Dynamic knowledge integration in socio-technical networks: an interpretive study of intranet use for knowledge integration

A major challenge facing firms competing in electronic business markets is the dynamic integration of knowledge within and beyond the firm, enabled by internet-based infrastructure and emergent fluid socio-technical networks. This paper explores how social actors dynamically employ intranets to integrate formal and informal knowledge within evolving socio-technical networks that emerge, permeate and extend beyond the organisational boundary. The paper presents two case studies that illustrate how static intranets can be useful for dynamically integrating knowledge when they are interwoven with other knowledge channels such as e-mail through which flows the informal knowledge needed to make sense of and situate formal organisational knowledge. The findings suggest that businesses should carefully examine how employees integrate intranets with other channels in their work, and the shaping of knowledge outcomes that flows from such use. There are practical implications for the proper skilling of thepeople who share and integrate knowledge in this way. The paper also provides a framework for dynamic knowledge integration in socio-technical networks, which can help underpin future research in this area.

Socio-technical factors influencing channel use for knowledge-sharing in regional SME networks

The economic sustainability of regional areas is dependent on cross-industry innovation and knowledge-sharing among Small and Medium Enterprises (SMEs). The web-based initiatives deployed in regions worldwide to facilitate SME knowledge-sharing have typically been unsuccessful. This paper argues that the main reason for these failures is the lack of understanding of the socio-technical factors which influence the use of web-based channels (websites, online forums and expertise databases) as well as the more conventional channels (face-to-face and e-mail). This paper reports the findings of interpretive case studies of two regional SME business networks. It evaluates the major channels on six socio-technical criteria: link strength; trustworthiness; tacitness; usability; durability and currency. None of the channels were strong against all socio-technical factors. This highlights the importance of achieving an appropriate mix of channels to facilitate SME knowledge-sharing.

Identification of implicit technical relationships in overlapping networks

Overlapping network techniques to expand the single sequential finish-to-start relationship between preceding and succeeding activities in scheduling construction projects have been developed for decades. The hidden logic relationships between activities and two virtual activities, Start and Finish, however limit the applications of these techniques in practice as they may lead to incorrect time parameters of some activities. In this research, a novel approach has been developed to identify those concealed relationships in a structured procedure by a two-dimensional nested diagram. An empirical study was carried out to demonstrate the network modification approach.

Actor-Network Theory (ANT) based visualisation of socio-technical facets of RFID technology translation: an Australian hospital scenario

In the early 2000s, Information Systems researchers in Australia had begun to emphasise socio-technical approaches in innovation adoption of technologies. The ‘essentialist' approaches to adoption (for example, Innovation Diffusion or TAM), suggest an essence is largely responsible for rate of adoption (Tatnall, 2011) or a new technology introduced may spark innovation. The socio-technical factors in implementing an innovation are largely flouted by researchers and hospitals. Innovation Translation is an approach that purports that any innovation needs to be customised and translated in to context before it can be adopted. Equally, Actor-Network Theory (ANT) is an approach that embraces the differences in technical and human factors and socio-professional aspects in a non-deterministic manner. The research reported in this paper is an attempt to combined the two approaches in an effective manner, to visualise the socio-technical factors in RFID technology adoption in an Australian hospital. This research investigation demonstrates RFID technology translation in an Australian hospital using a case approach (Yin, 2009). Data was collected using a process of focus groups and interviews, analysed with document analysis and concept mapping techniques. The data was then reconstructed in a ‘movie script' format, with Acts and Scenes funnelled to ANT informed abstraction at the end of each Act. The information visualisation at the end of each Act using ANT informed Lens reveal the re-negotiation and improvement of network relationships between the people (factors) involved including nurses, patient care orderlies, management staff and non-human participants such as equipment and technology. The paper augments the current gaps in literature regarding socio-technical approaches in technology adoption within Australian healthcare context, which is transitioning from non-integrated nearly technophobic hospitals in the last decade to a tech-savvy integrated era. More importantly, the ANT visualisation addresses one of the criticisms of ANT i.e. its insufficiency to explain relationship formations between participants and over changes of events in relationship networks (Greenhalgh & Stones, 2010).

To shut them up or to clarify: restraining the spread of rumors in online social networks

Restraining the spread of rumors in online social networks (OSNs) has long been an important but difficult problem to be addressed. Currently, there are mainly two types of methods 1) blocking rumors at the most influential users or community bridges, or 2) spreading truths to clarify the rumors. Each method claims the better performance among all the others according to their own considerations and environments. However, there must be one standing out of the rest. In this paper, we focus on this part of work. The difficulty is that there does not exist a universal standard to evaluate them. In order to address this problem, we carry out a series of empirical and theoretical analysis on the basis of the introduced mathematical model. Based on this mathematical platform, each method will be evaluated by using real OSN data.We have done three types of analysis in this work. First, we compare all the measures of locating important users. The results suggest that the degree and betweenness measures outperform all the others in the Facebook network. Second, we analyze the method of the truth clarification method, and find that this method has a long-term performance while the degree measure performs well only in the early stage. Third, in order to leverage these two methods, we further explore the strategy of different methods working together and their equivalence. Given a fixed budget in the real world, our analysis provides a potential solution to find out a better strategy by integrating both types of methods together. From both the academic and technical perspective, the work in this paper is an important step towards the most practical and optimal strategies of restraining rumors in OSNs.

Delay tolerant networks

The delay-tolerant networks (DTNs) are emerging research topics that have attracted keen research efforts from both academia and industry. Different from the traditional communication networks, DTNs consider an extreme network condition where a complete end-to-end path between the data source and destination may not exist, and the network is subject to dynamic node connections and unstable topologies. With the above features, DTNs find broad applications in the situations where legacynetworks cannot work effectively, such as data communications in rural areas, where stable communications infrastructure is not available or costly, and crucial areas, e.g., disaster rescue and battlefield communications. To summarize, the DTNs, as an important technology complementary to traditional networkings, can be widely applied to national welfare and the people’s livelihood.

Traffic matrix prediction and estimation based on deep learning in large-scale IP backbone networks

Network traffic analysis has been one of the most crucial techniques for preserving a large-scale IP backbone network. Despite its importance, large-scale network traffic monitoring techniques suffer from some technical and mercantile issues to obtain precise network traffic data. Though the network traffic estimation method has been the most prevalent technique for acquiring network traffic, it still has a great number of problems that need solving. With the development of the scale of our networks, the level of the ill-posed property of the network traffic estimation problem is more deteriorated. Besides, the statistical features of network traffic have changed greatly in terms of current network architectures and applications. Motivated by that, in this paper, we propose a network traffic prediction and estimation method respectively. We first use a deep learning architecture to explore the dynamic properties of network traffic, and then propose a novel network traffic prediction approach based on a deep belief network. We further propose a network traffic estimation method utilizing the deep belief network via link counts and routing information. We validate the effectiveness of our methodologies by real data sets from the Abilene and GÉANT backbone networks.