Communities of enterprise : developing regional SMEs in the knowledge economy

Purpose – This paper proposes the concepts of Communities of Enterprise (CoEs) and Virtual Communities of Enterprise (VCoEs) to describe business networking patterns in regional areas where there is no central organisational or industry focus and small and medium enterprises dominate the economy. Design/methodology/approach – Based on analysis of the literature this paper builds on theoretical understandings of knowledge management, clustering and regional development.
Findings – The concept of CoEs is most appropriate for regional areas characterised by many small enterprises in diverse industries. CoEs enhance development of regional clusters by contributing to their intellectual capital, innovation culture, value networks and social capital. The incorporation of ICT creates VCoEs which provide added potential by enabling regions to expand their learning potential through innovation.
Research limitations/implications – This paper provides a conceptual foundation for empirical research into regional network or cluster development using ICT.
Practical implications – Virtual Communities of Enterprise value creation potential is substantial but only when the socioeconomic elements of regional clusters are understood. The VCoE approach addresses the fact that without an industry focus it can be difficult to engage and link SMEs from different industries, although this is where the greatest potential
for value creation in regional clusters is to be found.
Originality/value – The Virtual Communities of Enterprise (VCoEs) concept specifically addresses the unique requirements of SMEs in regions. It has the potential to provide value for regions in a way few ICT based regional development initiatives have been able to achieve.

Patient and business rules extraction and formalisation using SVN and SBVR for automated healthcare

This paper describes advances in automated health service selection and composition in the Ambient Assisted Living (AAL) domain. We apply a Service Value Network (SVN) approach to automatically match medical practice recommendations to health services based on sensor readings in a home care context. Medical practice recommendations are extracted from National Health and Medical Research Council (NHMRC) guidelines. Service networks are derived from Medicare Benefits Schedule (MBS) listings. Service provider rules are further formalised using Semantics of Business Vocabulary and Business Rules (SBVR), which allows business participants to identify and define machine-readable rules. We demonstrate our work by applying an SVN composition process to patient profiles in the context of Type 2 Diabetes Management.

More than just a transaction: conceptualising industry-based networks for virtual trading communities

This paper addresses the questions of why failure in industry-based networks has been so persistent and whether it is possible to avoid failure and achieve success in internet based markets [iMarketplaces]. A better explanation of implementation failures is important for both improved empirical outcomes and theory building. We construct a theoretical framework based on Bijker’s technology frame (1995) and a contextualization typology developed by Nowotny, Scott and Gibbons (2001). The framework helps us understand how industry-based networks function, why they fail and how we can apply the framework to assist better empirical outcomes. In this paper we apply our framework to Food Connect Australia, a vertically integrated marketplace, representative of the first wave of B2B markets. Sponsors of these iMarketplaces were quick to see and exploit the opportunities online access offered to bring together large numbers of buyers and sellers in new ways. However a lack of understanding of firstly, what represented true value in these networks and secondly, how to achieve buy-in at sustainable levels, meant that many of these first wave sites failed. Application of our framework reveals why there has been a radical shift from the trading role originally envisioned for these sites to the information hub model of the iMarketplace that industry is now being urged to adopt (Berryman and Heck, 2001).

Virtual communities of practice for regional small businesses: bringing informal networks online

Virtual communities of practice (VCoPs) are online business networks which are increasingly used by large organisations as a key strategy for creating value in the knowledge-based economy of the 21st Century.This paper examines the applicability of VCoPs to cross-industry regionally clustered small business networks. Interviews conducted with government and industry informants in two regional areas of Australia indicate that these strategies used for establishing VCoPs are applicable to such small business networks. Both regions had regional networks with active member involvement displaying CoP characteristics. Significant social capital existed on which VCoPs could be built, and there were viable alternatives to satisfy the roles of sponsors and leaders. There were, however, significant impediments that will have to be addressed before VCoPs can be  implemented such as the apparent reluctance of many SME owners to use the Internet and ICT generally, and the preference for informal networking. Funding to ensure that VCoPs are sustainable was also an issue. VCoPs appear to be extremely useful in linking small businesses in regional areas and in the development of viable regional clusters.

Virtual communities of enterprise value creation potential for regional clusters

The research examines regionally networked small business practices. A new concept Virtual Communities of Enterprise is proferred to explain the nature of knowledge sharing both interpersonally and online, and its potential for creating value for small businesses and their regions. Social capital emerges as an essential pre-requisite for accessing value.

T-OSN : a trust evaluation model in online social networks

Now days, the online social networks (OSN) have gained considerable popularity. More and more people use OSN to share their interests and make friends, also the OSN helps users overcome the geographical barriers. With the development of OSN, there is an important problem users have to face that is trust evaluation. Before user makes friends with a stranger, the user need to consider the following issues: Can a stranger be trusted? How much the stranger can be trusted? How to measure the trust of a stranger? In this paper, we take two factors, Degree and Contact Interval into consideration, which produce a new trust evaluation model (T-OSN). T-OSN is aimed to solve how to evaluate the trust value of an OSN user, also which is more efficient, more reliable and easy to implement. Base on our research, this model can be used in wide range, such as online social network (OSN) trust evaluation, mobile network message forwarding, ad hoc wireless networking, routing message on Internet and peer-to-peer file sharing network. The T-OSN model has following obvious advantages compare to other trust evaluate methods. First of all, it is not base on features of traditional social network, such as, distance and shortest path. We choose the special features of OSN to build up the model, that is including numbers of friends(Degree) and contact frequency(Contact Interval). These species features makes our model more suitable to evaluate OSN users trust value. Second, the formulations of our model are quite simple but effective. That means, to calculate the result by using our formulations will not cost too much resources. Last but not least, our model is easy to implement for an OSN website, because of the features that we used in our model, such as numbers of friends and contact frequency are easy to obtain. To sum up, our model is using a few resources to obtain a valuable trust value that can help OSN users to solve an important security problem, we believe that will be big step - or development of OSN.

Exploring the global and local networks of national information system integration initiatives : a case study of national cultural collection integration from an actor-network perspective

Australian Museums Online (AMOL) was the earliest attempt to make Australia’s distributed cultural collections accessible from a single online resource. Despite early successes, significant achievements and the considerable value it offered certain groups, the project ran into operational difficulties and was eventually discontinued. By using Actor-Network Theory and analysing the global and local actor-networks, it is revealed that although the project originated from large, state museums, buy-in was restricted to individuals, rather than institutions and the most significant value was for smaller, regional institutions. Furthermore, although the global networks that governed the project could translate their visions through the local production networks, because the network’s underlying weaknesses were never addressed, over time this destablised the global networks. This case study offers advice for projects attempting to consolidate data sources from disparate sources, and highlights the importance of individual actors in championing the project.

Size invariance of fibrous networks of supramolecular soft materials during formation under critical volume confinement

The rheological properties of a hierarchically structured supramolecular soft material are mainly determined by the structure of its network. Controlling the thermodynamic driving force of physical gels (one type of such materials) during the formation has proven effective in manipulating the network structure due to the nature of nucleation and growth of the fiber network formation in such a supramolecular soft material. Nevertheless, it is shown in this study that such a property can be dramatically influenced when the volume of the system is reduced to below a threshold value. Unlike un-confined systems, the network structure of such a soft material formed under volume confinement contains a constant network size, independent of the experimental conditions, i.e. temperature and solute concentration. This implies that the size of the fiber networks in such a material is invariable and free from the influence of external factors, once the volume is reduced to a threshold. The observations of this work are significant in the control of the formation of fibrous networks in materials of this type.

Factors affecting SME owner-managers' willingness to share knowledge online in rural local business networks

This article integrates typically separate SME research on e-commerce, business networking, and knowledge management into a model explaining factors influencing the willingness of SME owner-managers to share knowledge online in business networks in rural districts. This is important because e-commerce can assist owner-managers, often dispersed in rural districts, to share knowledge between face-to-face networking events. The main factors associated with willingness to share knowledge online were their willingness to share knowledge face-to-face and their intensity of Internet use. Entrepreneurial factors such as owner-managers' expectations of rapid growth, trading outside the district, and seeking information about customers/competitors were indirectly associated with online sharing via intensity of Internet use only. The model suggests network coordinators could encourage online knowledge sharing by assisting owner-managers to see the business value of e-commerce and by ensuring that networking events are suitable for owner-managers, whether or not they have entrepreneurial goals, to facilitate face-to-face knowledge sharing.

Comparison between response surface models and artificial neural networks in hydrologic forecasting

Developing an efficient and accurate hydrologic forecasting model is crucial to managing water resources and flooding issues. In this study, response surface (RS) models including multiple linear regression (MLR), quadratic response surface (QRS), and nonlinear response surface (NRS) were applied to daily runoff (e.g., discharge and water level) prediction. Two catchments, one in southeast China and the other in western Canada, were used to demonstrate the applicability of the proposed models. Their performances were compared with artificial neural network (ANN) models, trained with the learning algorithms of the gradient descent with adaptive learning rate (ANN-GDA) and Levenberg-Marquardt (ANN-LM). The performances of both RS and ANN in relation to the lags used in the input data, the length of the training samples, long-term (monthly and yearly) predictions, and peak value predictions were also analyzed. The results indicate that the QRS and NRS were able to obtain equally good performance in runoff prediction, as compared with ANN-GDA and ANN-LM, but require lower computational efforts. The RS models bring practical benefits in their application to hydrologic forecasting, particularly in the cases of short-term flood forecasting (e.g., hourly) due to fast training capability, and could be considered as an alternative to ANN

Novel approach for information discovery in autonomous wireless sensor networks

Autonomous Wireless sensor networks(WSNs) have sensors that are usually deployed randomly to monitor one or more phenomena. They are attractive for information discovery in large-scale data rich environments and can add value to mission–critical applications such as battlefield surveillance and emergency response systems. However, in order to fully exploit these networks for such applications, energy efficient, load balanced and scalable solutions for information discovery are essential. Multi-dimensional autonomous WSNs are deployed in complex environments to sense and collect data relating to multiple attributes (multi-dimensional data). Such networks present unique challenges to data dissemination, data storage of in-network information discovery. In this paper, we propose a novel method for information discovery for multi-dimensional autonomous WSNs which sensors are deployed randomly that can significantly increase network lifetime and minimize query processing latency, resulting in quality of service (QoS) improvements that are of immense benefit to mission–critical applications. We present simulation results to show that the proposed approach to information discovery offers significant improvements on query resolution latency compared with current approaches.

Informal social networks within and between organisations: on the properties of interpersonal ties and trust

Purpose

– The purpose of this paper is to explore the underlying relational properties of security networks by focusing specifically on the relationship between formal and informal ties, and interpersonal and inter-organisational trust.

Design/methodology/approach

– The research is based on 20 qualitative interviews with senior members of police and security agencies across the field of counter-terrorism in Australia.

Findings

– The findings suggest that the underlying relational properties of security networks are highly complex, making it difficult to distinguish between formal and informal ties, interpersonal and inter-organisational trust. The findings also address the importance of informal ties and interpersonal trust for the functioning of organisational security networks.

Research limitations/implications

– The research is exploratory in nature and extends to a number of organisational security networks in the field of counter-terrorism in Australia. While it is anticipated that the findings will be relevant in a variety of contexts, further research is required to advance our knowledge of the implications and properties of informal social networks within defined network boundaries.

Practical implications

– The findings suggest that the functioning of security networks is likely to be highly dependent on the underlying social relationships between network members. This has practical implications for those responsible for designing and managing security networks.

Originality/value

– The paper calls attention to a very understudied topic by focusing on the dynamics of informal ties and interpersonal trust within organisational security networks.

Geospatial estimation-based auto drift correction in wireless sensor networks

Wireless sensor networks are often deployed in large numbers, over a large geographical region, in order to monitor the phenomena of interest. Sensors used in the sensor networks often suffer from random or systematic errors such as drift and bias. Even if they are calibrated at the time of deployment, they tend to drift as time progresses. Consequently, the progressive manual calibration of such a large-scale sensor network becomes impossible in practice. In this article, we address this challenge by proposing a collaborative framework to automatically detect and correct the drift in order to keep the data collected from these networks reliable. We propose a novel scheme that uses geospatial estimation-based interpolation techniques on measurements from neighboring sensors to collaboratively predict the value of phenomenon being observed. The predicted values are then used iteratively to correct the sensor drift by means of a Kalman filter. Our scheme can be implemented in a centralized as well as distributed manner to detect and correct the drift generated in the sensors. For centralized implementation of our scheme, we compare several krigingand nonkriging-based geospatial estimation techniques in combination with the Kalman filter, and show the superiority of the kriging-based methods in detecting and correcting the drift. To demonstrate the applicability of our distributed approach on a real world application scenario, we implement our algorithm on a network consisting of Wireless Sensor Network (WSN) hardware. We further evaluate single as well as multiple drifting sensor scenarios to show the effectiveness of our algorithm for detecting and correcting drift. Further, we address the issue of high power usage for data transmission among neighboring nodes leading to low network lifetime for the distributed approach by proposing two power saving schemes. Moreover, we compare our algorithm with a blind calibration scheme in the literature and demonstrate its superiority in detecting both linear and nonlinear drifts.

Passivity analysis of memristor-based complex-valued neural networks with time-varying delays

In this paper, the model of memristor-based complex-valued neural networks (MCVNNs) with time-varying delays is established and the problem of passivity analysis for MCVNNs is considered and extensively investigated. The analysis in this paper employs results from the theory of differential equations with discontinuous right-hand side as introduced by Filippov. By employing the appropriate Lyapunov–Krasovskii functional, differential inclusion theory and linear matrix inequality (LMI) approach, some new sufficient conditions for the passivity of the given MCVNNs are obtained in terms of both complex-valued and real-value LMIs, which can be easily solved by using standard numerical algorithms. Numerical examples are provided to illustrate the effectiveness of our theoretical results.