Indirect ties in knowledge networks:a social network analysis with ordered weighted averaging operators

This PhD thesis analyses networks of knowledge flows, focusing on the role of indirect ties in the knowledge transfer, knowledge accumulation and knowledge creation process. It extends and improves existing methods for mapping networks of knowledge flows in two different applications and contributes to two stream of research. To support the underlying idea of this thesis, which is finding an alternative method to rank indirect network ties to shed a new light on the dynamics of knowledge transfer, we apply Ordered Weighted Averaging (OWA) to two different network contexts. Knowledge flows in patent citation networks and a company supply chain network are analysed using Social Network Analysis (SNA) and the OWA operator. The OWA is used here for the first time (i) to rank indirect citations in patent networks, providing new insight into their role in transferring knowledge among network nodes; and to analyse a long chain of patent generations along 13 years; (ii) to rank indirect relations in a company supply chain network, to shed light on the role of indirectly connected individuals involved in the knowledge transfer and creation processes and to contribute to the literature on knowledge management in a supply chain. In doing so, indirect ties are measured and their role as means of knowledge transfer is shown. Thus, this thesis represents a first attempt to bridge the OWA and SNA fields and to show that the two methods can be used together to enrich the understanding of the role of indirectly connected nodes in a network. More specifically, the OWA scores enrich our understanding of knowledge evolution over time within complex networks. Future research can show the usefulness of OWA operator in different complex networks, such as the on-line social networks that consists of thousand of nodes.

Managing knowledge transfer across supply chain:a social network approach

This paper analyzes the theme of knowledge transfer in supply chain management. The aim of this study is to present the social network analysis (SNA) as an useful tool to study knowledge networks within supply chain, to monitor knowledge flows and to identify the accumulating knowledge nodes of the networks.

Managing supply chain knowledge in the new product development process:a social network analysis approach

The aim of this paper is to propose a conceptual framework for studying the knowledge transfer problem within the supply chain. The social network analysis (SNA) is presented as a useful tool to study knowledge networks within supply chain, to visualize knowledge flows and to identify the accumulating knowledge nodes of the networks. © 2011 IEEE.

Knowledge management in knowledge intensive service networks:a strategic management approach

Purpose – The main purpose of this paper is to analyze knowledge management in service networks. It analyzes the knowledge management process and identifies related challenges. The authors take a strategic management approach instead of a more technology-oriented approach, since it is believed that managerial problems still remain after technological problems are solved. Design/methodology/approach – The paper explores the literature on the topic of knowledge management as well as the resource (or knowledge) based view of the firm. It offers conceptual insights and provides possible solutions for knowledge management problems. Findings – The paper discusses several possible solutions for managing knowledge processes in knowledge-intensive service networks. Solutions for knowledge identification/generation, knowledge application, knowledge combination/transfer and supporting the evolution of tacit network knowledge include personal and technological aspects, as well as organizational and cultural elements. Practical implications – In a complex environment, knowledge management and network management become crucial for business success. It is the task of network management to establish routines, and to build and regularly refresh meta-knowledge about the competencies and abilities that exist within the network. It is suggested that each network partner should be rated according to the contribution to the network knowledge base. Based on this rating, a particular network partner is a member of a certain knowledge club, meaning that the partner has access to a particular level of network knowledge. Such an established routine provides strong incentives to add knowledge to the network's knowledge base Originality/value – This paper is a first attempt to outline the problems of knowledge management in knowledge-intensive service networks and, by so doing, to introduce strategic management reasoning to the discussion.

Knowledge mediation and overlapping in interfirm networks

Purpose – The literature on interfirm networks devotes scant attention to the ways collaborating firms combine and integrate the knowledge they share and to the subsequent learning outcomes. This study aims to investigate how motorsport companies use network ties to share and recombine knowledge and the learning that occurs both at the organizational and dyadic network levels. Design/methodology/approach – The paper adopts a qualitative and inductive approach with the aim of developing theory from an in-depth examination of the dyadic ties between motorsport companies and the way they share and recombine knowledge. Findings – The research shows that motorsport companies having substantial competences at managing knowledge flows do so by getting advantage of bridging ties. While bridging ties allow motorsport companies to reach distant and diverse sources of knowledge, their strengthening and the formation of relational capital facilitate the mediation and overlapping of that knowledge. Research limitations/implications – The analysis rests on a qualitative account in a single industry and does not take into account different types of inter-firm networks (e.g. alliances; constellations; consortia etc.) and governance structures. Cross-industry analyses may provide a more fine-grained picture of the practices used to recombine knowledge and the ideal composition of inter-firm ties. Practical implications – This study provides some interesting implications for scholars and managers concerned with the management of innovation activities at the interfirm level. From a managerial point of view, the recognition of the different roles played by network spanning connections is particularly salient and raises issues concerning the effective design and management of interfirm ties. Originality/value – Although much of the literature emphasizes the role of bridging ties in connecting to diverse pools of knowledge, this paper goes one step further and investigates in more depth how firms gather and combine distant and heterogeneous sources of knowledge through the use of strengthened bridging ties and a micro-context conducive to high quality relationships.

Networks of learning : how motorsport companies collaborate and share knowledge:evidence from the UK and Italian motorsport industries

The motorsport industry is a high value-added and highly innovative business sector. The UK’s leading racing car manufacturers are world class centres of research, development and engineering. However, individual firms in the sector do not have the range and depth of capabilities to compete independently in motorsport’s dynamic and competitive environment. Industry attention has therefore progressively focused on how networks of collaborating firms can work together to develop new products, improve business processes and reduce costs. This report presents findings from a three year Cardiff Business School study which examined the ways in which firms collaborate as part of wider networks. The research involved gathering data from over 120 firms in the UK and Italian motorsport sectors.

Curvature-driven smoothing: a learning algorithm for feed-forward networks

The performance of feed-forward neural networks in real applications can be often be improved significantly if use is made of a-priori information. For interpolation problems this prior knowledge frequently includes smoothness requirements on the network mapping, and can be imposed by the addition to the error function of suitable regularization terms. The new error function, however, now depends on the derivatives of the network mapping, and so the standard back-propagation algorithm cannot be applied. In this paper, we derive a computationally efficient learning algorithm, for a feed-forward network of arbitrary topology, which can be used to minimize the new error function. Networks having a single hidden layer, for which the learning algorithm simplifies, are treated as a special case.

Topographic mappings and feed-forward neural networks

This thesis is a study of the generation of topographic mappings - dimension reducing transformations of data that preserve some element of geometric structure - with feed-forward neural networks. As an alternative to established methods, a transformational variant of Sammon's method is proposed, where the projection is effected by a radial basis function neural network. This approach is related to the statistical field of multidimensional scaling, and from that the concept of a 'subjective metric' is defined, which permits the exploitation of additional prior knowledge concerning the data in the mapping process. This then enables the generation of more appropriate feature spaces for the purposes of enhanced visualisation or subsequent classification. A comparison with established methods for feature extraction is given for data taken from the 1992 Research Assessment Exercise for higher educational institutions in the United Kingdom. This is a difficult high-dimensional dataset, and illustrates well the benefit of the new topographic technique. A generalisation of the proposed model is considered for implementation of the classical multidimensional scaling (¸mds}) routine. This is related to Oja's principal subspace neural network, whose learning rule is shown to descend the error surface of the proposed ¸mds model. Some of the technical issues concerning the design and training of topographic neural networks are investigated. It is shown that neural network models can be less sensitive to entrapment in the sub-optimal global minima that badly affect the standard Sammon algorithm, and tend to exhibit good generalisation as a result of implicit weight decay in the training process. It is further argued that for ideal structure retention, the network transformation should be perfectly smooth for all inter-data directions in input space. Finally, there is a critique of optimisation techniques for topographic mappings, and a new training algorithm is proposed. A convergence proof is given, and the method is shown to produce lower-error mappings more rapidly than previous algorithms.

Combining spatially distributed predictions from neural networks

In this report we discuss the problem of combining spatially-distributed predictions from neural networks. An example of this problem is the prediction of a wind vector-field from remote-sensing data by combining bottom-up predictions (wind vector predictions on a pixel-by-pixel basis) with prior knowledge about wind-field configurations. This task can be achieved using the scaled-likelihood method, which has been used by Morgan and Bourlard (1995) and Smyth (1994), in the context of Hidden Markov modelling

Training Bayesian networks for image segmentation

We are concerned with the problem of image segmentation in which each pixel is assigned to one of a predefined finite number of classes. In Bayesian image analysis, this requires fusing together local predictions for the class labels with a prior model of segmentations. Markov Random Fields (MRFs) have been used to incorporate some of this prior knowledge, but this not entirely satisfactory as inference in MRFs is NP-hard. The multiscale quadtree model of Bouman and Shapiro (1994) is an attractive alternative, as this is a tree-structured belief network in which inference can be carried out in linear time (Pearl 1988). It is an hierarchical model where the bottom-level nodes are pixels, and higher levels correspond to downsampled versions of the image. The conditional-probability tables (CPTs) in the belief network encode the knowledge of how the levels interact. In this paper we discuss two methods of learning the CPTs given training data, using (a) maximum likelihood and the EM algorithm and (b) emphconditional maximum likelihood (CML). Segmentations obtained using networks trained by CML show a statistically-significant improvement in performance on synthetic images. We also demonstrate the methods on a real-world outdoor-scene segmentation task.

First year qualifying report: neural networks for extracting wind vectors from satellite scatterometer data

The ERS-1 Satellite was launched in July 1991 by the European Space Agency into a polar orbit at about km800, carrying a C-band scatterometer. A scatterometer measures the amount of radar back scatter generated by small ripples on the ocean surface induced by instantaneous local winds. Operational methods that extract wind vectors from satellite scatterometer data are based on the local inversion of a forward model, mapping scatterometer observations to wind vectors, by the minimisation of a cost function in the scatterometer measurement space.par This report uses mixture density networks, a principled method for modelling conditional probability density functions, to model the joint probability distribution of the wind vectors given the satellite scatterometer measurements in a single cell (the `inverse' problem). The complexity of the mapping and the structure of the conditional probability density function are investigated by varying the number of units in the hidden layer of the multi-layer perceptron and the number of kernels in the Gaussian mixture model of the mixture density network respectively. The optimal model for networks trained per trace has twenty hidden units and four kernels. Further investigation shows that models trained with incidence angle as an input have results comparable to those models trained by trace. A hybrid mixture density network that incorporates geophysical knowledge of the problem confirms other results that the conditional probability distribution is dominantly bimodal.par The wind retrieval results improve on previous work at Aston, but do not match other neural network techniques that use spatial information in the inputs, which is to be expected given the ambiguity of the inverse problem. Current work uses the local inverse model for autonomous ambiguity removal in a principled Bayesian framework. Future directions in which these models may be improved are given.

Towards an inter-organisational knowledge transfer framework for SMEs

This paper aims to develop a framework for SMEs to help them understand, and thus to improve, the process of knowledge exchange with their customers or suppliers. Through a review of the literature on knowledge transfer, organisational learning, social network theory and electronic networks, the key actors, key factors and their relationships in the process are identified. Finally, a framework containing all above points is proposed.

Marking high-technology market evolution through the foci of market stories:the case of local area networks

Previous research suggests that changing consumer and producer knowledge structures play a role in market evolution and that the sociocognitive processes of product markets are revealed in the sensemaking stories of market actors that are rebroadcasted in commercial publications. In this article, the authors lend further support to the story-based nature of market sensemaking and the use of the sociocognitive approach in explaining the evolution of high-technology markets. They examine the content (i.e., subject matter or topic) and volume (i.e., the number) of market stories and the extent to which content and volume of market stories evolve as a technology emerges. Data were obtained from a content analysis of 10,412 article abstracts, published in key trade journals, pertaining to Local Area Network (LAN) technologies and spanning the period 1981 to 2000. Hypotheses concerning the evolving nature (content and volume) of market stories in technology evolution are tested. The analysis identified four categories of market stories - technical, product availability, product adoption, and product discontinuation. The findings show that the emerging technology passes initially through a 'technical-intensive' phase whereby technology related stories dominate, through a 'supply-push' phase, in which stories presenting products embracing the technology tend to exceed technical stories while there is a rise in the number of product adoption reference stories, to a 'product-focus' phase, with stories predominantly focusing on product availability. Overall story volume declines when a technology matures as the need for sensemaking reduces. When stories about product discontinuation surface, these signal the decline of current technology. New technologies that fail to maintain the 'product-focus' stage also reflect limited market acceptance. The article also discusses the theoretical and managerial implications of the study's findings. © 2002 Elsevier Science Inc. All rights reserved.

Cultural impact of wireless networks:emerging experiences in the healthcare sector

Culture defines collective behavior and interactions among people in groups. In organizations, it shapes group identity, work pattern, communication schemes, and interpersonal relations. Any change in organizational culture will lead to changes in these elements of organizational factors, and vice versa. From a managerial standpoint, how to cultivate an organizational culture that would enhance these aforementioned elements in organizational workplace should thus be taken into serious consideration. Based on cases studies in two hospitals, this paper investigates how organizational culture is shaped by a particular type of information and communication technology, wireless networks, a topic that is generally overlooked by the mainstream research community, and in turn implicates how such cultural changes in organizations renovate their competitiveness in the marketplace. Lessons learned from these cases provide valuable insights to emerging IT management and culture studies in general and in wireless network management in the healthcare sector in particular.