Experts:the knowledge and power of expertise

In this book, Stehr and Grundmann outline the theoretical significance and practical importance of the growing stratum of experts, counsellors and advisors in contemporary society, and claim that the growing spectrum of knowledge-based occupations has led to the pluralisation of expertise. As decision makers in organizations and private citizens, for various reasons, increasingly seek advice from experts, the authors examine the nature of expert activity, and suggest that the role of experts needs to be distinguised from other roles such as professionals, scientists, or intellectuals. Experts, they argue, perform knowledge based activities that mediate between the context of knowledge creation and application. Existing approaches tend to restrict the role of the expert to scientists, or to conflate the roles of professionals with experts. In avoiding such restrictions, this book sets out a framework to understanding the growing role of expertise in a better way. Experts provides thought-provoking discussion that will be of interest to postgraduate students and academics working within the fields of social theory, knowledge, and consumption.

Supply chain performance measurement framework:case studies on the Thai manufacturers

One of the most significant paradigm shifts of modern business management is that individual businesses no longer compete as solely autonomous entities, but rather as supply chains. Firms worldwide have embraced the concept of supply chain management as important and sometimes critical to their business. The idea of a collaborative supply chain is to gain a competitive advantage by improving overall performance through measuring a holistic perspective of the supply chain. However, contemporary performance measurement theory is somewhat fragmented and fails to support this idea. Therefore, this research develops and applies an integrated supply chain performance measurement framework that provides a more holistic approach to the study of supply chain performance measurement by combining both supply chain macro processes and decision making levels. Therefore, the proposed framework can provide a balanced horizontal (cross-process) and vertical (hierarchical decision) view and measure the performance of the entire supply chain system. Firstly, literature on performance measurement frameworks and performance measurement factors of supply chain management will help to develop a conceptual framework. Next the proposed framework will be presented. The framework will be validated through in-depth interviews with three Thai manufacturing companies. The fieldwork combined varied sources in order to understand the views of manufacturers on supply chain performance in the three case study companies. The collected data were analyzed, interpreted, and reported using thematic analysis and analysis hierarchy process (AHP), which was influenced by the study’s conceptual framework. This research contributes a new theory of supply chain performance measurement and knowledge on supply chain characteristics of a developing country, Thailand. The research also affects organisations by preparing decision makers to make strategic, tactical and operational level decisions with respect to supply chain macro processes. The results from the case studies also indicate the similarities and differences in their supply chain performance. Furthermore, the implications of the study are offered for both academic and practical use.

Moving from HRM to knowledge productivity:a study of whether and how organizations become learning organizations

Practitioners and academics are in broad agreement that, above all, organizations need to be able to learn, to innovate and to question existing ways of working. This thesis develops a model to take into account, firstly, what determines whether or not organizations endorse practices designed to facilitate learning. Secondly, the model evaluates the impact of such practices upon organizational outcomes, measured in terms of products and technological innovation. Researchers have noted that organizations that are committed to producing innovation show great resilience in dealing with adverse business conditions (e.g. Pavitt, 1991; Leonard Barton, 1998). In effect, such organizations bear many of the characteristics associated with the achievement of ‘learning organization’ status (Garvin, 1993; Pedler, Burgoyne & Boydell, 1999; Senge, 1990). Seven studies are presented to support this theoretical framework. The first empirical study explores the antecedents to effective learning. The three following studies present data to suggest that people management practices are highly significant in determining whether or not organizations are able to produce sustained innovation. The thesis goes on to explore the relationship between organizational-level job satisfaction, learning and innovation, and provides evidence to suggest that there is a strong, positive relationship between these variables. The final two chapters analyze learning and innovation within two similar manufacturing organizations. One manifests relatively low levels of innovation whilst the other is generally considered to be outstandingly innovative. I present the comparative framework for exploring the different approaches to learning manifested by the two organizations. The thesis concludes by assessing the extent to which the theoretical model presented in the second chapter is borne out by the findings of the study. Whilst this is a relatively new field of inquiry, findings reveal that organizations have a much stronger chance of producing sustained innovation where they manage people proactively where people process themselves to be satisfied at work. Few studies to date have presented empirical evidence to substantiate theoretical endorsements to engage in higher order learning, so this research makes an important contribution to existing literature in this field.

Social capital and tacit knowledge sharing in organisational projects in Malaysian ICT companies

An increasing number of organisational researchers have turned to social capital theory in an attempt to better understand the impetus for knowledge sharing at the individual and organisational level. This thesis extends that research by investigating the impact of social capital on knowledge sharing at the group-level in the organisational project context. The objective of the thesis is to investigate the importance of social capital in fostering tacit knowledge sharing among the team members of a project. The analytical focus is on the Nahapiet and Ghoshal framework of social capital but also includes elements of other scholars' work. In brief, social capital is defined as an asset that is embedded in the network of relationships possessed by an individual or social unit. It is argued that the main dimensions of social capital that are of relevance to knowledge sharing are structural, cognitive, and relational because these, among other things, foster the exchange and combination of knowledge and resources among the team members. Empirically, the study is based on the grounded theory method. Data were collected from five projects in large, medium, and small ICT companies in Malaysia. Underpinned by the constant comparative method, data were derived from 55 interviews, and observations. The data were analysed using open, axial, and selective coding. The analysis also involved counting frequency occurrence from the coding generated by grounded theory to find the important items and categories under social capital dimensions and knowledge sharing, and for further explaining sub-groups within the data. The analysis shows that the most important dimension for tacit knowledge sharing is structural capital. Most importantly, the findings also suggest that structural capital is a prerequisite of cognitive capital and relational capital at the group-level in an organisational project. It also found that in a project context, relational capital is hard to realise because it requires time and frequent interactions among the team members. The findings from quantitative analysis show that frequent meetings and interactions, relationship, positions, shared visions, shared objectives, and collaboration are among the factors that foster the sharing of tacit knowledge among the team members. In conclusion, the present study adds to the existing literature on social capital in two main ways. Firstly, it distinguishes the dimensions of social capital and identifies that structural capital is the most important dimension in social capital and it is a prerequisite of cognitive and relational capital in a project context. Secondly, it identifies the causal sequence in the dimension of social capital suggesting avenues for further theoretical and empirical work in this emerging area of inquiry.

An advanced real-time predictive maintenance framework for large scale machine systems

This thesis introduces and develops a novel real-time predictive maintenance system to estimate the machine system parameters using the motion current signature. Recently, motion current signature analysis has been addressed as an alternative to the use of sensors for monitoring internal faults of a motor. A maintenance system based upon the analysis of motion current signature avoids the need for the implementation and maintenance of expensive motion sensing technology. By developing nonlinear dynamical analysis for motion current signature, the research described in this thesis implements a novel real-time predictive maintenance system for current and future manufacturing machine systems. A crucial concept underpinning this project is that the motion current signature contains infor­mation relating to the machine system parameters and that this information can be extracted using nonlinear mapping techniques, such as neural networks. Towards this end, a proof of con­cept procedure is performed, which substantiates this concept. A simulation model, TuneLearn, is developed to simulate the large amount of training data required by the neural network ap­proach. Statistical validation and verification of the model is performed to ascertain confidence in the simulated motion current signature. Validation experiment concludes that, although, the simulation model generates a good macro-dynamical mapping of the motion current signature, it fails to accurately map the micro-dynamical structure due to the lack of knowledge regarding performance of higher order and nonlinear factors, such as backlash and compliance. Failure of the simulation model to determine the micro-dynamical structure suggests the pres­ence of nonlinearity in the motion current signature. This motivated us to perform surrogate data testing for nonlinearity in the motion current signature. Results confirm the presence of nonlinearity in the motion current signature, thereby, motivating the use of nonlinear tech­niques for further analysis. Outcomes of the experiment show that nonlinear noise reduction combined with the linear reverse algorithm offers precise machine system parameter estimation using the motion current signature for the implementation of the real-time predictive maintenance system. Finally, a linear reverse algorithm, BJEST, is developed and applied to the motion current signature to estimate the machine system parameters.

The knowledge-based perspective of risk management in healthcare organisations

The aim of this research is to investigate how risk management in a healthcare organisation can be supported by knowledge management. The subject of research is the development and management of existing logs called "risk registers", through specific risk management processes employed in a N.H.S. (Foundation) Trust in England, in the U.K. Existing literature on organisational risk management stresses the importance of knowledge for the effective implementation of risk management programmes, claiming that knowledge used to perceive risk is biased by the beliefs of individuals and groups involved in risk management and therefore is considered incomplete. Further, literature on organisational knowledge management presents several definitions and categorisations of knowledge and approaches for knowledge manipulation in the organisational context as a whole. However, there is no specific approach regarding "how to deal" with knowledge in the course of organisational risk management. The research is based on a single case study, on a N.H.S. (Foundation) Trust, is influenced by principles of interpretivism and the frame of mind of Soft Systems Methodology (S.S.M.) to investigate the management of risk registers, from the viewpoint of people involved in the situation. Data revealed that knowledge about risks and about the existing risk management policy and procedures is situated in several locations in the Trust and is neither consolidated nor present where and when required. This study proposes a framework that identifies required knowledge for each of the risk management processes and outlines methods for conversion of this knowledge, based on the SECI knowledge conversion model, and activities to facilitate knowledge conversion so that knowledge is effectively used for the development of risk registers and the monitoring of risks throughout the whole Trust under study. This study has theoretical impact in the management science literature as it addresses the issue of incomplete knowledge raised in the risk management literature using concepts of the knowledge management literature, such as the knowledge conversion model. In essence, the combination of required risk and risk management related knowledge with the required type of communication for risk management creates the proposed methods for the support of each risk management process for the risk registers. Further, the indication of the importance of knowledge in risk management and the presentation of a framework that consolidates knowledge required for the risk management processes and proposes way(s) for the communication of this knowledge within a healthcare organisation have practical impact in the management of healthcare organisations.

Investigative framework for research into the design of manufacturing processes

This paper is based a major research project run by a team from the Innovation, Design and Operations Management Research Unit at the Aston Business School under SERC funding. International Computers Limited (!CL), the UK's largest indigenous manufacturer of mainframe computer products, was the main industrial collaborator in the research. During the period 1985-89 an integrated production system termed the "Modular Assembly Cascade'' was introduced to the Company's mainframe assembly plant at Ashton-under-Lyne near Manchester. Using a methodology primarily based upon 'participative observation', the researchers developed a model for analysing this manufacturing system design called "DRAMA". Following a critique of the existing literature on Manufacturing Strategy, this paper will describe the basic DRAMA model and its development from an industry specific design methodology to DRAMA II, a generic model for studying organizational decision processes in the design and implementation of production systems. From this, the potential contribution of the DRAMA model to the existing knowledge on the process of manufacturing system design will be apparent.

KEMNAD:a knowledge engineering methodology for negotiating agent development

Automated negotiation is widely applied in various domains. However, the development of such systems is a complex knowledge and software engineering task. So, a methodology there will be helpful. Unfortunately, none of existing methodologies can offer sufficient, detailed support for such system development. To remove this limitation, this paper develops a new methodology made up of: (1) a generic framework (architectural pattern) for the main task, and (2) a library of modular and reusable design pattern (templates) of subtasks. Thus, it is much easier to build a negotiating agent by assembling these standardised components rather than reinventing the wheel each time. Moreover, since these patterns are identified from a wide variety of existing negotiating agents (especially high impact ones), they can also improve the quality of the final systems developed. In addition, our methodology reveals what types of domain knowledge need to be input into the negotiating agents. This in turn provides a basis for developing techniques to acquire the domain knowledge from human users. This is important because negotiation agents act faithfully on the behalf of their human users and thus the relevant domain knowledge must be acquired from the human users. Finally, our methodology is validated with one high impact system.

A novel framework of training hidden Markov support vector machines from lightly-annotated data

Natural language understanding (NLU) aims to map sentences to their semantic mean representations. Statistical approaches to NLU normally require fully-annotated training data where each sentence is paired with its word-level semantic annotations. In this paper, we propose a novel learning framework which trains the Hidden Markov Support Vector Machines (HM-SVMs) without the use of expensive fully-annotated data. In particular, our learning approach takes as input a training set of sentences labeled with abstract semantic annotations encoding underlying embedded structural relations and automatically induces derivation rules that map sentences to their semantic meaning representations. The proposed approach has been tested on the DARPA Communicator Data and achieved 93.18% in F-measure, which outperforms the previously proposed approaches of training the hidden vector state model or conditional random fields from unaligned data, with a relative error reduction rate of 43.3% and 10.6% being achieved.

Knowledge integration for analyzing ChIP-seq

To capture the genomic profiles for histone modification, chromatin immunoprecipitation (ChIP) is combined with next generation sequencing, which is called ChIP-seq. However, enriched regions generated from the ChIP-seq data are only evaluated on the limited knowledge acquired from manually examining the relevant biological literature. This paper proposes a novel framework, which integrates multiple knowledge sources such as biological literature, Gene Ontology, and microarray data. In order to precisely analyze ChIP-seq data for histone modification, knowledge integration is based on a unified probabilistic model. The model is employed to re-rank the enriched regions generated from peak finding algorithms. Through filtering the reranked enriched regions using some predefined threshold, more reliable and precise results could be generated. The combination of the multiple knowledge sources with the peaking finding algorithm produces a new paradigm for ChIP-seq data analysis. © (2012) Trans Tech Publications, Switzerland.

An integrated environment for semantic knowledge work

In this demonstration, we will present a semantic environment called the K-Box. The K-Box supports the lightweight integration of knowledge tools, with a focus on semantic tools, but with the flexibility to integrate natural language and conventional tools. We discuss the implementation of the framework, and two existing applications, including details of a new application for developers of semantic workflows. The demonstration will be of interest to developers and researchers of ontology-based knowledge management systems, and semantic desktops, and to analysts working with cross-media information. © 2011 ACM.

Ontology-based knowledge capture and sharing in enterprise organisations

Despite years of effort in building organisational taxonomies, the potential of ontologies to support knowledge management in complex technical domains is under-exploited. The authors of this chapter present an approach to using rich domain ontologies to support sense-making tasks associated with resolving mechanical issues. Using Semantic Web technologies, the authors have built a framework and a suite of tools which support the whole semantic knowledge lifecycle. These are presented by describing the process of issue resolution for a simulated investigation concerning failure of bicycle brakes. Foci of the work have included ensuring that semantic tasks fit in with users’ everyday tasks, to achieve user acceptability and support the flexibility required by communities of practice with differing local sub-domains, tasks, and terminology.

A framework for evaluating semantic metadata

Because poor quality semantic metadata can destroy the effectiveness of semantic web technology by hampering applications from producing accurate results, it is important to have frameworks that support their evaluation. However, there is no such framework developedto date. In this context, we proposed i) an evaluation reference model, SemRef, which sketches some fundamental principles for evaluating semantic metadata, and ii) an evaluation framework, SemEval, which provides a set of instruments to support the detection of quality problems and the collection of quality metrics for these problems. A preliminary case study of SemEval shows encouraging results.

Enhancing enterprise knowledge processes via cross-media extraction

In large organizations the resources needed to solve challenging problems are typically dispersed over systems within and beyond the organization, and also in different media. However, there is still the need, in knowledge environments, for extraction methods able to combine evidence for a fact from across different media. In many cases the whole is more than the sum of its parts: only when considering the different media simultaneously can enough evidence be obtained to derive facts otherwise inaccessible to the knowledge worker via traditional methods that work on each single medium separately. In this paper, we present a cross-media knowledge extraction framework specifically designed to handle large volumes of documents composed of three types of media text, images and raw data and to exploit the evidence across the media. Our goal is to improve the quality and depth of automatically extracted knowledge.