Download e-book for iPad: Creating Collaborative Advantage Through Knowledge and by Suliman Hawamdeh

By Suliman Hawamdeh

Chosen from the distinguished 2006 overseas convention on wisdom administration held in Greenwich, London, this quantity represents a lot of the easiest and newest paintings by way of researchers and practitioners within the box of data administration (KM). It covers quite a lot of subject matters that come with social community research, innovation and creativity, KM instruments and applied sciences, social community applied sciences, collaboration and information sharing, matters in KM schooling and coaching, wisdom discovery (data mining, info warehousing, clever agents), wisdom association (meta info, taxonomies, ontology), and social and mental dimensions.

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Extra resources for Creating Collaborative Advantage Through Knowledge and Innovation (Series on Innovation and Knowledge Management) (Series on Innovation and Knowledge Management)

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In the tourism industry, for example, money invested in conducting market surveys to spawn innovative services is usually not recognized as a form of R&D expenditure. Furthermore, other than researchers, many occupational roles such as engineers and management staff are responsible to create new processes, systems, designs, and business models. Table 2 summarizes the participants’ opinions on the knowledge creation indicators. As one of the long-term measures to enhancing Singapore’s capabilities for knowledge creation, the participants concurred with the Ministry of Education’s move to foster innovation in students through “Thinking Schools, Learning Nation” vision (Goh, 1997).

The method for knowledge mapping used here is defined in Ermine et al. (2005). The criticality of a domain is defined as risks or opportunities estimation in a given sector of the company (Aubertin, 2005). We have chosen a grid of criticality constructed by the French Knowledge Management Club4. Figure 4 represents an overview of these criteria. Once the mapping of the knowledge domains had been set up, we carried out the estimation of these criteria for each knowledge domain. Holders Leadership Scarcity Specificity Confidentiality Externalisation Value Missions Values creation Utility Emergence Adaptability Use Criteria of criticality Sources identification Networks Capture Tacit knowledge Sources volume Fast evolution Operational difficulty Depth knowledge Complexity Use Appropriation History Environment Figure 4.

This map represents a description of PED know-how at a supra (Meta) level (Prax, 2005). It provides an addressing system of knowhow, which facilitates knowledge access. On the basis of this map, a criticality study was carried out. The evaluation of the criticality of a domain consists in attributing a grade according to each criterion of the analysis grid for each domain. The more critical the domain, the higher the grade. Each domain was evaluated independently from the others. The restitution of the results relating to each domain is synthesized graphically in a radar diagram.

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