Wyniki wyszukiwania - BazTech

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Organisational competence vs transferability of knowledge in cluster organisations and technology parks

Lis Anna Maria, Žemgulienė Jolanta

Engineering Management in Production and Services

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2020

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Vol. 12, no. 2

83--98

EN

The main paper aims to evaluate the impact of organisational competence on knowledge and information flows within cluster organisations and technology parks, with particular emphasis on innovative content knowledge. The paper addresses the research question: “What set of competencies of cooperating companies allows access to information and knowledge in cluster and parks structures?" The authors report their findings from a quantitative study carried out in four cluster organisations and three technology parks functioning in Poland. The research sample covered a total of 269 enterprises: 132 cluster members and 137 park tenants. The primary method of data collection was a survey questionnaire. Data analysis was conducted using the interdependence of variables, ANOVA, and logistic regression. The research showed that the surveyed enterprises from both analysed groups preferred cooperation with partners of a similar level of competence development and the same or complementary scope of competence. This set of competencies of cooperating organisations also guaranteed better access to information and knowledge resources, including confidential information and new knowledge. This study additionally indicated that the knowledge creation activities performed by the cooperating cluster organisations depended on the proximity of the competencies of organisations as well as on the nature of the information, disseminated within the cluster organisations. The theoretical contribution is related to the results obtained by analysing the phenomenon of information and knowledge dissemination in cluster and park structures, revealing the impact made by the competence proximity of cooperating organisations on the access to this such resources. Thus, the findings supplement the state-of-the-art knowledge of the concept of industrial clusters by presenting a broader view on cooperation developed in geographical proximity, based on a set of various partner competencies.

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Best approximation in quotient probabilistic normed space

Sen M., Nath S., Tripathy B. C.

Journal of Applied Analysis

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2017

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Vol. 23, nr 1

53--57

EN

In this article, we study the best approximation in quotient probabilistic normed space. We define the notion of quotient space of a probabilistic normed space, then prove some theorems of approximation in quotient space are extended to quotient probabilistic normed space.

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Knowledge-based clustering as a conceptual and algorithmic environment of biomedical data analysis

Pedrycz W., Gacek A.

Journal of Medical Informatics & Technologies

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2004

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Vol. 7

KB13--22

EN

While a genuine abundance of biomedical data available nowadays becomes a genuine blessing, it also posses a lot of challenges. The two fundamental and commonly occurring directions in data analysis deal with its supervised or unsupervised pursuits. Our conjecture is that in the area of biomedical data processing and understanding where we encounter a genuine diversity of patterns, problem descriptions and design objectives, this type of dichotomy is neither ideal nor the most productive. In particular, the limitations of such taxonomy become profoundly evident in the context of unsupervised learning. Clustering (being usually regarded as a synonym of unsupervised data analysis) is aimed at determining a structure in a data set by optimizing a given partition criterion. In this sense, a structure emerges (becomes formed) without a direct intervention of the user. While the underlying concept looks appealing, there are numerous sources of domain knowledge that could be effectively incorporated into clustering mechanisms and subsequently help navigate throughout large data spaces. In unsupervised learning, this unified treatment of data and domain knowledge leads to the general concept of what could be coined as knowledge-based clustering. In this study, we discuss the underlying principles of this paradigm and present its various methodological and algorithmic facets. In particular, we elaborate on the main issues of incorporating domain knowledge into the clustering environment such as (a) partial labelling, (b) referential labelling (including proximity and entropy constraints), (c) usage of conditional (navigational) variables, (d) exploitation of external structure. Presented are also concepts of stepwise clustering in which the structure of data is revealed via a series of refinements of existing domain granular information.