All

What are you looking for?

All
Projects
Results
Organizations

Quick search

  • Projects supported by TA ČR
  • Excellent projects
  • Projects with the highest public support
  • Current projects

Smart search

  • That is how I find a specific +word
  • That is how I leave the -word out of the results
  • “That is how I can find the whole phrase”

AHP-Based Micro and Small Enterprises' Cluster Identification

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F13%3A86099411" target="_blank" >RIV/61989100:27240/13:86099411 - isvavai.cz</a>

  • Alternative codes found

    RIV/61989100:27740/13:86099411

  • Result on the web

    <a href="http://dx.doi.org/10.1109/SOCPAR.2013.7054132" target="_blank" >http://dx.doi.org/10.1109/SOCPAR.2013.7054132</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/SOCPAR.2013.7054132" target="_blank" >10.1109/SOCPAR.2013.7054132</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    AHP-Based Micro and Small Enterprises' Cluster Identification

  • Original language description

    Micro and Small Enterprises' (MSEs) cluster is a group of small firms operating in a defined geographic location, producing similar products or services, cooperating and competing with one another, learning from each other to solve internal problems, setting common strategies to overcome external challenges, and reaching distance markets through developed networks. During recent years, identifying MSEs cluster has become a key strategic decision. However, the nature of these decisions is usually complex and involves conflicting criteria. The aim of this paper is to develop an AHP-based MSEs cluster identification model. As a result, quantitative and qualitative factors including geographical proximity, sectorial concentration, market potential, support services, resource potential and potential entrepreneurs are found to be critical factors in cluster identification. In this paper, linguistic values are used to assess the ratings and weights of the factors. Then, AHP model will be proposed in dealing with the cluster selection problems. Finally, a case study was taken to prove and validate the procedure of the proposed method. A sensitivity analysis is also performed to justify the results. (C) 2013 IEEE.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    IN - Informatics

  • OECD FORD branch

Result continuities

  • Project

    <a href="/en/project/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: IT4Innovations Centre of Excellence</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2013

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    2013 International Conference on Soft Computing and Pattern Recognition, SoCPaR 2013

  • ISBN

    978-1-4799-3400-3

  • ISSN

  • e-ISSN

  • Number of pages

    7

  • Pages from-to

    225-231

  • Publisher name

    IEEE

  • Place of publication

    New York

  • Event location

    Hanoj

  • Event date

    Dec 15, 2013

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000380467000038