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Cluster Analysis Of Charitable Organizations Of Ukraine Using K-Means Technology

Author

Listed:
  • Olha VYSOCHAN

    (Ph. D. in Economics, Associate Professor of the Department of Accounting and Analysis, Lviv Polytechnic National University, Lviv, Ukraine)

  • Oleh VYSOCHAN

    (Doctor of Economics, Professor of the Department of Accounting and Analysis, Lviv Polytechnic National University, Lviv, Ukraine)

  • Vasyl HYK

    (Ph. D. in Economics, Associate Professor of the Department of Accounting and Analysis, Lviv Polytechnic National University, Lviv, Ukraine)

Abstract

The work is devoted to the issue of segmentation of charitable organizations for structuring the sector of non-profit organizations of Ukraine using cluster analysis tools using software R for automated data processing. The four-cluster and five-cluster models were constructed using the K-means method, the suitability for clustering of which was checked using the Hopkins’ Index (H statistics). The developed four-cluster model demonstrated a significant level of validity in terms of correspondence between data and the stability of their structure. The basic indicators of financial and economic activity of charitable organizations were used as criteria for clustering: the number of staff, charitable assistance received and funds spent on the maintenance of the organization in the reporting period. It was found that the clusters of charitable organizations of Ukraine differ in the scale of activity, the number of funds raised, the number of costs for their own maintenance and the relationship between these indicators. The study demonstrated the existence in Ukraine of the most influential cluster of local charities that address social issues exclusively at the regional level, due to the small financial resources involved to support their activities. Such organizations are system-creating for the entire non-profit sector in Ukraine, their importance is manifested in the most rapid response to the needs of recipients through the implementation of small charitable projects.

Suggested Citation

  • Olha VYSOCHAN & Oleh VYSOCHAN & Vasyl HYK, 2021. "Cluster Analysis Of Charitable Organizations Of Ukraine Using K-Means Technology," REVISTA ADMINISTRATIE SI MANAGEMENT PUBLIC, Faculty of Administration and Public Management, Academy of Economic Studies, Bucharest, Romania, vol. 2021(37), pages 117-131.
    • Handle: RePEc:rom:rampas:v:2021:y:2021:i:37:p:117-131
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    References listed on IDEAS

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    1. Glenn Milligan & Martha Cooper, 1988. "A study of standardization of variables in cluster analysis," Journal of Classification, Springer;The Classification Society, vol. 5(2), pages 181-204, September.
    2. Jani KINNUNEN & Armenia ANDRONICEANU & Irina GEORGESCU, 2019. "The Role of Economic and Political Features in Classification of Countries-in-Transition by Human Development Index," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 23(4), pages 26-40.
    3. Hyuk-Rae Kim, 1997. "Korean NGOs: Global trend and prospect," Global Economic Review, Taylor & Francis Journals, vol. 26(2), pages 93-115.
    4. Anthony Bebbington & Roger Riddell, 1995. "The direct funding of Southern NGOs by donors: New agendas and old problems," Journal of International Development, John Wiley & Sons, Ltd., vol. 7(6), pages 879-893, November.
    5. Hildy Teegen & Jonathan P Doh & Sushil Vachani, 2004. "The importance of nongovernmental organizations (NGOs) in global governance and value creation: an international business research agenda," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 35(6), pages 463-483, November.
    6. Charrad, Malika & Ghazzali, Nadia & Boiteau, Véronique & Niknafs, Azam, 2014. "NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i06).
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    Cited by:

    1. Armenia ANDRONICEANU & Irina GEORGESCU, 2022. "Social Protection In Europe, A Comparative And Correlative Research," REVISTA ADMINISTRATIE SI MANAGEMENT PUBLIC, Faculty of Administration and Public Management, Academy of Economic Studies, Bucharest, Romania, vol. 2022(38), pages 31-45.

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    More about this item

    Keywords

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    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • L31 - Industrial Organization - - Nonprofit Organizations and Public Enterprise - - - Nonprofit Institutions; NGOs; Social Entrepreneurship
    • P43 - Political Economy and Comparative Economic Systems - - Other Economic Systems - - - Finance; Public Finance

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