Exploring the efficiency of Italian social cooperatives by descriptive and principal component analysis

被引:0
|
作者
Ericka Costa
Michele Andreaus
Chiara Carini
Maurizio Carpita
机构
[1] University of Trento and Euricse,Department of Computer and Management Sciences
[2] Euricse – European Research Institute on Cooperative and Social Enterprises (EURICSE),Department of Quantitative Methods
[3] University of Brescia and Euricse,undefined
来源
Service Business | 2012年 / 6卷
关键词
Social cooperatives; Italy; Regional development; Economic and financial indexes; Principal component analysis (PCA);
D O I
暂无
中图分类号
学科分类号
摘要
Over the last decade, independent agencies, institutions and research centres (ISTAT—National Statistic Office, Ministry of Economic Development, Confcooperative Legacoop, Unioncamere) have provided studies on the evolution of the cooperative movement in the Third Sector in Italy in order to monitor the development of these organizations over time and to evaluate their economic and employment impact in the country. Following a similar path, this study analyzes the contribution of social cooperatives in Italy at a regional level, highlighting the differences related to their longevity and fields of activity. Moreover, the article evaluates the efficiency and profitability of the social cooperative by adopting principal component analysis to economic and financial indexes.
引用
收藏
页码:117 / 136
页数:19
相关论文
共 50 条
  • [31] EMERGING GOVERNANCE PATTERNS FROM ITALIAN SOCIAL COOPERATIVES
    Rossignoli, Francesca
    Lionzo, Andrea
    INNOVATION, ENTREPRENEURSHIP AND SUSTAINABLE VALUE CHAIN IN A DYNAMIC ENVIRONMENT, 2015, : 2706 - 2709
  • [32] Principal component analysis
    Michael Greenacre
    Patrick J. F. Groenen
    Trevor Hastie
    Alfonso Iodice D’Enza
    Angelos Markos
    Elena Tuzhilina
    Nature Reviews Methods Primers, 2
  • [33] Principal component analysis
    Wallen, Hayley
    NATURE REVIEWS METHODS PRIMERS, 2022, 2 (01):
  • [34] Principal component analysis
    Bro, Rasmus
    Smilde, Age K.
    ANALYTICAL METHODS, 2014, 6 (09) : 2812 - 2831
  • [35] Principal component analysis
    Jake Lever
    Martin Krzywinski
    Naomi Altman
    Nature Methods, 2017, 14 : 641 - 642
  • [36] Principal component analysis
    Abdi, Herve
    Williams, Lynne J.
    WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS, 2010, 2 (04): : 433 - 459
  • [37] Principal component analysis
    School of Behavioral and Brain Sciences, University of Texas at Dallas, MS: GR4.1, Richardson, TX 75080-3021, United States
    不详
    Wiley Interdiscip. Rev. Comput. Stat., 4 (433-459):
  • [38] PRINCIPAL COMPONENT ANALYSIS
    WOLD, S
    ESBENSEN, K
    GELADI, P
    CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 1987, 2 (1-3) : 37 - 52
  • [39] Principal component analysis
    Hess, Aaron S.
    Hess, John R.
    TRANSFUSION, 2018, 58 (07) : 1580 - 1582
  • [40] PRINCIPAL COMPONENT ANALYSIS
    ARIES, RE
    LIDIARD, DP
    SPRAGG, RA
    CHEMISTRY IN BRITAIN, 1991, 27 (09) : 821 - 824