Applying the Balanced Scorecard and Predictive Analytics in the Administration of a European Funding Program

Alkinoos Psarras, Theodoros Anagnostopoulos, Nikos Tsotsolas, Ioannis Salmon and Lazaros Vryzidis
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Alkinoos Psarras: Department of Business Administration, University of West Attica, 12241 Athens, Greece
Theodoros Anagnostopoulos: Department of Business Administration, University of West Attica, 12241 Athens, Greece
Nikos Tsotsolas: Department of Business Administration, University of West Attica, 12241 Athens, Greece
Ioannis Salmon: Department of Business Administration, University of West Attica, 12241 Athens, Greece
Lazaros Vryzidis: Department of Business Administration, University of West Attica, 12241 Athens, Greece

Administrative Sciences, 2020, vol. 10, issue 4, 1-15

Abstract: The performance measurement of a great variety of enterprises is a highly complicated issue, especially taking into account that performance has a great many aspects and many variables which may, at times, be highly inconsistent with each other. The use of analytics and advanced machine learning promotes the decision-making process for each and every organizational structure. This paper combines the Balanced Scorecard and predictive analytics in order to assess the performance of a co-financed European Union program, which addressed 4071 Greek Small and Medium-sized Enterprises (SMEs) that requested funding. The application of predictive analytics tools and metrics in the available dataset of all addressed SMEs reveal the M5 Model Tree regressor to be an overall best prediction model for estimating the effect of the evaluation of companies’ funding proposals on their financial results after the finalization of the co-financed program.

Keywords: Balanced Scorecard; predictive analytics; program performance (search for similar items in EconPapers)
JEL-codes: L M M0 M1 M10 M11 M12 M14 M15 M16 (search for similar items in EconPapers)
Date: 2020
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