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A Change Management Approach with the Support of the Balanced Scorecard and the Utilization of Artificial Neural Networks

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  • Alkinoos Psarras

    (Department of Business Administration, University of West Attica, 12243 Athens, Greece)

  • Theodoros Anagnostopoulos

    (Department of Business Administration, University of West Attica, 12243 Athens, Greece)

  • Ioannis Salmon

    (Department of Business Administration, University of West Attica, 12243 Athens, Greece)

  • Yannis Psaromiligkos

    (Department of Business Administration, University of West Attica, 12243 Athens, Greece)

  • Lazaros Vryzidis

    (Department of Business Administration, University of West Attica, 12243 Athens, Greece)

Abstract

Artificial Intelligence (AI) has revolutionized the way organizations face decision-making issues. One of these crucial elements is the implementation of organizational changes. There has been a wide-spread adoption of AI techniques in the private sector, whereas in the public sector their use has been recently extended. One of the greatest challenges that European governments have to face is the implementation of a wide variety of European Union (EU) funding programs which have evolved in the context of the EU long-term budget. In the current study, the Balanced Scorecard (BSC) and Artificial Neural Networks (ANNs) are intertwined with forecasting the outcomes of a co-financed EU program by means of its impact on the non-financial measures of the government body that materialized it. The predictive accuracy of the present model advanced in this research study takes into account all the complexities of the business environment, within which the provided dataset is produced. The outcomes of the study showed that the measures taken to enhance customer satisfaction allows for further improvement. The utilization of the proposed model could facilitate the decision-making process and initiate changes to the administrational issues of the available funding programs.

Suggested Citation

  • Alkinoos Psarras & Theodoros Anagnostopoulos & Ioannis Salmon & Yannis Psaromiligkos & Lazaros Vryzidis, 2022. "A Change Management Approach with the Support of the Balanced Scorecard and the Utilization of Artificial Neural Networks," Administrative Sciences, MDPI, vol. 12(2), pages 1-15, May.
    • Handle: RePEc:gam:jadmsc:v:12:y:2022:i:2:p:63-:d:822964
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    References listed on IDEAS

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    1. Danilo Gambelli & Francesco Solfanelli & Stefano Orsini & Raffaele Zanoli, 2021. "Measuring the Economic Performance of Small Ruminant Farms Using Balanced Scorecard and Importance-Performance Analysis: A European Case Study," Sustainability, MDPI, vol. 13(6), pages 1-13, March.
    2. Francesco D’Acunto & Nagpurnanand Prabhala & Alberto G Rossi, 2019. "The Promises and Pitfalls of Robo-Advising," The Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 1983-2020.
    3. Kuziemski, Maciej & Misuraca, Gianluca, 2020. "AI governance in the public sector: Three tales from the frontiers of automated decision-making in democratic settings," Telecommunications Policy, Elsevier, vol. 44(6).
    4. Michaela Kotková Stříteská & Yee Yee Sein, 2021. "Performance Driven Culture in the Public Sector: The Case of Nordic Countries," Administrative Sciences, MDPI, vol. 11(1), pages 1-12, January.
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    Cited by:

    1. Ioannis Kosmas & Theofanis Papadopoulos & Georgia Dede & Christos Michalakelis, 2023. "The Use of Artificial Neural Networks in the Public Sector," FinTech, MDPI, vol. 2(1), pages 1-15, March.
    2. Boudy Bilal & Kaan Yetilmezsoy & Mohammed Ouassaid, 2024. "Benchmarking of Various Flexible Soft-Computing Strategies for the Accurate Estimation of Wind Turbine Output Power," Energies, MDPI, vol. 17(3), pages 1-36, February.

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