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

Psarras, Alkinoos; Anagnostopoulos, Theodoros; Tsotsolas, Nikos; Salmon, Ioannis; Vryzidis, Lazaros

doi:10.3390/admsci10040102

Cited by 5 publications

(8 citation statements)

References 38 publications

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“…In their previous research effort, Psarras et al (2020) utilized predictive analytics to assess the extent to which the financial measures of companies that participated in a funding program were influenced. As shown in Figure 1, this process affected the financial perspective of the government body which implemented the specific program.…”

Section: Ai In Decision-makingmentioning

confidence: 99%

“…For instance, other qualitative methods compare key performance indicators during and after a program with those prior to the program (Michalek 2012). This method was applied by Psarras et al (2020) who combined the BSC and predictive analytics to evaluate the performance of the co-financed EU financial program "Competitive reinforcement of the Greek Small and Medium-sized Enterprises (SMEs)". A Greek government body was assigned with the implementation and the supervision of the whole funding process.…”

Section: Introductionmentioning

confidence: 99%

“…Based on the research effort of Psarras et al (2020), this paper utilizes the data for the remaining perspectives of the government body's BSC which materialized the aforementioned financial program. More specifically, data extracted from the customers, the internal process, as well as the learning and innovation perspectives, are analyzed with the use of an ANN, which is the main contribution of this paper.…”

Section: Introductionmentioning

confidence: 99%

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

Psarras

Anagnostopoulos

Salmon

et al. 2022

Administrative Sciences

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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.

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Section: Ai In Decision-makingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Psarras

Anagnostopoulos

Salmon

et al. 2022

Administrative Sciences

Self Cite

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“…Several authors underline the relevance and contribution of big data analytics and the use of machine learning in predictive analytics, reinforcing the role and contribution of decision-making based on business environments (Lismont et al, 2017;Meyer et al, 2019;Psarras, et al, 2020). Quite important also is the methodology and all the processes to achieve the results.…”

Section: Literature Reviewmentioning

confidence: 99%

European Structural and Investment Funds 2021–2027: Prediction Analysis Based on Machine Learning Models

Santos¹

2022

Springer Proceedings in Political Science and International Relations

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This research presents several machine learning algorithms and prediction models to anticipate the European Structural and Investment Funds (ESIF) application in different European Union (EU) countries. These analyses start with data training from 2014 to 2020 ESIF, to test and predict the application of the future ESI Funds for 2021–2027. We deliver an analysis focused on the priorities of each fund, highlighting the differences between the programs in different time periods. In the framework of the European Regional Development Fund (ERDF), we will specifically address the assessment of the following themes: support innovation of small and medium-sized businesses, to greener, low-carbon, and resilient projects with enhanced mobility. In what concerns the European Social Fund (ESF), we will evaluate projects that promote and increase the EU’s employment, social, education, and skills policies, including structural reforms in these areas. Regarding the cohesion funds (CF), we will be targeting the improvements between the two ESIFs, looking at projects in the field of environment and trans-European networks in the area of transport infrastructure (TEN-T). In summary, we will be looking at the future of ESIF through the glasses of artificial intelligence.

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“…SMEs address turbulent and dynamic environments that characterize today's competitive arenas (Cosenz & Noto, 2015), thus, their managers must use more than financial measures to assess performance (Manville et al, 2019). Management control sys-tems (MCSs) (e.g., Balanced Scorecard and Tableau de Bord) support strategic planning and change management initiatives of SMEs by translating the organizational vision and mission into a set of objectives which then get interpreted by a series of financial and nonfinancial measures and key performance indicators (KPIs) (Psarras et al, 2020;Tarurhor & Osazevbaru, 2019). Most of the academic literature on MCSs takes a contingency approach to justify their use as determined by contextual factors (Bedford et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

SMEs Managers’ Perceptions of MCS: A Mixed Methods Approach

Curado

Jesús

Bontis

2022

Journal of Small Business Strategy

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The goals of this study are to explore the use of the Management Control Systems (MCS) by SMEs’ managers at the country level in order to identify the importance given to financial and nonfinancial measures, as well as key performance indicators. In this study, we use the behavioral accounting lens and adopt mixed methods approach to study the use of the MCS in Portuguese small to medium enterprises (SMEs): a correlational and a configurational analysis. Data was collected from a cross-sectional survey of 414 top managers of Portuguese SMEs across several industries. The results show that managers’ perceptions of the importance given to financial measures is positively and significantly related to the importance given to several nonfinancial measures. We take an original approach by addressing the managers’ perceptions to contribute to the understanding of Portuguese SMEs’ use of tools for strategy implementation: the use of different MCS. Additionally, the study discovers alternative configurations of individual and organizational conditions that lead to the managers’ perception of the importance given to financial and nonfinancial measures. This paper offers support for SMEs based on controlling strategy implementation by using MCS. The study’s limitations regard a relatively low response rate to the questionnaire (4.56%), which may be justified because data was collected during the COVID-19 pandemic. We offer alternative configurations that generate the perception of managers about the importance of using financial and nonfinancial measures. Our results enlighten the use of such tools in support of strategic accomplishment.

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

Cited by 5 publications

References 38 publications

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

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

European Structural and Investment Funds 2021–2027: Prediction Analysis Based on Machine Learning Models

SMEs Managers’ Perceptions of MCS: A Mixed Methods Approach

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