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Predicting Strategic Areas of a Financial Intermediation Services (SIF) Company Using BSC and PLS

Author

Listed:
  • Ion Stancu

    (Bucharest University of Economic Studies and Institute of Financial Studies, Romania)

  • Ion Alexandru Stancu

    (Geneva University and IATA Director, AME,)

  • Laura Elly Naghi

    (Bucharest University of Economic Studies and Institute of Financial Studies, Romania)

  • Dragos Bâlteanu

    (Romenergo, Romania)

Abstract

The Balanced Scorecard (BSC) analysis can identify relationships between different sectors of the company's activities and the interactions between them. Prof. Bernard Morard together with Dr. Alexandru Stancu and Dr. Christophe Jeannette from University of Geneva, Switzerland developed a way of identifying these relationships and interactions using the Partial Least Squares (PLS) regression technique. Their technique identifies the strategic areas (or strategic axes) by highlighting the groups of performance indicators with the highest correlation coefficient between them. The strategic axes can, in turn, identify performance sectors of the company. Our final model identified the interaction among the strategic areas of a financial intermediation services company (SIF) as well as the interaction between the SIF’s performance indicators and the group they are a part of. Our goal was to first apply a Principal Component Analysis to find the most important sectors for a SIF company (e.g. axis 1 = Capital and Results) and then to focus on 4 to 6 relevant performance indicators, that are strongly correlated with the respective strategic sectors (axes). The other indicators were discarded or were transferred to other axes where they have a significant weight, obviously, a little less than on the axis from which they were discarded. Once these economic judgments on the strategic areas were completed, we applied the PLS analysis to reveal the correlations between the strategic axes (sectors). These correlations highlight the intensity of interrelations within the company (SIF) and lead to possible strategic lines of interaction. Mainly, we intended: to identify relationships between different strategic axes (sectors) of the company's activities and the interactions between them using the Balanced Scorecard (BSC) analysis; to assign to these economic sectors the most appropriate name (for example, Axis 1 = CAPITAL and RESULTS and so on) and to retain maximum 6 relevant indicators for each axis; to reveal the correlations between strategic axes (sectors), highlighting the intensity of interrelations; to lead to the prediction of possible strategic lines of interaction within the company (SIF). Basically, BSC explains the relationship between the corporate governance variables and the company's performance. We intended that, besides the causal interrelations, we would also identify a logical relationship between the analyzed sectors of activity.

Suggested Citation

  • Ion Stancu & Ion Alexandru Stancu & Laura Elly Naghi & Dragos Bâlteanu, 2018. "Predicting Strategic Areas of a Financial Intermediation Services (SIF) Company Using BSC and PLS," The Audit Financiar journal, Chamber of Financial Auditors of Romania, vol. 20(47), pages 218-218, February.
  • Handle: RePEc:aud:audfin:v:20:y:2018:i:47:p:218
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    More about this item

    Keywords

    Strategic sectors (axes); Balanced Scorecard (BSC); Partial Least Squares (PLS); Principal Component Analysis (PCA); Corporate governance.;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • L25 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Firm Performance

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