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A Method For Identifying The Possible Causes Of Failure In The Case Of Service Companies

Author

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  • Camelia DELCEA

    (The Bucharest Academy of Economic Studies, Romania)

  • Maria DASCALU

    (The Bucharest Academy of Economic Studies, Romania)

Abstract

The core of our paperwork consists in determining a classification of qualitative and quantitative causes that are influencing service companies’ performance and financial health based on grey systems theory and fuzzy sub-sets. The quantitative causes of firms’ performance are mainly financial causes and can be represented by the financial changes, structural changes and by macroeconomic changes. All of these are objectively measurable. Instead, the qualitative causes are subjectively measurable and in most of the cases they are quantified using some experts’ opinion. For a better representation of reality, we will consider that those values are intervals and not numbers, situated in [0; 1]. In order to reduce the degree of subjectivity, we took advantage of the methods offered by fuzzy systems, mainly in construction of the expertons. Expertons are in fact intervals built using the f-fuzzy sub-set and the opinion of several experts over a certain problem. Furthermore, after constructing the expertons, we use the methods offered by grey systems theory and grey arithmetic to determine the degree of influence of each qualitative and quantitative cause on company’s performance. By classifying the causes and acting on the most important of them, the activity of the analyst can be really improved and the company’s performance will rise. The first part of our paper describes the evolutions of the methods used for establishing the companies’ financial health and some related works regarding the causes that affects it. After that, we focus on the methods offered by grey systems theory, on grey arithmetic, and on the steps that should be done to classify the causes, including also a numerical example. We conclude our paper with some remarks over the role and the utility of the grey systems theory and fuzzy in establishing the service companies’ performance and financial health.

Suggested Citation

  • Camelia DELCEA & Maria DASCALU, 2011. "A Method For Identifying The Possible Causes Of Failure In The Case Of Service Companies," Journal of Doctoral Research in Economics, The Bucharest University of Economic Studies, vol. 3(3), pages 48-61, September.
  • Handle: RePEc:aes:jdreco:v:3:y:2011:i:3:p:48-61
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    References listed on IDEAS

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    1. Subramanian, A. & Nilakanta, S., 1996. "Organizational innovativeness: Exploring the relationship between organizational determinants of innovation, types of innovations, and measures of organizational performance," Omega, Elsevier, vol. 24(6), pages 631-647, December.
    2. Pim Den Hertog, 2000. "Knowledge-Intensive Business Services As Co-Producers Of Innovation," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 4(04), pages 491-528.
    3. Altman, Edward I. & Haldeman, Robert G. & Narayanan, P., 1977. "ZETATM analysis A new model to identify bankruptcy risk of corporations," Journal of Banking & Finance, Elsevier, vol. 1(1), pages 29-54, June.
    4. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
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    More about this item

    Keywords

    Failure; Bankruptcy; Service Companies; Grey systems theory; Fuzzy sub-sets theory; Expertons; Diagnosis;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics

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