Skip to main content
SpringerLink
Log in

Goodness of Aggregation Operators in a Diagnostic Fuzzy Model of Business Failure

  • Conference paper
  • First Online:
Scientific Methods for the Treatment of Uncertainty in Social Sciences

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 377))

  • 768 Accesses

Abstract

The aim of the following paper is proposed a mechanism of analysis useful to verify the capacity of the Vigier and Terceño (2008) diagnostic fuzzy model to predict diseases. The model is enriched by the inclusion of aggregation operators because this allows reducing the detected map of causes or diseases in strategic areas of continuous monitoring. And at the same time this causes can be disaggregated once some alert indicator is identified. The capacity of explanation and prediction of estimated diseases are measured through this mechanism; and also are detected the monitoring key areas that warning insolvency situations. In this approach are introduced aggregation operators of causes of business failure, and a goodness measure using approximate solutions. This index of goodness allows testing the degree of fit of the predictions of the model. Also, as an example, the empirical estimation and the verification of the improvement proposal to a set of small and medium- sized enterprises (SMEs) of the construction sector are presented.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    This model is chosen because it has a comprehensive analysis of causes and symptoms over the pioneers models of Aluja Gil (1990) and Gil Lafuente (1996), and the recent proposals of Mărăcine and Delcea (2009), Scarlat et al. (2010), among others. These recents approaches taken as reference Vigier and Terceño model, derive the matrix of causes and symptoms by difference with the performance matrix, and therefore introduce a greater degree of subjectivity and a turn on in favor of experts point of views.

  2. 2.

    Revisions by Zmijewski (1984); Jones (1987); Balcaen and Ooghe (2006); Tascón and Castaño (2012), etc., can be consulted. They comment the main methodological problems of the traditional models and the new applied techniques.

  3. 3.

    According to Vigier y Terceño (2012), this methodology is consistent with Sánchez (1982) contributions regarding the general conditions imposed on the solution of fuzzy binary equations.

  4. 4.

    If only the firms constituted in formal societies are considered, as they are the only ones required to submit economic- financial statements, the percentage of representation is around 30 %.

References

  • Altman, E.: Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J. Fin. 23(4), 589–609 (1968)

    Article  Google Scholar 

  • Arias- Aranda, D., Castro, J.L., Navarro, M., Sánchez, J.M., Zurita, J.M.: A fuzzy expert system for business management. Expert Syst. Appl. 37(12), 7570–7580 (2010)

    Google Scholar 

  • Balcaen, S., Ooghe, H.: 35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems. Br. Account. Rev. 38(1), 63–93 (2006)

    Article  Google Scholar 

  • Beaver, W.: Financial ratios as predictors of failure. J. Account. Res. (Sel. Stud.) 4, 71–111 (1966)

    Article  Google Scholar 

  • Behbood, V., Lu, J., Zhang, G.: Adaptive inference-based learning and rule generation algorithms in fuzzy neural network for failure prediction. In: IEEE International Conference of Intelligent Systems and Knowledge Engineering, pp. 33–38 (2010)

    Google Scholar 

  • Behbood, V., Lu, J.: Intelligent financial warning model using fuzzy neural network and case-based reasoning. In: IEEE Symposium on Computational Intelligence for Financial Engineering and Economics, pp. 1–6 (2011)

    Google Scholar 

  • Brignole, D., Entizne, R., Vigier, H.: Análisis de Soluciones Aproximadas de Relaciones Binarias Fuzzy, pp. 20–21. VIII SIGEF Congress, Naples (Italy) (2001)

    Google Scholar 

  • Delcea, C., Scarlat, E.: The diagnosis of firm’s disease using the grey systems theory methods. In: IEEE International Conference of GSIS. Nanjing (China), pp. 755–762 (2009)

    Google Scholar 

  • Delcea, C., Scarlat, E., Maracine, V.: Grey relational analysis between firm’s current situation and its possible causes: A bankruptcy syndrome approach. Grey Syst. Theory Appl. 2(2), 229–239 (2012)

    Article  Google Scholar 

  • Dubois, D., Prade, H.: A review of fuzzy set aggregation connectives. Inf. Sci. 36(1/2), 85–121 (1985)

    Article  MATH  MathSciNet  Google Scholar 

  • Gil Aluja, J.: Ensayo sobre un Modelo de Diagnóstico Económico-Financiero. V Jornadas Hispano- Lusas de Gestión Científica. Vigo (Spain), pp. 26–29 (1990)

    Google Scholar 

  • Gil Lafuente, J.: El Control de las Actividades de Marketing. III SIGEF Congress. Buenos Aires (Argentina), vol. 244, pp. 1–21 (1996)

    Google Scholar 

  • Jones, F.L.: Current techniques in bankruptcy prediction. J. Account. Lit. 6(1), 131–164 (1987)

    Google Scholar 

  • Keasey, K., Watson, R.: Non financial symptoms and the prediction of small company failure: a test of Argenti’s hypothesis. J. Bus. Fin. Account. 14(3), 335–354 (1987)

    Article  Google Scholar 

  • Klir, G., Yuan, B.: A Review of Fuzzy Sets and Fuzzy Logic: Theory and Applications. Prentice Hall PTR, Upper Saddle River (1995)

    Google Scholar 

  • Klir, G., Folger, A.: Fuzzy Sets, Uncertainty, and Information. Prentice-Hall International, New York (1988)

    MATH  Google Scholar 

  • Maracine, V., Delcea, C.: How we can diagnose the firm’s diseases using grey systems theory. Econ. Comput. Econ. Cybern. Stud. Res. 3, 39–55 (2009)

    Google Scholar 

  • Regional Center for Economic Studies Bahía Blanca- Argentina (CREEBBA), Economic Activity Report, 112/ 137, last accessed 12 Jan 2015. http://www.creebba.org.ar/main/index.php?op=archivo_iae

  • Sánchez, E.: Solution of Fuzzy Equations with Extended Operations, Electronics Research Laboratory, pp. 8–10. University of California, Berkeley (1982)

    Google Scholar 

  • Scarlat, E., Delcea, C., Maracine, V.: Genetic-fuzzy-grey algorithms: a hybrid model for establishing companies’ failure reasons. In: IEEE International Conference (SMC), Istanbul (Turkey), pp. 955–962 (2010)

    Google Scholar 

  • Scherger, V., Vigier, H., Barberá- Mariné, G.: Finding business failure reasons through a fuzzy model of diagnosis. Fuzzy Econ. Rev. 19(1), 45–62 (2014)

    Google Scholar 

  • Tascón, M., Castaño, F.: Variables y modelos para la identificación y predicción del fracaso empresarial: Revisión de la investigación empírica reciente. RC- SAR 15(1), 7–58 (2012)

    Google Scholar 

  • Thapar, A., Pandey, D., Gaur, S.K.: Optimization of linear objective function with max-t fuzzy relation equations. Appl. Soft Comput. 9(3), 1097–1101 (2009)

    Article  Google Scholar 

  • Terceño, A., Vigier, H., Barberá-Mariné G., Scherger, V.: Hacia una integración de la Teoría del Diagnóstico Fuzzy y del Balanced Scorecard. In: XV SIGEF Conference on Economic and Financial Crisis. New Challenges and Perspectives, Lugo (Spain), pp. 364–379 (2009)

    Google Scholar 

  • Terceño, A., Vigier, H.: Economic—financial forecasting model of businesses using fuzzy relations. Econ. Comput. Econ. Cybern. Stud. Res. 45(1), 215–232 (2011)

    Google Scholar 

  • Terceño, A., Vigier, H., Scherger, V.: Application of a fuzzy model of economic—financial diagnosis to SMEs. In: Gil Aluja., J., Terceño, A. (eds.) Methods for Decision Making in an Uncertain Environment. Series on Computer Engineering and Information Science, vol. 6, pp. 146–162. World Scientific Publishing, Singapore (2012)

    Google Scholar 

  • Terceño, A., Vigier, H., Scherger, V.: Identificación de las causas en el diagnóstico empresarial mediante relaciones Fuzzy y el BSC. Actualidad Contable Fases 17(28), 101–118 (2014)

    Google Scholar 

  • Vigier, H., Terceño, A.: A model for the prediction of diseases of firms by means of fuzzy relations. Fuzzy Sets Syst. 159(17), 2299–2316 (2008)

    Article  MATH  Google Scholar 

  • Vigier, H., Terceño, A.: Analysis of the inconsistency problem in the model for predicting “diseases” of firms. Fuzzy Econ. Rev. 17(1), 73–88 (2012)

    Google Scholar 

  • Yager, R.R.: Families of OWA operators. Fuzzy Sets Syst. 59, 125–148 (1993)

    Article  MATH  MathSciNet  Google Scholar 

  • Yager, R.R.: Heavy OWA operators. Fuzzy Optim. Decis. Making 1(4), 379–397 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  • Yager, R.R.: Generalized OWA aggregation operators. Fuzzy Optim. Decis. Making 3(1), 93–107 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  • Yager, R.R.: Prioritized OWA aggregation. Fuzzy Optim. Decis. Making 8(3), 245–262 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  • Yager, R.R., Alajlan, N.: On characterizing features of OWA aggregation operators. Fuzzy Optim. Decis. Making 13(1), 1–32 (2014)

    Article  MathSciNet  Google Scholar 

  • Zimmermann, H.J.: Fuzzy Sets, Decision Making and Expert Systems, pp. 41–44. Kluwer Academic Publishers, Boston, Norwell (1987)

    Google Scholar 

  • Zmijewski, M.: Methodological issues related to the estimation of financial distress prediction models. J. Account. Res. 59–86 (1984)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Economic Department, Universidad Nacional Del Sur – CEDETS UPSO, 12 de Octubre 1198 (7º), 8000, Bahía Blanca, Argentina

    Valeria Scherger & Hernán P. Vigier

  2. Department of Business Management, Universitat Rovira I Virgili, Avinguda Universitat, 1, 43204, Reus, Spain

    Antonio Terceño-Gómez & M. Glòria Barberà-Mariné

Authors
  1. Valeria Scherger
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hernán P. Vigier
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Antonio Terceño-Gómez
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M. Glòria Barberà-Mariné
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Valeria Scherger .

Editor information

Editors and Affiliations

  1. Royal Academy of Economic and Financial Sciences (RACEF), Barcelona, Spain

    Jaime Gil-Aluja

  2. Department of Business Management, Universitat Rovira i Virgili Faculty of Business and Economics, Reus, Spain

    Antonio Terceño-Gómez

  3. Department of Business Administration, University of Girona, Girona, Spain

    Joan Carles Ferrer-Comalat

  4. Department of Management Control and Information Systems, University of Chile, Santiago, Chile

    José M. Merigó-Lindahl

  5. Department of Business Administration, University of Girona, Girona, Spain

    Salvador Linares-Mustarós

Appendices

Appendix A

Grouped membership matrix of causes (P*Max)

Areas

Learning and growth

Business process

Customers perspective

Finance

 

1

2

3

4

5

6

7

8

9

10

11

12

13

14

E1

1.00

0.57

0.83

0.67

1.00

1.00

1.00

1.00

0.37

0.83

0.50

1.00

1.00

0.86

E2

0.83

0.57

0.75

0.33

1.00

0.71

0.60

1.00

0.31

0.57

0.33

1.00

0.36

0.67

E3

0.73

0.53

0.79

0.83

0.73

0.71

0.80

1.00

0.40

0.71

0.75

1.00

0.83

0.71

E4

0.83

0.29

0.67

0.83

0.87

0.71

0.80

0.80

0.42

0.57

0.70

1.00

0.83

1.00

E5

0.71

0.57

0.83

0.50

0.73

0.64

0.80

0.79

0.37

0.73

0.63

1.00

0.67

0.72

E6

0.80

0.57

0.60

0.20

0.67

1.00

0.80

0.71

0.48

0.90

0.81

0.80

0.86

0.71

E7

0.80

0.57

0.60

0.20

0.63

1.00

0.80

0.71

0.48

0.90

0.81

0.80

0.86

0.80

E8

0.80

0.57

0.60

0.20

0.63

1.00

1.00

0.71

0.48

0.90

0.81

0.80

0.86

0.80

E9

1.00

0.63

0.80

0.67

1.00

0.71

1.00

1.00

0.40

1.00

0.75

0.80

1.00

1.00

E10

1.00

0.61

0.72

0.20

0.80

0.71

0.80

0.63

1.00

0.73

0.75

1.00

0.67

1.00

E11

1.00

0.57

1.50

0.83

0.93

0.79

0.80

0.80

0.47

0.86

0.58

0.81

0.83

1.00

E12

1.00

0.63

1.00

0.83

0.93

1.00

0.80

0.80

0.60

0.59

0.75

0.88

0.65

1.00

E13

1.00

0.63

0.80

1.00

0.80

0.71

0.80

1.00

0.43

0.83

0.75

1.00

0.83

0.69

E14

1.00

0.63

0.75

1.00

0.87

0.60

0.80

0.80

0.40

0.83

0.75

0.88

0.83

1.00

E15

0.8

0.57

0.80

0.67

0.80

0.80

0.80

0.80

0.48

0.83

0.61

1.00

0.65

1.00

Grouped membership matrix of causes (P*Min)

Areas

Learning and growth

Business process

Customers perspective

Finance

 

1

2

3

4

5

6

7

8

9

10

11

12

13

14

E1

0.20

0.12

0.57

0.20

0.13

0.20

0.20

0.13

0.20

0.43

0.20

0.20

0.20

0.27

E2

0.14

0.13

0.57

0.17

0.07

0.40

0.60

0.13

0.20

0.39

0.20

0.20

0.14

0.27

E3

0.14

0.13

0.60

0.17

0.07

0.53

0.60

0.13

0.26

0.29

0.20

0.22

0.14

0.17

E4

0.14

0.13

0.14

0.20

0.13

0.40

0.40

0.25

0.40

0.43

0.20

0.20

0.14

0.27

E5

0.14

0.13

0.71

0.33

0.25

0.53

0.60

0.13

0.20

0.57

0.20

0.20

0.14

0.20

E6

0.14

0.13

0.56

0.17

0.25

0.47

0.80

0.20

0.40

0.43

0.13

0.11

0.55

0.27

E7

0.14

0.13

0.56

0.17

0.25

0.47

0.80

0.20

0.40

0.43

0.13

0.11

0.55

0.27

E8

0.14

0.13

0.56

0.17

0.25

0.47

0.80

0.20

0.40

0.43

0.13

0.11

0.55

0.27

E9

0.14

0.14

0.43

0.17

0.13

0.40

0.80

0.13

0.37

0.43

0.20

0.20

0.14

0.27

E10

0.14

0.38

0.57

0.17

0.25

0.20

0.60

0.25

0.42

0.57

0.38

0.33

0.14

0.27

E11

0.14

0.24

0.14

0.20

0.14

0.20

0.20

0.38

0.40

0.29

0.20

0.33

0.14

0.24

E12

0.20

0.57

0.71

0.20

0.14

0.40

0.60

0.13

0.52

0.57

0.20

0.20

0.14

0.27

E13

0.17

0.53

0.29

0.20

0.14

0.40

0.60

0.29

0.40

0.57

0.20

0.40

0.14

0.27

E14

0.20

0.53

0.57

0.40

0.25

0.40

0.60

0.13

0.32

0.43

0.20

0.20

0.14

0.27

E15

0.20

0.25

0.57

0.20

0.25

0.40

0.60

0.13

0.40

0.57

0.20

0.33

0.14

0.27

Grouped membership matrix of causes (P*Mean)

Areas

Learning and growth

Business process

Customers perspective

Finance

 

1

2

3

4

5

6

7

8

9

10

11

12

13

14

E1

0.46

0.27

0.71

0.46

0.43

0.70

0.60

0.47

0.29

0.61

0.29

0.72

0.64

0.55

E2

0.29

0.40

0.67

0.23

0.46

0.50

0.60

0.50

0.25

0.46

0.26

0.49

0.26

0.47

E3

0.35

0.36

0.65

0.40

0.46

0.61

0.75

0.47

0.33

0.48

0.40

0.78

0.41

0.41

E4

0.45

0.19

0.52

0.51

0.46

0.54

0.65

0.54

0.41

0.48

0.38

0.59

0.48

0.58

E5

0.46

0.36

0.80

0.41

0.50

0.59

0.65

0.53

0.29

0.62

0.39

0.43

0.51

0.59

E6

0.39

0.39

0.57

0.18

0.45

0.66

0.80

0.45

0.44

0.63

0.35

0.41

0.71

0.52

E7

0.39

0.39

0.57

0.18

0.47

0.66

0.80

0.45

0.44

0.63

0.35

0.41

0.71

0.55

E8

0.39

0.39

0.57

0.18

0.47

0.66

0.85

0.45

0.44

0.63

0.35

0.41

0.71

0.55

E9

0.51

0.41

0.64

0.34

0.53

0.55

0.90

0.56

0.38

0.67

0.43

0.46

0.50

0.57

E10

0.42

0.52

0.65

0.18

0.51

0.53

0.75

0.51

0.71

0.62

0.61

0.55

0.41

0.59

E11

0.45

0.35

0.94

0.62

0.40

0.48

0.60

0.62

0.43

0.51

0.35

0.63

0.58

0.56

E12

0.59

0.59

0.85

0.62

0.43

0.70

0.70

0.57

0.56

0.58

0.43

0.69

0.42

0.56

E13

0.41

0.57

0.65

0.62

0.42

0.57

0.70

0.59

0.41

0.66

0.52

0.77

0.38

0.45

E14

0.48

0.57

0.67

0.80

0.52

0.53

0.75

0.47

0.36

0.61

0.43

0.61

0.49

0.57

E15

0.44

0.39

0.63

0.46

0.48

0.61

0.65

0.52

0.44

0.66

0.45

0.59

0.42

0.60

Appendix B

Estimated membership matrix of diseases (P′Max)

 

1

2

3

4

5

6

7

8

9

10

11

12

13

14

E1

0.57

0.57

0.61

0.67

0.87

0.80

0.80

0.93

0.31

0.83

0.51

1.00

0.83

0.67

E2

0.57

0.57

0.60

0.60

1.00

0.60

0.60

0.60

0.40

0.73

0.60

0.93

0.67

1.00

E3

0.67

0.43

0.61

0.80

1.00

0.60

0.80

0.80

0.27

0.59

0.63

0.80

0.55

1.00

E4

0.67

0.57

0.61

0.73

0.78

0.60

0.80

0.73

0.40

0.73

0.51

0.93

0.73

0.80

E5

0.83

0.57

0.60

0.67

0.63

0.60

0.80

0.80

0.40

0.73

0.63

1.00

0.67

0.92

E6

0.73

0.57

0.60

0.83

0.63

0.80

0.80

0.80

0.40

0.80

0.63

0.87

0.67

0.67

E7

0.57

0.57

0.60

0.67

0.63

0.80

0.80

1.00

0.40

0.83

0.63

1.00

0.83

0.73

E8

0.83

0.57

0.60

0.83

0.63

0.80

0.80

0.80

0.40

0.80

0.63

1.00

0.67

0.80

E9

0.67

0.57

0.61

1.00

1.00

0.60

0.80

0.87

0.33

0.83

0.63

1.00

0.83

1.00

E10

0.57

0.57

0.60

0.80

1.00

0.60

0.80

0.60

0.40

0.73

0.63

1.00

0.67

1.00

E11

0.67

0.57

0.61

0.67

0.89

0.67

0.67

0.67

0.40

0.67

0.51

0.80

0.67

0.89

E12

0.80

0.57

0.61

0.83

1.00

0.60

0.80

0.80

0.40

0.59

0.63

0.93

0.53

1.00

E13

0.67

0.57

0.61

0.93

1.00

0.60

0.80

0.80

0.40

0.83

0.63

0.93

0.83

1.00

E14

0.67

0.57

0.61

0.87

1.00

0.60

0.80

0.80

0.31

0.60

0.63

0.93

0.60

1.00

E15

0.83

0.57

0.60

0.67

0.80

0.80

0.80

0.80

0.31

0.83

0.51

1.00

0.50

1.00

Estimated membership matrix of diseases (P′Min)

 

1

2

3

4

5

6

7

8

9

10

11

12

13

14

E1

0.14

0.13

0.56

0.20

0.14

0.40

0.60

0.13

0.20

0.43

0.20

0.20

0.14

0.24

E2

0.14

0.13

0.56

0.20

0.14

0.40

0.60

0.13

0.26

0.43

0.20

0.20

0.14

0.24

E3

0.14

0.13

0.57

0.20

0.14

0.40

0.60

0.13

0.26

0.29

0.20

0.20

0.14

0.17

E4

0.14

0.13

0.57

0.20

0.14

0.40

0.60

0.13

0.26

0.43

0.20

0.20

0.14

0.24

E5

0.14

0.13

0.57

0.20

0.14

0.40

0.60

0.13

0.26

0.43

0.20

0.20

0.14

0.24

E6

0.14

0.13

0.56

0.20

0.14

0.40

0.60

0.13

0.26

0.43

0.20

0.20

0.14

0.24

E7

0.14

0.13

0.56

0.20

0.14

0.40

0.60

0.13

0.26

0.43

0.20

0.20

0.14

0.24

E8

0.14

0.13

0.56

0.20

0.14

0.40

0.60

0.13

0.26

0.43

0.20

0.20

0.14

0.24

E9

0.14

0.13

0.57

0.20

0.14

0.40

0.60

0.13

0.26

0.43

0.20

0.20

0.14

0.24

E10

0.14

0.13

0.56

0.20

0.14

0.40

0.60

0.13

0.26

0.43

0.20

0.20

0.14

0.24

E11

0.14

0.13

0.57

0.20

0.14

0.33

0.60

0.13

0.26

0.29

0.20

0.20

0.14

0.24

E12

0.14

0.13

0.56

0.20

0.14

0.40

0.60

0.13

0.26

0.29

0.20

0.20

0.14

0.24

E13

0.14

0.13

0.57

0.20

0.14

0.40

0.60

0.13

0.26

0.43

0.20

0.20

0.14

0.24

E14

0.14

0.13

0.57

0.20

0.14

0.40

0.60

0.13

0.20

0.33

0.20

0.20

0.14

0.24

E15

0.14

0.13

0.56

0.20

0.14

0.40

0.60

0.13

0.20

0.43

0.20

0.20

0.14

0.24

Estimated membership matrix of diseases (P′Mean)

 

1

2

3

4

5

6

7

8

9

10

11

12

13

14

E1

0.33

0.33

0.57

0.47

0.51

0.53

0.70

0.42

0.25

0.58

0.37

0.64

0.38

0.49

E2

0.32

0.33

0.57

0.51

0.47

0.51

0.60

0.42

0.33

0.58

0.35

0.65

0.38

0.49

E3

0.33

0.33

0.60

0.57

0.51

0.51

0.70

0.42

0.27

0.53

0.37

0.64

0.35

0.49

E4

0.32

0.33

0.60

0.57

0.47

0.53

0.65

0.42

0.33

0.58

0.33

0.61

0.38

0.49

E5

0.33

0.33

0.60

0.57

0.51

0.51

0.65

0.42

0.33

0.58

0.35

0.65

0.38

0.49

E6

0.33

0.33

0.57

0.51

0.41

0.53

0.70

0.42

0.33

0.58

0.35

0.65

0.38

0.49

E7

0.32

0.33

0.57

0.57

0.41

0.53

0.70

0.42

0.33

0.58

0.37

0.65

0.38

0.49

E8

0.33

0.33

0.57

0.57

0.40

0.53

0.70

0.42

0.33

0.58

0.37

0.65

0.35

0.49

E9

0.33

0.33

0.60

0.57

0.51

0.53

0.70

0.42

0.33

0.58

0.37

0.64

0.38

0.49

E10

0.32

0.33

0.57

0.53

0.47

0.51

0.70

0.42

0.33

0.58

0.37

0.61

0.38

0.49

E11

0.33

0.33

0.60

0.57

0.51

0.53

0.65

0.42

0.33

0.56

0.35

0.54

0.38

0.49

E12

0.33

0.33

0.57

0.57

0.51

0.53

0.70

0.42

0.33

0.53

0.37

0.64

0.36

0.49

E13

0.33

0.33

0.60

0.57

0.51

0.51

0.70

0.42

0.33

0.58

0.37

0.64

0.38

0.49

E14

0.33

0.33

0.60

0.57

0.51

0.51

0.70

0.42

0.25

0.56

0.37

0.64

0.36

0.49

E15

0.33

0.33

0.57

0.46

0.47

0.53

0.65

0.42

0.25

0.58

0.37

0.65

0.35

0.49

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Scherger, V., Vigier, H.P., Terceño-Gómez, A., Barberà-Mariné, M.G. (2015). Goodness of Aggregation Operators in a Diagnostic Fuzzy Model of Business Failure. In: Gil-Aluja, J., Terceño-Gómez, A., Ferrer-Comalat, J., Merigó-Lindahl, J., Linares-Mustarós, S. (eds) Scientific Methods for the Treatment of Uncertainty in Social Sciences. Advances in Intelligent Systems and Computing, vol 377. Springer, Cham. https://doi.org/10.1007/978-3-319-19704-3_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-19704-3_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19703-6

  • Online ISBN: 978-3-319-19704-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation