A fuzzy MCDM approach for evaluating banking performance based on Balanced Scorecard

By Hung-Yi Wu, Gwo-Hshiung Tzeng, Yi-Hsuan Chen. Expert systems with applications, 2009

535 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/73254f878ba3eaba48c09aa6d3af7767eff94e80

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Management Science and Operations Research

1. Edmundas Kazimieras Zavadskas
2. Zenonas Turskis
3. Serafim Opricovic
4. Arash Bahrammirzaee
5. Abbas Mardani
6. Zeshui Xu
7. Kannan Govindan
8. Huchang Liao

In Safety, Risk, Reliability and Quality

1. Edmundas Kazimieras Zavadskas
2. Serafim Opricovic
3. Arash Bahrammirzaee
4. Zenonas Turskis

Citations in the corpus

• A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems (2010, methodology): “In the same year, [212] used a fuzzy multiple criteria decision-making (FMCDM) approach for evaluating”
• Multiple criteria decision making (MCDM) methods in economics: an overview (2011, methodology): “Wu et al. (2009) Fuzzy AHP (FAHP) and the three MCDM analytical tools of SAW, TOPSIS, and VIKOR were respectively adopted to rank the banking performance and improve the gaps with three banks.”
• Fuzzy VIKOR with an application to water resources planning (2011, methodology): “The predefuzzification approach is used in (Wu et al., 2009), utilizing the COA (center of area) method to find ‘‘the Best Nonfuzzy Performance value (BNP)’’.”