Ranking corporate sustainability: a flexible multidimensional approach based on linguistic variables

Corporate sustainability implies a compromise between the present environmental, social, and economic needs of a firm's stakeholders and their future needs. Corporate sustainability is therefore a multidimensional concept. Nowadays, several independent rating agencies rate firms in terms of environmental, social, and governance (ESG) criteria. These ratings are usually used by main sustainability indices such as the Dow Jones Sustainability Index, FTSE4 Good, Stoxx Sustainability Index, or Euronext Vigeo Family to select companies to invest in. Only those firms performing better than the average of their sector are selected. However, although providing linguistic ratings about the performance of the firms in individual ESG criteria with respect to their sector, rating agencies do not usually provide overall ESG rates describing the global performance of the firms in terms of ESG. In this paper, we propose a flexible operator, linguistic ordered weighted geometric aggregating operator (LOWGA), which will allow us to define the fuzzy ESG performance of the firms based on the linguistic labels provided by the rating agencies. Once overall ESG scores have been obtained, we will use them together with financial criteria to rank the firms in terms of their sustainability using a suitable multiple criteria decision aid (MCDA) approach, namely, TOPSIS (technique for order preference by similarity to ideal solution).

[1]  Janusz Kacprzyk,et al.  The Ordered Weighted Averaging Operators , 1997 .

[2]  Thomas Dyllick,et al.  Beyond the business case for corporate sustainability , 2002 .

[3]  Francisco Herrera,et al.  A study of the origin and uses of the ordered weighted geometric operator in multicriteria decision making , 2003, Int. J. Intell. Syst..

[4]  A. Bilbao-Terol,et al.  Using TOPSIS for assessing the sustainability of government bond funds , 2014 .

[5]  E. Ballestero,et al.  Estimating the Ethical Achievement Levels of Mutual Funds by Synthetic Indicators , 2014 .

[6]  Ching-Hui Chang,et al.  Domestic open-end equity mutual fund performance evaluation using extended TOPSIS method with different distance approaches , 2010, Expert Syst. Appl..

[7]  Zeshui Xu,et al.  On generalized induced linguistic aggregation operators , 2006, Int. J. Gen. Syst..

[8]  Anna Maria Gil Lafuente,et al.  Decision making techniques with similarity measures and OWA operators , 2012 .

[9]  Francisco Herrera,et al.  Multiperson decision-making based on multiplicative preference relations , 2001, Eur. J. Oper. Res..

[10]  Stelios H. Zanakis,et al.  Multi-attribute decision making: A simulation comparison of select methods , 1998, Eur. J. Oper. Res..

[11]  Krishnendu Mukherjee,et al.  Analytic hierarchy process and technique for order preference by similarity to ideal solution: a bibliometric analysis 'from' past, present and future of AHP and TOPSIS , 2014, Int. J. Intell. Eng. Informatics.

[12]  Ronald R. Yager,et al.  On ordered weighted averaging aggregation operators in multicriteria decisionmaking , 1988, IEEE Trans. Syst. Man Cybern..

[13]  Ching-Lai Hwang,et al.  Multiple Attribute Decision Making: Methods and Applications - A State-of-the-Art Survey , 1981, Lecture Notes in Economics and Mathematical Systems.

[14]  Z. S. Xu,et al.  The ordered weighted geometric averaging operators , 2002, Int. J. Intell. Syst..

[15]  Z. S. Xu,et al.  An overview of operators for aggregating information , 2003, Int. J. Intell. Syst..

[16]  Morteza Yazdani,et al.  A state-of the-art survey of TOPSIS applications , 2012, Expert Syst. Appl..

[17]  Ching-Lai Hwang,et al.  Fuzzy Multiple Attribute Decision Making - Methods and Applications , 1992, Lecture Notes in Economics and Mathematical Systems.

[18]  Philip J. Fleming,et al.  How not to lie with statistics: the correct way to summarize benchmark results , 1986, CACM.

[19]  Francisco Herrera,et al.  A 2-tuple fuzzy linguistic representation model for computing with words , 2000, IEEE Trans. Fuzzy Syst..

[20]  W. Hsu,et al.  The sustainability balanced scorecard as a framework for selecting socially responsible investment: an effective MCDM model , 2009, J. Oper. Res. Soc..

[21]  Zeshui Xu,et al.  A method based on linguistic aggregation operators for group decision making with linguistic preference relations , 2004, Inf. Sci..

[22]  María Teresa Lamata,et al.  Doing good by doing well: a MCDM framework for evaluating corporate social responsibility attractiveness , 2018, Ann. Oper. Res..