**Abstract**

Performance evaluations in organizations are viewed as ideal instruments for evaluating and rewarding the employee's performance. While much emphasis is laid onto the administering of the evaluation techniques, not much thought has been laid out on assessing the contributions of each hierarchical level. Moreover the manifold decision making criteria can also impact the measurement of pertinent contributions because of their ambivalent characteristics. In such a scenario, intuitionistic fuzzy multi-criteria decision making can help strategists and policy makers to arrive at more or less accurate decisions. This paper restricts itself to six decision making criteria and adopts the intuitionistic fuzzy simple additive weighting (IFSAW) method and TOPSIS method to evaluate and rank the employee cadres. The results obtained were compared and both the methods revealed that the middle management displayed impeccable performance standards over their other counterparts.

**Keywords:** performance evaluation, organisation, intuitionistic fuzzy, IFSAW, TOPSIS

### **1. Introduction**

Organizational fit theories have long emphasized that appropriate selection strategies can lead to superior performance compared to firms that relatively overlook the employee selection based on fit theories [1]. The extent of fit between the individual and the organization determines the labor productivity [2–4] as well as the financial performance [5–8].

The other criterions that influence the overall organizational performance are informal learning [9], workplace competencies [10], organizational citizenship behavior [11] and the like.

While many employee focused parameters are relied on while determining the organizational performance, very few researches have essayed the contributions of each of the hierarchical cadres. Performance evaluations in organizations have traditionally focused on short-term financial and technical results. But modern organizations have not just demanded a generic short-term performance assessment, but an effective means to categorize employees as vital opportunities or threats. By using measurable performance results, with a focus on the entire organization, managers will be able to determine their progress toward longterm goals

and objectives [12]. Moreover, superior performance cannot be achieved by just delayering and de-staffing. Whilst these techniques can to a certain extent eliminate the imperfections within the system, it is the overall behaviors of the employees that need a volte-face. Explicit construal of roles of the employees and managers in particular, will ensure that the managers do not slip into the comfortable and familiar role structure of grand strategists, administrative controllers, and operational implementers. Each hierarchical level or cadre needs to exemplify its cardinal responsibilities that add distinct value to an organization [13]. Identifying, weighting and evaluating the various level of managers against various criteria can be assumed as a function of multi criteria decision making process.

While focus on HR metrics has been growing off late, there is still an element of bias and ambiguity regarding the criteria that are being used rather the greatest difficulty lies in the quantification of criteria being not clearly defined. The basis for the selection of criterions is the subjective judgements by the higher authorities in organisations. These judgements/verbal descriptions do not exhibit the characteristic of being classified into a dichotomous group and are therefore treated as linguistic variables. Also the relation between the different hierarchical levels and the criterions on the basis of which they are assessed are not known precisely. This provides a framework where a different methodology is required. Thus to understand such a structure a verbal description would suffice. A formal way of dealing with them is the linguistic approach by Zadeh [14]. Its basic feature is the use of linguistic variables which are the ones whose values are words or sentences in a language in place of numerical value and a fuzzy conditional statement for expressing the relation between linguistic variables. Here the meaning of a linguistic variable is equated with a fuzzy set while the meaning of the fuzzy conditional statement with a fuzzy relation. Since its inception about a decade ago, the theory of fuzzy sets has evolved in many directions, and is finding applications in a wide variety of fields in which the phenomena under study are too complex or too ill defined to be analyzed by conventional techniques. Fuzzy set theory (FST) [15] allows for subjective evaluation by the decision maker under conditions of uncertainty and ambiguity. It helps to express irreducible observations and measurement uncertainties which are intrinsic to the empirical data. It offers far greater resources for managing complexity and controlling computational cost and allows for conversion of linguistic variables to fuzzy numbers using membership functions. Membership functions assigns to each object a grade of membership denoted by μA(x) which ranges between zero and one. It maps every element of the universe of discourse X to the interval [0, 1] which is written as μ<sup>A</sup> : X ! ½ � 0, 1 . Each fuzzy set is completely and uniquely defined by one particular membership function. A "direct" use of verbal descriptions of those criteria via the concepts of the fuzzy set is proposed here.

A fuzzy set is defined by

$$\overline{A} = \left\{ (\mathfrak{x}, \mu\_{\overline{A}}(\mathfrak{x}))/\mathfrak{x} \in X, \mu\_{\overline{A}}(\mathfrak{x}) \in [0, 1] \right\}.$$

In the pair *<sup>x</sup>*, *<sup>μ</sup>A*ð Þ *<sup>x</sup>* the first element x belong to the classical set X, the second element *μA*ð Þ *x* belong to the interval [0, 1] which is called the membership function or grade of membership function. This membership function is represented with the help of fuzzy number. It represents the degree of compatibility or a degree of truth of x in *A*. The idea of fuzzy numbers was given by Dubois and Prade [16].

A fuzzy subset *A* of the real line R with membership function *μA*ð Þ *x* : *R* ! ½ � 0, 1 is called a fuzzy number if.

i. *A* is normal, (i.e.) there exist an element *x*<sup>0</sup> such that *μA*ð Þ¼ *x*<sup>0</sup> 1.

ii. *A* is fuzzy convex,

i.e. *μA*ð Þ *λx*<sup>1</sup> þ ð Þ 1 � *λ x*<sup>2</sup> ≥ min *μA*ð Þ *x*<sup>1</sup> , *μA*ð Þ *x*<sup>2</sup> *<sup>x</sup>*1, *<sup>x</sup>*<sup>2</sup> <sup>∈</sup>*R*, <sup>∀</sup>*λ*∈½ � 0, 1

iii. *μA*ð Þ *x* is upper continuous, and.

iv. supp *<sup>A</sup>* is bounded, where supp *<sup>A</sup>* <sup>¼</sup> *<sup>x</sup>*∈*<sup>R</sup>* : *<sup>μ</sup>A*ð Þ *<sup>x</sup>* <sup>&</sup>gt;<sup>0</sup> .

A fuzzy number *A* of the universe of discourse U may be characterized by a triangular distribution function parameterized by a triplet ð Þ *a*1, *a*2, *a*<sup>3</sup> (**Figure 1**).

Mikhailovich [17] used the fuzzy sets while solving the problem of factor causality. Dintsis [18] in his work dealt with the idea of implementing fuzzy logic for transforming descriptions of natural language to formal fuzzy and stochastic models. However, fuzzy sets lack in the idea of non -membership function. Whatever information is provided by fuzzy sets does not appear complete in context of decision making as there is no room for alternatives dissatisfying the attributes. Thus Atanassov [19] used the idea of membership value, nonmembership value as well as the hesitation index to characterize an intuitionistic fuzzy set. He opined that the sum of membership value and non-membership value lies between zero and one and the hesitation index is calculated as one minus the sum of membership value and non-membership value of an element of a set. In other words some hesitation about degree of belongingness of an element of a set exists. For a fuzzy set the hesitation index is zero. The fuzzy sets along with intuitionistic fuzzy sets can depict real life application areas defined by uncertainity. Some recent applications of fuzzy systems are found in the works of [20, 21].

**Figure 1.** *Membership function of TFN.*
