EVALUATION FRAMEWORK AND METHOD

A method of selection of the optimal model consists of several steps (Figure 1). First of all it is necessary to define the characteristics that form prism through which each of the proposed models will be seen. Application of the AHP method will determine the weight coefficients which are conditioned by the characteristics of the observed area. Assessment of achievement of each criterion in the application of a particular model is realized by fuzzy sets theory, where is obtained a universal matrix of contribution of each criteria with respect to the discussed model which is applied to each area. The final step is the application of TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) by which the selection of the most appropriate method of financing the universal service is made.

model evaluation criteria weight criteria performance measurment ranking of financing models

AHP _{characteristics}area universal

matrix fuzzy experts TOPSIS

Figure 1: The process of selecting models of financing

3.1Criteria for model evaluation

Models of financing the universal service may jeopardize the survival and development of operators in a given market. Models should provide contributions for public operators to compensate for the provision of universal service, allowing market entrance for the efficient, competitive operators and preventing the entry of inefficient operators. Operators should evenly divide the obligations arising from the necessity of providing universal service. Funding model should be'' well-balanced'' that is, to meet the criteria of fair competition. The criterion of social equality, in practice, is a normative concept which determines criteria and assesses which sectors of society need to be more privileged compared to others in terms of the postal service. This assessment is performed mainly in relation to universal service. Criteria should answer the question of whether financing model ensures that strata of society similar economic power get universal service at approximately same prices.

Any of the financing models, may it be the least reliant on financial support from the state, must comply with national and other positive regulations of the financial sector. This primarily relates to the compensation fund, which is filled with general or special taxation, and whose resources the state uses to subsidize universal service. Any transfer of state resources, direct or indirect, which provides certain competitive advantages to the recipient or lead to disorder in the liberalized market, is considered to be unlawful government act. This does not mean that any financial help from the state to the provider of universal service will be construed as an illegal intervention.

Calculation must be transparent in order to avoid favoring the recipient in relation to its competitors. The compensation should cover only the most basic expenses incurred by the provision of universal service, in special cases can be considered a minimum profit of the operator. When the selection of public operator is not made through public procurement procedure, the level of compensation must be determined by analyzing the costs that would theoretically occur in the case of efficient service delivery. From the perspective of this criterion, the principle of operation of the model of financing should be available and clear

to all market participants. All participants should be able to understand the way in which amount of compensation is determined, who pays contributions for financing the universal service and the way in which those contributions are set. Also, information on the establishment of the fund and entities who have established it must be available, and then how to access the resources of the fund and the availability of specific operators in a given time to have access to the fund. All participants, if they wish, must be provided with the opportunity to meet the requirements for funding from the fund. All this information must be verified and published.

Collection of contributions to small operators reduces the possibility of new entrants to the market, and in the long run leads to its polarization and negative consequences for users. Accordingly, European Commission has put forward a proposal that in case of covering all the costs of providing universal service with funds collected from contributions of other operators, perform the allocation of contributions. Collecting contributions should be non-discriminatory and based on the principle of proportionality, and if there is no higher level of market presence, small operators should be freed of this obligation. Proportionality means that the given model of funding achieved a reasonable balance between the projected goals and objectives of the global market. It needs to be ensured that the model minimally affects other market mechanisms and introduces minimal distortion in the global economic plan.

Feasibility is determined by the complexity of financial schemes and information necessary for their establishment and management. Complex mechanisms with complicated procedures of implementation and exploitation require greater funding at practical use and there is increased uncertainty about achieving the projected goals. This criterion has a direct impact on the transparency, in terms of easier or more difficult access to information relating to the current situation and progress in terms of achieving the objectives of the designed model of financing.

Sustainability is key criterion for achieving the minimum cost of establishing a specific financing model. The main aspect of this criterion is that the model, first of all need to act on clearly defined objectives to be achieved. Every action in the framework of the model is to provide a sustainable universal service.

Reliable financing model is one that ensures the long term sustainability of the universal service. The mechanism that provides long-term security financing is much more convenient than those with short and unpredictable financial arrangements. It is important that the model is based on economic principles.

Implementation of a financing model should provide minimal distortion of economic efficiency and increase profit of the operator. In practice, there are three main aspects that can support the concept of efficiency: allocative, productive and dynamic efficiency. Financing models that promote redistribution allows operators to raise prices to the level of service that will reflect the cost of providing service, including a reasonable profit based on invested funds. Despite the fact that allocative efficiency is directly contrary to the recognized principles of universal service operators, particularly those large, are increasingly trying to prove its necessity. Production efficiency is based on the obligation of the operator to provide services at the lowest possible cost, with the optimum use of all available technological resources. In this context, financing mechanisms should provide incentives in procurement and implementation of optimal technologies, which will enable full utilization of potential at an acceptable cost. The model should also facilitate the selection of optimal operator which will be entrusted with the provision of the universal service and thus avoid the increase of fixed costs of service. Dynamic efficiency refers to facilitate innovative process, which leads to the development of completely new and enhanced services. The advantage of the dynamic efficiency is in the possibilities of development of universal service based on the applied mechanism, through new ways of providing services or the introduction of entirely new services.

3.2 Analytic Hierarchy Process (AHP)

Analytical Hierarchy Process is a systemic procedure for determining the relative importance of a set of activities or of criteria (Kujačić, 2002). The approach is based on three main components. The first is defined in the request of necessity to decompose the problem into a hierarchical structure. The second component relates to the measurement scale expressing the priorities in the elements (Saaty, 1986).

Dependence of criteria and sub-criteria on the alternatives and mutual dependence of criteria is formed by a matrix whose values are defined by a numerical (1 through 9) or semantic scale. The third component is to define the measurement mechanism for setting priorities in the hierarchy and consistency of assessments provided by experts. Calculating the priority is reduced to the determination of the appropriate vector of weight coefficients.

3.3 The theory of fuzzy sets

A small piece of information about a particular problem can be seen as certain, or deterministic. Problem of uncertainty that arises in information can lead to deceiving if the blur of human decision-making is not taken into account. Fuzzy set theory provides a mathematical basis for the representation of ambiguity and vagueness in human systems (Ross, 2004). Starting from the development of the theory of fuzzy sets (Zadeh 1965), a large number of papers deal with the problem of uncertainty in decision-making based on the theory of fuzzy sets. In this direction, this paper endeavors to encompass fuzzy subjective assessment of experts in terms of the financing model for the universal service.

Fuzzy numbers can be regarded as a generalization of the concept of trust (Teodorović and Šelimć, 2012). That is every fuzzy number can be specified with confidence interval and affiliations functions. Under the term fuzzy numbers is considered to be limited, convex and normalized fuzzy set (Dubois and Prade, 1978). The statement of the extent to which it met certain criteria as linguistic variables (eg, part ") can be represented by triangular fuzzy numbers in the range of 0-5. Triangular fuzzy number can be defined as a triple (a, b, c) (Fig. 2), where the affiliations function is defined as follows (Zimmermann, 1991):

(1)

Figure 2: Triangular fuzzy number

Algebraic operations on triangular fuzzy numbers can be defined by means of arranged triplets:

(a1,b1,c1) (a2,b2,c2)=(a1+a2,b1+b2,c1+c2) (2) (a1,b1,c1) (a2,b2,c2)=(a1-a2,b1-b2,c1-c2) (3) K (a,b,c)=(Ka,Kb,Kc) (4) The concept of linguistic variable is very useful for dealing with situations that are too complex or ill-defined to describe understandably conventional quantitative expressions (Deng Yong and Chan, 2011). Each of these variables can be associated with affiliate function. In this paper, we used expressions in terms of the degree of achievement of the observed criteria: “unrealized”, “poorly realized”, “medium realized”, “very realized”, “and fully realized”. Experts were asked to give their opinion and each linguistic variable is demonstrate triangular fuzzy numbers in the range of 0 to 5 In order to provide a more objective evaluation of the fuzzy assessment, there was a aggregation of linguistic variables LVij alternative Ai for criterion Cj based

on the expression:

n

c

b

a

LV

LV

LV

n

c

b

a

LV

n i n i i i n i i n ij ij ij ij 1 2 1

...

1

,

,

. (5)

Equation (5) shows that the average performance can be expressed in the form of a new triangular fuzzy numbers (Buckely 1985). As the result of the synthesis is a new fuzzy number, it is necessary to realize defuzzification (a technique of converting fuzzy numbers into solid real numbers). Defuzzification procedure is positioning the BNP (Best Nonfuzzy Performance) value. Many defuzzification techniques are available (Zimmermann, 1991), but the common defuzzification methods include the center of area, first of maximums, last of maximums, and middle of maximums (MoM). This paper used Center of Area method taking into account ease of use, where defuzzification value can be obtained:

ij ij ij ij ij ij

c

a

b

a

a

BNP

/3

i, j.

(6)

3.4 TOPSIS

Technique for order performance by similarity to ideal solution (TOPSIS), was first developed by Hwang and Yoon (1981) for solving a MCDM problem. It is based on the selection of the optimal alternative that is closest to the positive ideal solution and farthest from the negative ideal solution. An ideal solution is composed of all best values attainable of criteria, whereas a negative ideal solution is made up of all worst values attainable of criteria. Take the objective space of the two criteria as example which is indicated in (Figure 3.), A+ _{and A}- _{are, respectively, the ideal solution and negative ideal solution, and observation A}

1 is

shorter in distance in regard to the ideal solution (A+_{) and negative ideal solution (A}-_{) than A}

2. As a matter of

fact, the ups and downs of these two observations regarding to ideal solutions cannot be compared because there exists some tradeoff between the ups and downs. However, TOPSIS can help resolving this problem because it has defined such „„relative closeness‟‟ so as to consider and correlate, as a whole, the distance to the ideal solution and the negative ideal solution.

Figure 3: Position A1 and A2 with respect to the ideal and the negative ideal solution The process of calculation is carried out through the following stages:

 forming normalized performance matrix,

 forming weighted normalized matrix by multiplying weight coefficients and criteria: ij

ij

ij

w

r

V

i, j.

(7) where wij is the weight of criterion j, and rij normalized value of the j-th performance

criteria for the i-th alternative;  determining the ideal solution:

m

i

J

j

V

J

j

V

A

max

_ij

,

min

_ij '

,

1,2,,

, (8)

 determining the ideal negative solution:

m

i

J

j

V

J

j

V

A

min

_ij

,

max

_ij '

,

1,2,,

(9) where J is associated with the benefits of the criteria, and J 'from the cost criterion,  calculating the distance between the ideal and the negative ideal solution of each alternative.

n j ij j i

V

V

S

1 2

i

1,2,,n

, (10) n j ij j i

V

V

S

₁ 2

i

1,2,,n

, (11)  calculating the relative proximity of each alternative to the ideal solution:

i i i i

S

S

S

R

i

1,2,,n

, (12)  ranking of alternatives in descending order Ri.

In document Accounting Information System as the Source of Corporate Competitive Advantage (Page 117-121)