A novel model for enterprise technological innovation capability evaluation with 2-tuple linguistic information

Abstract

Today rapid and drastic changes are taking place in technology. Technological innovation capability of a nation to knowledge becomes the crucial factor which raises national industry competition ability. Only in this way can an enterprise scientifically realize its technological innovation ability in its competition with its counterpart, employ appropriate innovation strategy, make full use of its advantages and achieve the best economic and social benefits. The aim of this paper is to study the evaluation problems for evaluating the enterprise technological innovation capability with 2-tuple linguistic information. The induced 2-tuple linguistic choquet ordered harmonic average (I-2TCOHA) operator is used to aggregate the 2-tuple linguistic information corresponding to each alternative and rank the alternatives and select the most desirable one(s). Finally an illustrative example for evaluating the enterprise technological innovation capability has been given to show the developed approach.

Keywords

Evaluation 2-tuple linguistic fuzzy information entrepreneurship enterprise technological innovation capability

1 Introduction

High-tech enterprises have become the dominant industry that can promote the development of socio-economy. The demand for knowledge of high-tech enterprises’ technological innovation has led to the emergence of innovation network [1, 2]. The cooperation between enterprises and resource sharing in innovation network is bound to be accompanied by knowledge transfer. Transferring knowledge quickly and efficiently are the key strategic elements in the development of high-tech enterprises technological innovation network. Therefore, the study of knowledge transfer in high-tech enterprises technological innovation network has important theoretical and practical significance. There are many factors that influence the activities of knowledge transfer in innovation network, and the relationship between these factors is very complex [3, 4]. How to find out the relationship and the mechanism between factors that influence knowledge transfer in high-tech enterprises technological innovation network, and the contribution to economic development of high-tech enterprises technological innovation network, how to choose partners, and how to determine the optimal time of knowledge transfer are the urgent issues to be considered when studying high-tech enterprises technological innovation network [5]. Many papers have mainly focused on the qualitative study of influencing factors, and quantitative study mainly focused on empirical models such as econometric model, game model, DEA or SEM, which is not operational to determine the mechanism between factors, choose partners and find out the optimal time of knowledge transfer [6 –8].

As a key link between the development of technology and the growth of economy, technological innovation has many important leaps-from scientific knowledge to technique knowledge, from technique knowledge to achievement in scientific research, from achievement in scientific research to manufacturing enterprises, from manufacturing enterprises to the ultimate consumers. There are many things with risk and uncertainty. The process of technological innovation is too complicated to sieve and to be eliminated. Therefore, technological innovation is a complicated social process with uncertainty. The concrete project of technological innovation is the basis of the national innovation system. One of the necessary conditions to set up the basis is to deal correctly with the evaluation and decision of the innovation project that need practical technology and method. The aim of this paper is to study the evaluation problems for evaluating the enterprise technological innovation capability with 2-tuple linguistic information. The induced 2-tuple linguistic choquet ordered harmonic average (I-2TCOHA) operator is used to aggregate the 2-tuple linguistic information corresponding to each alternative and rank the alternatives and select the most desirable one(s). Finally an illustrative example for evaluating the enterprise technological innovation capability has been given to show the developed approach.

2 Preliminaries

Herrera [9 –11] first introduced the 2-tuple fuzzy linguistic approach for overcoming the drawback of the classical computational models, which include the semantic model and symbolic model.

Let S ={ s_i |i = 1, 2, ⋯ , t } be a linguistic term set with odd cardinality. Any label, s_i represents a possible value for a linguistic variable, and it should satisfy the following characteristics [9 –11]:

(1) The set is ordered: s_i > s_j, if i > j; (2) Max operator: max(s_i, s_j) = s_i, if s_i ≥ s_j; (3) Min operator: min(s_i, s_j) = s_i, if s_i ≤ s_j. For example, S can be defined as $\begin{matrix} S = {s_{1} = extremely poor, s_{2} = very poor, \\ s_{3} = poor, s_{4} = medium, s_{5} = good, \\ s_{6} = very good, s_{7} = extremely good} \end{matrix}$

Definition 1. [12] Let f be a positive real-valued function on X and m be a fuzzy measure on X. The induced Choquet ordered averaging operator of dimension n is a function I - COA : (R⁺× R⁺) → R⁺, which is defined to aggregate the set of second argument of a list of 2-tuples (〈 u₁, f₁ 〉 , 〈 u₂, f₂ 〉 , ⋯ , 〈 u_n, f_n 〉) according to the following expression:

$\begin{matrix} {I - COA}_{m} (〈 u_{1}, f_{1} 〉, 〈 u_{2}, f_{2} 〉, \dots, 〈 u_{n}, f_{n} 〉) \\ = \sum_{j = 1}^{n} f_{σ (j)} [m (A_{σ (j)}) - m (A_{σ (j - 1)})] \end{matrix}$ (1) where (σ (1) , σ (2) , ⋯ , σ (n)) is a permutation of (1, 2, ⋯ , n), such that u_σ(i-1) ≥ u_σ(i) for all j = 2, ⋯ , n, i.e., 〈u_σ(j), f_σ(j)〉 is the 2-tuple with u_σ(j) the jth largest value in the set (u₁, u₂, ⋯ , u_n), A_σ(k) ={ x_σ(j)|j ≤ k }, for k ≥ 1, and A_σ(0) = φ.

In the following, we shall introduce the induced 2-tuple linguistic choquet ordered harmonic average (I-2TCOHA) operator based the I-COA operator and ordered harmonic average operator.

Definition 2. [13] Let X (x₁, x₂, ⋯ , x_n) be a finite set, m be a fuzzy measure on X, and (〈 u₁, (r₁, a₁) 〉 , 〈 u₂, (r₂, a₂) 〉 , ⋯ , 〈 u_n, (r_n, a_n) 〉) be a set of 2-tuple, An I-2TCOA operator of dimension n is a mapping I-2TCOHA: Sⁿ→ S, furthermore,

$\begin{matrix} I - 2 TCOHA (〈 u_{1}, (r_{1}, a_{1}) 〉, 〈 u_{2}, (r_{2}, a_{2}) 〉, \\ \dots, 〈 u_{n}, (r_{n}, a_{n}) 〉) \\ = Δ (\frac{1}{\sum_{j = 1}^{n} \frac{(m (A_{σ (j)}) - m (A_{σ (j - 1)}))}{Δ^{- 1} (r_{σ (j)}, a_{σ (j)})}}) \end{matrix}$ (2) where u_j in 2-tuple 〈u_j, (r_j, a_j)〉 is referred to as the order-inducing variable and (r_j, a_j) as the argument variable, (σ (1) , σ (2) , ⋯ , σ (n)) is a permutation of (1, 2, ⋯ , n), such that u_σ(j-1) ≥ u_σ(j) for all j = 2, ⋯ , n, A_(i) ={ x₍₁₎, x₍₂₎, ⋯ , x_(i) } when i ≥ 1 and A_σ(0) = φ.

The 2-tuple linguistic model is a kind of new information processing method. It takes 2-tuple to represent linguistic assessment information and carry out operation. The basic concept of linguistic 2-tuple is symbolic translation. The 2-tuple linguistic representation and computational model has received more and more attention since its appearance [14 –29].

3 A study on enterprise technological innovation capability evaluation with 2-tuple linguistic information

With the development of the globalization of the world economy and the upsurge of knowledge economy, a new technological revolution will set off across the world. The characteristics of this worldwide trend embody the transformation from conventional industries to knowledge based industries, the closer combination of technology and economy, the shortening of product life cycle and the emergence of high technology one after another. The accumulation of knowledge and innovation of technology are becoming the keys to the success of enterprises in this global competition, and significant factors to measure the science and technology competitiveness of a country. After China joined WTO, the world has experienced an unprecedented fierce competition. The innovative capacities and activities of technology have become the impetus and source which determine the survival and development of enterprises. Technological innovation capacity has been considered publicly as a crucial factor of improving the enterprises’ competitiveness. Industrial enterprise is the pillar of national economy and the main part of technological innovation, which indicates that the level of technological innovation capacity concerns the overall development of national economy. How to evaluate the technological innovation capacity in industrial enterprises objectively, scientifically and effectively with combination of relative theoretical analysis of its own development characteristics will be particularly important to those corporations that compete to comprehend their own technological innovation capacity in the industries with appropriate strategies, enhance their competitive advantages and gain the best economic and social benefits. In this section, we shall utilize the induced 2-tuple linguistic choquet ordered harmonic average (I-2TCOHA) operator to study the evaluation problems for evaluating the enterprise technological innovation capability with 2-tuple linguistic information. Let A ={ A₁, A₂, ⋯ , A_m } be a discrete set of alternatives, G ={ G₁, G₂, ⋯ , G_n } be the set of attributes, whose weight vector is ω = (ω₁, ω₂, ⋯ , ω_n), with ω_j ≥ 0, j = 1, 2, ⋯ , n, $\sum_{j = 1}^{n} ω_{j} = 1$ . Suppose that $R = {(r_{ij})}_{m \times n}$ is the 2-tuple linguistic evaluation matrix, where r_ij ∈ S are the attribute values, which take the form of 2-tuple linguistic variables, given by the decision maker for the alternative A_i ∈ A with respect to the attribute G_j ∈ G.

In what follows, we shall apply the I-2TCOHA operator to solve the evaluation problems evaluation problems for evaluating the enterprise technological innovation capability with 2-tuple linguistic information

Step 1. Calculate the fuzzy measure of attribute of G_j (j = 1, 2, ⋯ , n) and attribute sets of G.

Step 2. Due to the fact that the attitudinal character is very complex because it involves the opinion of different members of the board of directors, the experts use order-inducing variables to represent it and construct the inducing variables matrix U = (u_ij) _m×n.

Step 3. Transforming linguistic decision matrix R = (r_ij) _m×n into 2-tuple linguistic decision matrix $\tilde{R}$ = (r_ij, 0) _m×n.

Step 4. We utilize the decision information given in matrix $\tilde{R}$ , and the I-2TCOHA operator

$\begin{matrix} {\tilde{r}}_{i} = I - 2 TCOHA (〈 u_{ij}, (r_{ij}, a_{ij}) 〉, \\ 〈 u_{ij}, (r_{ij}, a_{ij}) 〉, \dots, 〈 u_{ij}, (r_{ij}, a_{ij}) 〉) \\ = Δ (\frac{1}{\sum_{j = 1}^{n} \frac{(m (A_{σ (j)}) - m (A_{σ (j - 1)}))}{Δ^{- 1} (r_{σ (ij)}, a_{σ (ij)})}}) \end{matrix}$ (3)

to derive the overall preference values ${\tilde{r}}_{i} (i = 1, 2, \dots, m)$ of the alternative A_i.

Step 5. Rank all the alternatives A_i (i = 1, 2, ⋯ , m) in accordance with the overall 2-tuple linguistic preference values ${\tilde{r}}_{i} (i = 1, 2, \dots, m)$ of the alternative A_i.

4 Numerical example

The high technological enterprises are the main carriers of the regional innovation capacity. Accompanied by the development of high technological, the focus of the competition within high technological companies began to transfer from markets to struggles for innovative talent and emerging technologies. Innovative talents, the most active factor of the main carrier of emerging technologies and entrepreneurial activities, bear the entire process of technical and management innovation, even the real value of innovation activities. Due to the scarcity of innovative talent, competition for talent flares up, how to effectively choose and maintain innovative talent has become one of the important strategic of high technological enterprises. And how to recognize innovative talents through evaluation methods is the primary process of the implementation of this strategy. There is quite a few analysis about qualities evaluation of innovative talents at domestic and abroad. Domestic scholars are mostly trying to explain the innovative talents mechanism, instead of the doctrine of the innovative quality of talent evaluation. Compared to foreign researches, whose focus is placed on the technical means for measuring innovation capability, mainly on the analytical tools effectiveness, but also tend to ignore the composition of the innovative quality of our manpower system. And both domestic and foreign researches rarely analysis from a perspective of practical application. For the high technological enterprises are the densest units that innovative talents gather, the aim of this section is to study the illustrative example for evaluating the enterprise technological innovation capability with 2-tuple linguistic information. There is a panel with five possible high technological enterprises A_i (i = 1, 2, 3, 4, 5) to evaluate. The four attributes include innovation resources input ability (G₁), research and development ability (G₂), manufacturing capacity and marketing ability (G₃) and innovation output capacity (G₄), respectively. The five possible high technological enterprises A_i (i = 1, 2, ⋯ , 5) are to be evaluated using the 2-tuple linguistic information under the above four attributes, The linguistic decision matrix is shown in Table 1.

Then, we utilize the proposed procedure to get the most desirable high technological enterprises.

Step 1. Suppose the fuzzy measure of attribute of G_j (j = 1, 2, ⋯ , n) and attribute sets of G as follows: $\begin{matrix} μ (G_{1}) = 0.20, μ (G_{2}) = 0.30, μ (G_{3}) \\ = 0.25, μ (G_{4}) = 0.15 \\ μ (G_{1}, G_{2}) = 0.50, μ (G_{1}, G_{3}) \\ = 0.55, μ (G_{1}, G_{4}) = 0.45 \\ μ (G_{2}, G_{3}) = 0.40, μ (G_{2}, G_{4}) \\ = 0.55, μ (G_{3}, G_{4}) = 0.50 \\ μ (G_{1}, G_{2}, G_{3}) = 0.60, μ (G_{1}, G_{2}, G_{4}) \\ = 0.75, μ (G_{1}, G_{3}, G_{4}) = 0.70 \\ μ (G_{2}, G_{3}, G_{4}) = 0.80, μ (G_{1}, G_{2}, G_{3}, G_{4}) = 1.00 \end{matrix}$

Step 2. The experts use order-inducing variables to represent the complex attitudinal character involving the opinion of different members of the board of directors. The results are shown in Table 2.

Step 3. Transforming linguistic decision matrix R= (r_ij) _5×4 into 2-tuple linguistic matrix $\tilde{R}$ = (r_ij, 0) _m×n. The results are shown in Table 3.

Step 4. We utilize the information given in the $\tilde{R}$ , and the I-2TCOHA operator to calculate the values ${\tilde{r}}_{i}$ of the high technological enterprises A_i (i = 1, 2, 3, 4, 5). The results are shown in Table 4.

Step 5. Rank all the high technological enterprises A_i (i = 1, 2, 3, 4, 5) in accordance with the values ${\tilde{r}}_{i}$ of the high technological enterprises A_i (i = 1, 2, 3, 4, 5). The ordering of the high technological enterprises is shown in Table 5. Therefore, the best mechanical product is A₃.

5 Conclusion

With the acceleration of economic globalization, technology innovation as the core competitiveness is the crucial factors to business success. From now on, China’s models in technology innovation have exposed some abuses in introduction, digestion, absorption, development and so on. So just copying the Western management theories, methods, and techniques can not improve our management system. Defects also exist. Under the immature circumstances of market economy, we need to trending China’s enterprises to information process while achieving industrialization mission. But the weak industrial basis makes the technology innovation management of China’s enterprises to be the biggest barrier to participation in the global competition. Today rapid and drastic changes are taking place in technology. Technological innovation capability of a nation to knowledge becomes the crucial factor which raises national industry competition ability. Only in this way can an enterprise scientifically realize its technological innovation ability in its competition with its counterpart, employ appropriate innovation strategy, make full use of its advantages and achieve the best economic and social benefits. The aim of this paper is to study the evaluation problems for evaluating the enterprise technological innovation capability with 2-tuple linguistic information. The induced 2-tuple linguistic choquet ordered harmonic average (I-2TCOHA) operator is used to aggregate the 2-tuple linguistic information corresponding to each alternative and rank the alternatives and select the most desirable one(s). Finally an illustrative example for evaluating the enterprise technological innovation capability has been given to show the developed approach.

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