Zhang Xiao-mei, and Zhang Jia-xin

College of Economics and Management, Northeast Agricultural University, Harbin 150030, China

Introduction

Heilongjiang Province as the largest province of corn cultivation and production, has made outstanding contributions on the development of the national corn industry (Liu, 2013).Since 2008, the total area of corn in Heilongjiang Province has been ranked first in China, which is far more than other provinces.However, the corns through corn processing are less than 20%, because the majority of them are sold directly in the form of raw materials, so that farmers only earn meager income (Li, 2016).The corn deep processing industry technology innovation strategic alliance is established in Qiqihar City in order to improve the technological innovation capacity of corn industry and to promote the development of corn deep processing industry.The alliance puts Qiqihar University as its lead unit, and attracts 26 union governing units from the enterprises, universities and research institutes engaged in corn deep processing.The establishment of the alliance plays an important role on achieving the increase of the fiscal and farmers'income, the adjustment of the industrial structure and the transformation from food production to food processing.

In recent years, the corn deep processing industry technology innovation strategic alliance has made remarkable achievements and made important contributions to the promotion on the deep processing industry, technological innovation and level in Heilongjiang Province.However, the structure and function of the alliance are changing in certain extent,which will have a profound impact on the operational performance of the alliance and also a direct impact on the development and stability of the alliance.Owing to the continuous expansion of alliance members, the differences of the resource allocation and demand preferences among members were found out, it was necessary to study the influencing factors of the operational performance of the corn deep processing industry technology innovation strategic alliance in Heilongjiang Province, in order to find out the existing problems, then the improvement measures were put forward to ensuring the stable development of the alliance.

Evaluation Index of Corn Processing Industry Technological Innovation Alliance

The operational performance of industrial technology innovation strategic alliance should be divided into three dimensions: behavioral attitude, alliance operation process and alliance operation results through releasing questionnaires to 23 provinciallevel pilot technology innovation centers in Hubei Province (Yang, 2013).Similarly, this paper studied the operational performance of the alliance from three aspects.

Behavioral attitude among members of union

The industrial technology innovation and strategic alliances during the operation had the feature of instability, so the research on the behavior and attitudes among the alliance members was the basis of analyzing operational performance.The behavioral attitude was divided into cooperative satisfaction and cooperative intensity.

Cooperative satisfaction

Cooperation among the alliance members was the basis on the effective operation of the Union.In order to improve the operation mechanism and the running performance of the alliance, it was necessary to clarify the satisfaction degree of the cooperation process among the members, mainly including information communication satisfaction, cooperation process satisfaction, management process satisfaction and goal complete satisfaction (Veugelers and Casiman,2005).

Cooperative intensity

The cooperative intensity was the guarantee of longterm cooperation among the alliance members.In order to carry out more effectively technology researches and development activities, the cooperative intensity in the union should be strengthened to enhance the overall operational performance of the alliance (Yanget al., 2012).The cooperation intensity was divided into the special invested funds, frequency of cooperation between members, the comparison between the cooperative income and the external enterprises' income, and the default rate of the alliance members.

Operation process of alliance

Operation process was the key to the alliance operation.In order to better study the process of alliance operation, it was divided into two aspects: cooperative ability and environmental uncertainty.

Cooperative ability

Union was composed of a number of members,so member units must have cooperation with each other in order to develop and enhance the overall competition and comprehensive strength of the Union (Collins and Hittm, 2006).And the level of collaboration among members had a direct impact on the operational performance of the alliance, so the study of the performance of the alliance must be clear the level of collaboration among members.The level of collaboration among alliance members through the communication and coordination ability of the alliance members, the sharing degree of alliance member resources, the stability of alliance, the complementary knowledge of alliance members and the success rate of technology development were studied in the following.Environmental uncertainty

The change of the policy or the change of the market demand could affect the running performance of the alliance.Therefore, the uncertainty of the environment was an important factor affecting the operational performance of the alliance, mainly including the changes of the corn industry-related laws, regulations,and policy, the uncertainty of the number of corn farmers in the region, customer demand instability and the changes in crop varieties (Lv, 2013).

Alliance operation process

The result of the alliance operation was the most direct indicator of the operational performance of the alliance.The results of the alliance were divided into three parts: integration effect, economic effect and social effect.

Integration effect

Alliance was composed of enterprises, universities,research institutes and other institutions, whose main purposes were to enhance the level and ability of industry science and technology innovation.Therefore,the integration effect was the most direct result of the operation of the alliance.The integration effect was divided into the union jointly publishing papers,establishing industry technical guidelines, the union receiving the patent situation, the development of new product innovation and scientific research and technological achievements (Candace and Thomas, 2009).Economic effect

The economic effect was the foundation of the development of the alliance and the most important source of power, which directly reflected the level of operational performance of the alliance.Therefore,the economic effect was also an extremely important of performance result (Luo and Zheng, 2014).The economic effect could be mainly expressed as the contribution rate of profit, the supply of corn products market, the income obtained from the alliance technology or patent transferred, and the cost reduction rate of comparable products.

Social effect

The alliance can promote the integration of production and research, enhance scientific research and innovation ability, solve the industrial common and key technical problems, and ultimately achieve the rapid development of social and technological innovation(Leng and Zhang, 2015).Therefore, the alliance running performance in addition to considering the alliance's integration and economic results, also considered the impact of the alliance to the society.The evaluation of social results could be divided into the following aspects: the joint development of technical personnel, the breakthroughs of the key common technical difficulties of industrial development, the improvement of industrial technology innovation capacity, corporate tax situation, ecological and environmental protection construction.

Multi-level Fuzzy Comprehensive Evaluation Method

Determine set of factors for evaluation of object

The subjective factors and objective factors of the alliance operation performance should be taken into account the determinants of the operational performance of the corn deep processing industry technological innovation alliance.Supposed that innovation alliance provided corn processing industry technical operation performance wasA, criterion layer wasB, the sub-criterion layer wasC, the index layer wasD, andm,n,iandjwere the numbers of each stage of evaluation.

Determine fuzzy weight vector of evaluation factor

Firstly, determining the fuzzy weight vector was to decompose a complex problem into various constituent factors.Then, these factors were grouped according to the distribution relationship in order to form an orderly hierarchical structure.Finally, determine the relative importance of decision factors sorting through the two pairs of ways.Specific steps were as follows:

a.The relationship among the various factors in the system was analyzed, and the hierarchical structure of the system was established, including the target layer, the criterion layer and the program layer;b.the elements of the same level and the last level were compared, and two comparative judgment matrix were constructed, generally using one to nine scale method (Table 1) to determine the judgment matrix; c.the maximum eigenvalue max according to Matrix calculated was determined and compared to the relative weight of the factor criterion weightsWi.At the same time, in order to avoid the existence of inconsistencies in the constructed judgment matrix, it was necessary to test the consistency of the judgment matrix.It was usually tested with two indicators:C.I.andC.R., the specific steps were as the followings:

Table 1　Judgment matrix scale

IfC.R.value was less than 0.1, indicating that the judgment matrix passed the consistency test, and the obtained weight value could be applied.

Overall weight of index layer

In order to determine the influence of the index on the target layer, the overall weights of the index layer were calculated from the weight of index layer to the total weight of the target layer.

Determine rating level set

The classification level of the comment set was determined according to the actual situation, generally expressed inVi(iwas the evaluation level).

Evaluate single factor and establish fuzzy relation matrix R

The process of determining membershipVfrom a factor was called a single factor fuzzy evaluation.Similarly, available membership of other factors can be obtained, and ultimately the fuzzy relation matrixRwas obtained:

Rijrepresented the membership of a factor on the comment setV.In determining the affiliation, the expert scoring method was used generally.Accord-ing to the results of the expert scoring statistics, determined its membership and satisfy a relationship:

Multi-factor fuzzy evaluation

The fuzzy comprehensive evaluation model was:

Among them,Wivector was the weight vector of thei-th index, andRimatrix was the fuzzy relation matrix of thei-th index to the comment setV.The resultingBirepresented the degree of membership of the object being evaluated fromVj-level fuzzy subset as a whole.

Operational Performance Evaluation of Corn Deep Processing Industry Technology Innovation Strategic Alliance

Summary of research objects

The corn deep processing industry technology innovation strategic alliance was formally established in 2012, whose main research content and product development direction were the corn deep processing.This alliance organized 26 members to participate in cooperative innovation, integrated the industry's advantages of resources and attracted five large-scale corn processing enterprises in Heilongjiang Province, namely China Oil And Foodstuffs Corporation Biochemical Energy Biochemical Energy(Zhaodong) Co., Ltd., Heilongjiang Longfeng Maize Development Co., Ltd., Heilongjiang Chengfu Food Co., Ltd., Heilongjiang Universal Green Food Development Co., Ltd.and Heilongjiang Haotian Corn Development Co., Ltd.This alliance carried out 36 national spark plan major projects and applied technology research and development program major projects of Heilongjiang Province.In terms of product innovation, in collaboration with universities, research institutes on technology research and development and technological innovation, the varieties of the company's corn deep processing was increasing,and the number of processing was also rising.In terms of academic exchange, alliance members often participated in academic exchanges at home and abroad, in order to timely grasp and update the trends and directions of development, to enhance their levels of scientific research and innovation, and to ensure that prospective coalition corn in some areas.

However, there were still many deficiencies in the management mechanism, distribution of benefits and mode of operation in the development of the alliance,and it was necessary to establish a more perfect operation mechanism to improve the operational performance of the alliance.

Establishment of performance evaluation index system of corn deep processing industry technological innovation alliance operation

The strategic alliance of technological innovation of corn deep processing industry in Heilongjiang Province was selected as the research object.Considering that the alliance was still in the early stage of operation and the technical achievements of the alliance were in the development stage, the operation process would be a certain degree of influence on the future cooperation results of the union in the case of lacking achievement index.Therefore, lessons were drawn from the research results of Yanget al.(2012) and others, and combined the actual situation of the strategic alliance of technological innovation in Heilongjiang Province.The main factors that affected the performance of the strategic alliance of technological innovation in corn industry were summarized as three parts, namely, the behavior attitude of alliance members, the operation of the alliance and the results of the alliance.As running performance indicator system of corn deep processing industry technology innovation strategic alliances had no cross structure, it could be divided into: the target layer (A), the criteria layer (B), sub-criteria layer (C)and index layer (D).In summary, the hierarchical structure of the operation performance factors of the corn industry technology innovation strategic alliance is shown in Table 2.

Table 2　Factors of operation performance of corn deep processing industry technology innovation strategic alliance

Determination of weight of index

The expert scoring method was used to collect and analyze the data, the experts were senior managers and technical staffs of the corn deep processing industry technology innovation strategic alliance in Heilongjiang Province, who had enough cognition about the alliance.The main contents of the interview came from the recognition and sequencing of the relative importance of the factors.The judgment matrix was constructed according to one to nine scale method, and the weights of each evaluation index were calculated according to the steps (1) to (6) and took consistency test.

Determination of weight of criterion layer

The construction judgment matrix A-B could be obtained according to the relative importance of the performance evaluation factors of the criterion layer,and the specific situation was as the followings:

⑤ 低 LWR組（LWR≤0.248）患者 94例（36.6%），高LWR組 （LWR＞0.248）患者163例（63.4%）。兩組患者的各臨床資料之間，差異無統(tǒng)計(jì)學(xué)意義。

Consistency test of judgment matrix:

Maximum eigenvalueλmax=3.0037; standardized feature vectorW=[0.645 0.230 0.125]; according to the consistency formula, calculatedC.I.=0.0018,R.I.=0.52,C.R.=0.0035＜0.1.It was clear that the judgment matrix had passed the consistency check, which wasB1,B2andB3, respectively, the weight of 0.125, 0.230 and 0.645.

Determination of weight of sub-criterion layer

The determination of the weight of each factor in the criterion layer was the same as that of the criterion layer weight.The relative importance of the factors in the sub-criterion layer could be compared to determine the matrixB1-C,B2-CandB3-C, and the specific situation was as the followings:

According to the above judgment matrix, the corresponding weight vector was obtained and the consistency test was carried out.The judgment matrixB1-C,B2-CandB3-Cwere passed the consistency check.The weights of the calculated sub-criteria layers were as the followings:

Determination of weight of index layer

The determination of the weight of each factor of the index layer was the same as that of the criterion layer and the sub-criterion layer weight.The relative importance of each factor in the index layer could be compared to determine the matrixesC1,C2,C3,C4,C5,C6andC7:

Calculation of overall weight of indicator layer Based on the weight value of each index, calculated the total weight of A layer to D layer to understand the effect of each index on the operational performance of the alliance.It could be found the general arrangement of D layer to B layer, and then derived the total arrangement of D layer to A layer, as shown in Table 3.

Determine evaluation set value of object to be judged

Operating performance factors set of the corn deep processing industry technology innovation strategic alliance wasD={D1,D2,D3, …, andD31}; the evaluation set was V={V1,V2,V3,V4andV5}={high, higher,generally, low and very low}.

Ten experts on the operational performance of the corn industry technology innovation strategic alliance in accordance were organized with the five levels of judgments to evaluate these factors.Finally,the evaluation results after statistics are shown in Table 4.

Table 4　Results of evaluation of performance indicators

Operational performance comprehensive evaluation

According to the result of the evaluation of the index,the membership degreerijof the single factorDidecision classVj(j=1, 2, 3, 4 and 5) was calculated, and the single factor evaluation setRij=(Ri1,Ri2,Ri3,Ri4andRi5) (i=1, 2, 3, … and 31) could be obtained.

The weight of the factors of each sub-criterion layer and the evaluation matrix of each factor was taken as primary fuzzy comprehensive evaluation.According to the formulaCi=WiRi,Cicould be available.

From the obtainedC1,C2,C3,C4,C5,C6andC7, the evaluation matrixRiof the sub-criterion layer could be obtained.

The result of the second level fuzzy comprehensive evaluation was:

Finally, it was necessary to set the value on the evaluation level, got the final comprehensive evaluation value, and judged the running performance level.The performance evaluation was carried out by expert evaluation method.The performance level was divided into one to nine levels, and the assignment wasV1=9,V2=7,V3=5,V4=3 andV5= 1, the greater the value, the higher the performance.According to the formulaB`=BV, the final evaluation value of the operational performance of the alliance could be gotten.

According to the literature on the performance level of the standard, seven to nine set as very high, five to seven set as higher, three to five set as general, and one to three set as low.As a result of this calculation was 6.7289 (between five and seven), therefore, the operational performance of the corn deep processing industry technology innovation strategic alliance in Heilongjiang Province was higher.So, there were still some problems to be solved.

Conclusions

The corn deep processing industry technology innovation strategic alliance in Heilongjiang Province was taken as the object, building the hierarchical structure of the alliance operation performance factors,using AHP and fuzzy comprehensive evaluation to quantify the calculation of the impact of the factor,sorting the importance of each factor, and evaluating the operational performance of the alliance by the numerical value.

According to the total weight of each index in the table, some relevant conclusions about the operational performance of alliance could be drawn:

Firstly, the innovation of new product development(D21) and the transformation of scientific research and technology achievements (D22) had the most obvious impact on the operational performance of the alliance,accounting for 11.6% of the total weigh, which proved that the alliance focus on scientific and technological innovation and achievements.However, the degree of satisfaction of information communication (D1) and management process (D3) were the weakest, only 0.2%of the total weight.

Secondly, by measuring the total weight of the operational performance of the alliance, it could indirectly reflect the influence of the sub-criterion layer and the second criterion layer.From the total weight table of these indexes, it could be seen that the total weight ofD1-D4was almost 0.002, which had a very low impact on the operational performance of the alliance, which showed that the satisfaction degree of the alliance members (C1) was corresponding lower.

Finally, according to category attribution to divide,D21andD22were indicators of the results level, whileD1andD3were indicators of behavioral attitudes.It showed that the operational performance of the alliance was affected by the result level, and the influence of behavioral attitude was lower.From the overall arrangement of the column, the proportion of the results level was the highest, followed by the process level, and finally behavioral attitudes.Therefore,according to the degree of influence, top three of the factors in the criterion layer should be the result of the alliance operation, the process of the alliance operation and the behavioral attitude of the alliance members.

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