Operational Performance Evaluation of Corn Processing Industry Technological Innovation Alliance Based on Survey Data in Heilongjiang Province

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.

Rij represented the membership of a factor on the comment set V.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:

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 (Yang et 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.

在简短的开幕式后，上海财经大学图书馆馆长朱为群、康考迪亚大学图书馆馆长吉莲·柏德丽（Guylaine Beaudry）分别作了题为《转型、开放和共享：大学图书馆的发展之路》《康考迪亚大学（加拿大蒙特利尔）韦伯斯特图书馆的转型》的主旨报告。中国矿业大学图书馆馆长宋迎法主持主旨报告。

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 was A, criterion layer was B, the sub-criterion layer was C, the index layer was D, and m, n, i and j were the numbers of each stage of evaluation.

Determine fuzzy weight vector of evaluation factor

According to the result of the evaluation of the index,the membership degree rij of the single factor Di decision class Vj(j=1, 2, 3, 4 and 5) was calculated, and the single factor evaluation set Rij=(Ri1, Ri2, Ri3, Ri4 and Ri5) (i=1, 2, 3, … and 31) could be obtained.

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 weights Wi.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.and C.R., the specific steps were as the followings:

Table 1　Judgment matrix scale

Judgment matrix　Definition One　A and B are equally important Three　A is slightly more important than B Five　A is more important than B Seven　A is strongly more important than B Nine　A is absolutely important than B Two, four, six and eight　Between the two adjacent judgment scales Reciprocal　A is more important than B, then B is more important than A

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.

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

2.1.2 草莓专业合作社。草莓专业合作社位于孙扎齐乡雀儿盘村，合作社通过科学栽培、全程标准化生产，果实自然成熟，并且硕大饱满、无公害。园内还有多种绿色蔬菜可供选择，每年春季，草莓采摘成为周边游客旅游度假的热门体验项目［1］。

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

Evaluate single factor and establish fuzzy relation matrix R

解放战争时期的“三查三整”运动，建国初期的整党运动，改革开放时期的“三讲”教育，“三严三实”专题教育，是在一部分党组织中进行集中教育活动。

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

Cooperative satisfaction

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

Multi-factor fuzzy evaluation

The fuzzy comprehensive evaluation model was:

Among them, Wi vector was the weight vector of the i-th index, and Ri matrix was the fuzzy relation matrix of the i-th index to the comment set V.The resulting Bi represented the degree of membership of the object being evaluated from Vj-level fuzzy subset as a whole.

洪口乡方言是目前宁德方言中唯一的鼻音韵尾保留[n、ŋ]的乡镇。洪口方言还需进一步的深度调查，并与中古音进行比较。

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.

钟明文等[15]研究表明隧道施工产生的围岩塑性区主要集中在拱脚处，在拱脚附近需要加长锚杆的长度，可以保证隧道围岩的稳定性，本文数值计算结果反映了类似规律，在台阶法基础上设置临时仰拱和琐脚锚管后，不仅减小了初支因弯矩产生的应力，还能充分利用锚管的锁脚作用，能够较好的控制地层变形。台阶法拱顶变形量为25.3 mm，与之相比，临时仰拱台阶法变形量会降低22.3%，能控制围岩变形，保证隧道安全。而且，与CD法和CRD法相比[16]，临时仰拱台阶法施工更加灵活，能加快施工进度，降低造价。综合考虑，依托工程最终选用临时仰拱台阶法施工。

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 Yang et 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

Target layer　Criteria layer　Sub-criteria layer　Index layer Corn deep processing industry technology innovation strategic alliance operation performance (A)Behavioral attitude (B1)Cooperation satisfaction (C1)Information communication satisfaction (D1)Satisfaction of process of cooperation (D2)Satisfaction in management process (D3)Satisfaction of objectives (D4)Cooperation intensity(C2)Special funds of alliance (D5)Comparison of cooperation frequency of alliance (D6)Comparison of cooperation frequency with external units (D7)Comparison of cooperative income with the external enterprises of alliance (D8)Operational process (B2)Level of collaboration (C3)Communication and coordination ability of alliance members (D9)Sharing degree of alliance member resources (D10)Stability of alliance (D11)Complementary knowledge of alliance members (D12)Success rate of technology development (D13)Environmental uncertainty (C4)Changes of the corn industry-related laws, regulations and policy(D14)Uncertainty of number of corn farmers in region (D15)customer demand instability (D16)Changes in crop varieties (D17)Operational results (B3)Integration effect (C5)Union jointly publishing papers (D18)Establishing industry technical guidelines (D19)Union receiving the patent situation (D20)Development of new product innovation (D21)Scientific research and technological achievements (D22)Economic effect (C6)Contribution rate of profit (D23)Supply of corn products market (D24)Income obtained from alliance technology or patent transferred (D25)Cost reduction rate of comparable products (D26)Social effect (C7)Joint development of technical personnel (D27)Breakthroughs of key common technical difficulties of industrial development (D28)Improvement of industrial technology innovation capacity,corporate tax situation (D29)Corporate tax situation (D30)Ecological and environmental protection construction (D31)

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:

Consistency test of judgment matrix:

印度属于多民族国家，而且还受到中东及西洋文化的影响，因此，这里的饮食随着地区和宗教的变化而变化。南印度与北印度的饮食习惯差异很大，所以这些饮食习惯的形成并不是由于食物味道的好坏，而是因为宗教信仰的原因。不同的城市，不同的家庭，都有自己的饮食特色。印度菜口味较浓，但愈往北口味愈淡。

Maximum eigenvalue λmax=3.0037; standardized feature vector W=[0.645 0.230 0.125]; according to the consistency formula, calculated C.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 was B1, B2 and B3, 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 matrix B1-C, B2-C and B3-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 matrix B1-C, B2-C and B3-C were passed the consistency check.The weights of the calculated sub-criteria layers were as the followings:

Determination of weight of index layer

高温制曲为开放式过程，各种环境微生物均参与到该过程中。随着发酵温度升高，酱香功能嗜热菌就占据优势地位。当曲块温度达到较高温度时，翻曲、降温换气，为优势功能菌的继续生长、繁殖、代谢以及酱香味产生创造有利条件。推测在郎酒等酱香型白酒的高温制曲过程中，其多个工艺步骤，包括升温、翻曲以及换气等，定向培养、筛选形成了一个相对稳定的微生物菌群，确立了优势菌群。

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 matrixes C1, C2, C3, C4, C5,C6 and C7:

According to the above judgment matrix, the corresponding weight vector was obtained and the consistency test was carried out.The results showed that C1, C2, C3, C4, C5, C6 and C7 passed the consistency test and calculated the weight of each factor.

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.

Table 3　D layer of indicators on A layer of the total row

Layer　B1 0.125 B2 0.230 B3 0.645 The total weight of E layer Wi D1　0.019　0　0　0.002 D2　0.030　0　0　0.004 D3　0.016　0　0　0.002 D4　0.044　0　0　0.006 D5　0.163　0　0　0.020 D6　0.194　0　0　0.024 D7　0.152　0　0　0.019 D8　0.073　0　0　0.009 D9　0　0.080　0　0.018 D10　0　0.086　0　0.020 D11　0　0.131　0　0.030 D12　0　0.038　0　0.009 D13　0　0.140　0　0.032 D14　0　0.068　0　0.016 D15　0　0.022　0　0.005 D16　0　0.125　0　0.029 D17　0　0.039　0　0.009 D18　0　0　0.065　0.042 D19　0　0　0.038　0.025 D20　0　0　0.065　0.042 D21　0　0　0.180　0.116 D22　0　0　0.180　0.116 D23　0　0　0.092　0.059 D24　0　0　0.092　0.059 D25　0　0　0.018　0.012 D26　0　0　0.048　0.031 D27　0　0　0.062　0.040 D28　0　0　0.033　0.021 D29　0　0　0.010　0.006 D30　0　0　0.020　0.013 D31　0　0　0.016　0.010

Determine evaluation set value of object to be judged

Operating performance factors set of the corn deep processing industry technology innovation strategic alliance was D={D1, D2, D3, …, and D31}; the evaluation set was V={V1, V2, V3, V4 and V5}={high, higher,generally, low and very low}.

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

Table 4　Results of evaluation of performance indicators

Target layer Criteria layer Sub-criteria layer　Index layer　Weights　Very high　Higher　Generally　Lower　Very low A B1 C1 D1　0.176　1　4　2　3　0 D2　0.275　0　2　5　2　1 D3　0.148　3　2　4　1　0 D4　0.401　3　3　1　3　0 C2 D5　0.280　2　3　3　1　1 D6　0.333　0　5　2　2　1 D7　0.261　1　3　4　2　0 D8　0.126　3　4　2　1　0 B2 C3 D9　0.168　2　2　1　5　0 D10　0.181　3　2　2　3　0 D11　0.276　5　3　1　1　0 D12　0.080　4　2　3　1　0 D13　0.295　4　3　2　1　0 C4 D14　0.272　5　3　2　0　0 D15　0.088　0　1　5　3　1 D16　0.483　4　3　3　0　0 D17　0.157　6　3　1　0　0 B3 C5 D18　0.123　1　3　5　1　0 D19　0.072　1　2　4　1　2 D20　0.123　5　3　2　0　0 D21　0.341　4　3　2　1　0 D22　0.341　5　3　2　0　0 C6 D23　0.368　3　4　2　1　0 D24　0.368　6　3　1　0　0 D25　0.071　1　2　3　2　2 D26　0.193　3　2　2　2　1 C7 D27　0.441　3　4　2　1　0 D28　0.234　4　4　1　1　0 D29　0.069　5　3　2　0　0 D30　0.142　1　1　5　1　2 D31　0.114　2　3　2　2　1

Operational performance comprehensive evaluation

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:

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 formula Ci=WiRi, Ci could be available.

From the obtained C1, C2, C3, C4, C5, C6 and C7, the evaluation matrix Ri of the sub-criterion layer could be obtained.

从花型、花期、花径、花量等观赏性指标观察，大花月季系列单朵花径大、花色丰富、花期长、具芳香味等优良特性，观赏性最好，但花量小，其中观赏性表现最好的为梅郎口红、大紫光、粉扇、金奖章、彩云、月季王朝、美国粉、萨曼莎等；丰花月季系列单朵花径偏小，但花量大、花色纯正、株型丰满、整齐，其中观赏性表现最好的为红帽子、仙境、世纪之春、欢笑、满堂红等；藤本月季花径变化大、花色丰富、攀援性好，其中观赏性最好的为御用马车、藤彩虹等。

The result of the second level fuzzy comprehensive evaluation was:

我觉得这是娱乐业的未来。这样的事，我们每个人在未来都将做得到。有一天，你可以跟功夫巨星一起演武打戏，你可以跟歌唱巨星一起开演唱会。我们最大的娱乐快感，并不在于看“你”怎么表演，而在于看完你怎么表演之后，换“我”来。

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 was V1=9,V2=7, V3=5, V4=3 and V5= 1, the greater the value, the higher the performance.According to the formula B`=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.

阿东觉得长久这样，也不是个事。便再次跟父亲商量，要不要干脆告诉阿里，姆妈已经死了，姆妈不会再回家来了，这样索性断了他的念想。

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:

考虑到三峡水库蓄水后上游来沙减少的实际情况，应科学合理地控制长江中下游河道采砂的总量。根据沿程主要控制测站的输沙量变化以及区间各段的冲淤情况，分不同时段和不同河段合理调整已有规划。如前所述，三峡水库蓄水后前20年，冲刷主要集中在荆江河段，上游来沙减少对汉口以下河段的影响不大。因此，此时宜昌—武汉河段的年度采砂总量应考虑该段冲刷剧烈的实际情况适当酌减，而汉口以下河段的年度采砂控制总量可大致维持已有规划。同时，考虑到水文系列年自身的丰、中、枯的差异，可适当根据宜昌年下泄沙量的不同情况，对中下游各河段年度采砂控制总量进行动态调整。

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 of D1-D4 was 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,D21 and D22 were indicators of the results level, while D1 and D3 were 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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