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Discrete Dynamics in Nature and Society
Volume 2016, Article ID 4793851, 13 pages
http://dx.doi.org/10.1155/2016/4793851
Research Article

The Analysis of Vertical Transaction Behavior and Performance Based on Automobile Brand Trust in Supply Chain

1Contemporary Business and Trade Research Center, Zhejiang Gongshang University, Hangzhou 310018, China
2School of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou 310018, China
3National Economic and Technological Development Zone of Xiangyang Government, Xiangyang, Hubei 441000, China

Received 10 March 2016; Accepted 19 April 2016

Academic Editor: Elmetwally Elabbasy

Copyright © 2016 Guanglan Zhou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The nontrust behaviors among the automobile supply chain members lead to a trust crisis situation. Under such circumstances, this paper studies the mutual influences of trust, enterprise behavior, and transaction performance on the independent brand automobile supply chain. The business behavior concept which consists of information sharing, joint action, and specific asset investment is proposed. Then, the paper tests the reliability and validity of the collected data through Structural Equation Modeling (SEM). Through empirical test and analysis on mutual relationship among vertical transaction enterprise behaviors, trust, and transaction performance, the vertical transaction enterprise behaviors can be regulated, so as to improve the efficiency of independent brand automobile supply chain.

1. Introduction

In recent years, the auto market of China has created miracles one by one. The sales are the largest and China has become the third largest automobile producer in the world. In China, most car companies are just beginning to use the supply chain management mode. And the cooperation effect between suppliers and manufacturers is not very ideal. It is greatly difficult to construct independent brand. One of the important reasons is the trust crisis among the automobile enterprises. It contains two perspectives. (A) The mutual trust level is not high enough. (B) There is an issue on asymmetry of the trust degree. The trust crisis generally is characterized by the following aspects. (1) The partners lack long-term cooperation intention. (2) The stronger side pushes down the price in the negotiations. (3) The company instigates the partners to compete with each other. (4) The company blocks the information channel to the partners. (5) The company is inclined to release the confidential R&D information for the sake of short-term interests. (6) The contract implementation is not satisfactory. (7) There exists occasional fraud among the partners. (8) The company shirks the liability sometimes. (9) There is insufficient necessity investment on the production and service among the partners ‎[14].

The nontrust automobile supply chain enterprise behaviors above lead to a great increase in the product cost and ultimately affect the economic benefit of the members. In view of the low trust between the enterprises and the poor efficiency in cooperation of the independent brand car supply chain, the problem that the enterprise faces is how to establish trust between enterprises in view of our country’s cultural background and what actions should the enterprises in the supply chain take to improve the trust so as to improve the trade performance.

This paper will find the enterprise behavior of the greatest impact on the performance of independent brand automobile supply chain and inspect the correlation between trust, enterprise behavior, and the automobile supply chain trading performance through the questionnaire survey on independent brand automotive suppliers.

2. Theoretical Basis and Literature Review

Trust is the soul and foundation of supply chain management. It is also the key factor to maintain long-term cooperation among the enterprises, the trust mechanism of which has been investigated worldwide. In recent years, with the rise of relationship marketing in the developed countries, the trust mechanism more becomes an important area of scholars’ research. There are some differences among the different scholars’ understanding of trust, but most scholars regard it as a single dimension variable to study. With the in-depth research on trust, more and more scholars adopt the concept of multidimensional trust to reveal the complexity of this variable. Table 1 lists the dimensions of trust of some scholars at home and abroad.

Table 1: The dimensions of trust.

Based on the previous literature research on trust and combined with the study of independent brand automobile supply chain in China, the trust classification method is from Sako (1992) ‎[5]; that is to say, trust is divided into contract-based trust, ability-based trust, and kindness-based trust.

The enterprise behaviors include the internal behavior and cross-organizational behavior. And this paper studies the cross-organizational cooperative behavior. It introduces two authoritative divisions of enterprise behavior dimension in the supply chain. In the study of the relationship between enterprise behavior and trust in supply chain, Johnston et al. (2004) [6] thought that the enterprise behaviors can be divided into three dimensions: common responsibility, sharing plan, and flexible arrangement. Srinivasan and Brush (2006)‎ [7] believed that there were mainly three behaviors, specific asset investment activities, information sharing, and the degree of mutual relations effort, between enterprises in supply chain when they study the behavior between the virtual supply chain partners. Beyond Srinivasan and Brush (2006)‎ [7], Qian (2010) ‎[8] studied two kinds of supply chain enterprise behaviors: joint action and special asset investment. In the study of Shelanski and Klein (1995) ‎[9], the authors measured the independent brand automobile supply chain vertical transaction enterprise behaviors from three perspectives: information sharing, joint action, and specific asset investment.

Trade performance is the ultimate embodiment of supply chain effect, and it is usually evaluated by objective indexes such as profit rate, logistics cost, and customer satisfaction. Moreover, there are also other evaluation methods. Table 2 lists the subdivision indexes of cooperative performance of some scholars at home and abroad.

Table 2: The subdivision indexes of cooperative performance.

By synthesizing all the opinions of each expert, the transaction performance of the independent brand automobile supply chain in the paper will be measured by the market share, the timeliness and flexibility of product delivery, the profitability, the ability to research independent brand, the management level, the ability to innovate, and the competitive position in the industry.

3. Research Hypotheses and Questionnaire Design

3.1. Variables
3.1.1. The Measurement of Trust

Since the actual independent brand automobile supply chain generally lacks trust, the automobile sector lacks reliability in terms of delivery and small capacity of auto-ancillary firms leading to shortages and lack of availability of components and lack of partnership among partners [1012]. In the paper, trust is divided into contract-based trust, ability-based trust, and kindness-based trust. According to the actual situation of the independent brand automobile supply chain, the specific indexes of trust are shown in Table 3.

Table 3: The specific indexes of trust.
3.1.2. The Measurement of Enterprise Behavior

Through the analysis and comparison of each expert’s opinions and according to the situation of independent brand automobile supply chain, the measurement indexes of information sharing include order information, demand information, product research information, inventory information, and production information finally. The specific indexes of information sharing are shown in Table 4 [13].

Table 4: The specific indexes of information sharing.

Through the analysis and comparison of each expert’s opinions and according to the situation of independent brand automobile supply chain, the measurement indexes of joint action finally include making, organizing, and implementing the product’s monthly plan and long-term planning together, forecasting the product’s annual demand together, solving the business problems and establishing the conflict solution mechanism of system together, and joining efforts for product research together. The specific indexes of joint action are shown in Table 5 [14].

Table 5: The specific indexes of joint action.

According to the practical situation and combined with the research results, the special investment in this paper mainly refers to the special assets such as material, human resources, management, brand, and relationship in the supplier input in order to enhance the coordination of the manufacturer and consolidate its advantage position when competing in the same industry, for example, specialized production plant and equipment, specialized distribution center, and common development center. The specific indexes of specific asset investment are shown in Table 6 [15, 16].

Table 6: The specific indexes of specific asset investment.
3.1.3. The Measurement of Transaction Performance

The cooperative performance between the enterprises in the independent brand automobile supply chain can be measured by 7 indexes: the market share, the timeliness and flexibility of product delivery, the profitability, the ability to research independent brand, the management level, the ability to innovate, and the competitive position in the industry, which was shown in Table 7 ‎[17].

Table 7: The specific indexes of specificity of cooperative performance.
3.2. Research Hypotheses
3.2.1. The Relationship between Enterprise Behavior and Trust

(1) The Relationship between Information Sharing and Trust. There are several literatures studying the relationship in information sharing. Lansbury et al. (2007) ‎[18] think that the share of certain key information can enhance the mutual understanding between the cooperative enterprises and help them to build trust mechanism, which can solve some unnecessary disputes. Dyer (1997) ‎[19] believes that the share of information such as demand inventory information is the key factor to keep trust between enterprises in the supply chain. Doney and Cannon (1997) ‎[20] think that the share of confidential information can show that the partner is trustworthy and has good motives and intentions. Anderson et al. (1987)‎ [21] think that information sharing promotes trust between enterprises, and it can maintain a long-term cooperative relation between them. In the independent brand automobile supply chain, if one shares information with the other, the latter will feel trusted, and he will also adopt or maintain an attitude of trust, so as to achieve a virtuous circle of trust relationship between each other. That is to say, information sharing can promote trust between members in the supply chain.

Based on the analysis above, the following two hypotheses are put forward to verify:H1: information sharing has a significant effect on trust in the independent brand automobile supply chain.H2: in the independent brand automobile supply chain, the higher the level of trust, the higher the degree of information sharing among enterprises.

(2) The Relationship between Joint Action and Trust. In order to make the cooperation successful, it is very important for the partners to collaborate the planning and coordination. Through the literature, Thomé et al. (2014) ‎[22] found that the joint action degree taken by the enterprises in the supply chain directly determines the successful cooperation. Joint action and information sharing jointly promote the development of trust. By taking joint action, the enterprises in the supply chain can better cope with the uncertainty of dynamic environment and constantly enhance the level of trust between them in the cooperation.

Trust is also conducive to the further deepening of joint action. It is believed that trust can improve the closer combination of process and operation between the partners, which can make them strengthen joint action. In addition, the trust between them is a motivator of the joint action.

Based on the analysis above, the following two hypotheses are put forward to verify:H3: joint action has a significant effect on trust in the independent brand automobile supply chain.H4: in the independent brand automobile supply chain, the higher the level of trust, the easier the joint action.

(3) The Relationship between Specific Asset Investment and Trust. To some certain extent, specific asset investment locked the relationship between enterprises in supply chain. It is the tangible evidence, which is worthy of trust, of sincerity cooperation between them. Moreover, the conversion cost of specific asset investment is very high. This reduces the willingness of changing partners and enhances the intention of taking long-term cooperation between them. Trust can strengthen the special investment intention between partners. Once the mutual trust relationship is established, they will maintain a long-term cooperative relation, so as to reduce the risk of specific asset investment in the supply chain and promote the special asset investment between partners. In the independent brand automobile supply chain, trust can reduce the transaction costs between enterprises; promote the enterprises to carry on the asset investment such as special relationship and technology; limit the opportunism of supply chain, so as to improve the ability to build independent brand in the whole supply chain.

Based on the analysis above, the following two hypotheses are put forward to verify:H5: specific asset investment has a significant effect on trust in the independent brand automobile supply chain.H6: in the independent brand automobile supply chain, the higher the level of trust, the higher the degree of specific asset investment.

3.2.2. The Impact of Enterprise Behavior on Trade Performance

The worldwide research consistently recognizes that information sharing can reduce the bullwhip effect greatly and it can also reduce the information distortion and information risk in the supply chain. This greatly reduces the uncertainty of the supply chain and improves the trade performance between enterprises. Through the in-depth study of the partnership in the automobile supply chain of Japan, Dyer (1997)‎ [19] found that specific asset investment can improve the performance of enterprises. The partners can promote the trading activities developed smoothly and strengthen their strategic cooperation relationship through the specific asset investment such as product technology, production flow, and the inner relation between operation and management, so as to improve the efficiency and competitiveness of the entire automobile supply chain.

Based on the analysis above, the following three hypotheses are put forward to verify:H7: in the independent brand automobile supply chain, information sharing has a significant positive influence on trade performance.H8: in the independent brand automobile supply chain, joint action has a significant positive influence on trade performance.H9: in the independent brand automobile supply chain, specific asset investment has a significant positive influence on trade performance.

3.2.3. The Impact of Trust on Trade Performance

The impacts of trust on cooperative performance mainly include the following two aspects. First, under the conditions of certain transaction cost, the higher the degree of trust between enterprises is, the lower the exchange cost and interactive cost between enterprises will be and the higher the possibility of successful transaction will be, which can improve the profitability of enterprises. Secondly, trust promotes knowledge sharing. And the knowledge can create innovative products in time. This is conducive to the building of independent brand and can respond to market changes timely. In addition, Panayides and Lun (2009) ‎[23] also pointed out that trust had a positive influence on the cooperative relationship and trade performance of supply chain. Chen and Zhang (2011)‎ [24] used structural equation model with the survey data of 256 supply chain enterprises to indicate that trust between enterprises of the supply chain has a significant positive impact on cooperative performance. Pan and Zhang (2006)‎ [25] discussed the effect of trust between partners in the supply chain on cooperative performance by the way of questionnaire survey. And they demonstrated that there was a positive correlation among organizational trust, personal trust, and cooperative performance.

Based on the analysis above, the following hypothesis is put forward to verify:H10: in the independent brand automobile supply chain, trust has a significant positive influence on trade performance.

3.3. Building of the Conceptual Model

Through the analysis above, this paper establishes a theoretical model including three factors and their relationship. The factors, respectively, are trust, enterprise behavior (information sharing, joint action, and specific asset investment) in the supply chain, and cooperative performance. The specific conceptual model is shown in Figure 1.

Figure 1: Hypotheses of the conceptual model.
3.4. Questionnaire Design

The questionnaire is designed after referring to the related research. According to the characteristics of independent brand automobile supply chain, this can ensure the rationality, pertinence, and language accuracy of the questionnaire as far as possible.

Overall, the project questionnaire is formed by the following two parts.

The first part is the basic information of the investigator and his organization. It includes his name, position, and the name, size, and nature of his organization. Also, it includes the role of his organization in the whole automotive supply chain. This helps to confirm that the samples are representative, to screen the effective questionnaire, and to get the descriptive characteristics of the sample.

The second part is the main part of the questionnaire. That is the index design, including trust, enterprise behavior (information sharing, joint action, and special asset investment), and cooperation performance in the automobile supply chain. The paper uses a seven-point Likert scale questionnaire for measurement. That is to say, each related problem has seven descriptive scales (1, 2, 3, 4, 5, 6, and 7). And 1 means the investigator strongly disagreed with the problem statement, 2 means he disagreed, 3 means he slightly disagreed, 4 means he is neutral, 5 means he slightly agreed, 6 means he agreed, and 7 means he strongly agreed. The investigator can choose the most appropriate scale according to the specific situation.

4. Data Analysis

4.1. Sample Analysis

In this study, the descriptive statistical analysis of the variables is gained by SPSS 17.0, and the mean of each dimension is shown in Table 8.

Table 8: The mean of each dimension.

From Table 8, we can see that the mean of each variable is generally from 5 and 6, but there are also some differences. First of all, the mean of trust is 5.364, which means that there exists a certain degree of trust in the independent brand automobile supply chain of China. Secondly, the mean of joint action is maximal among the three dimensions of corporate behavior. This shows that, in the independent brand automobile supply chain of China, the degree of joint action between partners is high in the process of cooperation. They value joint development and joint decision-making and planning, and there are certain communications such as cooperative emotion and corporate culture, which can help to ensure the consistency of their objectives. The mean of information sharing is followed. This shows that there exists a certain degree of information sharing between partners at present, which can help them to timely understand all kinds of information such as order, customer requirement, and product development. The mean of special asset investment is the lowest. And, through the analysis of the results, we can find that, in the independent brand automobile supply chain of China, the partners pay more attention to the management of specific asset investment, while the physical, brand, and human relations and other aspects of the specific asset investment are far from enough. In addition, the mean of cooperative performance is the lowest, a value of 5.138, which shows that the expected result of cooperation between partners is low in the current situation, and they are less satisfied with the cooperative results.

4.2. Analysis of Reliability and Validity
4.2.1. Analysis of Reliability

Firstly, the reliability (refers to the reliable degree) of the scale should be tested before data analysis to measure the internal consistency degree of the results. The higher the value, the higher the reliability of the scale. At present, the reliability of the scale is often measured through the coefficient Cronbach . The higher the value of , the higher the reliability of the scale. Generally, when the value of Cronbach is larger than 0.8, this means that the scale has high reliability; when the value is between 0.7 and 0.8, this means that the reliability is acceptable and the value is preferably above 0.7. If the value is below 0.6, this means that the scale is not qualified and needs to be modified and designed again.

The formula of Cronbach is as follows:

where is the total number of evaluation projects in the scale, is the variance of the th project, and is the sum of all the projects’ variances.

By using SPSS17.0 software to calculate the Cronbach of trust, enterprise behavior, and trade performance to test the internal consistency of the scale, the results are shown in Table 9. The Cronbach of each scale and each dimension is all above 0.8, which means that the scale is of good reliability.

Table 9: The Cronbach of the variables.
4.2.2. Test of Validity

Validity can reflect the accuracy degree of the researched problem measured in the scale. The higher the validity is, the more the results can show the real features of measuring things the researchers desired. Generally, the test of content validity and construct validity is needed. In the study, the test of validity mainly measures the construct validity, which can be specifically measured by the exploring factor analysis method.

The scale should be measured if it is suitable for factor analysis through KMO (Kaiser-Meyer-Olkin) and Bartlett’s test of sphericity before analysis. KMO is an index used to compare the simple correlation coefficient with partial correlation coefficient between variables. Its value ranges between 0 and 1. When the value is closer to 1, it indicates the stronger correlation between variables; that is to say, it is more suitable for factor analysis than not. Kaiser gave the measure standard of KMO: a value above 0.9 indicates a very suitable situation; a value between 0.8 and 0.9 indicates a suitable situation; a value between 0.7 and 0.8 indicates a neutral situation; a value below 0.5 indicates an unsuitable situation. Bartlett’s test of sphericity is also a test method of describing the correlation between variables. And the statistical value is obtained according to the determinant of the correlation matrix. If the value is larger and the corresponding concomitant probability is less than the significance level of the user thought, the null hypothesis should be rejected, and the correlation coefficient cannot be unit matrix. And this means that there is a correlation between the original variables and it is more suitable for factor analysis than not.

The test data in Table 10 shows that the KMO values of trust, information sharing, joint action, specific asset investment, and trade performance, respectively, are 0.830, 0.819, 0.863, 0.892, and 0.877. The values are all consistent with expectations, which means the data can be handled by factor analysis. Meanwhile, Bartlett’s test of sphericity values of the variables, respectively, are 319.467, 279.460, 214.018, 444.317, and 283.280, and the degrees of freedom, respectively, are 28, 10, 10, 10, and 21. The values of significant probability are all 0.000, less than 0.01, which illustrates that the correlation coefficient matrix is not a unit matrix; the data is relevant; and there is a common factor between overall correlation matrixes. This suggests that the data is suitable for factor analysis.

Table 10: The KMO and Bartlett’s test of sphericity.

The principal component analysis results of trust are shown in Table 11. Three factors are extracted through a rotating shaft, including contract-based trust, ability-based trust, and kindness-based trust. And they are consistent with the conceptual structure. The factor loading of each item is larger than 0.5, which indicates that the convergence of each measurement item is good and it has the characteristic of single dimension obviously.

Table 11: The factor analysis results of trust.

The mathematical model of principal component analysis is as follows:

The matrix is expressed as follows:

The principal component analysis of trust results from SPSS is shown in Table 11.

In the same way, by using the principal component analysis to analyze the items of enterprise behavior, the results are shown in Table 12. The extracted factors explained the variance, 71.702%, higher than the requirement, 60%. The factor loading of each item is larger than 0.5, which indicates that the convergence of each measurement item is good and it has the characteristic of single dimension obviously.

Table 12: The factor analysis results of enterprise behavior.

By using the principal component analysis to analyze the items of trade performance, the results are shown in Table 13. The extracted factors explained the variance, 73.512%, higher than the requirement, 60%. The factor loading of each item is larger than 0.5, which indicates that the convergence of each measurement item is good and it has the characteristic of single dimension obviously.

Table 13: The factor analysis results of trade performance.
4.3. Structural Equation Model and Hypothesis Test

Structural equation model integrates two kinds of statistical methods: factor analysis and path analysis. In fact, it is a multiple linear regression model with a dependent variable in multivariate extension. Hence,

where is a vector containing observation points of the dependent variable, is a unit vector which presents the intercept of , is a continuous distribution or classification (code) independent variable matrix, is a regression weight vector, and is a residual vector or error or signifies that the remaining score cannot be explained by the model.

The LISREL method used in the process of AMOS modeling is the maximum likelihood method and the estimated function is as follows:

where is the trace of matrix , is the sample covariance of matrix consisting of all variables, is the covariance matrix estimated by the model, and and are all positive definite matrices and there is an inverse matrix of .

The relationship among trust, enterprise behavior, and trade performance in the independent brand automobile supply chain is analyzed by the structural equation analysis software AMOS17.0. According to the modeling requirements of structural equation, the measurement model composed of latent variables and observed variables should be established first. In the study, the measurement model composed of trust, enterprise behavior, and trade performance is established, as shown in Figure 2. In the model, when trust becomes the latent variable, there are 8 observed variables: TC1 and TC2; TA1, TA2, and TA3; TG1, TG2, and TG3. When information sharing becomes the latent variable, there are 5 observed variables: IS1, IS2, IS3, IS4, and IS5. When joint action becomes the latent variable, there are 5 observed variables: JE1, JE2, JE3, JE4, and JE5. When specific asset investment becomes the latent variable, there are 5 observed variables: SI1, SI2, SI3, SI4, and SI5. When trade performance becomes the latent variable, there are 7 observed variables: JP1, JP2, JP3, JP4, JP5, JP6, and JP7.

Figure 2: The path analysis diagram.
4.3.1. The Inspection and Evaluation of the Fit Degree of Model

After parameter estimation to the structure equation model, the fit degree of the model also needs to be inspected and evaluated. The basic situation of model fitting is shown in Table 14. The fit degree of the model is evaluated by three aspects: basic fit degree, overall fit degree, and internal fit degree.

Table 14: The basic situation of model fitting.

(1) Basic Fit Degree. The factor loading of each dimension index is between 0.5 and 0.95. All factor loading values of each dimension reached the significant level 0.01 and there is no negative error, which indicates that the theoretical model is fully consistent with the basic fit degree.

(2) Overall Fit Degree. The overall fit degree is a statistical indicator that investigates the fit degree of data of theoretical structure model. And it is measured mainly by three indexes: absolute fit index, relative fit index, and information index. The evaluation result is shown in Table 15.

Table 15: The analysis of fit degree of the model.

(a) Absolute Fit Index. In the model, the value of is 395 and the value of is 921.332, so , and GFI = 0.91 > 0.9, RMR = 0.074 < 0.08, and RMSEA = 0.054 > 0.05. As can be seen, with the exception that the value of RMSEA index is slightly larger than the reference value above the indexes, the other indexes are all in accordance with the standards. In general, there does not exist a significant difference between the observed covariance matrix and the estimated covariance matrix. And the fit degree of sample data and model is good.

(b) Relative Fit Index. As can be seen from Table 15, NFI = 0.934, NNFI = 0.961 > 0.95, and CFI = 0.92. The value of NFI and CFI is slightly less than the reference value above the indexes, but it is still in the bearable range. The NNFI index is in accordance with the standards. In addition, some literatures indicate that NNFI is a good index of evaluating the model, and this index in the model meets the requirement. Therefore, the relative fit index in this study is relatively good.

(c) Information Index. As can be seen from Table 15, PNFI = 0.49 and PGFI = 0.62. The value of PNFI is slightly less than the reference value, and the PGFI is in accordance with the standards, which means that there is no high complexity of the model.

(i) Internal Fit Degree. The CR values of trust, information sharing, joint action, specific asset investment, and trade performance, respectively, are 0.712, 0.734, 0.756, 0.724, and 0.743, all above 0.7. And the AVE values, respectively, are 0.673, 0.696, 0.685, 0.692, and 0.704, all above 0.5, and meet the requirement. Thus, the internal fit degree of the model in the study is in accordance with the standards.

According to the fit degree indexes above, we can see that the fit degree of the structural equation model is ideal, so the results can be used to validate the research hypothesis.

4.3.2. The Results of Hypothesis Test

The hypothesis is validated according to the related path analysis data. And the summarized result can be seen in Table 16. Most research hypotheses have been supported, which are validated by the data, such as H1, H3, H4, H6, H7, H8, H9, and H10. But H2 and H5 are not supported at a level. Information sharing has a significant influence on trust, specific asset investment, and trade performance, and the influence coefficients are 2.78, 0.699, and 0.714. The effect of specific asset investment on trade performance is not obvious, and the influence coefficient is 0.104. There is a mutual influence between joint action and trust, and the influence coefficient is 0.22. Information sharing, joint action, and specific asset investment have a positive effect on trade performance, and the influence coefficients are 0.150, 0.173, and 0.104.

Table 16: Results of hypothesis test.

Consequently, eight hypotheses are verified and supported through the empirical analysis, which conclude the relationship among trust, enterprise behavior, and transaction performance.

5. Conclusion

In the vertical transaction of the independent brand automobile supply chain, information sharing, joint action, specific asset investment, and trust have a significant positive effect on improving the trade performance. Specifically, it is suggested that (1) trust has a positive influence on joint action; (2) joint action dimensionality has a positive influence on trust; the information sharing dimensionality and specific asset investment dimensionality have a significant positive influence on trust; (3) the information sharing dimensionality, joint action dimensionality, and specific asset investment dimensionality have a significant positive influence on transaction performance; (4) trust has a positive influence on transaction performance.

Therefore, the cooperative partners should actively promote information sharing, joint action, and special asset investment to improve trade performance. At the same time, they also should build a good trust mechanism to enhance the level of trust between them and ensure their cooperation is sustained and stable. Above all, this can be improved from the following aspects. The automobile manufacturing enterprise should invest special technology and talent assets in its parts suppliers, such as exchanging the technology and management personnel to do a short-term product research and to communicate with each other in terms of supply chain management. The manufacturers can also send the excellent engineer to the suppliers to resolve the problem of product quality. And the suppliers should actively respond to the appeal of developing independent brand by the manufacturer and be involved in it. And they should also invest special technology, product development, and brand equity to improve the performance of their products and to assist the manufacturer in the development of independent brand.

Competing Interests

The authors declare that they have no competing interests.

Acknowledgments

This paper is supported by humanities and social science research project of the Ministry of Education of the People’s Republic of China (nos. 10YJA630030, 14YJCZH226, 14JJD630011, and 11YJC630019), Foundation of Department of Education of Zhejiang Province (Y201329545), Key Laboratory of Electronic Business and Logistics Information Technology of Zhejiang Province (2011E10005), Innovative Group of e-Business Technology of Zhejiang Province (2010R50041), Natural Science Foundation of China (nos. 71203196 and 71401156), Zhejiang Provincial Natural Science Foundation of China (LY14F020002, LY7100673, LY12G03015, and LQ12G01007), and Zhejiang Provincial 2011 Collaboration Innovation Centre (15SMGK26YB, 15SMGK28YB). The authors thank them heartedly for supporting the paper funds.

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