Discrete Dynamics in Nature and Society
Volume 2016 (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

Guanglan Zhou,¹ Yulian Fei,² and Jun Hu³

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 ‎[1–4].

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.

References
Dimensions

Lewicki and Bunker (1996) [26]
Calculation-based trust, understanding-based trust, identification-based trust

Lewicki et al. (1998) [27]
Cognition-based trust, emotion-based trust

Nooteboom (1999) [28]
Ability-based trust, intention-based trust

Sako and Helper (1998) [29]
Contract-based trust, ability-based trust, kindness-based trust

Das and Teng (2001) [30]
Kindness-based trust, ability-based trust

Zhao et al. (2008) [31]
Intelligence-based trust, ability-based trust, contract-based trust, emotion-based trust

Yang (2006) [32]
Calculation-based trust, relationship-based trust

Xu and Xie (2007) [33]
Contract-based trust, ability-based trust, kindness-based 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.

References
The evaluation indexes

Geringer and Hebert (1991) [34]
The satisfaction degree of cooperation, the survival rate of cooperation, the stability of cooperation, and the duration of cooperation.

McGee et al. (1995) [35]
The cooperative performance is divided into absolute performance and relative performance. And the absolute performance is measured by customer satisfaction, logistics cost, profitability, and relationship continuity; the relative performance is measured by the degree of reaching the goal, the degree of profit, and profit growth rate.

Ariño (2003) [36]
Result performance, including financial performance, union life, organizational effectiveness, and the degree of reaching the goal; process performance, including the satisfaction degree in the process of cooperation, spillover effect, and the change frequency of contract.

Wu et al. (2005) [37]
The realization degree of cooperative target, the improvement of profitability, the satisfaction degree of cooperation, and the willingness to continue cooperation.

Huang and Wan (2007) [38]
The realization degree of cooperative target, profitability, knowledge learning, market share, competition advantage, the satisfaction degree of partner, and the competitive position.

Jia et al. (2007) [39]
The productive relationship, the satisfaction with the relationship, the achievement of expected goals, a lot of revenue gained from the cooperation, the great improvement of the enterprise’s market value, the greater competitive advantage brought to the enterprises in the supply chain, and the long-term competitiveness brought to them.

Ambrose et al. (2007) [40]
The cooperative performance is evaluated from short-term performance and long-term performance. The authors believe believes that the short-term performance between members of the supply chain is to get profits through the market efficiency, and they emphasize the period cost, income, and profits. The long-term performance depends on the stable relationship between them and persistent value maximization.

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 [10–12]. 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.

⁢Dimensions
The measurement items

Trust
Contract-based trust
TC1: if the cooperative enterprise does not fulfill the contract signed with us, its market and industry credibility will be seriously affected, and it must afford huge economic losses.

TC2: we believe that the cooperation enterprises will strictly perform the contract.

Trust⁢
Ability-based trust⁢
TA1: the ability of the cooperative enterprise is worthy of trust, and we believe that they are able to do the current work.

TA2: we believe that our partners can bring profit to us.

TA3: we are willing to take long-term cooperation with the enterprises cooperative now.

Trust⁢
Kindness-based trust⁢
TG1: we believe that the cooperative enterprises will not obtain profit by using our problems.

TG2: we believe that when the cooperative enterprises make major decisions they will consider our interests.

TG3: we believe that the cooperative enterprises will not cooperate with others rather than us even if there is a better choice.

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.

Dimensions
The measurement items

Information sharing⁢
IS1: there is good order information sharing between partners.

IS2: there is good demand information sharing between partners.

IS3: there is good product research information sharing between partners.

IS4: there is good inventory information sharing between partners.

IS5: there is good production information sharing between partners.

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.

Dimensions
The measurement items

Joint action⁢
JE1: the partners make, organize, and implement the product’s monthly plan and long-term planning together.

JE2: the partners forecast the product’s annual demand together.

JE3: the partners solve the business problems together.

JE4: the partners establish the conflict solution mechanism of the system together.

JE5: the partners join efforts for product research together.

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.

Dimensions
The measurement items

Specific asset investment⁢
SI1: the cooperative enterprise is willing to invest special physical assets, such as distribution center, production and service place, equipment, and facilities.

SI2: the cooperative enterprise is willing to invest special management assets; for example, the cooperative enterprise will bring its advanced production process and management concept to the supply chain in order to maintain the supply chain management.

SI3: the cooperative enterprise is willing to invest special human resources assets, such as professional and technical personnel, management personnel, and sales personnel.

SI4: the cooperative enterprise is willing to invest special brand assets, such as trademark, brand, and patent.

SI5: the cooperative enterprise is willing to invest special relationship assets, such as the investment to maintain the cooperative relationship.

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.

Dimensions
The measurement items

Transaction performance⁢
JP1: the market share of the enterprise increases significantly through cooperation.

JP2: the timeliness and flexibility of product delivery of the enterprise improve significantly through cooperation.

JP3: the profitability of the enterprise improves significantly through cooperation.

JP4: the management level of the enterprise improves significantly through cooperation.

JP5: the ability to innovate of the enterprise improves significantly through cooperation.

JP6: the ability to research independent brand of the enterprise improves significantly through cooperation.

JP7: the competitive position in the industry of the enterprise improves significantly through cooperation.

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.

Variable
Dimension
Numbers of item
Mean

Trust

8
5.364

Enterprise behavior⁢
Information sharing
5
5.324

Joint action
5
5.331

Specific asset investment
5
5.258

Trade performance

7
5.138

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.

Variable
Dimension
Cronbach
Number of items

Trust
—
0.865
8

Enterprise behavior⁢
Information sharing
0.947
5

Joint action
0.928
5

Specific asset investment
0.953
5

Trade performance
—
0.967
7

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.

Variable
Dimension
KMO
Bartlett’s test of sphericity

Trust
—
0.830
Sig. 0.000, approx. Chi-Square 319.467, df 28

Enterprise behavior
Information sharing
0.819
Sig. 0.000, approx. Chi-Square 279.460, df 10

Joint action
0.863
Sig. 0.000, approx. Chi-Square 214.018, df 10

Specific asset investment
0.892
Sig. 0.000, approx. Chi-Square 283.280, df 10

Trade performance
—
0.877
Sig. 0.000, approx. Chi-Square 444.317, df 21

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.

Variable
Dimension
Item
Factor loading

Contract-based trust
Ability-based trust
Kindness-based trust

Trust
Contract-based trust
TC1
0.952
0.056
0.032

TC2
0.588
0.431
0.277

Trust
Ability-based trust
TA1
0.115
0.898
0.309

TA2
0.119
0.898
0.273

TA3
0.028
0.907
0.250

Trust
Kindness-based trust
TG1
−0.003
0.477
0.775

TG2
0.021
0.388
0.841

TG3
0.170
0.123
0.899

Total variance explained: 74.556%

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.

Variable
Dimension
Item
Factor loading

Contract-based trust
Ability-based trust
Kindness-based trust

Enterprise behavior
Information sharing
IS1
0.409
0.801
0.255

IS2
0.581
0.699
0.266

IS3
0.470
0.658
0.410

IS4
0.330
0.809
0.264

IS5
0.171
0.811
0.381

Enterprise behavior
Joint action
JE1
0.264
0.349
0.820

JE2
0.499
0.208
0.740

JE3
0.556
0.548
0.364

JE4
0.365
0.370
0.774

JE5
0.380
0.349
0.683

Enterprise behavior
Specific asset investment
SI1
0.765
0.343
0.329

SI2
0.757
0.403
0.364

SI4
0.664
0.323
0.495

SI3
0.817
0.301
0.368

SI5
0.680
0.481
0.432

Total variance explained: 71.702%

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.

Variable
Dimension
Factor loading

Trade performance

Trade performance⁢
JP1
0.755

JP2
0.873

JP3
0.924

JP4
0.883

JP5
0.929

JP6
0.892

JP7
0.942

Total variance explained: 73.512%

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.

Latent variable
Item
Standardized load

CR
AVE

Trust
TC1
0.854
17.366
0.743
0.712
0.673

TC2
0.812
16.534
0.726

TA1
0.798
16.797
0.651

TA2
0.847
16.576
0.682

TA3
0.816
15.468
0.698

TG1
0.805
14.355
0.738

TG2
0.769
15.685
0.698

TG3
0.774
13.678
0.665

Information sharing
IS1
0.824
14.353
0.704
0.734
0.696

IS2
0.808
13.566
0.723

IS3
0.786
17.354
0.654

IS4
0.779
—
0.685

IS5
0.811
16.685
0.746

Joint action
JE1
0.803
15.547
0.789
0.756
0.733

JE2
0.755
13.425
0.719

JE3
0.822
—
0.725

JE4
0.827
14.554
0.610

JE5
0.783
15.745
0.639

Specific asset investment
SI1
0.846
16.798
0.747
0.724
0.685

SI2
0.834
15.279
0.694

SI4
0.781
13.578
0.678

SI3
0.760
14.680
0.738

SI5
0.801
16.464
0.705

Trade performance
JP1
0.832
17.465
0.754
0.743
0.704

JP2
0.852
17.325
0.687

JP3
0.794
16.379
0.758

JP4
0.815
15.346
0.640

JP5
0.835
13.687
0.729

JP6
0.824
15.688
0.684

JP7
0.784
14.578
0.645

(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.

Fit index
Absolute fit index⁢
Relative fit index
⁢Information index

GFI
RMR
RMSEA
NFI
NNFI
CFI
PNFI
PGFI

Standard of measurement

>0.9
<0.08
<0.05
>0.95
>0.95
>0.95
>0.5
>0.5

The model
2.33
0.91
0.074
0.054
0.934
0.961
0.92
0.62
0.49

Results
Ideal
Slightly ideal
Slightly ideal
Ideal
Slightly ideal
Ideal
Slightly ideal
Ideal
Slightly ideal

(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.

Hypothesis
Relationship
Path coefficient

Results

H1
Information sharing → trust
2.78
9.7
Supported

H2
Trust → information sharing
−0.08
−0.34
Not supported

H3
Joint action → trust
0.22
1.67
Supported

H4
Trust → joint action
0.197
3.604
Supported

H5
Specific asset investment → trust
0.699
4.548
Supported

H6
Trust → specific asset investment
−1.89
−1.21
Not supported

H7
Information sharing → trade performance
0.150
1.59
Supported

H8
Joint action → trade performance
0.173
3.024
Supported

H9
Specific asset investment → trade performance
0.104
0.35
Supported

H10
Trust → trade performance
0.714
6.338
Supported

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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