Open Journal of Social Sciences, 2014, 2, 167-172
Published Online September 2014 in SciRes.
How to cite this paper: Zhang, Q. (2014) Influence of Relationship between Supply Chain Members on Innovation Perfor-
mance. Open Journal of Social Sciences, 2, 167-172.
Influence of Relationship between Supply
Chain Members on Innovation Performance
Qi Zhang
Economics School of Beijing Wuzi University, Beijing, Chi na
Email: zhangqb
Received June 2014
In order to pursue development in fierce competition, enterprises have attached greater and
greater importance to their innovation abilities. This paper analyzes the theory model about how
the relationship between supply chain members can influence innovation performance. It is found
that inter-firm trust and learning can help to promote relationship governance, and further en-
hance the innovation performance of the enterprises.
Learning, Trust, Relationship Governance, Innovation Performance
1. Introduction
When enterprises try hard to maintain and improve competitiveness, organization innovation has got more and
more attention. High uncertainty of the market and upgrading of technology turn out to present a severe test for
enterprises to maintain competitive advantages. In this rapidly changing environment, it is more and more diffi-
cult for a single enterprise to get the latest knowledge and independently develop comprehensive technology.
Innovative research has switched its attention from individual research to research of the whole internet [1].
People pay attention to the study of the organizational creativity and innovation performance of the industry
cluster and the whole region [2]. How to make use of resources between enterprises to manage their relationship,
thus promoting the innovation ability and competitiveness of the whole cluster? This article tries to integrate the
micro enterprise characteristics and macro industry characteristics, and combine characteristics of the relation-
ship between enterprises and external environment features of the cluster, in order to explore the process of lo-
calized innovation of enterprises in the cluster.
2. Literature Review and Theoretical Assumptions
In recent years, in spite the fact that research on relationship between enterprises has achieved fruitful results in
relationship governance element, operation mechanism and relationship value, most of the research stays in re-
search on the relationship governance between the alliance and network. It should be pointed out that, the estab-
lishment and maintenance of the cooperative/partner relationship needs a lot of resources, and it is not appropri-
ate for all enterprises to establish long-term relationship. Enterprises should deploy limited resources to match
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the construction of the relationship [3]. It turns out that existing research ignores the study of characteristics of
the relationship itself. In an industrial cluster, enterprises are embedded in a closer relational network. A poten-
tial assumption is that there exists high level of trust and knowledge sharing between enterprises, but due to
fierce competitive conditions caused by close geographical position, geopolitical competition relations become
more complex.
2.1. Relationship Governance and Innovation Performance
Relationship governance, brought up in the relational contract theory, is a kind of middle type of organization
between market-oriented organization and hierarchical organization. The transaction cost theory emphasizes the
role of hierarchical governance mechanism while the relational contract theory is concerned about the impact of
social factors. From the perspective of relationship behavior process, relationship governance reflects the extent
to which joint action could be organized, including joint planning and joint problem solving. Joint planning, as a
predicting behavior, is a forecast for production status and distribution of rights and obligations in the relation-
ship. Joint problem solving, being a reaction behavior, is to cooperatively solute new problems, situations and
disputes caused by various reasons such as environment changes in the management process. Both sides choose
to reduce opportunistic behaviors and take joint actions according to norms and their expectations of future
long-term cooperation. Joint planning behaviors enable enterprises to establish good mutual expectations and
make efforts to cooperate, speed up knowledge sharing and improve knowledge absorptive capacity, further to
solve the problems arising in the course of cooperation and to strengthen innovation ability [4].It is obvious that
improvement of relationship governance could strengthen the cognitive ability, information integration ability
and the ability to remove bottlenecks, thus to promote an enterprise's innovation ability. Therefore, we assume
H1: Level of the relationship governance between enterprises is positively related to the improvement of in-
novation performance.
2.2. Trust, Relationship Governance and Innovation Performance
Trust reflects level of protocol fairness and commitment redemption. Trust is an important factor affecting
long-term relationship orientation, and also is the essential condition of relationship governance. Firstly, trust
can lower opportunistic behaviors before or after transaction, forming a lower level of hierarchical governance
structure [5]. Secondly, considering from the perspective of social system theory, economic behaviors are em-
bedded in the society, especially in the cluster where peer enterprises gathered in close geographical location,
and business relationship and social relations between business owners are closely intertwined, forming trust
based on township, geographical relationship, and blood relationship, Trusted trading companies will pay more
efforts beyond the contract to overcome the difficulties [6]. So a high level of trust between enterprises improves
relationship governance with norms as the core and reduces transaction costs. It is obvious that mutual trust be-
tween enterprises could help strengthen relationship governance level [7], take joint actions, as we mentioned
before, further improve innovation performance. Therefore, we assume that:
H2: Trust between enterprises could improve relationship governance level.
H3: Mutual trust between enterprises improves innovation performance through relationship governance.
2.3. Inter-Firm Learning, Relationship Governance and Innovation Performance
Inter-firm learning refers to the process of trading parties passing and transferring knowledge [8]. In this process,
shared information is usually special and implicit, and has scenario particularity and integrity. It is difficult to
decode and express this kind of knowledge, making knowledge transferring challenging [9]. Obtaining valuable
information of trading partners can reduce opportunistic behaviors, allowing both sides to better cope with the
changes of business environment [10]. Especially, during enterprise management practice, due to supplier clas-
sification and certification management, reduction of the number of suppliers increased the dependence of both
sides. In order to maintain synchronized development and respond to the new changes of the market, enterprises
should keep common progress to make sure that knowledge provided could promote supervision and control
enterprise production coordination, product development process and so on [11]. Besides, they can reflect mar-
ket changes together. Especially, with the suppliers involved in the core business and participating in early re-
search and development, inter-firm learning enables technical problems to be recognized and diagnosed in the
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early stage, providing a smoother communication platform for experts solving problems jointly. Furthermore,
recognition of technology resources for both companies is more likely to happen, enabling technology develop-
ment to be better planned and forecasted. So, knowledge exchange between trading partners has a positive im-
pact on relationship governance between enterprises, provided with the above conclusion that good relationship
governance positively leads to better innovation performance, it therefore promotes innovation performance ac-
cordingly. Therefore, we put forward the following hypotheses:
H4: Inter-firm learning has positive impacts on relationship governance.
H5: Inter-firm learning promotes enterprises’ innovation performances through relationship governance.
3. Research Method
3.1. Sample and Data Collection
A total of 600 questionnaires were sent to enterprises in the Yangtze River Delta region, and finally 194 ones are
valid. Manufacturing enterprises account for 50 percent. Manufacturing and distributing type of enterprises ac-
count for 32.5%. Wholesaling and distributing type of enterprises account for 8.2%. Retailing enterprises ac-
count for 3.6% and other types of enterprises account for 5.7%.
3.2. Measurement
The measurement scale in the study of innovation performance is modified from Bell’s (2005) study and in-
cludes three items. Relationship governance variables include three items [12], in order to reflect how the two
sides of the particular partnership can reduce and manage opportunistic behaviors according to norms and ex-
pectations of long-term cooperation in the future [13]. In this study, industry, size, sales revenue, total assets are
set as control variables. Each variable’s reliability (Cronbachs α coefficient) and results of exploratory factor
analysis are shown in Table 1 and Table 2.
Table 1. Items and results of exploratory factor analysis.
Variables and Items Code CITC VARIMAX Factor Loadings after Rotation
F1 F2 F3 F4 F5
Dependent Variables
Innovation Performance (IP) α = 0.79
IP1 0.847 0.836
IP2 0.859 0.811
IP3 0.810 0.783
Relationship governance (RG) α = 0.66
RG1 0.826 0.758
RG2 0.824 0.758
RG3 0.705 0.557
Inter- Firm Trust (IFT) α = 0.67
IFT1 717 0.750
IFT 2 0.747 0.666
IFT 3 0.715 0.611
IFT 4 0.709 0.594
Inter-Firm Learning (IFL) α = 0.90
IFL 1 0.929 0.837
IFL 2 0.904 0.829
IFL 3 0.902 0.819
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Table 2. Variables’ mean, standard deviation and correlation coefficient matrix.
Variable 1 2 3 4
1. Trust 1
2. Learning 0.399** 1
3. Relationship governance 0.353** 0.535** 1
4. Innovation Performance 0.394** 0.348** 0.330** 1
Mean 30.771 30.377 30.904 4.242
Standard Deviation 0.562 0.971 0.655 0.677
a N = 194. b Numbers in diagonal are reliabilities.*p < 0.05; **p < 0.01.
4. Data Analysis Results (Table 3)
As shown in Model 1, trust and learning has a significantly positive impact on innovation performance (β Trust=
0.329; p < 0.001; β Learning = 0.205; p < 0.01). According to the test of null hypothesis 1, relationship gover-
nance contributes to the improvement of innovation performance. The Step 3 in Model 1, for example, suggests
that the former has a significantly positive influence on the latter. In the test of null hypothesis 2 and 4, relation-
ship governance is taken as the dependent variable. As shown in the Step 2 of Model 2, trust and learning has a
significantly positive impact on relationship governance (β Trust = 0.168; p < 0.05; β Learning = 0.428; p <
0.001). Adding the independent variables, trust and learning, increased R2, the degree of explained variation of
the dependent variable, relationship governance, by 0.256, which is significant at the significance level of p <
0.001 (F = 7.979, p < 0.001).
In the test of null hypothesis 3 and 5, as it is mentioned above, the trust and learning in the corporation has a
significantly positive impact on innovation performance (See Step 2 of Model 1). Besides, the result of Model 1
reveals that relationship governance has a significantly positive influence on innovation performance. What is
more, the effect is reduced when taking relationship governance into consideration (β trust = 0.302; p < 0.001; β
learning = 0.136; p < 0.05). It shows that the relationship between independent variables and dependent va-
riables is partially intermediated, which supports null hypothesis 3 and 5.
5. Research Conclusions and Implications
This thesis explores the localized innovation performance in a cluster considering the characteristics of industri-
al structure of the cluster. The research shows that the features of corporations’ relationships affect the innova-
tion performance via relationship governance, which is a partial intermediary variable. It means that the higher
the level of trust and learning between companies, the better the level of relationship governance. The innova-
tion performance, therefore, is improved. The study suggests that the external links improve the integration and
optimization of information and professional knowledge at the corporation level. Besides, it motivates compa-
nies to innovate the reaction system to the changing of external needs through the relationship governance based
on norms. This thesis also studies the scene effect of relationship governance. Besides illustrating the influence
of relationship governance on innovation performance, the thesis introduces the characteristics of cluster’s in-
dustrial environment, considers about the scene effect of cluster atmosphere and explores diverse effects of dif-
ferent relationship types on relationship governance. When the industries are highly similar, trust between cor-
porations could increase the efficiency of relationship governance more. However, if the level of relationship
governance is so high that it forms a high embedding degree of relations, the increase of innovation level will be
restrained. The ideal embedding degree of relations, hence, is supposed to be moderate [14]. The research of [15]
also proves that certain embedding degree of relations among the network promotes the performance, but it will
reduce when the embedding degree exceeds the limitation.
Firstly, relationship governance could improve the efficiency of innovation performance. The supervisors will
maintain and adjust the corporation relations better in order to dig the resources and capacities holding in the re-
lations through joint action and integrated behaviors. The supervisors should encourage and cultivate the trust
and learning among companies to promote the lever of relationship governance. Secondly, the supervisors need
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Table 3. Results of regression analysis.
Variables Innovation Performance
Step 1
Step 2
Independent Variables (Main Effects)
Step 3
Intermediary Variables
Std. β Std. β Std. β
Industry 0.048 0.028 0.035
Business Type 0.120 0.129 0.129
Enterprises Scale 0.099 0.120 0.124
Sales Revenue 0.002 0.018 0.021
Total Assets 0.102 0.121 0.128
Location 0.062 0.019 0.032
Trust 0.329*** 0.302***
Learning 0.205** 0.136*
Relationship governance 0.159**
R2 0.030 0.228 0.245
R20.197** 0.108***
F 0.909 6.407*** 6.242***
p < 0.10; *p < 0.05; **p < 0.01; ***p < 0.001 (two-tails test).
to emphasize on the trust building among companies. Here needs to point out that when the level of relationship
governance reaches a certain degree, the improvement of innovation performance will be blocked. The relations
turn to have the lock-in effect and bring about the inertia of relations behaviors. Therefore, the supervisors need
to build a complete system of performance evaluation and avoid the lock-in effect resulting from the exorbitant
level of relationship governance. This thesis only discusses about the adjustment function of the industrial
structure of a cluster. The further study could draw into the features of companies’ internal resources and indus-
trial environment features to enrich the theoretical model.
This thesis is supported by the 2013 Beijing Municipal Education Commission’s specific funding program: Re-
search on Public Service Incubator of Small Enterprises of Creative Industries in Beijing, and 2011 Beijing
Language and Culture University’s social-science project: Research on Internalization of RMB (Project No.
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