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An empirical research on SMES' vertical network relationships and

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An empirical research on SMES' vertical network relationships and Powered By Docstoc
					     An empirical research on SMEs’ vertical network relationships and

                     technological innovation performance

                                   MO Yan1,Linghu Kebo2
1 School of Economics and Management, ZheJiang Sic-Tech University, P.R.China, moyan@zist.edu.cn
2 School of Economics and Management, ZheJiang Sic-Tech University, P.R.China, romath@163.com

Abstract: Applying the multiple linear regression analysis, this paper studies on SMEs‟ vertical
network relationships and technological innovation performance in social network angle mainly based on
questionnaire of 200 SMEs in ZheJiang. It is confirmed that the enterprises‟ Vertical network
relationships significantly influence their technological innovation performance. Another two findings of
the paper are that the vertical network relationships‟ dimensions have the different significant influences
on the technological innovation performance between the high-tech enterprises and traditional enterprises;
the high-tech enterprises are better at using their enterprises-suppliers network relationships to improve
their technological innovation performance. These findings have significant implications at providing
suggestions when SMEs manage the vertical network relationships.
Keywords: vertical network relationships, technological innovation performance, Social network angle

1 Introduction
Recent work on competitiveness has emphasized the importance of business networking of
innovativeness (Luke Pittaway, 2004). Focusing on the resource requirements of firms, they are induced
to form network relationships with other firms as a way of obtaining access to technical or commercial
resources they lack(Ahuja,2000). Enterprises‟ innovative networks have significant influences on the
technological innovation performance .And now more and more enterprises pay so much attention to the
management of the network to improve their performance and make more profit.
   Especially the vertical network relationships‟ two factors, enterprise-suppliers network relationships
and enterprise-customers network relationships, became more and more important in enterprises‟
innovation process.
   Gemünden (1996) had proposed that collaboration with suppliers is of critical importance in the firm‟s
innovative network. The integration of suppliers in the innovation process has been highlighted as one of
the factors leading to frame-breaking innovation (Kaufmann and Tödtling, 2001). The value of including
suppliers in new product development innovation has been widely documented in the supply chain
literature ( Lincoln et al. 1998).So constructing the enterprise-suppliers network relationships are critical
for each other (kamp,2005).
   Luke (2004) proposed that the importance of networking with business customers is confirmed. And it
is shown to offer many benefits. Many theoretical and empirical studies have shown that customer
interaction is necessary for achieving product innovation success (Gemünden, 2000).
   There are so many researchers have do many works on that how the vertical network influence on the
technological innovation performance. But almost no one do the empirical analysis on small and medium
enterprises‟ vertical network relationships and technological innovation performance.And little paper is to
distinguish the function differences between in the high-tech enterprises and traditional enterprises with
empirical analysis. So this paper will do the empirical analysis to solve these problems.

2 Theoretical Background
2.1 network relationships :social network angle
This paper puts the “relationships “into the social network, which is represented as “network
relationships”. It means the connections between the nodes in the network, which is the basis of the
network actives. Johanson and Mattsson (1987) proposed that the network relationships are Mutual
Orientation.
   This paper mainly considers the connection relationships between the network nodes. In personal
network, Granovetter (1973) employs the 4 dimensions, communicating frequency; emotion; intimacy
and reciprocal degree, to measure the relationships. And Nooteboome and GilSing (2003) employ 6
dimensions to measure the enterprises‟ innovation network relationships. They are range, special
inventory, persistence, communicating frequency, personal trust. The Pluralism of network relationships
means multi-connection between nodes. For one case, two nodes‟ connections include different trade
relations (Ha Hoanga and Bostjan Antoncic, 2005). In addition, network relationships also include the
asymmetry and the position property of nodes.

2.2 Concept of enterprises’ vertical network relationships
Gemünden (2000) had provided the fig of the innovations partners and their contributions. According to
that, we have known that the enterprises‟ innovation network relationships could be divided to 3 groups.
First one called vertical network relationships: the relationships between the enterprise and suppliers,
customers; Second one called horizontal network relationships: the relationships between the enterprise
and competitors, other enterprises; third one called external social network relationships: the relationships
between enterprises and Administration, Research and training institutes, Consultants, Distributors.
   This paper is focused on the vertical network relationships. In another word, this paper makes research
on the network relationships which are built by enterprises and suppliers, customers.
   Enterprises-suppliers network relationships means one protocol relationship that suppliers and the
buying enterprises make in a period .So they can share information, risk and make more profit together.
Krause(2007) had made one study on how the enterprises‟ all these factors(long-term relation promise ,
cognitive capital, target and value, structural capital, information sharing ,supplier evaluation, supplier
exploring, and relation capital, relation length, buyer dependent, supplier dependent) influence the
improvement of enterprises, cost, quality, due date, flexibility of improvement. Fynes (2002) pointed out
that the enterprises-suppliers network relationships determine the degrees of their collaborations. He
thought trust is the most important dimension while measuring the network relationships, and also there
are other dimensions, such as: satisfaction, depending, communication, promise, and collaboration.
Humphreys and Chan (2004) concluded the 8 dimensions to measure enterprises-suppliers performance
by factor analysis. They are supplier exploring, strategic targets, efficient communication, long-term
promise, top managers‟ support, supplier evaluate, suppliers‟ strategic targets, trust.
   The enterprises-customers network relationships not only just include the business relations in sale,
such as: contract signing, order processing, and consignment, gathering, but also contain kinds of
relationships after sale. Enterprises apply different channels to communicate with customers, trust each
other, and satisfy the customers‟ requirements. Enterprises hope to build the customers‟ inclination to
their products. These indexes are used to measure enterprises-customers network relationships.

2.3 Technological innovation performance
Domestic and foreign scholars‟ understandings of technological innovation performance focus on the
input-output efficiency and results. It was confirmed that technological innovation activities influence
enterprises‟ collaboration performance. While there is little paper study the quantity relation between the
network relationships and technological innovation performance. This paper will do the work.
   In social capital angle, Zhang Fanghua (2006) proposed that the vertical network relationships as
vertical social capital will improve the enterprises‟ technological innovation performance by getting
enterprises‟ information, knowledge and funds. Ransley and Rogers (1998) had used 7 factors to
measure      technological innovation performance based on the enterprises‟ R&D practices. The 7
factors are technological tactics, items‟ choosing and management, key capability, validity, external
consciousness, technological transfer and workers. Chinese national statistic department set these factors
which include technological exploring input capital, researcher, research output, technological transfer,
sale of new products to measure the technological innovation performance. Until now, there are no
uniform indexes to measure it in academic circles. Yin Jianhai (2008) proposed the index system to
measure technological innovation performance of enterprises based on EBSC.

3 Methodology
3.1 Definitions and Measures of Variables
This paper considers the vertical network relationships of small and medium enterprises and uses 8
dimensions to measure the two factors of vertical network relationships.
   The 4 dimensions of the enterprises-suppliers network relationships(X) are:
X1: the amount of suppliers which the enterprise contacts
X2: frequent degree of contact between the enterprise and suppliers
X3: trust degree of the enterprise and suppliers
X4: reciprocal degree of the enterprise and suppliers
   And the 4 dimensions of enterprises-customers network relationships(Y) are:
Y1: the amount of customers which the enterprise contacts
Y2: frequent degree of contact between the enterprise and customers
Y3: trust degree of the enterprise and customers
Y4: reciprocal degree of the enterprise and customers
   Then we use the following 6 dimensions to measure technological innovation performance: the
percentage of new product innovation success; percentage of improve product; reduce degree of workers‟
cost; shorting degree of the time from the enterprises get order to they shipment products; improvement
degree of working efficiency; reduce degree of consuming the raw and processed materials. Then we will
calculate the six dimensions‟ average value , so we get the value of technological innovation
performance.
   In addition, this paper„s research object is small and medium enterprises, so we don‟t think over the
enterprise scale „s influence on technological innovation performance. But we have to take account in the
enterprise‟s industry property. In the end of 1970s, industrial organization economics had proved that
industry‟s configuration is the main factor of enterprises‟ performance. SCP model is one of theoretical
framework which analysis the relation of industry configuration and profit rate. Now that different
industries‟ profit rates are significant difference, so we must consider industry‟s influences on
enterprises‟ technological innovation performance. Finally, this paper considers the industry property as
the control variable. We divide the enterprises into 2 groups: high-tech enterprises and traditional
enterprise. The former one includes biological and new medicine industry; new materials and new energy
industry; electronic information technology industry; high technology service industry. The later one
contains machinery industry; textile industry; resources and environment technology industry; chemical
industry; paper making industry and so on. The paper will figure out their different influences on
technological innovation performance in the following empirical analysis.

3.2 Questionnaire, Sample and Data Collection
This paper has researched that how the vertical network relationships influence the innovation. The
questionnaires have been mainly designed to measure the variables in the model by applying 7 points
Likert scale. And also we have collected respondents‟ individual messages and their organizational
messages.
   This paper considers small and medium enterprises as our research objects. It mainly used e-mail to
distribute and retrieve the questionnaires as well as went straight to the company for questionnaires. We
have taken back 190 copies of questionnaires while sent out 200 copies .There are 170 valid
questionnaires with the valid rate of 85%. Our research samples include the industries of IT, electronic
communication, biochemical medicine, and manufacturing, Textile, Plastics Chemical, papermaking,
which mainly located in ZheJiang.
3.3 Assessment of Validity and Reliability Test
We must take a Validity and Reliability Test for our sample. In order to assess the reliability of the
questionnaires precisely, the Cronbach‟s α of all scales are calculated. All the values of α are more than
0.8, representing that the scales are highly internal reliable. Then we do KMO and Battle tests to all
variables. It shows that these variables can be made the factor analysis. The results show that all items
load onto their predicted latent variables very strongly, and the correlation matrix of items also shows
that the majority of inter-item correlations between those variables are all low. So the test results show
that our sample has good construct validity.

3.4 Empirical Analysis
Firstly we divide our database into 2 groups by industry enterprise. The first one includes 60 high-tech
enterprises; the other one includes 110 traditional enterprises. In this paper, we use multivariate linear
regression analysis to examine the relationship among the dimensions of enterprises‟ vertical network
relationships and technological innovation performance.
   Then we do the regression analysis to the high-tech enterprises. We get the value of R2 is 0.981, it
means that the regression equation is highly success. And the regressions pass the variance significance
test. The detailed data of high-tech enterprises‟ regression coefficient and significance test are showed in
the Tab.1.
                     Tab.1 high-tech enterprises’ regression coefficient and significance test
                                      Unstandardized      Standardized
                                        Coefficients      Coefficients       t       Sig.     Collinearity Statistics
                                                 Std.
Independent      Dependent             B         Error        Beta                            Tolerance       VIF
                (Constant)              .778       .113                    6.888       .000
                X1                      .139       .040            .165    3.490       .001        .185        5.414
technological   X2                     -.023       .014           -.039   -1.668       .102        .738        1.354
innovation      X3                      .176       .042            .197    4.223       .000        .190        5.274
performance     X4                      .136       .043            .174    3.194       .003        .138        7.224
                Y1                      .162       .054            .200    2.998       .004        .142        7.042
                Y2                      .046       .019            .062    2.453       .018        .651        1.537
                Y3                      .097       .036            .128    2.720       .009        .186        5.390
                Y4                      .149       .039            .182    3.815       .000        .181        5.535
  From the Tab.1, we can find all two factors of vertical network relationships significantly influence on
technological innovation performance. In the enterprises-suppliers network relationships, X1, X3, X4 are
positive correlation. Especially trust degree of the enterprise and suppliers represented as X4 has the
highest regression coefficient, which is the most important dimension in the enterprises-suppliers network
relationships. And X2 shows negative correlation with technological innovation performance, but it
doesn‟t pass the significance test. We can understand that by considering the high connection cost and
knowledge spillover effect in high-tech enterprises.
   Then in the enterprises-customers network relationships, 4 dimensions are positive correlation with
technological innovation performance and pass the significant test. We also find that Y1, Y4 have the
high regression coefficient. The customers could offer a lot of actual demand information of the market.
So the amount of customer is important in product innovation process. Nowadays, customers are more
inclined to buy the products which can satisfy them more. So the reciprocal degree of the enterprise and
customers has accessibly significant influences on technological innovation performance. In the high
–tech industry, the two factors show more important when the enterprises are likely to make innovation
products to satisfy the customers.
   In the following paper, we will do the regression analysis to the traditional enterprises. The value of R2
is 0.941, and the regressions pass the variance significance test. The more detailed analysis data are
showed in Tab.2.
The analysis results show that X1 is negative correlation with technological innovation performance in
enterprises-suppliers network relationships, but not pass the significance test. Since the cost of
maintenance huge amount of suppliers is so high in traditional industry. And their demands of supply
often are stabilization not as in high-tech industry. So they don‟t contact with many suppliers. Now we
can understand why X1 is negative correlation with technological innovation performance. The other
dimensions in the network relationships are significantly positive correlation with technological
innovation performance. But X4 doesn‟t pass the significance test. While in the enterprises-customers
network relationships, Y2, Y3 are the main factors to influence technological innovation performance.
This point is so different with that in high-tech industry. In another word, traditional enterprises should
pay more attentions to the frequent degree of contact between the enterprise and customers and the trust
degree of the enterprise and customers than high-tech enterprises. Finally, it is confirmed that their
enterprises-suppliers network relationships make more influences (35.4%) on technological innovation
performance than that (64.6%) in enterprises-customers network relationships.
   Comparing the two tables, we can easily find that high-tech enterprises are better at using
enterprises-suppliers network relationship to improve their technological innovation performance. Despite
the differences of industry, the different levels of managing vertical network relationships are obviously
influence their technological innovation performance. And the key dimensions of network management
are significantly different between high-tech enterprises and traditional enterprises.
                   Tab.2 traditional enterprises’ regression coefficient and significance test
                                       Unstandardized         Standardized                          Collinearity
                                         Coefficients         Coefficients         t      Sig.       Statistics

 independent            Dependent       B       Std. Error        Beta                           Tolerance        VIF
                        (Constant)      .619         .113                        5.450    .000
                         X1             -.041        .030                -.051   -1.384   .169        .423    2.366
                         X2             .130         .049                .144    2.651    .009        .196    5.100
 technological           X3             .160         .055                .174    2.885    .005        .161    6.219
 innovation              X4             .078         .049                .087    1.597    .113        .196    5.114
 performance             Y1             .135         .056                .153    2.689    .009        .141    7.081
                         Y2             .163         .043                .197    3.819    .000        .219    4.575
                         Y3             .171         .051                .211    3.330    .001        .145    6.880
                         Y4             .102         .037                .121    2.726    .008        .293    3.409


4. Discussion and Conclusion
From the theoretical research and the later empirical analysis results, this paper has the following
conclusions:
1) The enterprises‟ Vertical network relationships significantly influence on their technological
innovation performance. The key roles of suppliers and customers are more and more prominent in
innovation network. SMEs should take more attentions to the vertical network relationships.
2) Our empirical analysis results show that the vertical network relationships‟ dimensions have the
different significant influences on the technological innovation performance between the high-tech
enterprises and traditional enterprises. In high-tech industry, trust degree of the enterprise and suppliers;
the amount of customers which the enterprise contacts; reciprocal degree of the enterprise and customers
are the main factors to influence technological innovation performance. While in traditional industry, all
the dimensions of the enterprises-customers network relationships significantly influence the
technological innovation performance .But in the enterprises-suppliers network relationships, all the
dimensions except trust degree of the enterprise and suppliers make little effort to the technological
innovation performance. Even X1, the amount of suppliers which the enterprise contacts, is negative
correlation with the technological innovation performance.
3) Comparing the two tables, we can easily find that high-tech enterprises are better at using
enterprises-suppliers network relationship to improve their technological innovation performance. Despite
the differences of industry, the different levels of manage vertical network relationships significantly
influence their technological innovation performance. This point suggests that our small and medium
enterprises need to improve their levels of network relationships‟ management when we have limited
innovation capital. We can‟t afford high cost of managing all the factors of network relationships, so
SMEs need to focus on the main factors which this paper had pointed out to improve our enterprises‟
technological innovation performance.
                                              References
[1] Luke Pittaway, Networking and innovation: a systematic review of the evidence. International Journal
of Management Reviews, 2004, 9: 137-168.
[2] Ahuja, G., The duality of collaboration: inducements and opportunities in the formation of interfirm
linkages. Strategic Management Journal, 2000, 21: 317-343.
[3] Gemünden, H.G., Network configuration and innovation success: an empirical analysis in German
high-tech industries. International Journal of Research in marketing, 1996, 13: 449-462.
[4] Kaufmann, A. and Tödtling, F., System of innovation in traditional industrial regions: the case of
Styria in a comparative perspective.   Regional Studies, 2000, 34: 29.
[5] Kamp, B., Formation and evolution of buyer-supplier relationships: conceiving Dynamism in Actor
Composition of business Net-work. Industrial Marketing management, 2005, 34(7): 658-668.
[6] Lincoln, J. and Ahmadjian, C., and Mason, E. Organizational learning and purchase-supply relation in
Japan: Hitachi, Matsushita and Toyota compared. California Management Review, 1998, 40: 241.
[7] Krause, D.R.and Handfield, R.B., The Relationships between Supplier Development, Commitment,
Social Capital Accumulation and Performance Improvement. Journal of Operations Management, 2007,
25(2):528-545.
[8] Fynes, B. and Voss C, The Moderating Effect of Buyer supplier Relationships on Quality Practices
and Performance. International Journal of Operations & Production Management, 2002, 22: 589-613.
[9] Humphreys, P.K. and LiW, L., ChanL, Y., The Impact of Supplier Development on Buyer-supplier
Performance. Omega, 2004, 32(2): 131 - 143.
[10] D.L. Ransley and J.L. Rogers,A Consensus on Best R&D Practices.Research Technology
Management, 1998,5-6:19-26.
[11] Yin Jianhai and Yang Jianhua, The index system study on technological innovation performance of
enterprises based on EBSC. Science Research Management, 2008, 1:1-7.
[12] Zhang Fang-hua, Firm‟s Social Capital and S&T Innovation Performance:Concept Model and
Empirical Analysis. R & D MANAGEMENT, 2006, 6:47-53.
[13] Johanson, J. and L.G. Mattsson, Interorganizational Relations in Industrial System: A Network
Approach compared With the Transaction—Cast Approach. International Studies of Management and
Organization, 1987, 27(1): 34-48.
[14] Granovetter, M., The Strength of Weak Ties. American Journal of Sociology, 1973, 78(6):1360-1380.
[15] Nooteboom, B. and Gilslng, V.A., Density and Strength of Ties in Innovation networks: A
Competence and Governance View. Working paper, 2004, 19(1):1-29.
[16] Ha Hoanga and Bostjan Antoncic, Network-Based Research in Entrepreneurship: A Critical
Review.Journal of Business Venturing,2003, 18 (2):165-187.

				
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