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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, firstname.lastname@example.org 2 School of Economics and Management, ZheJiang Sic-Tech University, P.R.China, email@example.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  Luke Pittaway, Networking and innovation: a systematic review of the evidence. 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