面向整体技术能力演进的企业集群技术学习机制研究
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摘要
企业集群的普遍存在和蓬勃发展是浙江省区域经济发展的一个重要特色。但是，目前看来，这些集群都不同程度地面临着技术能力不足的问题，从而影响了其产业升级和可持续发展。鉴于此，本文着眼于集群技术能力的演进，探讨了集群技术学习的问题，目的在于总结一种能有效整合集群内部各学习主体的学习机制，以推动集群整体的技术学习和能力发展。
本文首先基于对企业集群结构特征的分析提出了集群技术能力和集群中技术能力势差(以及高位势企业和低位势企业)的概念，并在此基础上指出，集群技术能力的理想演进表现为集群高位势企业和低位势企业各自的技术能力都能稳步提升。显然，此种理想演进路径必然在微观上要求高低位势企业皆能持续有效地开展技术学习活动，而这一方面取决于充足的学习动力，另一方面则有赖于合理的学习过程。为了实现这两个条件，本文提出，应该在集群中引入一种基于分工的技术学习机制。该机制的基本思想是借助内生的动力因素和合理的外部激励设计，来引导集群高位势企业面向跨区域网络开展外向型技术学习以引进高价值知识流，而低位势企业则借助当地网络平台从前者的知识扩散中进行跟进式学习，表现在知识的空间运动形态上，是一种“知识引进——知识扩散”模式。在该模式下，集群整体的知识存量得以拓展，技术能力持续发展。
事实上，集群技术学习的分工机制的形成和良好运转需要一个广义政策体系的支撑，因此本文最后从集群企业、集群中间机构和公共部门以及当地政府三个方面讨论了该政策体系的设计问题，以更好地推动集群中的技术学习分工和集群技术能力演进。
本论文的研究基于案例研究的方法，在前面的理论探讨过程中，先后解剖了温州柳市低压电器集群、绍兴纺织产业集群、上虞风机产业集群和杭州软件产业集群的技术能力演进和技术学习动态，其结论较好地支持了本文的理论探索。
In Zhejiang province,a large number of enterprise clusters have played a significant role in regional economic development. However,by modern standards,these clusters are more or less confronted with the problem of shortage of technological capability,which hampers the upgrading of industry and sustainable development of region. So,this dissertation focuses on the evolution of technological capability of these clusters and starts out to discuss the issue of technological learning of them. The total objective of our discussion is to conclude a reasonable and effective learning mechanism for these clusters that can integrate clustering members to coordinated technological learning activities so as to push the capability development of their cluster.
By analyzing the structure model of enterprise cluster,the dissertation firstly puts forward the concepts of technological capability of cluster and capability gap among clustering enterprises (from the latter concept,two types of clustering enterprises are classified,that is high-level-capability enterprises and low-level-capability ones). Based on these two concepts,the dissertation suggests that the ideal evolution of clusters' technological capability is resulted from the situation that both high-level-capability enterprises' and low-level-capability ones' technological capability are stably developing. That is to say,the ideal capability evolution path of cluster has to depend on the continuous and efficient technological learning activities of both the two types of clustering enterprises on micro level. To realize this situation,the dissertation proposes that a technological learning mechanism that is characterized in learning division should be introduced into cluster. Under this mechanism,driven by s
ome endogenous motivation factors and suitable exogenous motivation arrangement,the high-level-capability enterprises are expected to conduct external-to-cluster learning activities in the interregional network so as to introduce high-value knowledge flow into the cluster where they are located,and the low-level-capability enterprises,however,are expected to utilize local network to learn from the knowledge diffusion from their high-level-capability neighbors. Considering the dynamic of spatial transfer of knowledge,the above learning mechanism for cluster also reflects the "knowledge induction-knowledge diffusion" model,which,if run efficiently,will promote clusters' knowledge base to extend and upgrade and their technological capability to continuously evolve.
In fact,the formation and operation of the learning division mechanism for cluster has to need support from an extensive policy system,which is constituted of
the policy of clustering enterprises,the policy of coordinating institution and public service institution and the policy of local government.
Accompanied with the theoretical exploration,empirical studies are also conducted in the dissertation,which are in the form of case study. The analyzed cases include the electric component industry cluster of Liushi in Wenzhou,the textile industry cluster of Shaoxing,the software industry of Hangzhou and so on. The conclusion of above case studies has satisfyingly supported the theoretical discussion.
本文首先基于对企业集群结构特征的分析提出了集群技术能力和集群中技术能力势差(以及高位势企业和低位势企业)的概念，并在此基础上指出，集群技术能力的理想演进表现为集群高位势企业和低位势企业各自的技术能力都能稳步提升。显然，此种理想演进路径必然在微观上要求高低位势企业皆能持续有效地开展技术学习活动，而这一方面取决于充足的学习动力，另一方面则有赖于合理的学习过程。为了实现这两个条件，本文提出，应该在集群中引入一种基于分工的技术学习机制。该机制的基本思想是借助内生的动力因素和合理的外部激励设计，来引导集群高位势企业面向跨区域网络开展外向型技术学习以引进高价值知识流，而低位势企业则借助当地网络平台从前者的知识扩散中进行跟进式学习，表现在知识的空间运动形态上，是一种“知识引进——知识扩散”模式。在该模式下，集群整体的知识存量得以拓展，技术能力持续发展。
事实上，集群技术学习的分工机制的形成和良好运转需要一个广义政策体系的支撑，因此本文最后从集群企业、集群中间机构和公共部门以及当地政府三个方面讨论了该政策体系的设计问题，以更好地推动集群中的技术学习分工和集群技术能力演进。
本论文的研究基于案例研究的方法，在前面的理论探讨过程中，先后解剖了温州柳市低压电器集群、绍兴纺织产业集群、上虞风机产业集群和杭州软件产业集群的技术能力演进和技术学习动态，其结论较好地支持了本文的理论探索。
In Zhejiang province,a large number of enterprise clusters have played a significant role in regional economic development. However,by modern standards,these clusters are more or less confronted with the problem of shortage of technological capability,which hampers the upgrading of industry and sustainable development of region. So,this dissertation focuses on the evolution of technological capability of these clusters and starts out to discuss the issue of technological learning of them. The total objective of our discussion is to conclude a reasonable and effective learning mechanism for these clusters that can integrate clustering members to coordinated technological learning activities so as to push the capability development of their cluster.
By analyzing the structure model of enterprise cluster,the dissertation firstly puts forward the concepts of technological capability of cluster and capability gap among clustering enterprises (from the latter concept,two types of clustering enterprises are classified,that is high-level-capability enterprises and low-level-capability ones). Based on these two concepts,the dissertation suggests that the ideal evolution of clusters' technological capability is resulted from the situation that both high-level-capability enterprises' and low-level-capability ones' technological capability are stably developing. That is to say,the ideal capability evolution path of cluster has to depend on the continuous and efficient technological learning activities of both the two types of clustering enterprises on micro level. To realize this situation,the dissertation proposes that a technological learning mechanism that is characterized in learning division should be introduced into cluster. Under this mechanism,driven by s
ome endogenous motivation factors and suitable exogenous motivation arrangement,the high-level-capability enterprises are expected to conduct external-to-cluster learning activities in the interregional network so as to introduce high-value knowledge flow into the cluster where they are located,and the low-level-capability enterprises,however,are expected to utilize local network to learn from the knowledge diffusion from their high-level-capability neighbors. Considering the dynamic of spatial transfer of knowledge,the above learning mechanism for cluster also reflects the "knowledge induction-knowledge diffusion" model,which,if run efficiently,will promote clusters' knowledge base to extend and upgrade and their technological capability to continuously evolve.
In fact,the formation and operation of the learning division mechanism for cluster has to need support from an extensive policy system,which is constituted of
the policy of clustering enterprises,the policy of coordinating institution and public service institution and the policy of local government.
Accompanied with the theoretical exploration,empirical studies are also conducted in the dissertation,which are in the form of case study. The analyzed cases include the electric component industry cluster of Liushi in Wenzhou,the textile industry cluster of Shaoxing,the software industry of Hangzhou and so on. The conclusion of above case studies has satisfyingly supported the theoretical discussion.
引文
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[2] Baptista R and Swarm P, Do firms in clusters innovate more? Research Policy, 1998, 27: 525-540.
[3] Bell M, "Learning" and the accumulation of work and indigenous technological capability in the developing countries, Technological Capability in the Third World, Edited by Fransman M and King K, 1984:187-210.
[4] Belussi F and Arcangeli F, A typology of networks: flexible and evolutionary firms, Research Policy, 1998, 27: 415-428.
[5] Bessant J and Francis D, Using learning networks to help improve manufacturing competitiveness, Technovation, 1999,19(6-7): 373-381.
[6] Birkinshaw J, Upgrading of industry clusters and foreign investment, International Studies of Management & Organization, 2000, 30(2): 93-113.
[7] Capello R, Spatial transfer of knowledge in high technology milieux: learning versus collective learning processes, Regional Studies, 1999, 33(4): 353-365.
[8] Carrincazeaux C, Lung Y and Rallet A, Proximity and localization of corporate R&D activities, Research Policy, 2001, 30: 777-789.
[9] Cohen WM and Levinthal DA, Innovation and learning: the two faces of R&D, Economics Journal, 1989, 99(9): 569-596.
[10] Cohen WM and Levinthal DA, Absorptive capability: a new perspective on learning and innovation, Administrative Science Quarterly, 1990, 35:128-152.
[11] Cooke P, Uranga MG and Etxebarria G, Regional innovation systems: institutional and organizational dimensions, Research Policy, 1997, 26: 475-491.
[12] De Bernardy M, Reactive and proactive local territory: co-operation and community in Grenoble, Regional Studies, 1999, 33(4): 343-352.
[13] DeBresson C and Amesse F, Networks of innovators: a review and introduction to the issue, Research Policy, 1991, 20: 363-379.
[14] Dutton IM and Thomas A, Treating progress function as a managerial opportunity, Academy of Management Review, 1984, (2).
[15] Enright MJ, Regional clusters and multinational enterprise: independence, dependence, or interdependence? International Studies of Management & Organization, 2000, 30(2): 114-138.
[16] Feldman MP, The geography of innovation, London: Kluwer Academic Publishers, 1994.
[17] Freeman C, Networks of innovators: a synthesis of research issues, Research Policy, 1991, 20: 499-514.
[18] Gulyani S, Effects of poor transportation on lean production and industrial clustering: evidence from the Indian auto industry, World Development, 2001, 29(7):1157-1177.
[19] Henderson D, Building interactive learning networks: lessons from the Welsh medical technology forum, Regional Studies, 1999, 33(8): 783-787.
[20] Ivarsson I, Competitive industry clusters and inward TNC investments: the case of Sweden, Regional Studies, 1999, 33(1): 37-49.
[21] Izushi H, Conflict between two industrial networks: technological adaptation and inter-firm relationships in the ceramics industry in Seto, Japan. Regional Studies, 1997, 31(2): 117-129.
[22] Izushi H, Can a development agency foster cooperation among local firms? The case of the Welsh Development Agency's supplier association programme, Regional Studies, 1999, 33(8): 739-750.
[23] Keeble D and Wilkinson F, Collective learning and knowledge development in the evolution of regional clusters of high technology SMEs in Europe, Regional Studies, 1999, 33(4): 295-303.
[24] Keeble D, Lawson C, Moore B and Wilkinson F, Collective learning processes, networking and 'institutional thickness' in the Cambridge region, Regional Studies, 1999, 33(4): 319-332.
[25] Krugman P, Increasing returns and economic geography, Journal of Political Economy, 1991, 99: 483-499.
[26] Lane PJ and Lubatkin M, Relative absorptive capacity and inter-organizational learning, Strategic Management Journal, 1998, 19:461-477.
[27] Lawson C and Lorenz E, Collective learning, tacit knowledge and regional innovative capacity, Regional Studies, 1999, 33(4): 305-317.
[28] Lei D, Slocum JW and Pitts RA, Building cooperative advantage: managing strategic alliances to promote organizational learning, Journal of World Business, 1997, (3).
[29] Linsu Kim, Imitation to innovation: the dynamics of Korea's technological learning, Boston: Harvard Business School Press, 1997.
[30] Lissoni F, Knowledge codification and the geography of innovation: the case of Brescia mechanical cluster, Research Policy, 2001, 30:1479-1500.
[31] Longhi C, Networks, collective learning and technology development in innovative high technology regions: the case of Sophia-Antipolis, Regional Studies, 1999, 33(4): 333-342
[32] Lundvall BA, National systems of innovation: towards a theory of innovation and interactive learning, London: Printer Publishers, 1992.
[33] Lundvall BA, Interaction between producer and user, Journal of Management, 1988, (3).
[34] Makusen A, Sticky places in slippery space: a typology of industrial districts, Economic Geography, 1996, 72(3): 293-313.
[35] Mcevily B and Zaheer A, Bridging ties: a source of firm heterogeneity in competitive capacity, Strategic Management Journal, 1999, 20:1133-1156.
[36] Meyer-Stamer J, Path dependence in regional development: persistence and change in three industrial clusters in Santa Catarina, Brazil, World Development, 1998, 26(8): 1495-1511.
[37] Niosi J and Bas TG, The competencies of regions: Canada's clusters in biotechnology, Small Business Economics, 2001, 17:31-42.
[38] Nonaka I and Takeuchi D, Managing the new product development process: how the Japanese companies learn and unlearn, Readings of the management of innovation, Edited by Tushman M and Moore W, Cambridge: Ballinger, 1988.
[39] Padmore T and Gibson H, Modelling systems of innovation: Ⅱ.A framework for industrial cluster analysis in regions, Research Policy, 1998, 26: 625-641.
[40] Park SO and Markusen A, Generalizing new industrial districts: a theoretical agenda and an application from a non-Western economy. Environment and Planning A, 1995, 27(1): 84-104.
[41] Pinch S and Henry N, Paul Krugman's geographical economics, industrial clustering and the British motor sport industry, Regional Studies, 1999, 33(9):815-827.
[42] Porter ME, Competitive advantage of nations, Harvard Business Review, 1991, 68(2): 82.
[43] Porter ME, Clusters and the new economics of competition, Harvard Business Review, 1998, 76(6): 77-90.
[44] Rosenberg N, Perspectives on technology, London: Cambridge University Press, 1976.
[45] Saxenian A, Regional advantage: culture and competition in Silicon Valley and Route 128, Cambrige: Harvard University Press, 1994.
[46] Schmitz H, From ascribed to earned trust in exporting clusters, Journal of International Economics, 1999, 48:139-150.
[47] Sternberg R and Tamasy C, Munich as Germany's No.1 high technology region: empirical evidence, theoretical explanations and the role of small firm/large firm relationships, Regional Studies, 1999, 33(4): 367-377.
[48] Storper M and Harrison B, Flexibility, hierarchy and regional development: the changing structure of industrial production systems and their forms of governance in the 1990s, Research Policy, 1991, 20: 407-422.
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