After 30 years of development, especially since Chinese government proposed the "Mass Entrepreneurship and Innovation" initiative in 2014, the number of China′s Technology Business Incubators (TBIs) has gained a dramatic growth. In 2017, the total number reached 4,069, making China the largest country in the world with the number of TBIs. Also, China′s TBIs have helped 40,000 technology companies to obtain 194 billion yuan of venture capital. The number of listed incubatees after graduation reached 2,777 which accounts for 1/7 of the listed companies on the GEM, and 1/10 of the listed companies in the New Third Board in 2007. However, the incubator research theory on China′s and global TBIs is still in its youth stage, and academic research lags far behind the practice due to the shortage of data.
The performance of TBIs depends on three main factors: the city, the incubator and the incubator. Existing research has the most adequate understanding of the role of incubators in incubation performance, and the importance of physical assets, service capabilities, knowledge reserves, financial support, and social networks has been confirmed. In recent years, studies have begun to focus on the impact of cities on incubation performance, especially the impact of factors such as urbanization, urban wealth, human capital, and technological development on technology business incubators. However, as far as we know, the literature on the effect of incubatees′ quality on hatching performance is quite limited. The comparative analysis of the three factors is even rarer.
Therefore, this paper hopes to use the latest large-scale statistical data of China′s technology incubator to enrich the incubator research theory and bridge the gap between theory and practice by answering three questions: First, whether cities, incubators and incubators have a positive impact on incubation performance? Second, the impact of the three main factors on the incubation performance is weak and weak? Third, which specific variables have the greatest impact on incubation performance? Through the research in this paper, in addition to supplementing existing theoretical research, it can also provide some reference for the work of policy makers, incubator managers and start-up entrepreneurs.
Based on the resource-based theory, this article compares the effects of resources of cities, TBIs and incubatees on incubation performance with the application of the data of 857 TBIs from 33 biggest cities in China from 2015 to 2017. We use Fisher discriminant analysis approach to conduct our research. First, we adopt research variables, both dependent and independent ones, on the basis of the literature research. Second, we test the effective predictive ability of the independent variables and construct the general discriminant equation. Third, we compare the discriminant equation models of the three types of high, medium and low results. Eventually we can obtain the final intensity comparison of the impact of the resources of the city, TBI and incubatees on incubation performance.
The study found that the resources of cities, TBIs and incubated enterprises all have positive impacts on the performance of incubation. Among them, the impact of incubated enterprises′ resources is the strongest, the TBIs′ is second, and the impact of cities′ resources on incubation performance is the weakest. The impact of incubated enterprises′ resources on incubation performance is the strongest, consistent with the conclusions of the relevant research literature on incubatee selection, which confirms the practical significance and value of the incubator setting in the selection indexes. Meanwhile, this also gives us another inspiration, that is, simply looking at the performance of the TBIs may not accurately reflect the level of their operations. We should consider to introduce some new evaluation methods which can eliminate the impact of incubatees as much as possible.
Second, TBIs′ resources also have an important influence on incubation performance, and the impact is only slightly lower than the incubatees′. This is consistent with the conclusions of previous large-scale incubator studies which proves the value of TBIs and provides a theoretical basis for incubator managers to improve the TBIs′ qualities of physical and consulting services. In addition, the combination of the above two conclusions confirms that the incubator and the incubatee can only achieve better results if they complement and work with each other.
Interestingly, the impact of urban innovation resources on incubation performance is the weakest which indicates the impact of urban macro factors on incubators and incubatees is not so strong as people intuitively imagined. Taking into account the data set used in this paper, maybe it also shows that the gap of the innovation environment among China′s largest cities has been converging. Furthermore, from the perspective of specific variables, human resources are the most important. Human resources variables are the most powerful indicators of prediction to incubation performances. Incubator service personnel including the number and quality of entrepreneurial instructors, and the reserves of technicians in the incubatees are very effective indicators of incubation performance.
This paper has some policy implications. First TBIs must establish screening and selecting indicators on incubatees. Establishing indicators for incubating enterprises is conducive to optimizing the allocation of resources, avoiding the phenomenon of "bad money drives out good", and promoting the positive interaction and development between enterprises within the incubator. Second, TBIs managers must upgrade the incubators′ resources and capabilities. While paying attention to physical assets and hardware configuration, they should pay more attention to the configuration and upgrade of "software" and strengthen the service capabilities of management and service personnel training, entrepreneurial tutors, and external networks. The third is to accumulate the human capital of TBIs and incubatees. It is necessary to establish effective talent introduction, incentive, training and service mechanisms to continuously improve human capital. TBIs should strengthen the training of employees and continuously improve the professional ability and professional level of the employees.
Key words
technology business incubator /
incubation performance /
comparative research /
discriminant analysis
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
References
[1]张志宏等.中国创业孵化30年：1987-2017[M], 2017，科学技术文献出版社。
Zhangzhihong et al. China Business Incubation 30 Years:1987-2017[M], 2017, Science and Technology Documentation Press.
[2]科学技术部火炬高技术产业开发中心和首都科技发展战略研究院.中国创业孵化发展报告2018[M], 2018，科学技术文献出版社。
Torch High Technology Industry Development Center of the Ministry of Science and Technology,Capital Institute of Science and Technology Development Strategy. China Business Incubation Development Report 2018[M],2018, Science and Technology Documentation Press.
[3]冯金余.科技企业孵化器的创新驱动效应研究[J].科研管理,2017，38(11):38-47.
Feng Jinyu. Research on the Innovation Driving Effect of Technology Business Incubators[J]. Science Research Management, 2017，38(11):38-47.
[4]Harper-Anderson E.,Lewis D.A.. What Makes Business Incubation Work? Measuring the Influence of Incubator Quality and Regional Capacity on Incubator Outcomes[J].Economic Development Quarterly, 2018,32(1):60-77.
[5]Rice M. P.,J. Matthews. Growing New Ventures, Creating New Jobs: Principles and Practices of Successful Business Incubation[M],Published by Praeger. 1995,32-33.
[6]Mian S.A..US University-sponsored Technology Incubators: an Overview of Management Policies and Performance[J]. Technovation, 1994, 14(8):515-528.
[7]McAdam M.,R. McAdam, High Tech Start-ups in University Science Park Incubators: the Relationship Between the Start-up’s Lifecycle Progression and Use of the Incubator's Resources[J].Technovation, 2008, 28(5):277-290.
[8]van Weele M., F. J. van Rijnsoever, F. Nauta. You Can’t Always Get What You Want: How Entrepreneur’s Perceived Resource Needs Affect the Incubator’s assertiveness[J]. Technovation, 2017, 59(2017):18-33.
[9]Carpenter R.,B. Petersen. Capital Market Imperfections, High-tech Investment, and New Equity Financing[J].The Economic Journal, 2002,112(477):54-72.
[10]Lai W. H., C. C. Lin. Constructing Business Incubation Service Capabilities for Tenants at Post-entrepreneurial Phase[J]. Journal of Business Research, 2015, 68(11):2285-2289.
[11]Stam W., S. Arzlanian, T. Elfring. Social Capital of Entrepreneurs and Small Firm Performance: a Meta-analysis of Contextual and Methodological Moderators[J]. Journal of Business Venturing, 2014,29(1):152-173.
[12]PeEr A., T. Keil. Are All Startups Affected Similarly by Clusters? Agglomeration, Competition, Firm Heterogeneity, and Survival[J]. Journal of Business Venturing, 2013, 28(3):354-372.
[13]Rantisi N. M., D. Leslie, S. Christopherson. Placing the Creative Economy: Scale, Politics, and the Material[J]. Environment and Planning A, 2006,38(2006):1789-1797.
[14]Acs Z. J.,L. Szerb. The Global Entrepreneurship Index (GEINDEX)[J]. Foundations and Trends in Entrepreneurship, 2009, 5(5):341-435.
[15]Pe?a I.. Business Incubation Centers and New Firm Growth in the Basque Country[J]. Small Business Economics, 2004, 22(3-4):223-236.
[16]Florida R.. The Rise of the Creative Class[M], 2002, Published by Basic Books.
[17]胡永平.技术专家高管、风险投资与创业企业技术研发——基于创业期差异视角的实证研究[J].经济经纬,2018，35（5）:108-114.
HU Yong-ping. Technical Expert Executives，Venture Capital and Technological R＆D of Venture Enterprises——An Empirical Study Based on Differences in Venture Stages[J]. Economic Survey, 2018，35（5）:108-114.
[18]钟卫东, 孙大海,施立华.创业自我效能感、外部环境支持与初创科技企业绩效的关系——基于孵化器在孵企业的实证研究[J].南开管理评论,2007,10(5):68-74.
Zhong Weidong, Sun Dahai, Shi Lihua. Relationships among Entrepreneurial Self-efficacy , External Environmental Support and Performance of Newly established High-Tech Venture: An Empirical Study Based on Tenant Companies of Business Incubators[J].Nankai Business Review, 2007,10(5):68-74.
[19]关成华,袁祥飞.直接投资、财政补贴和税收优惠——基于全国孵化器数据的比较研究[J].北京社会科学,2018,8(2018):107-119.
Guan Chenghua, Yuan Xiangfei. Direct Investment, Financial Subsidies and Tax Incentives——Comparative Analysis Based on National Incubator Data[J].Beijing Social Science,2018,8(2018):107-119.
[20]Yin B.,J. Luo. How Do Accelerators Select Startups? Shifting Decision Criteria Across Stages[J]. IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT, 2018, 65(4):574-589.
[21]Merrifield D.B.. New Business Incubators[J].Journal of Business Venturing, 1987, 2(4):277-284.
[22]Wulung R. S., K. Takahashi, K. Morikawa. An Interactive Multi-objective Incubatee Selection Model Incorporating Incubator Manager Orientation[J]. Operational Research, 2014, 14(3):409-438.
[23]Hackett S. M.,D. M. Dilts. A Real Options-driven Theory of Business Incubation[J].The Journal of Technology Transfer, 2002, 29(1):41-54.
[24]Aerts K.,P. Matthyssens, K.Vandenbempt. Critical Role and Screening Practices of European Business Incubators[J]. Technovation, 2007, 27(5):254-267.
[25]Bergek A.,C. Norrman. Incubator Best Practice: a Framework[J]. Technovation, 2008, 28(1-2):22-28.
[26]李庆博,刘西明.科技企业孵化器创新效率评估及关系研究[J].科技管理研究,2018，2018（4）:59-63.
LI Qingbo, LIU Ximing. Incubator Innovation Efficiency Evaluation and Relationship[J]. Research Science and Technology Management Research,2018，2018（4）:59-63.
[27]赵天燕,郭文.江苏科技企业孵化器运营效率研究[J].江苏社会科学, 2018，2018（3）:264-272.
Zhao Tianyan, Guo Wen.Study on the Operation Efficiency of Jiangsu Science and Technology Business Incubator[J].Jiangsu Social Sciences, 2018, 2018（3）:264-272.
[28]Fukugawa F.. Is the Impact of Incubator’s Ability on Incubation Performance Contingent on Technologies and Life Cycle Stages of Startups?: evidence from Japan[J].International Entrepreneurship & Management Journal，2018,2018(14):457-478.
[29]Amélie Charles, Etienne Redor, Constantin Zopounidis. The determinants of the existence of a critical mass of women on boards: A discriminant analysis[J]. Economics Bulletin, 2015,35(3):185-197.
[30]IDDA Lorenzo, FURESI Roberto, PULINA Pietro. Human Capital and Farm Households: A Map of Management Models in Italy[J]. Economics of Agriculture, 2018, 2018(57): 45-56.
[31]Asma Sghaier,Sami Ben Jabeur,Boutheina Bannour. Using partial least square discriminant analysis to distinguish between Islamic and conventional banks in the MENA region[J].Review of Financial Economics,2018,36(2):133-148
[32]施锡铨,邹新月.典型判别分析在企业信用风险评估中的应用[J].财经研究, 2001,27(10):53-57.
Shi Xiquan, Zou Xinyue.The Appliation of Canonical Discriminate Analysis in Credit Risk Evaluation of Enterprise[J].The Study of Finance and Economics,2001,27(10):53-57.
[33]方洪全,曾勇,何佳. 多标准等级判别模型在银行信用风险评估中的应用研究[J].金融研究,2004,2004（9）:92-100.
Fand Hongquan, Zeng Yong, He Jia. Application Research of Multi-standard Grade Discriminant Model in Bank Credit Risk Assessment[J]. Journal of Financial Research, 2004, 2004（9）:92-100.
[34]高培业，张道奎.企业失败判别模型实证研究[J].统计研究,2000,2000（10）:46-51.
Gao Peiye, Zhang Daokui. An Empirical Study on Enterprise Failure Discrimination Model[J].Statistical Research, 2000, 2000（10）:46-51.
[35]朱宗乾,朱怡琼,廖貅武,王万东.ERP实施中的员工抵制风险及其评估模型[J].系统管理学报, 2017, 26(1):107-116.
Zhu Zongqian, Zhu Yiqiong, Liao Xiuwu, Wang Wandong. Evaluation Model of Employees Resistance in ERP Implementation Based on Bayes Classification Analysis[J].Journal of Science and Management, 2017, 26(1):107-116.
[36]Xiao, L.,D. North. The Role of Technological Business Incubators in Supporting Business Innovation in China: a Case of Regional Adaptability? [J]. Entrepreneurship & Regional Development, 2018,30(1-2):29-57.
[37]Florida R., Mellander C.,Stolarick K. Inside the black box of regional development—human capital, the creative class and tolerance[J].Journal of economic geography,2008,8(5):615-649.
PDF(1274 KB)
