American Journal of Management Science and Engineering

| Peer-Reviewed |

Reasons for the Failure of Technology Incubator - Failure Mechanism and Empirical Study of Technology Incubation Platform Under the Background of Big Data

Received: 11 October 2019    Accepted:     Published: 08 November 2019
Views:       Downloads:

Share This Article

Abstract

In recent years, the technology incubation platform is facing a new ecological environment. The background of big data brought by cloud computing and big data has increased the random disturbance effect on technology incubation platform. The failure of some technology incubation platforms has caused academic controversies. This paper conducts theoretical research and empirical test for these academic controversies, and the empirical conclusions of this paper provide a more comprehensive and reasonable explanation for current academic controversies. In order to describe the failure phenomena of technology incubation platform, this paper innovatively proposes the concept of failure effects and failure coefficients, constructs failure effects model and deduces the failure mechanism formula by using the principle of Stochastic Frontier Analysis (SFA). On the basis of literature research, combined with the background characteristics of the big data, 4 dependent variables and 14 random influence variables were selected, and the Chinese technology incubator platform was taken as an example to empirically analyze failure effects model. The paper finds that the independent variables can be divided into three categories: positive, negative and partially irrelevant. When corresponding to negatively correlate variables or unrelated variables, dependent variables will show the failure phenomenon, that is, partial failure of technology incubation platform.

DOI 10.11648/j.ajmse.20190404.12
Published in American Journal of Management Science and Engineering (Volume 4, Issue 4, July 2019)
Page(s) 66-75
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Failure Effects, The Background of Big Data, Technology Incubation Platform, Stochastic Frontier Analysis (SFA), Mechanisms

References
[1] Ferguson R & Olofsson C. Science parks and the development of NTBFs. Location, survival and growth [J]. Journal of Technology Transfer, 2004, 29 (1): 5–17.
[2] Sutherland D. China’s science parks: production bases or a tool for institutional reform? [J]. Asia Pacific Business Review, 2005, 11 (1): 83–104.
[3] Harwit E. High-technology incubators: fuel for China’s new entrepreneurship? [J]. The China Business Review, 2002, 29 (4): 26–29.
[4] Tamasy C. Rethinking technology-oriented business incubators: developing a robust policy instrument for entrepreneurship, innovation, and regional development? [J]. Growth and Change, 2007, 38 (3): 460–473.
[5] Salador E, Rolfo S. Are incubators and science parks effective for research spin-offs? Evidence from Italy [J]. Science and Public Policy, 2011, 4: 170-184.
[6] Tondolo L P, BEL. Business incubators: many public investments, much noise, and the results? [J]. Journal of Management and Planning. Salvador, 2016, 17 (2): 298-313.
[7] Develear E J, Nijkamp P. The incubator hypothesis: revitalization of metropolitan areas? [J]. Annals of Regional Science, 1988, 22 (3): 48-56.
[8] Dutt N, Hawn O, Vidal M, Chatterji A, Mcgahan A. How open system intermediaries address institutional failures: the case of business incubators in emerging-market countries [J]. Academy of Management Journal, 2015, 59 (3): 818-840.
[9] Zhu Xiumei. Empirical Research on Innovation Path and Mechanism of High-tech Industry Clusters [J]. Chinese Industrial Economy, 2008 (10): 42-48. (in Chinese)
[10] Rice M P. Co-production of business assistance in business incubators- -An exploratory study [J]. Journal of Business Venturing, 2002 (17): 163-187.
[11] Aiger, Lovell, Schmidt, Meeusen. Formulation and estimation of stochastic frontier production function models [J]. Journal of Econometrics, 1977 (6): 21-37.
[12] Smilor RW. Managing the incubator system: critical success factors to accelerate new company development [J]. IEEE Transactions on Engineering Management, 1987, 34 (3): 146–155.
[13] Park G, Shin K, Han ST. A Study on the present conditions of technology business incubator and its efficient operation [J]. The Korean Small Business Review, 1999, 21 (2): 111–138.
[14] Lee JJ, Kim JS, Chun H K. A Study on the management and financial independence of university technology business incubators (UTBIs) in information and telecommunication industry [J]. Korean Small Business Review, 1999, 21 (2): 185–206.
[15] Hansen MT, Chesbrough HW, Sull DN. Networked incubators: houses of the new economy [J]. Harvard Business Review, 2000, 78 (5): 74–84.
[16] Pace G. Incubators as catalysts of academic spin-offs: evidence from the Israeli case-study [R]. The 42nd European Regional Science Association Congress, held 27–31 August 2002, Dortmund, Germany.
[17] Colombo MG, Delmastro M. How effective are technology incubators? Evidence from Italy [J]. Research Policy, 2002, 31 (7): 1103–1122.
[18] Link A N, Scott J T. The economics of university research parks [J]. Oxford Review of Economic Policy, 2007, 23 (4): 661–674.
[19] Schwartz M. Beyond incubation: an analysis of firm survival and exit dynamics in the post-graduation period [J]. Journal of Technology Transfer, 2009, 34 (4): 403–421.
[20] Mian SA. U.S. University ponsored technology incubator: an overview of management, policies, and performance [J]. Technovation, 1994, 14 (8): 515–529.
[21] Bakkali C. For a tool for measuring and controlling the performance of incubators [J]. Management International, 2013, 17 (3): 140-152.
[22] Li Chenguang, Zhang Yongan. An Empirical Study on the Impact of Regional Innovation Policy on Enterprise Innovation Efficiency [J]. Scientific Research Management, 2014, 5 (9): 25-34. (in Chinese)
[23] Lee S S, Jerome S, Osteryoung. A comparison of critical success factors for effective operations of university business incubators in the United States and Korea [J]. Journal of Small Business Management, 2004, 42 (4): 418–426.
[24] Zhang Li, Zhou Yongtao, Qi Ruqing. Analysis of incubator performance based on panel data of incubating enterprises [J]. Soft Science, 2016, 30 (11): 5-9 (in Chinese)
[25] Huang Hong, Xu Yuehui. Research on Operational Performance and Regional Differences of Chinese Technology Business Incubators [J]. Exploring Economic Issues, 2013 (7): 144-151 (in Chinese)
[26] AL-Mubaraki H, Holgoer. Measuring the effectiveness of business incubators: A four dimensions approach from a gulf cooperation council perspective [J]. Journal of Enterprising Culture, 2011, 19 (4): 435-452.
[27] Schwatrz M. Incubating an illusion? Long-term incubator firm performance after graduation [J]. Growth and Change, 2011, 42 (4): 491-516.
[28] Femandeza M, Francisco J, Jimeneza B, Juan R, Roura C. Business incubation: innovative services in an entrepreneurship ecosystem [J]. The Service Industries Journal, 2015, 35 (14): 783-800.
[29] Wu Wenqing, Yu Kexin, Liu Wenyi, Li Chaoqun. Research on Technology Business Incubator and Venture Capital Cooperation under Fair Preference [J]. Journal of Tianjin University (Social Science Edition), 2018 (2): 116-120. (in Chinese)
Author Information
  • Business School, Beijing Wuzi University, Beijing, China

  • Business School, Beijing Wuzi University, Beijing, China

  • Business School, Beijing Wuzi University, Beijing, China

Cite This Article
  • APA Style

    Lv Bo, Zhi Yechao, Gu Qiaoling. (2019). Reasons for the Failure of Technology Incubator - Failure Mechanism and Empirical Study of Technology Incubation Platform Under the Background of Big Data. American Journal of Management Science and Engineering, 4(4), 66-75. https://doi.org/10.11648/j.ajmse.20190404.12

    Copy | Download

    ACS Style

    Lv Bo; Zhi Yechao; Gu Qiaoling. Reasons for the Failure of Technology Incubator - Failure Mechanism and Empirical Study of Technology Incubation Platform Under the Background of Big Data. Am. J. Manag. Sci. Eng. 2019, 4(4), 66-75. doi: 10.11648/j.ajmse.20190404.12

    Copy | Download

    AMA Style

    Lv Bo, Zhi Yechao, Gu Qiaoling. Reasons for the Failure of Technology Incubator - Failure Mechanism and Empirical Study of Technology Incubation Platform Under the Background of Big Data. Am J Manag Sci Eng. 2019;4(4):66-75. doi: 10.11648/j.ajmse.20190404.12

    Copy | Download

  • @article{10.11648/j.ajmse.20190404.12,
      author = {Lv Bo and Zhi Yechao and Gu Qiaoling},
      title = {Reasons for the Failure of Technology Incubator - Failure Mechanism and Empirical Study of Technology Incubation Platform Under the Background of Big Data},
      journal = {American Journal of Management Science and Engineering},
      volume = {4},
      number = {4},
      pages = {66-75},
      doi = {10.11648/j.ajmse.20190404.12},
      url = {https://doi.org/10.11648/j.ajmse.20190404.12},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ajmse.20190404.12},
      abstract = {In recent years, the technology incubation platform is facing a new ecological environment. The background of big data brought by cloud computing and big data has increased the random disturbance effect on technology incubation platform. The failure of some technology incubation platforms has caused academic controversies. This paper conducts theoretical research and empirical test for these academic controversies, and the empirical conclusions of this paper provide a more comprehensive and reasonable explanation for current academic controversies. In order to describe the failure phenomena of technology incubation platform, this paper innovatively proposes the concept of failure effects and failure coefficients, constructs failure effects model and deduces the failure mechanism formula by using the principle of Stochastic Frontier Analysis (SFA). On the basis of literature research, combined with the background characteristics of the big data, 4 dependent variables and 14 random influence variables were selected, and the Chinese technology incubator platform was taken as an example to empirically analyze failure effects model. The paper finds that the independent variables can be divided into three categories: positive, negative and partially irrelevant. When corresponding to negatively correlate variables or unrelated variables, dependent variables will show the failure phenomenon, that is, partial failure of technology incubation platform.},
     year = {2019}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Reasons for the Failure of Technology Incubator - Failure Mechanism and Empirical Study of Technology Incubation Platform Under the Background of Big Data
    AU  - Lv Bo
    AU  - Zhi Yechao
    AU  - Gu Qiaoling
    Y1  - 2019/11/08
    PY  - 2019
    N1  - https://doi.org/10.11648/j.ajmse.20190404.12
    DO  - 10.11648/j.ajmse.20190404.12
    T2  - American Journal of Management Science and Engineering
    JF  - American Journal of Management Science and Engineering
    JO  - American Journal of Management Science and Engineering
    SP  - 66
    EP  - 75
    PB  - Science Publishing Group
    SN  - 2575-1379
    UR  - https://doi.org/10.11648/j.ajmse.20190404.12
    AB  - In recent years, the technology incubation platform is facing a new ecological environment. The background of big data brought by cloud computing and big data has increased the random disturbance effect on technology incubation platform. The failure of some technology incubation platforms has caused academic controversies. This paper conducts theoretical research and empirical test for these academic controversies, and the empirical conclusions of this paper provide a more comprehensive and reasonable explanation for current academic controversies. In order to describe the failure phenomena of technology incubation platform, this paper innovatively proposes the concept of failure effects and failure coefficients, constructs failure effects model and deduces the failure mechanism formula by using the principle of Stochastic Frontier Analysis (SFA). On the basis of literature research, combined with the background characteristics of the big data, 4 dependent variables and 14 random influence variables were selected, and the Chinese technology incubator platform was taken as an example to empirically analyze failure effects model. The paper finds that the independent variables can be divided into three categories: positive, negative and partially irrelevant. When corresponding to negatively correlate variables or unrelated variables, dependent variables will show the failure phenomenon, that is, partial failure of technology incubation platform.
    VL  - 4
    IS  - 4
    ER  - 

    Copy | Download

  • Sections