The Construction and Empirical Study of the Maker Space Input-Output Model Under the Perspective of Resource Sharing
Volume 8, Issue 6, December 2019, Pages: 322-332
Received: Nov. 3, 2019;
Published: Dec. 3, 2019
Views 468 Downloads 109
Lv Bo, Business School, Beijing Wuzi University, Beijing, China
Yang Jing, Business School, Beijing Wuzi University, Beijing, China
Gu Qiaoling, Business School, Beijing Wuzi University, Beijing, China
Qi Meiru, Business School, Beijing Wuzi University, Beijing, China
Maker space is a shared network platform that brings together many social entities to innovate and start businesses. It has new features such as openness, resource sharing, and social subject diversity. The traditional methods of input factors measured by the organization’s original human, financial, material, research and development are no longer suitable for the development of today's era and economic application. Input-output model needs to be redefined and re-explored. This paper defines the diversified social subject structure of maker space under the perspective of resource sharing. According to the measurability standard of input variables, crowd-sourcing, public support, crowds, crowd-funding, and public research are used as new input variables. A nonlinear input-output model embodying the characteristics of resource sharing is constructed, and through the method of empirical analysis, the output effect of the new input variables is empirically studied. In order to improve the output of the maker space, this paper formulates the input-output path diagram in the field of resource sharing based on the output effect coefficient, and proposes countermeasures and suggestions for how to effectively invest in the innovative space, start up business, policies and public research. The input-output graph of this paper can better reflect the characteristics of the space, and the method is also applicable to other fields of empirical analysis.
The Construction and Empirical Study of the Maker Space Input-Output Model Under the Perspective of Resource Sharing, Social Sciences.
Vol. 8, No. 6,
2019, pp. 322-332.
Xiang Chunling. On the role of various social subjects in social management innovation [J]. Journal of the Party School of the Central Committee of the Communist Party of China, 2011, 5: 89-93. (in Chinese)
Beijing Maker Space Alliance, etc. 2016 Beijing Maker Space Blue Book [R]. 2016. (in Chinese)
Chen Dejin. Comparative Analysis and Empirical Enlightenment of Foreign Maker Space Business Models [J]. Science Management Research, 2017, 3:110-113. (in Chinese)
Wang Youmei, Ye Aimin. From Hackerspace to Maker Space: functional model and service path based on innovation 2.0 [J]. Research on Audio-Visual Education, 2015, 11: 5-12. (in Chinese)
Wang Liping, Liu Xiaolong. Research on the characteristics and operational mechanism of the integration of “four maker” in the maker space from the perspective of valuemaker space [J]. China Science and Technology Forum, 2017, 3: 110-116. (in Chinese)
Zhang Shaoli, Zheng Xiaoqi. Research on the Construction and Realization Path of College Maker Space [J]. Modern Education Management, 2017, 7: 54-59. (in Chinese)
Zhang Yuli, Bai Feng. Research on Spatial Evolution and Optimization of Maker Space Based on Dissipation Theory [J]. Science and Technology Management, 2017, 1: 22-28. (in Chinese)
Bigliardi B, Dormio A I, Nosella A, et al. Assessing science parks’ performances: Directions from elected Italian case studies [J]. Technovation, 2006, 26 (4):489-505.
Schwartz, M., and C. Hornych. Specialization as strategy for business incubators: An assessment of the Central German Multimedia Center [J]. Technovation, 2008, 28 (7): 436–449.
Michael Schwartz. Incubating and illusion? Long-term incubator firm performance after graduation [J]. Growth and Change, 2011, 42 (4): 491–516.
Barbero J, Casillas J C, Ramos A. Revisiting incubation performance: How incubator typology affects results [J]. Technological Forecasting and Social Change, 2012, 79 (5): 888-902.
Wann, Jong-Wen. University-based incubators’ performance evaluation: a benchmarking approach [J]. Benchmarking: An International Journal, 2017, 24 (1): 34-49.
Chen Wei, Xiang Liyao, Yu Rongjian. The Maker Space Entrepreneurship Ecosystem: Characteristics, Structure, Mechanism and Strategy——Taking Hangzhou Dream Town as an Example [J]. Business Economics and Management, 2015, 11: 35-48. (in Chinese)
Wen Meirong, Ma Ruoxi. Analysis of the Key Performance Indicator System for Constructing Public Policy Evaluation——Taking X City Trial Implementation of Maker Space Performance Evaluation System as an Example [J]. 2017, 3: 93-99. (in Chinese)
Li Yanping, Chen Wu. Current Status and Prospects of China’s Maker Space Research [J]. China Science and Technology Forum, 2017, 5: 12-18. (in Chinese)
JiaTianming, Lei Lianghai. Research on the Connotation, Type and Profit Model of Maker Space [J]. Contemporary Economic Management, 2017, 6: 13-18. (in Chinese)
Grimaldi R, Grandi A. Business incubators and new venture creation--an assessment of incubating models [J]. Technovation, 2005, 3: 11-12. (in Chinese)
Wang Liping, Liu Xiaolong. Research on the characteristics and operational mechanism of the integration of “four maker” in the maker space from the perspective of value maker space [J]. China Science and Technology Forum, 2017, 3: 109-116. (in Chinese)
Sarkis J. Analysis of the operational efficiency of major airports in the United States [J]. Journal of operations management, 2000, 2: 335-351.
Yang, Chih-Hai, Motohashi Kazuyuki, Chen Jong-Rong. Are new technology-based firms located on science parks really more innovative?--Evidence from Taiwan [J]. Research Policy, 2009, 38 (1): 77-85.
Wu Jianwei, Zhao Chunyan, Nan Shijing. Can State-level Incubators Improve the R&D Efficiency of Technology Enterprises——Based on the Tendency Matching Method Verification [J]. Science and Technology Progress and Countermeasures, 2017, 10: 76-82. (in Chinese)
Zhang Jiao, Yin Qun. Research on the Differences of Operational Efficiency of Business Incubators in China——Based on DEA and Cluster Analysis Methods [J]. Science of Science and Technology Management, 2010, 5: 171-177. (in Chinese)
Yang Wenbiao, Hu Hanhui. Analysis of Operational Efficiency of National Technology Business Incubator Based on DEA [J]. Statistics and Decision Making, 2015, 22: 175-178. (in Chinese)
Zhang Daning, Fu Xiaowei, Yi Pingtao. Research on the Development Path of Shenyang Maker Space Industry Cluster——Based on Operational Efficiency Measurement [J]. Journal of Northeastern University (Social Science), 2017, 1: 34-40. (in Chinese)
Liu Zhiyang. Optimizing the Ecological Environment of Innovation and Entrepreneurship--A Survey Report on the Development of Maker Space in China [N]. Guangming Daily, 2016, 12-7, 11 Edition. (in Chinese)
Milton Friedman. Monetary variability: United States and Japan [J]. Journal of Money Credit & Banking, 1983, 15 (3): 339-343.
Yang Lin, Qu Xiaodong. Summary of Research on Maker Space: Connotation Analysis, Theoretical Interpretation and Development Strategy [J]. Journal of Xi’an University of Finance and Economics, 2019, 32 (03): 121-128. (in Chinese)
Wu Yi, Jin Xia Ying, Zhang Wenyi. Maker Space: Research Summary and Prospects [J]. Technical Economy, 2018, 37 (12): 76-81. (in Chinese)