| 国内外区域创新研究方法综述 |
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| 引用本文： | 苏屹,刘艳雪.国内外区域创新研究方法综述[J].科研管理,2019,40(9):14-24. |
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| 作者姓名： | 苏屹 刘艳雪 |
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| 作者单位： | 哈尔滨工程大学经济管理学院,黑龙江哈尔滨 150001;清华大学经济管理学院,北京100084;哈尔滨工程大学经济管理学院,黑龙江哈尔滨,150001 |
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| 基金项目： | 国家自然科学基金资助项目（71403066，2015-2017；71774036，2018-2021；71872057，2019-2022）；教育部人文社科青年项目（18YJC630245，2018-2020）；黑龙江省社会科学基金项目（17GLH21，2018-2019；18GLB023，2019-2020）；黑龙江省自然科学基金项目（QC2018088，2018-2020）；中央高校基本科研费专项基金（3072019CFW0901，2019-2019）。 |
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| 摘 要： | 区域创新作为地区经济发展的重要驱动力，引起了国内外学者的广泛关注，学者们采用纷杂的研究方法对区域创新问题进行研究。通过对近十年文献的查阅，梳理出国内外研究区域创新问题的三类比较典型方法：统计分析方法、前沿面分析方法、系统分析方法，并对三类研究方法进行比较分析。在此基础上，针对区域创新面对的新挑战，从大数据分析技术和人工智能两个方面，提出了未来区域创新应该重点关注的研究方法。
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| 关 键 词： | 区域创新 统计分析方法 前沿面分析方法 系统分析方法 |
A literature review on the regional innovation research methods |
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| Su Yi,Liu Yanxue .A literature review on the regional innovation research methods[J].Science Research Management,2019,40(9):14-24. |
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| Authors: | Su Yi Liu Yanxue |
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| Institution: | 1. School of Economics and Management, Harbin Engineering University, Harbin 150001, Heilongjiang, China; ;
2. School of Economics and Management, Tsinghua University, Beijing 100084, China |
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| Abstract: | In March 2019, China’s ‘two sessions’ (the National People’s Congress and Chinese People’s Political Consultative Conference) pointed out that it is necessary to focus on strengthening the scientific and technological innovation development plan, to strengthen the docking of various science and technology, talents, and industrial planning in various provinces and cities, and to study and plan regional science and technology innovation strategies. Regional innovation is the basis for planning regional science and technology innovation strategy. How to apply scientific methods to study regional innovation is particularly important. Through extensive reading of domestic and foreign literature on regional innovation, this paper finds that scholars are very concerned about regional innovation research, and the research methods adopted are also quite diverse. This paper attempts to sort out the research methods related to regional innovation, in order to lay a foundation for scholars to carry out relevant research.
First of all, based on China Knowledge Network (CNKI) and Web of Science databases, this paper uses “regional innovation” and “regional innovation” as the title and key words to retrieve the literature. Then, taking the important journals identified by the Ministry of Management Science of the National Natural Science Foundation of China, the journals sponsored by the National Social Science Foundation, SCI, SSCI, etc. as the criteria, this paper select “Scientific Research Management”, “China Soft Science”, “Technological Forecasting and Social Change”, “Journal of Cleaner Production” , etc. The representative classical literature are selected from these journals for detailed analysis. Due to space limitations, this paper only lists 40 main references at the end. This paper sorts out three typical methods for studying regional innovation problems: statistical analysis methods, frontier analysis methods and system analysis methods.
In thesecond part, regional innovation research based on statistical analysis methods is divided into two categories: regression analysis, dimensionality reduction and classification analysis. The regression analysis method is divided into two sub-categories: classical cross-section data analysis and non-classical cross-section data analysis. When using classical interface data analysis methods to study regional innovation, scholars mainly focus on linear regression analysis, time series regression analysis and spatial regression analysis methods. When using non-classical cross-section data analysis methods to study regional innovation, scholars focused on panel data regression analysis methods and discrete data regression analysis methods. Using regression analysis methods, scholars mainly studied the factors influencing regional innovation, technological innovation, regional innovation capability, and regional innovation performance. Scholars use the method of dimensionality reduction and classification analysis to study the regional innovation ability and innovation efficiency as well as the evaluation of innovation performance. The method of dimensionality reduction mainly refers to two aspects of factor analysis and projection pursuit analysis. The classification method mainly refers to cluster analysis.
In thethird part, regional innovation research based on frontier analysis methods is divided into two categories: data envelopment analysis (DEA) and stochastic frontier analysis (SFA). Scholars have applied the data envelopment analysis (DEA) methods and its improved model to study the efficiency measurement and performance evaluation of regional innovation systems, and the stochastic frontier analysis (SFA) method to study the influencing factors of regional innovation efficiency.
In thefourth part, regional innovation research based on system perspective is divided into four perspectives: regional innovation research based on system science analysis methods, regional innovation research based on social network analysis methods, the regional innovation research based on game theory analysis methods, regional innovation research based on ecological theory analysis methods. Applying system science analysis methods, the scholars mainly focus on the measurement of coupling coordination degree and order degree in various regions, the evaluation of innovation ability and collaborative evolution of regional innovation systems. Applying social network analysis methods, the scholars focus on the evolution path of innovation cooperation network, the influence factors of regional innovation network and innovation performance. Applying game theory analysis methods, the scholars focus on the main bodies of regional innovation system, such as enterprises and academic institutions, government-industry-university research, enterprises and enterprises. And the game mechanism among various innovators and the influencing factors of strategy selection are studied. Applying ecological theory analysis methods, scholars regard enterprises, universities and other innovators in the regional innovation system as different populations in the ecosystem. At the same time, relevant theories and models of ecology are introduced to study the coupling mechanisms, the dynamic evolution, the population effects and the population relationships in the innovation ecosystem.
In the fifth part, the relationship among statistical analysis method, frontier analysis method and system analysis method is discussed. The unreliability of statistical data determines the limitations of quantitative research methods. In view of the new challenges faced by regional innovation, this paper prospects the research methods that should be focused on in the future regional innovation from the following two aspects. (1) The arrival of the era of big data has spawned new regional concepts. Based on the big data platform, various scientific and technological resources can be fully integrated to meet the increasingly diverse and personalized demand for technological innovation, and the efficiency of innovation and the conversion rate of scientific and technological achievements can be improved greatly. Therefore, it will be an effective way to study regional innovation by using big data method. (2) With the advent of the electronic information age, the artificial intelligence methods will become the research methods of regional innovation. As the system theory is excessively abstract and rational, some research hypotheses are divorced from reality. Artificial intelligence research method, which integrates rationality and reality reasonably, will become a new research method of regional innovation research. |
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