Research on the Evaluation of Regional Scientific and Technological Innovation Capabilities Driven by Big Data
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsThe authors presented RSTI capability evaluation model based on AHP-SMO human-machine fusion. The manuscript is well structured. A collaborative evaluation scheme of RSTI capability is presented. Figures 2, 4 , 6, 8, and 9 could be avoided. Practical and theoretical contributions were presented. Authors could compare their results with similar research and approaches. References are valid and well selected.
Comments on the Quality of English LanguageProof reading
Author Response
Thank you very much! We compare the proposed method with existing studies. For example, Paredes-Frigolett et al. (2014) adopted multi-criteria decision-making (MCDM) method to rank the performance of national innovation systems of Argentina, Chile, and Portugal. Hwangbo and Park (2021) adopted AHP to study the policy instruments related to the development of a regional innovation system in the Mekong Delta. Ture et al. (2019) used TOPSIS to evaluate the performance of 27 EU member countries in terms of each EU 2020 Strategy. Stanković et al. (2021) used VIKOR to rank the European cities according to their urban development indicators. However, research results show that many approaches become complex and challenging to maintain consistency within and among evaluators when there are many criteria. Compared with the methods in the above studies, the AHP-SMO method in this paper simplifies the evaluation criteria and evaluation objects, makes it easier for experts to reach a consensus, and effectively integrates expert opinions with machine learning insights to form complementary advantages.
Reviewer 2 Report
Comments and Suggestions for AuthorsThe article focuses on evaluation of regional scientific and technological innovation capability. For the purpose, the authors analyze new opportunities provided by big data and artificial intelligence. By doing this, the authors intend to demonstrate human-machine interaction in assessment of dynamic and significant phenomenon of regional scientific and technological innovation. The aim of the article ensures innovative design of the research and novel findings in the field. The choice to evaluate innovation capability by using big data and artificial intelligence is well grounded by demonstrating possible benefits for new knowledge and expertise. The authors explain the design of research in detail and provide discussion on possible benefits and shortages throughout the article. Systematical review of factors and evaluation models is provided. The novel contribution of this research appears also through elaboration of collaborative evaluation scheme for assessment of regional scientific and technological innovation capability. Overall, the article offers valuable scientific discussion on assessment approach for regional scientific and technological innovation capability.
Given complexity of the theme and design of the present article, some recommendations may be provided to the authors for possible improvements. It is recommended to the authors to define hypothesis/research question. It is recommended to the authors to precisely define a type of article – case study of region or methodological article. At present form, the article does not provide clear understanding whether the authors try to elaborate new assessment approach or just explain methodology and apply it for the case study of region. In conclusions, the authors write that they construct evaluation method. In case of methodological article, the present version does not ensure sufficient discussion/comparison/analysis with the existing and widespread assessment practices including also the newest sources. In case of focusing on case study of region, the article does not ensure regional background and sufficient discussion on previous scientific experience for regional scientific and technological innovation capability assessment at regional level. One more recommendation relates to the possible expansion of literature with more recent sources. In the present version, the authors little ground discussion and analysis with the newest research findings in such dynamic theme as innovation, big data, artificial intelligence in regional context. Overall assessment of the research is positive and the authors are endorsed to make some improvements.
Comments on the Quality of English LanguageThe authors need to proofread the article on sentence structure and style. For example, see lines 370-371: Based on the characteristics of RSTI capability, the evaluation criteria of RSTI capability ...
Author Response
1.It is recommended to the authors to define hypothesis/research question.
Answer：Therefore, the research problem of this paper is how to use multi-source heterogeneous big data, and combine the advantages of expert evaluation and machine learning to build an integrated model to scientifically evaluate RSTI capability.
2.In the present version, the authors little ground discussion and analysis with the newest research findings in such dynamic theme as innovation, big data, artificial intelligence in regional context.
Answer：At present, there are few studies on the evaluation of regional scientific and technological innovation ability using big data. Some studies only focus on how to use big data to analyze the innovation ability of industries and enterprises[43-46]. There are still a few studies that focus on the impact of government data opening on urban innovation capacity [47], but lacks in-depth analysis of evaluating urban innovation capacity based on big data. Therefore, this study enriches relevant theories and methods.
[43] Bai, Y., & Song, Y. (2022). CNS: Research on Regional Evaluation and Distribution Characteristics of Enterprise Technological Innovation Capability Based on Internet of Things and Big Data. International Journal of Cooperative Information Systems, 31(03n04), 2150004.
[44] Li, H., Zhang, Q., & Zheng, Z. (2020). Research on enterprise radical innovation based on machine learning in big data background. The Journal of Supercomputing, 76, 3283-3297.
[45] Ngo, N. D. K. (2019, July). A Method for Innovation Capability Evaluation in Banking. In 2019 16th International Conference on Service Systems and Service Management (ICSSSM) (pp. 1-6). IEEE.
[46] Guo, F., & Liu, Q. (2020, June). Research on Manufacturing Green Innovation Capability Based on Big Data Mining—Taking the New Energy Vehicle Companies as an Example. In IOP Conference Series: Earth and Environmental Science (Vol. 510, No. 3, p. 032023). IOP Publishing.
[47] Luo, Y., Tang, Z., & Fan, P. (2021). Could government data openness enhance urban innovation capability? An evaluation based on multistage DID method. Sustainability, 13(23), 13495.
3.In case of methodological article, the present version does not ensure sufficient discussion/comparison/analysis with the existing and widespread assessment practices including also the newest sources.
Answer：We compare the proposed method with existing studies. For example, Paredes-Frigolett et al. (2014) adopted multi-criteria decision-making (MCDM) method to rank the performance of national innovation systems of Argentina, Chile, and Portugal. Hwangbo and Park (2021) adopted AHP to study the policy instruments related to the development of a regional innovation system in the Mekong Delta. Ture et al. (2019) used TOPSIS to evaluate the performance of 27 EU member countries in terms of each EU 2020 Strategy. Stanković et al. (2021) used VIKOR to rank the European cities according to their urban development indicators. However, research results show that many approaches become complex and challenging to maintain consistency within and among evaluators when there are many criteria. Compared with the methods in the above studies, the AHP-SMO method in this paper simplifies the evaluation criteria and evaluation objects, makes it easier for experts to reach a consensus, and effectively integrates expert opinions with machine learning insights to form complementary advantages.
Reviewer 3 Report
Comments and Suggestions for AuthorsThe paper can hold a contribution to literature. I suggest some revisions before having a publication.
The paper demonstrates an adequate understanding of the relevant literature in the field.
The claim of filling a literature gap is valid. However, the mention of Anhui Province as the object to study the evaluation of the RSTI capability has to be justified properly, as it might raise questions about the generalizability of the findings.
The language is generally clear.
Conclusions should be expanded to better present the results of the study.
The structure of the paper could be improved for better flow, with a clear introduction, literature review, methods section, results, and discussion, each serving its purpose distinctly.
Author Response
Due to the integration of Anhui province into the Yangtze River Delta, it has made great progress in the field of scientific and technological innovation. For example, Hefei, the provincial capital of Anhui province, is known as a city of science and technology, with the most intensive layout of national major scientific projects besides the capital Beijing, and has made outstanding achievements in fields such as chips and artificial intelligence. Hefei's scientific and technological innovation has also led to the common progress of other cities in Anhui province. Therefore, many provinces and regions in China pay great attention to the innovative development path of Anhui Province, and send delegations to learn the advanced experience of innovative development in Anhui province. This paper chooses Anhui province as a good representative example. The research results of this paper also play a positive role in the improvement of scientific and technological innovation ability in other regions.
