Developing a Framework for Evaluating and Predicting Management Innovation in Public Research Institutions
Round 1
Reviewer 1 Report
Innovation is difficult to predict because there are too many factors affected and there is a large amount of uncertainty. The method used in this paper to predict innovation is not desirable.
Author Response
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Reviewer 2 Report
Introduction
The introduction should be rewritten. The research question should be clearly proposed. The title of this paper is “Developing a framework for evaluating and predicting innovation of Public Research Institutions”. After reading the first paragraph, I would think that the research question is how to achieve sustainable innovation in response to rapid changes in the external environment. The authors should avoid causing this misunderstanding to readers. A review of previous research should be conducted. The last sentence of the second paragraph pointed out the lack of research on the measurement and prediction of innovation. However, the authors did not describe what past research has been done and the research gaps that exist. This statement seems strange to readers. Besides, the authors should have clearly stated the advantages of this study’s approach to measuring and predicting innovation compared to other methods.
On p.2 the authors stated that “Unlike traditional statistical methods, data mining is used in many social and natural sciences.” In reality, traditional statistical methods are widely used in the social and natural sciences.
Literature review and related work
There is only a brief mention of innovation predicting in the last paragraph of Section 2.1 on p.3. The authors should introduce the existing methods of predicting innovation and their shortcomings, and explain how the method in this study addresses these problems.
In the first paragraph of Section 2.2 on p.3, the connection between the spread of new knowledge and the place where innovation activities take place is a bit confusing. What viewpoint are the authors trying to express with the discussion on the spread of new knowledge?
The following literature may be helpful and can be included:
Hage J, Dewar R. Elite values versus organizational structure in predicting innovation[J]. Administrative science quarterly, 1973: 279-290.
Jun S P, Lee J S, Lee J. Method of improving the performance of public-private innovation networks by linking heterogeneous DBs: Prediction using ensemble and PPDM models[J]. Technological Forecasting and Social Change, 2020, 161: 120258.
Yan Y, Li J, Zhang J. Protecting intellectual property in foreign subsidiaries: An internal network defense perspective[J]. Journal of International Business Studies, 2022, 53(9): 1924-1944.
In the first paragraph of Section 2.3 on p.4, the authors again stated that “Unlike traditional statistical methods, data mining is used in many social and natural sciences.” It is not uncommon for traditional statistical methods to be used in the social and natural sciences. The advantages of data mining techniques over traditional statistical methods in predicting innovation are not included.
Research framework
In Section 3.1 on p.4 and p.5, the authors should describe in detail how to calculate the Catch-up index of public institutions. What data are involved and where do the data come from? The meaning of the symbols in Figure 1 on p.5, such as DMU, is not mentioned.
Does the fact that this study uses only Korean data affect the applicability of the technical framework and findings of this study in other settings?
What changes in policies related to research institutes led to the decline in innovation in the 2017-2018 and 2019-2020 periods on p.9?
Why is the 10-fold cross validation method more appropriate than the training-test method for analysis in the face of new environments due to infectious diseases after 2020 on p.10?
The evaluation of the data mining technique for the high-innovation institutions’ prediction reported in Section 4.3 is not consistent with the data shown in Table 3 on p.11.
Why are data mining techniques better at predicting innovation of high-innovation public research institutions than low-innovation ones on p.11? Some explanations are welcome here.
Discussion
Please provide some data to support the conclusions presented in Section 5 on p.12.
Given that data mining techniques insufficiently predict innovation of low-innovation public research institutions, do these conclusions still hold for them?
Conclusion
On p.13, line 16, there is a large number of studies exploring the measurement of innovation.
Please replace:
“The organization with successful innovation” with “An organization with successful innovation” on p.1
“gaining significant competitive advantage” with “gaining a significant competitive advantage” on p.1
“important factor” with “an important factor” on p.1
“explain the techniques used in the research” with “explains the techniques used in the research” on p.2
“explain the implication” with “explains the implication” on p.2
“Literature review and relate work” with “Literature review and related work” on p.2
“performance related pay were increased” with “performance-related pay was increased” on p.12
“performance related pay decrease” with “performance related pay decreases” on p.12
Author Response
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Author Response File: Author Response.pdf
Reviewer 3 Report
I enjoyed reading your paper and this topic sounds interesting. The comments are provided. Before resubmitting, the authors should consider the following:
1. The authors should explain how sustainable innovation relates to business environment. What is it? How do the organizations apply the sustainable innovation in their business activities?
2. Line 46, the authors pointed out that related research is insufficient. The authors have to elaborate this point.
3. How do big data relate to the measurement and prediction of innovation? The authors have to explain this.
4. What are your research questions (RQs) or research objectives (ROs)? The authors need to develop RQs/ROs and explain why the study is needed and what are the justifications for undertaking this study. Even though the authors mentioned the objective, it is not convincing readers. What is the gap(s) of research?
5. Line 81-87, please check. What is that?
6. Line 146, what is the value of innovation? There are many aspects of values.
7. Section s lacks the review of the evaluation of innovation. Since the authors pointed out two main activities which are the evaluation and the prediction of innovation.
8. Line 195, what does ‘FS’ stand for?
9. What is a database of collected data? What is the scope of the research institutes? Please elaborate.
10. Based on Table 1, what is the measurement of innovation? Is it a patent? Please elaborate.
11. How did the authors classify high and low innovation?
12. What is the content in Section 4.2? It seems a literature review of the data mining technique. It is not a result.
13. Based on the research objective, what is a proposed framework for evaluating and predicting innovation of public research institutes?
14. The authors should discuss the findings with literatures to see the same/similar/different findings with others.
15. The authors should provide the limitations of this study and suggest future research direction.
Author Response
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Round 2
Reviewer 1 Report
Although the theme of the paper has been adjusted, the practicality and effectiveness of the results are still questionable.
Author Response
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Reviewer 2 Report
Thanks. The authors has addressed my questions.
Author Response
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Reviewer 3 Report
The authors have extensively revised the manuscript. It is now ok for publication.
Author Response
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