Start Switch for Innovation in “Construction Sequencing”: Research Funding

Round 1

Reviewer 1 Report (Previous Reviewer 1)

Comments and Suggestions for Authors

I think that the authors have worked out the article quite succinctly, eliminated the comments, and the article can be accepted for publication

Author Response

Dear Reviewer 1,                  

I would like to thank you for your very helpful comments.

The comments have helped me to complete the paper.

I would like to thank you again.

Best regards.

Reviewer 2 Report (New Reviewer)

Comments and Suggestions for Authors

 1、In the introduction of this article I suggest adding some real data to make it easier to demonstrate and to make the conclusion simple and logical.

2、The author divides the process of cluster formation into aggregation and innovation. Can we further illustrate the model construction with diagrams?

3、In this paper, China, Italy, the United Kingdom, the United States, Japan, Russia, France, Germany and South Korea are selected as the research objects of knowledge-intensive industries, and it is suggested that the author analyze whether the research conclusions are generalized and heterogeneous.

4、The authors explain the contributions and limitations of this paper in their conclusion, and I would like to know how they intend to further their research.

5、In addition, the author further examines the value added ranking of knowledge-intensive industries, and can it be compared with the research results and proposed conclusions of others?

Comments on the Quality of English Language

It can be improved further.

Author Response

Dear Reviewer 2,

I would like to thank you for your comments.  They helped me significantly to improve my paper.

My responses are as follows:

Reviewer 2: Comments and responses:

1. In the introduction of this article, I suggest adding some real data to make it easier to demonstrate and to make the conclusion simple and logical.

Response:　

p.5.

Tables 3.1~3.7 show the value added, patents, papers, research expenditure per researcher, and research expenditure per capita data used in this study. This study analyzes the relationship between research expenditure per researcher and the amount of value added created. No relationship was found between research expenditure per head of population and the creation of value added.

The relationship between research expenses per researcher and knowledge/technology services is the same for the first-ranked US and the ninth-ranked Russia. For research expenses per researcher, those of China, South Korea, Japan, Italy, UK, and France are around 50% of the first-ranked US. The countries with knowledge/technology services around 10% of the US, which is in first place, are Japan, Germany, France, the UK, and South Korea. However, Germany is tied with the US in first place. The country with the largest population (China, 1.4 billion) has 28% of the value added of the US.

2. The author divides the process of cluster formation into aggregation and innovation. Can we further illustrate the model construction with diagrams?

Response:　

p.1.

According to Fujita (2003), a cluster is the formation of an agglomeration and the activation of innovation within it. Cluster formation involves an industrial agglomeration step and an innovation activation step. Kanai and Ishida (2000) noted that studying the process of cluster formation is essential for the success of cluster policies. As shown in Figure 1, Kuchiki and Tsuji (2010) divided this process into two steps: Step I is agglomeration and Step II is innovation.

2. 8: 2.1

As shown in Figure 1 and Table 2, the functions of the segments are (i) the “master switch,” (ii) “accelerator,” and (iii) “brake.” Kuchiki and Sakai (2023) identified the master switch on the basis of the results of Krugman (1991) and Alonso (1964). Kuchiki (2023) and Kuchiki (2024) identified the accelerator and the brake, respectively, by drawing on the work of Helpman and Krugman (1985).

2. 8: 2.1

Figure 2 illustrates the process of cluster formation. In this study, the process of cluster formation involves Step I (agglomeration) and Step II (innovation). This study focuses on the "process" of segment building for activating innovation (Step II). This concept is defined as the sequencing of segments in the efficient construction of segments that forms an agglomeration. In the process of activating Step II (innovation), if the starting segment works, innovation will proceed and added value will be generated.

2. 8: 2.1

Then, we extend the model of Fujita and Thisse (2003) to prove the hypothesis that research expenditure per researcher Granger-causes value added. In this study, we define a “start switch” as the segment acting as the starting point for the process of innovation, as shown in Figure 2.

3. In this paper, China, Italy, the United Kingdom, the United States, Japan, Russia, France, Germany and South Korea are selected as the research objects of knowledge-intensive industries, and it is suggested that the author analyze whether the research conclusions are generalized and heterogeneous.

Response:　

p.11.

In this section, we aim to understand the results for the nine countries for which data were analyzed. These data cover knowledge-intensive industries and five types of manufacturing. The situation in the nine countries is outlined in terms of value added, patents, the number of publications, research expenditure per researcher, and research expenditure per capita. The nine countries were chosen because, with the exception of Russia, their policies have had a significant impact on research expenditure and value added per researcher and because of their high world rankings. In particular, we tried to identify the underlying preconditions that indicate that research expenditure per capita Granger-causes value added.

Table 3.1 shows that for both the added value of knowledge and technology services and for knowledge and technology manufacturing in knowledge-intensive industries, there are only eight countries with a value added of more than 5% of that of the country that ranks first, taking the value added of the country that ranks first as 100%. One country whose value added is below 3% of that of the country in first place is Russia. Table 3.5 shows that in the four manufacturing sectors, with the exception of electronics, the top seven countries have a value added of more than 5% of that of the top-ranked country, taking the value added of the country that ranked first as 100%. In addition, Russia is the only country where the value added is below 2% of that of the country in first place. In other words, the top eight countries in the knowledge-intensive and manufacturing industries occupy the top positions in the global value-added ranking.

This study therefore analyzed nine countries, including the top eight countries for knowledge-intensive industries, and Russia, as a country below the threshold of the global value-added rankings.

4. The authors explain the contributions and limitations of this paper in their conclusion, and I would like to know how they intend to further their research.

Response:　

p.21.

Fourth, as stated above regarding the actions of Japanese Nobel laureates, outside of the US, research funding per capita is low, and obtaining research funding is the first major obstacle for researchers in various research efforts. In this sense, it is essential to recognize that funding is the starting segment of research. For example, Nobel laureate Yamanaka (2024) also identified research funding activities in Japan as an important issue in his IPS cell research. However, further empirical research is needed on this point.

5. In addition, the author further examines the value added ranking of knowledge-intensive industries, and can it be compared with the research results and proposed conclusions of others?

Response:　 We are very sorry, but at present we have no plans to further research this issue.

6．Comments on the Quality of English Language

It can be improved further.

Response:　 The English editing of my paper has been done.

We certify that the following article Start Segment for Innovation in “Construction Sequencing”: Research Funding Akifumi Kuchiki * has undergone English language editing by MDPI. The text has been checked for correct use of grammar and common technical terms, and edited to a level suitable for reporting research in a scholarly journal. MDPI uses experienced, native English speaking editors. Full details of the edit

s to Reviewer FileEditViewInsertFormatToolsTableHelp Paragraph                        

Dear Reviewer 2,

I would like to thank you for your comments.  They helped me significantly to improve my paper.

My responses are as follows:

Reviewer 2: Comments and responses:

1. In the introduction of this article, I suggest adding some real data to make it easier to demonstrate and to make the conclusion simple and logical.

Response:　

p.5.

Tables 3.1~3.7 show the value added, patents, papers, research expenditure per researcher, and research expenditure per capita data used in this study. This study analyzes the relationship between research expenditure per researcher and the amount of value added created. No relationship was found between research expenditure per head of population and the creation of value added.

The relationship between research expenses per researcher and knowledge/technology services is the same for the first-ranked US and the ninth-ranked Russia. For research expenses per researcher, those of China, South Korea, Japan, Italy, UK, and France are around 50% of the first-ranked US. The countries with knowledge/technology services around 10% of the US, which is in first place, are Japan, Germany, France, the UK, and South Korea. However, Germany is tied with the US in first place. The country with the largest population (China, 1.4 billion) has 28% of the value added of the US.

2. The author divides the process of cluster formation into aggregation and innovation. Can we further illustrate the model construction with diagrams?

Response:　

p.1.

According to Fujita (2003), a cluster is the formation of an agglomeration and the activation of innovation within it. Cluster formation involves an industrial agglomeration step and an innovation activation step. Kanai and Ishida (2000) noted that studying the process of cluster formation is essential for the success of cluster policies. As shown in Figure 1, Kuchiki and Tsuji (2010) divided this process into two steps: Step I is agglomeration and Step II is innovation.

2. 8: 2.1

As shown in Figure 1 and Table 2, the functions of the segments are (i) the “master switch,” (ii) “accelerator,” and (iii) “brake.” Kuchiki and Sakai (2023) identified the master switch on the basis of the results of Krugman (1991) and Alonso (1964). Kuchiki (2023) and Kuchiki (2024) identified the accelerator and the brake, respectively, by drawing on the work of Helpman and Krugman (1985).

2. 8: 2.1

Figure 2 illustrates the process of cluster formation. In this study, the process of cluster formation involves Step I (agglomeration) and Step II (innovation). This study focuses on the "process" of segment building for activating innovation (Step II). This concept is defined as the sequencing of segments in the efficient construction of segments that forms an agglomeration. In the process of activating Step II (innovation), if the starting segment works, innovation will proceed and added value will be generated.

2. 8: 2.1

Then, we extend the model of Fujita and Thisse (2003) to prove the hypothesis that research expenditure per researcher Granger-causes value added. In this study, we define a “start switch” as the segment acting as the starting point for the process of innovation, as shown in Figure 2.

3. In this paper, China, Italy, the United Kingdom, the United States, Japan, Russia, France, Germany and South Korea are selected as the research objects of knowledge-intensive industries, and it is suggested that the author analyze whether the research conclusions are generalized and heterogeneous.

Response:　

p.11.

In this section, we aim to understand the results for the nine countries for which data were analyzed. These data cover knowledge-intensive industries and five types of manufacturing. The situation in the nine countries is outlined in terms of value added, patents, the number of publications, research expenditure per researcher, and research expenditure per capita. The nine countries were chosen because, with the exception of Russia, their policies have had a significant impact on research expenditure and value added per researcher and because of their high world rankings. In particular, we tried to identify the underlying preconditions that indicate that research expenditure per capita Granger-causes value added.

Table 3.1 shows that for both the added value of knowledge and technology services and for knowledge and technology manufacturing in knowledge-intensive industries, there are only eight countries with a value added of more than 5% of that of the country that ranks first, taking the value added of the country that ranks first as 100%. One country whose value added is below 3% of that of the country in first place is Russia. Table 3.5 shows that in the four manufacturing sectors, with the exception of electronics, the top seven countries have a value added of more than 5% of that of the top-ranked country, taking the value added of the country that ranked first as 100%. In addition, Russia is the only country where the value added is below 2% of that of the country in first place. In other words, the top eight countries in the knowledge-intensive and manufacturing industries occupy the top positions in the global value-added ranking.

This study therefore analyzed nine countries, including the top eight countries for knowledge-intensive industries, and Russia, as a country below the threshold of the global value-added rankings.

4. The authors explain the contributions and limitations of this paper in their conclusion, and I would like to know how they intend to further their research.

Response:　

p.21.

Fourth, as stated above regarding the actions of Japanese Nobel laureates, outside of the US, research funding per capita is low, and obtaining research funding is the first major obstacle for researchers in various research efforts. In this sense, it is essential to recognize that funding is the starting segment of research. For example, Nobel laureate Yamanaka (2024) also identified research funding activities in Japan as an important issue in his IPS cell research. However, further empirical research is needed on this point.

5. In addition, the author further examines the value added ranking of knowledge-intensive industries, and can it be compared with the research results and proposed conclusions of others?

Response:　 We are very sorry, but at present we have no plans to further research this issue.

6．Comments on the Quality of English Language

It can be improved further.

Response:　 The English editing of my paper has been done.

We certify that the following article Start Segment for Innovation in “Construction Sequencing”: Research Funding Akifumi Kuchiki * has undergone English language editing by MDPI. The text has been checked for correct use of grammar and common technical terms, and edited to a level suitable for reporting research in a scholarly journal. MDPI uses experienced, native English speaking editors. Full details of the editing service can be found at ► https://www.mdpi.com/authors/english. Basel, Switzerland October 2024

ing service can be found at ► https://www.mdpi.com/authors/english. Basel, Switzerland October 2024

Reviewer 3 Report (New Reviewer)

Comments and Suggestions for Authors

The proposed manuscript endeavors to address a certain gap in scholarly literature regarding the connections between research funding and innovation in industry. On the other hand, we have certain suggestions for improvement:

 

In the introduction, the author's references to Porter (1990), Porter (1998), Grashof and Fornahl (2020), Knoben et al (2015), Fang (2015), and Hernandez (2020) look more like a litany of very laconic summaries with only somewhat frail efforts at dissections/comparison. Perhaps the author(s) could try to write some more material on the ways in which these authors address aspects of this study. The author may wish to consider the idea of writing with more clarity in this critical literature discussion. This reviewer felt confused about the sentence fragment of "Fang (2014) finds this discrepancy is found," since the reviewer had no idea about the discrepancy to which Fang made a claim. A good yardstick for clarity would rest in the determination over whether a first-year graduate student in economics with only the most basic undergraduate background in the field would understand this literature section.

 

The author(s) should endeavor to demonstrate the relevance of every chapter and subsection to the central thesis of the manuscript (optimally at the very beginning of each chapter and subsection). For example, in section 2.3, we immediately find ourselves saddled with a highly specific detail on a specific kind of business model of Taiwan Semiconductor Manufacturing Company (TSMC), but the author(s) have to constantly connect the separate sections to the main argument of the monograph. We appreciate the presence of subsections to nominally facilitate the comprehension proclivities shown by the reading audience, but the presence of subsections does not excuse a scholar from the necessity of constantly reminding readers over the relevance of those individual subsections. In Chapter 3, we find a remark over the origins (United States National Science Foundation) of certain statistics, but again, our main interest would rest in the relevance of Chapter 3 to the main argument of the manuscript.

 

Sometimes, the manuscript reads more like a purely empirical exercise in the presentation of data, as opposed to the interpretation of data. Of course, in the empirical sciences, certain debates persist over the appropriateness and intensity with which authors should discuss the interpretation of data. Still, in order to provide some argumentative framework, the author(s) would do well to at least occasionally touch on the subtext (implied realities, as opposed to obvious facts) pregnant within the data. In this way, the reader might figure out the relevance of the data to the actual thesis of the article. We had this feeling in section 3.1 (Ranking of knowledge-intensive industries in terms of value added), but other sections arguably carried this running theme of data presentations outpacing any efforts to closely analyze the data's relevance to the substantiation of the thesis. In defense of the writing/argumentation of the author(s), at the end of section 3.3, one can see a sentence in which the author(s) propose to analyze "research expenditures per researcher," but readers would have appreciated a continuous effort to dissect the data throughout the paper, and not just after the presentation of paragraphs/compilations of data..

 

In the conclusion, we would have liked a little more insight on the "other methods" that the author offers as a way to reexamine the conclusions (final paragraph of the conclusion). In the same paragraph, we would have appreciated an exposition of the actual reasons for the so-called essential status of "analyzing the relationship between the conclusions of this paper and research expenditures." It seems that the author assumes that the audience shares the author's presupposition on the essential nature of this query, but the author might clarify the cogency of the conclusions by explaining this phenomenon in some more depth.

Author Response

Dear Reviewer 3,

I would like to thank you for your comments.  They helped me significantly to improve my paper.

My responses are as follows:

Reviewer 3: Comments and responses:

The proposed manuscript endeavors to address a certain gap in scholarly literature regarding the connections between research funding and innovation in industry. On the other hand, we have certain suggestions for improvement:

1．In the introduction, the author's references to Porter (1990), Porter (1998), Grashof and Fornahl (2020), Knoben et al (2015), Fang (2015), and Hernandez (2020) look more like a litany of very laconic summaries with only somewhat frail efforts at dissections/comparison. Perhaps the author(s) could try to write some more material on the ways in which these authors address aspects of this study. The author may wish to consider the idea of writing with more clarity in this critical literature discussion. This reviewer felt confused about the sentence fragment of "Fang (2014) finds this discrepancy is found," since the reviewer had no idea about the discrepancy to which Fang made a claim. A good yardstick for clarity would rest in the determination over whether a first-year graduate student in economics with only the most basic undergraduate background in the field would understand this literature section.

Response:　 

Certainly, as Reviewer suggests, this section was not well explained; Porter's analysis was deleted in the course of writing this paper, so I deleted this section.

p.4-5.

In prior studies, Hernandez (2020) analyzed innovation in relation to how it is adopted in clusters and how it can contribute to cluster-level, regional, and “national” development and competitiveness. They discussed important factors that were not considered in Porter's diamond model, such as the importance of the "multinational activity effect" and the role of the government in regulating regional and international interactions.

University–industry linkages (UILs) and the national innovation system (NIS) play a key role in explaining innovation according to Hershberg et al. (2007). Brimble and Doner (2007) found few UILs and weak NISs in the case of Thailand; Thai industry showed little interest in innovation at that time. Conversely, high-tech zones in China were built in close proximity to universities and public research institutes with the goal of promoting UILs, as shown by Chen and Kenney (2007) and Kuchiki (2021). Wu (2007) analyzed UILs in the context of NISs and found that UILs were exceptionally active in Beijing. However, it was not concluded that a UIL, an NIS, or any other segment was the starting segment for Step II.

Kanai and Ishida (2000) emphasized the importance of the cumulative process, as it takes time to build any segment of an agglomeration. The Mind Tools Content Team (2024) followed Weiss and Legrand (2011) and described a four-stage innovation process.

The above studies can be summarized regarding the purpose of this study as follows. First, these studies explored what determinants are involved in activating innovation in cluster formation, and they also explored the process of innovation activation. Integrating these ideas, this study focuses on the process of segment building for innovation in Step II of the cluster policy, as shown in Figure 2. The process of innovation proceeds through the construction of segments. We will attempt to identify a start switch for innovation.

2. The author(s) should endeavor to demonstrate the relevance of every chapter and subsection to the central thesis of the manuscript (optimally at the very beginning of each chapter and subsection). For example, in section 2.3, we immediately find ourselves saddled with a highly specific detail on a specific kind of business model of Taiwan Semiconductor Manufacturing Company (TSMC), but the author(s) have to constantly connect the separate sections to the main argument of the monograph. We appreciate the presence of subsections to nominally facilitate the comprehension proclivities shown by the reading audience, but the presence of subsections does not excuse a scholar from the necessity of constantly reminding readers over the relevance of those individual subsections.

Response:　

              p.10: 2.3.

In this subsection, Equation (8) is obtained via Equation (7). This equation is used to define the master switch in building an agglomeration. In this subsection, we study the case of a semiconductor agglomeration formed by Taiwan's TSMC, which built a factory in Kumamoto Prefecture.

In Step I of the industrial agglomeration shown in Figure 1, the master switch was to use subsidies to attract TSMC to Kumamoto Prefecture. As a result of the master switch being pushed, the semiconductor agglomeration in Kumamoto Prefecture started to form clusters. This is explained below.

Note that the hypothesis presented in the previous subsection of this article is the hypothesis regarding the starting switch of Step II following Step I (agglomeration). The following subsections test this hypothesis.

In this subsection, Equation (8) is also proposed for the master switch of agglomeration as a valid suggestion for the cluster policy. In other words, it has implications regarding what the policy should be for the threshold of transport costs in a broad sense, which should be lowered. The following equation was obtained by partial differentiation of C in Equation (7) via σ.

∂ C / ∂σ = 2 (σ - 1) μ (σ − μ) –2 [(σ + μ) / (σ − μ)]–σ > 0,

since  σ > 1 and 0 < μ < 1.            (8)

This equation shows that the smaller σ is, the smaller C is. In other words, as a breaking condition for the start of a knowledge industry-related agglomeration, agglomeration does not begin unless the reduction in its transport costs is greater than the threshold C in the case of heterogeneous goods and services in knowledge-intensive industries with lower elasticity of substitution.

TSMC created the dedicated IC semiconductor foundry business model when it was founded in 1987. In 2023, TSMC served 528 customers and manufactured 11,895 products for various applications covering a variety of end markets, including high-performance computing, smartphones, the Internet of Things (IoT), automotive, and digital consumer electronics (TSMC (2024)).

TSMC is capable of producing two nano semiconductors. This product has a very low elasticity of substitution σ. Theoretically, the breaking condition from symmetric equilibrium to agglomeration equilibrium cannot be established unless the transport costs in a broad sense are very low.

TSMC decided to expand its plant to Kumamoto Prefecture in Japan on October 14, 2021 (Nikkei (2024)). At that time, Japan's Ministry of Economy, Trade and Industry decided to provide JPY 476 billion (USD 3 billion; USD 1 = JPY 155) in subsidies for the first plant to manufacture 12 nano semiconductors (Ohta (2021)).

Subsequently, TSMC decided to expand to a second plant, and METI announced more subsidies. The subsidy for the second plant, which produces semiconductors of 6 nanometers with high product differentiation, i.e., a smaller σ, should be increased to up to JPY 732 billion (USD 4.72 billion), thereby lowering the threshold of transport costs. It can be interpreted that the "subsidy" broadly contributed to the reduction in transport costs.

Fifty-six companies announced their intention to establish or expand operations in Kumamoto by February 2024 (Ministry of Economy, Trade and Industry (2024)). TSMC's effect has led to a semiconductor-related industrial agglomeration around Kumamoto Prefecture. The agglomeration includes major Japanese semiconductor companies such as Ebara Corporation, Renesas Electronics, Tokyo Electron Kyushu, Mitsubishi Electric, and Fujifilm. New buildings, expansions, and new factories were constructed not only in Kumamoto Prefecture but also throughout Kyushu Island, including Rohm Semiconductor in Fukuoka Prefecture and Japan Semiconductor in Ohita Prefecture.

3. In Chapter 3, we find a remark over the origins (United States National Science Foundation) of certain statistics, but again, our main interest would rest in the relevance of Chapter 3 to the main argument of the manuscript.

Response:　

p.5.

Tables 3.1~3.7 show the value added, patents, papers, research expenditure per researcher, and research expenditure per capita data used in this study. This study analyzes the relationship between research expenditure per researcher and the amount of value added created. No relationship was found between research expenditure per head of population and the creation of value added.

The relationship between research expenses per researcher and knowledge/technology services is the same for the first-ranked US and the ninth-ranked Russia. For research expenses per researcher, those of China, South Korea, Japan, Italy, UK, and France are around 50% of the first-ranked US. The countries with knowledge/technology services around 10% of the US, which is in first place, are Japan, Germany, France, the UK, and South Korea. However, Germany is tied with the US in first place. The country with the largest population (China, 1.4 billion) has 28% of the value added of the US.

        p.11:3

In this section, we aim to understand the results for the nine countries for which data were analyzed. These data cover knowledge-intensive industries and five types of manufacturing. The situation in the nine countries is outlined in terms of value added, patents, the number of publications, research expenditure per researcher, and research expenditure per capita. The nine countries were chosen because, with the exception of Russia, their policies have had a significant impact on research expenditure and value added per researcher and because of their high world rankings. In particular, we tried to identify the underlying preconditions that indicate that research expenditure per capita Granger-causes value added.

Table 3.1 shows that for both the added value of knowledge and technology services and for knowledge and technology manufacturing in knowledge-intensive industries, there are only eight countries with a value added of more than 5% of that of the country that ranks first, taking the value added of the country that ranks first as 100%. One country whose value added is below 3% of that of the country in first place is Russia. Table 3.5 shows that in the four manufacturing sectors, with the exception of electronics, the top seven countries have a value added of more than 5% of that of the top-ranked country, taking the value added of the country that ranked first as 100%. In addition, Russia is the only country where the value added is below 2% of that of the country in first place. In other words, the top eight countries in the knowledge-intensive and manufacturing industries occupy the top positions in the global value-added ranking.

This study therefore analyzed nine countries, including the top eight countries for knowledge-intensive industries, and Russia, as a country below the threshold of the global value-added rankings.

The relationship between research expenditure per researcher and the value added of knowledge/technology services is the same for the US, which is in first place, and Russia in ninth place. In terms of research expenditure per researcher, China, South Korea, Japan, Italy, the UK and France have around 50% of that of the US. Countries with knowledge/technology services around 10% of that of the US are Japan, Germany, France, the UK, and South Korea. Germany is tied with the US in first place. In China, which has a population of 1.4 billion, the value added is 28% of that of the US.

4. Sometimes, the manuscript reads more like a purely empirical exercise in the presentation of data, as opposed to the interpretation of data. Of course, in the empirical sciences, certain debates persist over the appropriateness and intensity with which authors should discuss the interpretation of data. Still, in order to provide some argumentative framework, the author(s) would do well to at least occasionally touch on the subtext (implied realities, as opposed to obvious facts) pregnant within the data. In this way, the reader might figure out the relevance of the data to the actual thesis of the article. We had this feeling in section 3.1 (Ranking of knowledge-intensive industries in terms of value added), but other sections arguably carried this running theme of data presentations outpacing any efforts to closely analyze the data's relevance to the substantiation of the thesis. In defense of the writing/argumentation of the author(s), at the end of section 3.3, one can see a sentence in which the author(s) propose to analyze "research expenditures per researcher," but readers would have appreciated a continuous effort to dissect the data throughout the paper, and not just after the presentation of paragraphs/compilations of data..

 Response:　

        p.12: 3.

The relationship between research expenditure per researcher and the value added of knowledge/technology services is the same for the US, which is in first place, and Russia in ninth place. In terms of research expenditure per researcher, China, South Korea, Japan, Italy, the UK and France have around 50% of that of the US. Countries with knowledge/technology services around 10% of that of the US are Japan, Germany, France, the UK, and South Korea. Germany is tied with the US in first place. In China, which has a population of 1.4 billion, the value added is 28% of that of the US.

5. In the conclusion, we would have liked a little more insight on the "other methods" that the author offers as a way to reexamine the conclusions (final paragraph of the conclusion).

 Response:　

p.21.

However, there are many remaining issues. First, it is essential to re-examine our conclusions by examining other data and other methods, such as causal inference tools, Bayesian causality, and behavioral economics. For example, causal inference tools would ensure that the observed outcomes were a direct result of the intervention and were not confounded by other variables. Bayesian causality provides a probabilistic framework for understanding how rational agents should update their beliefs and make decisions under uncertainty. Behavioral economics provides a theoretical foundation for understanding decision-making.

6. In the same paragraph, we would have appreciated an exposition of the actual reasons for the so-called essential status of "analyzing the relationship between the conclusions of this paper and research expenditures." It seems that the author assumes that the audience shares the author's presupposition on the essential nature of this query, but the author might clarify the cogency of the conclusions by explaining this phenomenon in some more depth.

Response:　

p.21

Fourth, as stated above regarding the actions of Japanese Nobel laureates, outside of the US, research funding per capita is low, and obtaining research funding is the first major obstacle for researchers in various research efforts. In this sense, it is essential to recognize that funding is the starting segment of research. For example, Nobel laureate Yamanaka (2024) also identified research funding activities in Japan as an important issue in his IPS cell research. However, further empirical research is needed on this point.

Round 2

Reviewer 3 Report (New Reviewer)

Comments and Suggestions for Authors

The author(s) of this particular manuscript have broadly (and in much detail) addressed the proposed revisions point for point. Although we have the sense of the authors doing a lot more explaining in the "response letter" as opposed to the actual manuscript itself, we nonetheless feel that this paper has reached a stage for which the "minor revisions" status seems appropriate.

Author Response

Dear Reviewer 3,

Thank you very much for your valuable comments.

 

My responses are as follows:

 

I have made the minor corrections to the content and English throughout, based on the attached English editing. With your help, I hope it is now ready for submission. Thank you for your consideration.

 

Best regards.

Author

 

“We certify that the following article Start Segment for Innovation in “Construction Sequencing”: Research Funding Akifumi Kuchiki * has undergone English language editing by MDPI. The text has been checked for correct use of grammar and common technical terms, and edited to a level suitable for reporting research in a scholarly journal. MDPI uses experienced, native English speaking editors. Full details of the editing service can be found at ► https://www.mdpi.com/authors/english. Basel, Switzerland October 2024 english-85612” 

 

This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The article is written on a relevant topic. The article analyzes the steps, drivers and inhibitors of agglomeration formation. The article is aimed at expanding theoretical knowledge in the field of spatial economics. Complements existing research in this field. In the article, the authors propose an algorithm for the formation of agglomeration from the position of increasing the efficiency of mesosystems based on the theory of spatial economics. The article analyzes the causal relationships of the costs of mesosystem development with their performance indicators, including innovation and patent activity.

 

Comments on the article:

1. In the abstract, specify the hypothesis of the study and the results obtained. Expand the annotation.

2. From an economic point of view, the limit of integration in the formula to infinity does not seem logical (justification is needed)

3. What was the empirical basis for the calculations? It is necessary to justify the reliability of the sample and its "statistical purity"

4. In tables 4,5,6, a justification should be given *, **, ***

5. In tables 4,5,6, it is necessary to give a justification for the yellow fill

6. The list of references should be supplemented. There are few fresh sources in it, over the last 5 years. A lot of old articles.

7. In conclusion and in the discussion, it is necessary to justify how the results obtained by the authors relate to other studies in this field.

Author Response

Please find the attached. 

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The paper promises an empirical test of what it describes as a synthesis of different conceptualizations of economic clusters. In particular, it aims to identify what it calls “master switches” that have a significant effect on agglomeration and then the innovation performance of these agglomerations.

The paper explicitly refers to Porter’s diamond model of business environment conditions in its title. But in its analysis, it never draws on the core elements of the diamond approach. It reduces the diamond to narrow factor input conditions, for innovation in particular R&D expenditures. Such expenditures are clearly related to innovation outputs. But this would be suggested by a standard innovation production function; there is no inclusion of the systemic effects of all diamond elements.

In general, the discussion of the different schools of research on clusters lacks depth. The traditional economic geography literature cited develops powerful models where clusters emerge due to the existence of multiple equilibria in models with multiple locations, transportation costs, and external economies of scale in production. The Porter approach focuses on a richer qualitative framework of these external economies and focuses on a broader set of business environment conditions in each location that affect both firm-level efficiency and external economies.

The “sequencing approach” with the notion of “master switches” suggests that there specific input factors or transportation assets that create levels at which cluster dynamics or output performance “switch” once the values on them is reached. As far as transportation costs are concerned, this is consistent with the level of transportation costs at which multiple equilibria and those agglomerations emerge. There is no explanation as to why this should occur in innovation production. Porter’s approach does not explicitly suggest such trigger points. The empirical analysis in this paper also does not trace such trigger points but seems to assume a stable relationship between the level of transportation costs/R&D expenditure and the level of agglomeration/innovation.

The paper talks about clusters but never discusses the role of location. All of the empirical analysis is at the country level – for large countries with a large number of regions in which clusters are located.

The innovation data analysis does not include any quality or per capita analysis. China has undoubtedly become a key part of the global innovation system. But an approach that has China leading in all relevant knowledge fields seems not particularly robust.

In the end, the empirical analysis conducted provides no insights on clusters, the role of the diamond, or “master switches” (as far as I understand them). It ends up supporting the clear link between innovation inputs and outputs, which is not a new observation. I do like the attempt to bring a new conceptual lens to the overall discussion. And while I have not heard much about “sequencing economics” I would be interested to see this developed. But the paper in its current form does not achieve this.

Author Response

Please find the attached. 

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

The authors have done a good job in reducing the paper to what its empirical analysis supports, which is the positive link between R&D investments and innovation outcomes. But this is not a new finding, and there is no attempt to develop how it might add to what is already known about this not particularly surprising relationship.

The paper has in this form no relationship to Porters diamond model or clusters. It fails to recognize that R&D spending is fully captured in the diamond structure. The empirical analysis does also not indicate how the "switch" mechanism is operationalized in the econometric setup. 

Comments on the Quality of English Language

Language is fine