Digital Economy, Industry–Academia–Research Collaborative Innovation, and the Development of New-Quality Productive Forces

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The paper successfully contextualizes its subject within existing literature and theoretical frameworks. However, consider elaborating on the key concept of "new-quality productive forces" in the introduction. Providing a concise definition and linking it explicitly to global trends in digital economies and collaborative innovation would make it more accessible to a broader audience.

 

The research design and hypotheses are well-articulated. However, the description of the spatial econometric and threshold models could be simplified for clarity. Adding a brief explanation of how these methods align with the research objectives and their advantages over other potential methods would help readers unfamiliar with these techniques.

 

The arguments are logical and well-supported by the findings. The paper could further strengthen its discussion by including more practical implications for policymakers or industry stakeholders. For instance, how can these findings inform actionable strategies to enhance industry-academia-research collaboration in regions with varying levels of digital economy development?

 

The article demonstrates strong engagement with current and relevant literature. To further enrich the manuscript, it might be useful to include international examples or comparisons that highlight how other countries leverage the digital economy and collaborative innovation to develop productive forces.

 

• Expand on "new-quality productive forces" early in the introduction to ensure accessibility for a global audience unfamiliar with the term.
• Provide a brief justification for the use of spatial econometric and threshold models, explaining their alignment with the research questions.
• Consider integrating practical recommendations for policymakers and stakeholders, making the findings actionable.

Comments on the Quality of English Language

The English is clear but can be improved to enhance readability and precision. For example, some sentences are long and could be broken into shorter, more concise statements. Engaging a professional editor or using advanced grammar-checking tools could help refine the language further.

Author Response

Comments 1: The paper successfully contextualizes its subject within existing literature and theoretical frameworks. However, consider elaborating on the key concept of "new-quality productive forces" in the introduction. Providing a concise definition and linking it explicitly to global trends in digital economies and collaborative innovation would make it more accessible to a broader audience.

Response 1: We recognize that clearly defining the concept of "new-quality productivity" in the introduction and closely linking it to the global trends of digital economy and collaborative innovation are crucial for enhancing the readability and appeal of our paper. In the revised manuscript, we have made the following improvements:(1) Concise Definition of "New-quality Productivity": At the outset of the introduction, we provide a clear and concise definition, emphasizing that new-quality productivity is a highly efficient and high-quality productivity driven by technological innovation and supported by breakthroughs in key disruptive technologies. It surpasses the traditional resource-intensive model and aligns with high-quality development, reflecting the new integration and connotations of the digital era. (2) Establishing Links with the Digital Economy: We elaborate on the intrinsic connection between new-quality productivity and the digital economy. We point out that the Fourth Industrial Revolution, led by the digital economy and artificial intelligence, is driving human progress and technological innovation at an unprecedented pace, with the digital economy emerging as a powerful driver for the formation of new-quality productivity. (3) Relating to the Global Trend of Collaborative Innovation: We also delve into the convergence points between new-quality productivity and the global trend of collaborative innovation. We emphasize that in the context of globalization, cross-domain and cross-industry collaborative innovation has become a vital force in driving technological advancements and industrial upgrading, with new-quality productivity being a concentrated manifestation of this trend. Through collaborative innovation, different entities can share resources, knowledge, and technologies, accelerating breakthroughs and applications of key disruptive technologies, and propelling the continuous leap of new-quality productivity. Through these improvements, we aim to more clearly define the concept of "new-quality productivity" and make its close connection with the global trends of digital economy and collaborative innovation more explicit, thereby attracting a wider audience to pay attention to and deeply understand our research topic.

 

Comments 2: The research design and hypotheses are well-articulated. However, the description of the spatial econometric and threshold models could be simplified for clarity. Adding a brief explanation of how these methods align with the research objectives and their advantages over other potential methods would help readers unfamiliar with these techniques.

Response 2: In the revised manuscript, we will make the following adjustments:(1) Simplifying Technical Descriptions: We will streamline the technical details of spatial econometrics and threshold models, retaining the core concepts and methodological frameworks to ensure that the descriptions are both accurate and easily understandable. (2) Emphasizing the Rationale for Model Selection: We will clearly articulate why these methods were chosen. For instance, in the selection of the threshold model, we will explain that the mutually reinforcing relationship between the digital economy and industry-university-research collaborative innovation may encounter a "threshold" effect due to discoordination between them, which hinders their joint promotion of new-quality productivity development. Consequently, the article constructs a panel threshold model equation with the digital economy and industry-university-research collaborative innovation as threshold variables. (3) Comparing Methodological Advantages: We will briefly compare the advantages of these methods with other potential approaches. For example, in the revised manuscript, we highlight the advantages of the SDM (Spatial Durbin Model) over the SAR (Spatial Autoregressive Model) and SEM (Spatial Error Model), which is the reason why the SDM was chosen for this study.

 

Comments 3: The arguments are logical and well-supported by the findings. The paper could further strengthen its discussion by including more practical implications for policymakers or industry stakeholders. For instance, how can these findings inform actionable strategies to enhance industry-academia-research collaboration in regions with varying levels of digital economy development?

Response 3: We have supplemented the policy recommendation section with the following content to further refine the relevant strategies:(1) Assess Digital Economy Development and Invest in Infrastructure: Policy makers should first assess the development status of the digital economy in various regions, identify digital divides, and invest in infrastructure such as high-speed internet and data centers to bridge the technological gap and provide a solid material foundation for industry-university-research collaboration.(2) Establish Regional Digital Resource Sharing Platforms: Encourage data interoperability, technology transfer, and knowledge sharing between developed and underdeveloped regions through the establishment of regional digital resource sharing platforms, thereby reducing the costs of industry-university-research collaboration and enhancing cooperation efficiency. (3) Customize Policies Based on Digital Economy Maturity: Tailor industry-university-research cooperation policies according to the digital economy maturity of different regions. In regions with a relatively backward digital economy, priority can be given to supporting basic research and application demonstration projects, while in more developed digital economy regions, higher-level innovation cooperation such as joint R&D centers and innovation alliances should be encouraged. (4) Strengthen Talent Cultivation in the Digital Economy and Industry-University-Research Collaboration: Increase efforts to cultivate talents related to the digital economy and industry-university-research collaboration, including setting up special funds, providing scholarships, and attracting external high-end talents, especially those with rich experience and innovative capabilities in the digital economy field.

 

Comments 4: The article demonstrates strong engagement with current and relevant literature. To further enrich the manuscript, it might be useful to include international examples or comparisons that highlight how other countries leverage the digital economy and collaborative innovation to develop productive forces.

Response 4: In response to your suggestions, we have added a literature review on the impact of digital economy and collaborative innovation on productivity in foreign contexts to our article. The supplementary content is as follows: Extensive research and discussions have been conducted both domestically and internationally on how the digital economy and collaborative innovation jointly drive productivity growth. In the realm of international research, In the realm of foreign research, on the one hand, scholars have pointed out that the digital economy has exhibited a significant positive effect on overall total factor productivity in Europe, emphasizing its key role in enhancing regional economic efficiency (Naqeeb U.R and Giulia N, 2023). Further, research on the manufacturing industry in Spain has found that the complementary application of digital technologies has a sustained promotional effect on long-term productivity improvement, indicating that the deep integration of digital technologies with traditional industries can stimulate new growth drivers (Maria T.B et al., 2021). Additionally, the construction of early-stage digital economy infrastructure has been proven to have a profound and positive impact on subsequent regional productivity enhancement, highlighting the importance of proactive planning for the digital economy (Emmanouil T, 2021). On the other hand, scholars have revealed the new opportunities brought by the combination of digital platforms and collaborative innovation. This combination not only accelerates the generation and diffusion of innovation outcomes but also increasingly becomes a core force driving the transformation of business operation models and efficiency improvement (Salvatore E.D.F, 2017). At the same time, the synergistic effect of innovation and information technology (IT) capital has also been proven to be an effective way to enhance productivity, with the two complementing each other and jointly promoting high-quality economic and social development (Landon K, 2014).

 

Comments 5: Expand on "new-quality productive forces" early in the introduction to ensure accessibility for a global audience unfamiliar with the term.

Response 5: In the introduction, we provide a clear and concise definition right at the beginning, emphasizing that new-quality productivity is an efficient and high-quality productivity driven by technological innovation and supported by breakthroughs in key disruptive technologies. It not only surpasses the traditional resource-intensive model but also aligns with high-quality development, reflecting the new integration and connotations of the digital era.

 

Comments 6: Provide a brief justification for the use of spatial econometric and threshold models, explaining their alignment with the research questions.

Response 6: The SDM model not only integrates the respective advantages of the SAR and SEM spatial models but also takes into account the spatial correlation between dependent and independent variables. Moreover, the SDM model can simultaneously capture the effects of spatial lag terms and spatial error terms, which neither the SAR nor the SEM model can achieve alone, making it more accurate in describing the relationships between spatial data. Meanwhile, in the empirical tests conducted in this paper, based on the Wald test and R² values, it was also confirmed that the SDM model has the best fitting effect, thus justifying its selection. In terms of the selection of the threshold model, the digital economy and industry-university-research collaborative innovation complement and interact with each other. The effect of their joint promotion of new-quality productivity development may encounter a "threshold" due to the lack of coordination between them. Therefore, a panel threshold model equation is constructed with the digital economy and industry-university-research collaborative innovation as threshold variables.

 

Comments 7: Consider integrating practical recommendations for policymakers and stakeholders, making the findings actionable.

Response 7: We fully recognize the importance of integrating practical recommendations for policy makers and stakeholders to make survey results actionable. We have supplemented the policy recommendations section based on the conclusion. For example, encourage the use of flexible and diverse cooperation models, such as project cooperation, talent exchange, and co building laboratories, to adapt to the actual needs of different regions, industries, and enterprises, and improve the pertinence and effectiveness of cooperation; The government should play a guiding role in promoting industry university research cooperation through policy incentives, financial support, and other means. At the same time, it should respect market laws and encourage enterprises to independently choose cooperation partners and methods based on market demand. Regular evaluation and dynamic adjustment, etc.

 

Comments 8: The English is clear but can be improved to enhance readability and precision. For example, some sentences are long and could be broken into shorter, more concise statements. Engaging a professional editor or using advanced grammar-checking tools could help refine the language further.

Response 8: Regarding the issue of sentence length you mentioned, I will attempt to break down long sentences into shorter, more easily understandable statements to ensure that the information is conveyed more directly and effectively. At the same time, I will also pay closer attention to the choice of grammar and wording to ensure the accuracy and professionalism of the expression. Furthermore, hiring a professional editor or using advanced grammar-checking tools is indeed a great suggestion. These resources can help me further refine my language and improve the overall quality of the text. I will actively consider utilizing these tools to enhance my writing.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

1. Literature review and evaluation section. The authors concentrate more on the theoretical connotations of new quality productivity for the literature review and lack a brief overview of existing empirical studies. For example, in China, some scholars have analyzed the impact of different macro variables such as business environment and financial agglomeration on the development of new quality productivity. Therefore, it is recommended that the authors add more literature on the empirical analysis of new quality productivity.

2. Theoretical analysis section. When the authors discuss how the digital economy affects the development of new quality productivity, it is recommended to add corresponding theoretical basis. In addition, the authors need to elaborate on the connection between digital economy and new quality productivity, avoiding platitudes and supplementing corresponding literature evidence. For example, statements such as “digital economy not only integrates and configures traditional elements but also facilitates the penetration of new elements” lack literature evidence.

3. The authors chose the sample data year to be 2012-2022, please explain why this time period was chosen for the sample and in particular why the start date was chosen to be 2012.

4. Explained variable construction section. Firstly, the authors have not explained the method of calculating the weights, so it is recommended to add it. Secondly, when the authors describe the variables, please choose the same words as in the table. For example, in the table, “worker” is used, but the authors use “labor” in their statement. Finally, the authors need to give the rationale for the selection of the sub-indicators.

5. There are some grammatical errors in the article, and the authors are advised to read it over and over again, check it, and embellish the language to avoid using too many longer sentences.

Comments on the Quality of English Language

There are some grammatical errors in the article, and the authors are advised to read it over and over again, check it, and embellish the language to avoid using too many longer sentences.

Author Response

Comments 1: Literature review and evaluation section. The authors concentrate more on the theoretical connotations of new quality productivity for the literature review and lack a brief overview of existing empirical studies. For example, in China, some scholars have analyzed the impact of different macro variables such as business environment and financial agglomeration on the development of new quality productivity. Therefore, it is recommended that the authors add more literature on the empirical analysis of new quality productivity.

Response 1: Based on your suggestions, we have not only included a brief overview of existing empirical research in the article but also added a literature review on the digital economy and collaborative innovation's impact on productivity in foreign studies. The supplemented content is as follows: Section on New-Quality Productivity, Regarding the influencing factors of new-quality productivity, research has primarily focused on macro-financial policies, such as financial agglomeration (Ren X Y et al., 2024), digital inclusive finance (Cui G R, 2024), science and technology financial policies (Huang X L and Xu H D, 2024), and green finance (Mao X M and Wang R Z, 2024; Wu W X and Chen X Y, 2024). These policies have played a crucial role in cultivating and enhancing new-quality productivity. At the same time, some scholars have delved into more specific factors, exploring the profound impact of effective allocation of data elements (Fu Z, 2024), the enabling role of ESG (Environmental, Social, and Governance) (Liu X X and Cao C Z, 2024), and the enhancement of human capital (Nie X and Liu L X, 2024) on the development of new-quality productivity. Foreign Research Section: In the realm of foreign research, on the one hand, scholars have pointed out that the digital economy has exhibited a significant positive effect on overall total factor productivity in Europe, emphasizing its key role in enhancing regional economic efficiency (Naqeeb U.R and Giulia N, 2023). Further, research on the manufacturing industry in Spain has found that the complementary application of digital technologies has a sustained promotional effect on long-term productivity improvement, indicating that the deep integration of digital technologies with traditional industries can stimulate new growth drivers (Maria T.B et al., 2021). Additionally, the construction of early-stage digital economy infrastructure has been proven to have a profound and positive impact on subsequent regional productivity enhancement, highlighting the importance of proactive planning for the digital economy (Emmanouil T, 2021). On the other hand, scholars have revealed the new opportunities brought by the combination of digital platforms and collaborative innovation. This combination not only accelerates the generation and diffusion of innovation outcomes but also increasingly becomes a core force driving the transformation of business operation models and efficiency improvement (Salvatore E.D.F, 2017). At the same time, the synergistic effect of innovation and information technology (IT) capital has also been proven to be an effective way to enhance productivity, with the two complementing each other and jointly promoting high-quality economic and social development (Landon K, 2014).

 

 Comments 2: Theoretical analysis section. When the authors discuss how the digital economy affects the development of new quality productivity, it is recommended to add corresponding theoretical basis. In addition, the authors need to elaborate on the connection between digital economy and new quality productivity, avoiding platitudes and supplementing corresponding literature evidence. For example, statements such as “digital economy not only integrates and configures traditional elements but also facilitates the penetration of new elements” lack literature evidence.

Response 2: In response to the issues you pointed out, we have made modifications to the theoretical hypothesis section of the manuscript. For example: (1) We have added corresponding theoretical foundations, such as Marx's theory of productivity and synergy theory. (2) We have enriched and increased the arguments for how the digital economy affects new-quality productivity. For instance: Fourth, the rise of the internet and digital platforms has brought unprecedented changes in business models to traditional industries. Take the sharing economy as an example, which is an important component of the digital economy. It has not only greatly expanded the market scope and reduced transaction costs but also increased market activity and flexibility (Gao S Y et al., 2017). This new business model not only meets the diversified needs of consumers but also promotes the effective utilization and optimal allocation of resources. Fifth, the development of the digital economy has also driven the rapid progress of cutting-edge technologies such as information technology, artificial intelligence, and blockchain (Shi Y, 2022). Through changes in quality, efficiency, and dynamics, these technologies provide strong support for innovation-driven sustainable development. They not only reshape production processes and value chains but also promote productivity innovation and transformation and upgrading across the entire society, laying a solid foundation for constructing a new development pattern in the digital economy era. (3) We have added literature evidence to the corresponding theoretical foundations. For example: The digital economy, with its unique characteristics, has nurtured the vigorous development of new organizational forms, new business models, and emerging industries. It is not only adept at efficiently integrating and optimizing traditional production factors (Zhou L and Ma J, 2024) but also greatly promotes the deep integration and widespread penetration of new elements (Shi D, 2022). This characteristic not only changes the comparative advantage pattern of the labor force but also accelerates the flow of labor from traditional industries to emerging industries, thereby achieving more precise and efficient resource allocation (Bai P W and Zhang Y, 2021).

 

Comments 3: The authors chose the sample data year to be 2012-2022, please explain why this time period was chosen for the sample and in particular why the start date was chosen to be 2012.

Response 3: The data from 2012 to 2022 was selected as the sample for the following reasons: (1) Around 2012, the digital economy began to enter a stage of rapid development globally. With the popularization of internet technology and the widespread use of mobile devices, new-generation information technologies such as big data, cloud computing, and artificial intelligence gradually matured and were applied in various fields, laying a solid foundation for the rise of the digital economy. (2) The period of 2012-2022 was chosen also because the data from this period is relatively complete and easily accessible. With the continuous improvement of the statistical system and the increasing level of information disclosure, the data from this period can more accurately reflect the development status of the digital economy and new-quality productivity.

 

Comments 4: Explained variable construction section. Firstly, the authors have not explained the method of calculating the weights, so it is recommended to add it. Secondly, when the authors describe the variables, please choose the same words as in the table. For example, in the table, “worker” is used, but the authors use “labor” in their statement. Finally, the authors need to give the rationale for the selection of the sub-indicators.

Response 4: Firstly, we will present the calculation formula for indicator weights in the section on explained variables in this paper; secondly, we will carefully check for consistency between the main text and the table content; thirdly, the following is a brief overview of the reasons for selecting the secondary indicators: As the core component of new-quality productive forces, the characteristics and value of new-quality laborers can be comprehensively summarized from four aspects: human capital investment, labor output, laborer skills, and labor productivity, encompassing six indicators: scientific investment, educational investment, number of R&D personnel, innovative R&D, human capital structure, and output per capita. These four aspects not only deeply reflect the internal qualities and external performances of new-quality laborers but also closely relate to the overall efficiency and development potential of new-quality productive forces. Firstly, human capital investment is a crucial indicator for measuring the growth and development of new-quality laborers. It covers investments in education, training, and other aspects, directly determining laborers' knowledge levels, skill proficiency, and innovative capabilities. In the context of new-quality productive forces, continuous human capital investment is key to enhancing laborers' comprehensive qualities and adapting to technological changes and industrial upgrades. Secondly, labor output reflects the productivity and contribution of new-quality laborers. High-output laborers can create more value for enterprises and society, driving sustained economic development. The improvement of labor output not only depends on laborers' skills and efforts but is also influenced by various factors such as the production environment and technological conditions, serving as a direct manifestation of new-quality laborers' effectiveness. Furthermore, laborer skills are one of the core elements of new-quality productive forces. Laborers with advanced skills and innovative capabilities can more effectively utilize new-quality means of labor, improving production efficiency and driving industrial upgrades. The enhancement of skills is not only crucial for individual career development but also serves as a driving force for the continuous progress of new-quality productive forces. Lastly, labor productivity is a comprehensive indicator for measuring the productivity of new-quality laborers. It comprehensively considers the relationship between labor input and output, reflecting laborers' production achievements per unit of time. Improving labor productivity is one of the core goals of new-quality productive force development and a key path to achieving high-quality economic development. In summary, summarizing new-quality laborers from the four aspects of human capital investment, labor output, laborer skills, and labor productivity provides a comprehensive and accurate revelation of their internal characteristics and external performances, offering strong support for the research and practice of new-quality productive forces.

As an important component of new-quality productive forces, the characteristics and value of new-quality objects of labor can be summarized from two aspects: emerging industries and ecological environment, mainly including five indicators: strategic emerging industries, emerging industry activity, environmental protection efforts, pollutant emissions, and pollutant treatment. This not only reveals the crucial role of new-quality objects of labor in the modern economy but also reflects their far-reaching impact on sustainable development. Firstly, from the perspective of emerging industries, new-quality objects of labor represent the latest achievements in technological innovation and industrial transformation. With technological advancements, more and more emerging technologies and products are being applied in production practices, becoming new objects of labor. For example, the development of new-generation information technology, biotechnology, new energy, and new materials has spawned numerous new-quality objects of labor with high technological content and added value. These objects of labor not only drive the rapid development of emerging industries but also provide strong support for the transformation and upgrading of traditional industries. By continuously expanding and optimizing objects of labor, new-quality productive forces can exert their effects in a wider range of fields, driving the optimization and upgrading of economic structures and the transformation of economic growth patterns. Secondly, from the perspective of the ecological environment, new-quality objects of labor embody the requirements of green development concepts and sustainable development strategies. Driven by new-quality productive forces, people are increasingly focusing on the environmental friendliness and resource efficiency of objects of labor. For example, by developing clean energy, promoting circular economy, and strengthening waste recycling and utilization, more green, low-carbon, and environmentally friendly new-quality objects of labor can be created. These objects of labor not only help reduce environmental pollution and ecological damage but also improve resource utilization efficiency, promoting sustainable economic and social development.

As a key element of new-quality productive forces, the characteristics and roles of new-quality means of labor can be deeply summarized from three aspects: infrastructure improvement, energy consumption levels, and technological innovation, including five indicators: traditional infrastructure, digital infrastructure, telecommunications penetration, overall energy consumption, and R&D investment. This not only comprehensively reflects the core position of new-quality means of labor in modern production but also reveals their important role in promoting high-quality economic and social development. Firstly, the improvement of infrastructure is the foundation for new-quality means of labor to exert their effectiveness. In modern production, efficient and convenient infrastructure such as transportation networks, information networks, and energy supply systems provide strong support for the efficient use of new-quality means of labor. The improvement of infrastructure not only enhances production efficiency but also promotes the optimal allocation of resource elements, laying a solid foundation for the rapid development of new-quality productive forces. Secondly, energy consumption levels are an important indicator for measuring the efficiency and environmental performance of new-quality means of labor. With the increasingly tense global energy situation and heightened environmental protection awareness, new-quality means of labor are increasingly focusing on controlling and optimizing energy consumption during their design and application processes. By adopting energy-saving technologies and improving energy utilization efficiency, new-quality means of labor can ensure production efficiency while reducing energy consumption, minimizing environmental pollution, and achieving a win-win situation for both economic and ecological benefits. Lastly, technological innovation is the core driving force for the continuous evolution of new-quality means of labor. With technological advancements, new-quality means of labor continue to emerge, with significant improvements in performance, functionality, and efficiency. Technological innovation not only drives the upgrading and replacement of new-quality means of labor but also spawns new production methods and business models, injecting strong momentum into the sustained development of new-quality productive forces.

 

Comments 5: There are some grammatical errors in the article, and the authors are advised to read it over and over again, check it, and embellish the language to avoid using too many longer sentences.

Response 5: Indeed, the accuracy and fluency of language are crucial for the expression of an article. I will follow your advice to carefully read, check, and polish the language of the article repeatedly, ensuring that each sentence is clear and precise, and avoiding the use of lengthy and complex sentence structures. At the same time, I will pay special attention to the correctness of grammar and spelling, striving to make the article even more perfect.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

1. The paper is coherent, but the introduction needs to deepen the background and necessity of studying the relationship between the digital economy and new quality productivity. It is recommended to supplement the relevant literature review to strengthen the theoretical support and frontier nature of the research.

2. It is suggested that the author provide more literature support for each hypothesis, including relevant theoretical frameworks, to enhance the credibility of the hypotheses.

3. The author is advised to elaborate on why specific spatial econometric models and panel threshold models were chosen, including the advantages and limitations of these models.

4. It is recommended that the author use different methods or datasets for robustness tests and provide a detailed explanation of the reasons for choosing these methods or datasets to verify the reliability of the research results.

5. In the conclusion, it is encouraged that the author include a discussion on future research directions, encouraging future researchers to continue exploring this field.

Author Response

Comments 1: The paper is coherent, but the introduction needs to deepen the background and necessity of studying the relationship between the digital economy and new quality productivity. It is recommended to supplement the relevant literature review to strengthen the theoretical support and frontier nature of the research.

Response 1: A thorough analysis of the background and necessity of deepening the relationship between the digital economy and new-quality productivity, along with a comprehensive literature review, is vital for enhancing the theoretical depth and cutting-edge nature of the paper. Based on the suggestions, we have deepened the introduction section as follows: Firstly, at the beginning of the introduction, we have added a description of the current status of the digital economy (In the context of today's global economic integration and rapid development of information technology, the digital economy has become a new engine driving economic and social development. It not only reshapes the operational models of traditional industries but also spawns a multitude of emerging industries, injecting new vitality into economic growth) before introducing the concept of new-quality productivity, in order to better reflect the historical background of studying the relationship between the digital economy and new-quality productivity. Secondly, we have enriched the literature section on the digital economy and new-quality productivity with the following content: The booming development of the digital economy provides powerful technical support and market space for the enhancement of new-quality productivity. The widespread application of digital technologies has reduced transaction costs (Pang R Z et al., 2023), improved resource allocation efficiency (Yi E W et al., 2023), and promoted industrial upgrading and innovation capability enhancement (Chen X D and Yang X X, 2021; Zhao C Y et al., 2021). At the same time, the continuous development of new-quality productivity also poses higher requirements for the digital economy, demanding more efficient, intelligent, and green digital services to support it (Liu Y J and Ji Y X, 2024). This illustrates the necessity of studying the relationship between the digital economy and new-quality productivity. Furthermore, we have also added a literature review on foreign research on the digital economy, innovation collaboration, and productivity, thereby reinforcing the necessity of our study on the digital economy and new-quality productivity.

 

Comments 2: It is suggested that the author provide more literature support for each hypothesis, including relevant theoretical frameworks, to enhance the credibility of the hypotheses.

Response 2: Based on your suggestions, we have enriched the theoretical hypothesis section and strengthened the literature review and integration. For example, in Theoretical Hypothesis 1, we have expanded and added more evidence to support the argument that the digital economy influences new-quality productivity. We have also increased the literature evidence for the corresponding theoretical basis of Hypothesis 1. Specifically, the digital economy, with its unique characteristics, has nurtured the vigorous development of new organizational forms, innovative business models, and emerging industries. It not only excels at efficiently integrating and optimizing traditional production factors (Zhou L and Ma J, 2024), but also significantly promotes the deep integration and widespread penetration of new factors (Shi D, 2022). This characteristic not only alters the comparative advantage structure of the labor force but also accelerates the flow of labor from traditional industries to emerging industries, thereby achieving more precise and efficient resource allocation (Bai P W and Zhang Y, 2021). Furthermore, we have added corresponding theoretical foundations, such as Marx's theory of productivity and synergy theory.

Comments 3: The author is advised to elaborate on why specific spatial econometric models and panel threshold models were chosen, including the advantages and limitations of these models.

Response 3: The SDM model not only integrates the respective advantages of the SAR and SEM spatial models but also considers the spatial correlation between dependent and independent variables. Moreover, the SDM model can simultaneously capture the effects of both spatial lag terms and spatial error terms (which the SAR and SEM models cannot achieve simultaneously), making it more accurate in describing the relationships between spatial data. Additionally, in the empirical tests conducted in this paper, based on the Wald test and R² values, it was confirmed that the SDM model has the best fitting effect, thus justifying its selection. The digital economy and industry-university-research collaborative innovation complement and interact with each other, and their joint promotion of new-quality productivity development may encounter a "threshold" due to incongruities between them. Therefore, a panel threshold model equation is constructed with the digital economy and industry-university-research collaborative innovation as threshold variables.

 

Comments 4: It is recommended that the author use different methods or datasets for robustness tests and provide a detailed explanation of the reasons for choosing these methods or datasets to verify the reliability of the research results.

Response 4: This paper conducts robustness checks from the following aspects: The method of extracting the keyword frequency of "digital economy" from provincial government work reports to comprehensively evaluate the digital economy variable may introduce the risk of sample bias due to potential exaggerated representations of digital economy content in government reports. To address this, the paper refers to the measurement methods of the digital economy proposed by Zhong W et al. (2023) and Liu J et al. (2020), and selects a total of 11 indicators from three dimensions: digital information infrastructure, the degree of enterprise digitization, and the development level of digital industries, to construct a comprehensive index for measuring the development level of the digital economy. The paper also follows the research approach of Song Y (2020) by dividing the measurement of the digital economy into direct and indirect effects. The direct effect is measured through three indicators: software product sales, information technology service fees, and the number of websites, while the indirect effect is assessed through three indicators: total e-commerce sales, the number of computers used, and total e-commerce purchases. The variation coefficient method is applied to these indicators to estimate the development level of the digital economy, and the regression analysis is conducted again by replacing the original digital economy variable. The results are shown in Table 6. The regression results in Table 6 indicate that after replacing the measurement method of the digital economy, apart from slight changes in coefficient sizes, there are no significant differences in significance and direction. This demonstrates that the research conclusions of this paper have a certain degree of stability and persuasiveness. To better verify the robustness of the conclusions, the paper additionally reports the estimation results of multiple spatial panel models in the independent effect analysis of the digital economy and industry-university-research collaborative innovation, in order to validate whether the empirical results are robust.

 

Comments 5: In the conclusion, it is encouraged that the author include a discussion on future research directions, encouraging future researchers to continue exploring this field.

Response 5: This paper has thoroughly analyzed the intrinsic relationship and interaction mechanism between the digital economy and new-quality productivity from a macro perspective, elucidating the promotive effect of the widespread development of the digital economy on the enhancement of new-quality productivity. However, while this macro perspective provides a crucial theoretical framework for us to grasp this complex phenomenon, there are still several research directions worth further exploring: (1) Refining the study of spatial effects: Further analyze the specific differences in the impact of the digital economy and industry-university-research collaborative innovation on new-quality productivity across different geographical spaces (such as cities, city clusters, provinces, regions, etc.), and explore the trends in spatial effects over time, i.e., whether the spatial spillover effects of the digital economy and industry-university-research collaborative innovation strengthen or weaken as time progresses. (2) Deepening the study of matching effects: Delve into the matching mechanisms between the digital economy and industry-university-research collaborative innovation, including the specific impacts of matching degrees, matching modes, matching paths, etc., on the enhancement of new-quality productivity. Through case studies or empirical analyses, explore the best practices for matching the digital economy and industry-university-research collaborative innovation in different industries and types of enterprises.

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Strengths

• Novel Topic: The study addresses a highly relevant topic, focusing on the interplay between the digital economy, industry-academia-research collaboration, and new-quality productive forces, with significant implications for high-quality economic development.
• Theoretical Framework: The article provides a clear and systematic theoretical framework, which strengthens its contribution to existing literature.
• Empirical Analysis: The use of spatial econometric models and panel threshold models offers a robust methodological approach, allowing for comprehensive insights into the relationships studied.
• Policy Implications: The conclusions include actionable recommendations, bridging the gap between theoretical research and practical application.

Suggestions for Improvement

1. Contextualization:

   • While the article refers to existing literature, a more detailed review of recent international studies on the digital economy's role in productivity could provide stronger contextualization.
   • Add further discussion comparing China's context with other countries' experiences to enhance global relevance.

2. Research Design and Methods:

   • Clearly articulate the rationale for selecting the study period (2012–2022). Could extending the analysis to more recent years (if data permits) yield richer insights?
   • Explain how the study addresses potential limitations of spatial econometric models, such as issues related to spatial heterogeneity or endogeneity.

3. Presentation of Results:

   • While the results are detailed, they could be summarized more effectively for readability, particularly the empirical findings on spatial effects and threshold effects.
   • Use visuals (e.g., heat maps for spatial correlation or charts illustrating threshold effects) to enhance the interpretation of the findings.

4. Clarity of Arguments:

   • The argument that the digital economy's effect is stronger than that of collaborative innovation could benefit from a deeper exploration of why this might be the case and how collaborative innovation might be enhanced to bridge this gap.
   • Elaborate on the mechanisms through which digital economy thresholds affect productivity gains, as this is crucial for understanding the broader implications.

5. Conclusions and Future Research:

   • Expand the conclusion section to provide a more detailed discussion of the study's limitations and areas for further research, such as sector-specific analysis or micro-level investigations.
   • Suggest more specific future research directions on the interplay between digital technologies and collaborative innovation at the firm or institutional level.

English Language Quality

• The English is clear and does not impede understanding, but it could be improved for better readability and precision. Consider refining sentence structures and avoiding overly long sentences in some sections.

Author Response

Comments 1: Contextualization:

• While the article refers to existing literature, a more detailed review of recent international studies on the digital economy's role in productivity could provide stronger contextualization.
• Add further discussion comparing China's context with other countries' experiences to enhance global relevance.

Response 1: In response to your comments, we have added a literature review on the role of the digital economy in productivity growth in foreign research to our article. The supplemented content is as follows: In the realm of international research, scholars have extensively explored the multifaceted positive impacts of the digital economy on productivity development. On one hand, studies have shown that the digital economy has exerted a significant positive effect on the overall total factor productivity of Europe, highlighting its central role in enhancing regional economic efficiency ^[4]. Further in-depth analysis of the Spanish manufacturing sector has revealed that the complementary application of digital technologies can continuously drive long-term productivity growth, fully demonstrating that the deep integration of digital technologies with traditional industries can stimulate new economic growth drivers ^[5]. Additionally, research has confirmed that the early construction of digital economy infrastructure has far-reaching and lasting positive impacts on subsequent regional productivity growth, emphasizing the importance of proactive strategic planning for the digital economy ^[6]. Meanwhile, the digital economy has demonstrated its powerful catalytic effect in multiple fields. For example, it can significantly promote the development of blockchain technology, providing strong support for emerging technological areas ^[7]; in 39 African countries, the widespread development of the digital economy has greatly contributed to economic growth ^[8]; the digital economy can also serve as a catalyst for achieving a circular economy, driving the green transformation of economic models ^[9]; for the West African region, the digital economy has provided more employment opportunities for youth, alleviating employment pressure ^[10]; furthermore, the digital economy has significantly accelerated the pace of economic globalization and international labor division, expanding its scope and making global economic connections closer ^[11]; finally, the interaction between the digital economy and international trade has also had a significant positive impact on economic growth in regions such as Africa, injecting new vitality into global economic cooperation and development ^[12].

After reviewing the literature both domestically and internationally, we have added a discussion on the commonalities and differences in experiences between China and other countries as follows: In summary, the digital economy has exerted a certain impact on productivity both domestically and internationally, with certain commonalities and differences observed. The commonality lies in the fact that the digital economy has become a key driver of economic growth and productivity enhancement in both developed and developing countries. The widespread application of digital technologies, such as cloud computing, big data, and artificial intelligence, has greatly promoted the improvement of production efficiency and the innovation of business models. The differences are mainly reflected in the stages, speeds, and policy environments of digital economy development across countries. As a latecomer in the digital economy, China has achieved rapid rise through government guidance, market-driven forces, and corporate innovation, among other efforts, and has taken the lead in certain fields. Other countries, such as those in Europe, although having started earlier, are still exploring and making progress in the deep application and cross-sector integration of the digital economy.

 

Comments 2: Research Design and Methods:

• Clearly articulate the rationale for selecting the study period (2012–2022). Could extending the analysis to more recent years (if data permits) yield richer insights?
• Explain how the study addresses potential limitations of spatial econometric models, such as issues related to spatial heterogeneity or endogeneity.

Response 2: The reasons for selecting data from 2012 to 2022 as the sample in this paper are as follows: (1) Around 2012, the digital economy began to enter a stage of rapid development globally. With the popularization of internet technology and the widespread use of mobile devices, new-generation information technologies such as big data, cloud computing, and artificial intelligence gradually matured and were applied in various fields, laying a solid foundation for the rise of the digital economy. (2) The period from 2012 to 2022 was chosen because the data during this time are relatively complete and easily accessible. With the continuous improvement of the statistical system and the enhancement of information disclosure, the data from this period can accurately reflect the development status of the digital economy and new-form productivity. We are well aware of the importance of data updating and have made great efforts to update the relevant information to the latest time point. However, unfortunately, due to practical difficulties in obtaining certain data (especially data on industry-university-research collaborative innovation), we were unable to fully update the data as we had hoped. We deeply apologize for this deficiency and will continue to work hard in the future to provide the latest and most accurate data to everyone more timely.

To address the potential spatial heterogeneity in the spatial econometric model, we employed the Moran's I index based on a geographic matrix to test the spatial correlation between the digital economy, industry-university-research collaborative innovation, and new-form productivity. Additionally, we utilized the Spatial Durbin Model to examine the relationships among these three elements. As an extension of the Spatial Lag Model and the Spatial Error Model, the Spatial Durbin Model takes into account the spatial correlation of both dependent and independent variables, further enhancing the model's ability to handle spatial heterogeneity. Furthermore, we introduced spatial lag terms into the spatial econometric analysis to mitigate the impact of endogeneity issues on the model's estimation results.

Comments 3: Presentation of Results:

• While the results are detailed, they could be summarized more effectively for readability, particularly the empirical findings on spatial effects and threshold effects.
• Use visuals (e.g., heat maps for spatial correlation or charts illustrating threshold effects) to enhance the interpretation of the findings.

Response 3: We fully acknowledge the importance of enhancing the readability of results and providing effective summaries, particularly for the empirical findings related to spatial effects and threshold effects. In the revised manuscript, we will take the following measures: We will provide a more concise summary of the results section, especially highlighting the key findings of spatial effects and threshold effects, to ensure that readers can quickly grasp the core conclusions of the study. For instance, in the section analyzing the independent effects of the digital economy and industry-university-research collaborative innovation, we have refined the conclusions and explained the reasons behind the phenomena to improve the readability of the results. Additionally, based on the threshold effect analysis methods used in existing research, we have conducted corresponding threshold effect analyses on the data in this paper. Please refer to Tables 8 and 9 for the specific results. Through these improvements, we believe that we can significantly enhance the readability of the paper and the explanatory power of the results, better presenting our research findings.

 

Comments 4: Clarity of Arguments:

• The argument that the digital economy's effect is stronger than that of collaborative innovation could benefit from a deeper exploration of why this might be the case and how collaborative innovation might be enhanced to bridge this gap.
• Elaborate on the mechanisms through which digital economy thresholds affect productivity gains, as this is crucial for understanding the broader implications.

Response 4: Here are the specific reasons why the impact of the digital economy on new-form productivity is greater than that of industry-university-research collaborative innovation, as well as suggestions on how to strengthen the latter to bridge this gap: The digital economy has a greater impact on new-form productivity than industry-university-research collaborative innovation, and its direct effect is greater than its indirect effect. This may be attributed to several factors. First, the digital economy, with data as its key production factor, accelerates information flow and optimizes resource allocation, directly driving a leap in production efficiency. This aligns closely with the high-efficiency and intelligence goals pursued by new-form productivity. Second, the digital economy has nurtured emerging industries and promoted the digital transformation of traditional industries. Its breadth and depth of impact far exceed that of collaboration in a single field, injecting new impetus into economic growth. Third, compared to the complexity of coordinating multiple parties in industry-university-research collaborative innovation, the digital economy, through technological innovation and model reform, can more directly and swiftly respond to market demands, driving the rapid development of new-form productivity. To bridge the gap between the digital economy and industry-university-research collaborative innovation in promoting new-form productivity, several measures should be taken. Firstly, we should strengthen the long-term cooperation mechanism among industry, universities, and research institutions, clarifying the division of labor and responsibilities. Secondly, we should increase policy and financial support, establishing special funds for this purpose. Thirdly, we should optimize talent cultivation and recruitment strategies, cultivating and attracting high-quality talents. Fourthly, we should promote the integration of digital technology with industry-university-research collaborative innovation, building digital platforms. Lastly, we should facilitate the transformation and industrialization of scientific and technological achievements, shortening the cycle from research and development to market launch.

We have further supplemented the detailed explanation of how the threshold value of the digital economy affects new-form productivity, as follows: Based on the results of the threshold effect test, this paper continues to conduct regression analysis on the model. The panel threshold regression results in Table 9 indicate that, with the rapid iteration of digital technologies and the swift development of the digital economy, the resulting innovative environment has enhanced the level of industry-university-research collaborative innovation across society and gradually exerted a positive impact on new-form productivity, becoming a strong driving force. This suggests that the digital economy is an effective means of unleashing the benefits of industry-university-research collaborative innovation. By strengthening such collaboration through the digital economy, we can enhance total factor productivity and maintain the sustained momentum for the development of new-form productivity. The regression results, with industry-university-research collaborative innovation as the threshold, reveal that when the level of such collaboration is below 0.628, the coefficient of the digital economy's impact on new-form productivity is positive but not significant. This is due to the low level of collaboration at this stage, leading to inefficient technology transfer and inadequate resource integration capabilities, thereby preventing the digital economy from fully realizing its potential in driving the development of new-form productivity. Additionally, an imperfect policy environment or inadequate implementation may also constrain the improvement of industry-university-research collaborative innovation, further affecting the driving role of the digital economy. When the level of industry-university-research collaborative innovation falls between 0.628 and 1.499, the coefficient of the digital economy's impact on new-form productivity shifts from insignificant to significantly positive. At this stage, collaborative innovation enhances the conversion and application capabilities of scientific and technological achievements, creating a synergistic effect with the digital economy to jointly promote the rapid development of new-form productivity. As the level of industry-university-research collaborative innovation continues to rise, exceeding 1.499, the regression coefficient of the digital economy's impact on new-form productivity becomes significantly positive and increases in value. High-level collaborative innovation strengthens technological innovation capabilities, forming a stronger synergy with the digital economy to accelerate the development of new-form productivity.

 

 

Comments 5: Conclusions and Future Research:

• Expand the conclusion section to provide a more detailed discussion of the study's limitations and areas for further research, such as sector-specific analysis or micro-level investigations.
• Suggest more specific future research directions on the interplay between digital technologies and collaborative innovation at the firm or institutional level.

Response 5: This paper profoundly analyzes the intrinsic connection and interaction mechanism between the digital economy and new-quality productivity from a macro perspective, clearly elucidating the significant role of the booming digital economy in enhancing new-quality productivity. However, constrained by the difficulty in data acquisition, several directions remain worthy of further exploration and excavation. Specifically, they are as follows:

(1) This paper only analyzes the impact of the digital economy on new-quality productivity from a macro perspective. Future research can further focus on the micro-enterprise perspective to deeply explore the specific role and operation mechanism of the digital economy on new-quality productivity at the micro level.

(2) By specifying the research object to a particular industry, such as manufacturing or resource-based enterprises, a more in-depth analysis can be conducted on the specific role and impact mechanism of the digital economy on new-quality productivity within that industry, providing more targeted strategic suggestions for industry digital transformation and productivity enhancement.

(3) Explore how companies or institutions leverage digital technologies (such as artificial intelligence, big data, cloud computing, etc.) to optimize the process of industry-university-research collaborative innovation, including project selection, cooperation modes, resource allocation, etc., thereby constructing a collaborative innovation model based on digital technologies to improve cooperation efficiency and outcome quality. Additionally, quantify the impact of digital technology application in industry-university-research collaborative innovation on cooperation performance, including technological innovation achievements, economic benefits, social benefits, etc., to reveal the actual value of digital technology in such collaborations and provide data support for company or institutional decision-making. Furthermore, investigate the influence of the current policy and regulatory environment on the application of digital technology in industry-university-research collaborative innovation, including laws and regulations related to intellectual property protection, data security, privacy protection, etc., to provide policy and regulatory suggestions for companies or institutions to promote industry-university-research collaborative innovation using digital technology in a legal and compliant manner.

(4) Further analyze the specific differences in the impact of the digital economy and industry-university-research collaborative innovation on new-quality productivity across different geographical spaces (such as cities, city clusters, provinces, regions, etc.), and explore the temporal changes in spatial effects, i.e., whether the spatial spillover effects of the digital economy and industry-university-research collaborative innovation increase or decrease over time.

 

Comments 6: The English is clear and does not impede understanding, but it could be improved for better readability and precision. Consider refining sentence structures and avoiding overly long sentences in some sections.

Response 6: Regarding the issue of sentence length you mentioned, I will try to break down long sentences into shorter and easier to understand statements to ensure that information is conveyed more directly and effectively. At the same time, I will also pay more attention to grammar and word choice to ensure accuracy and professionalism in expression.

Author Response File: Author Response.docx

Reviewer 3 Report

Comments and Suggestions for Authors

accept.

Author Response

Thank you for the opinions of the reviewers, which have helped me improve my article. Wishing you academic longevity