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Promoting technological innovation is important to address the complexity of major engineering challenges. Technological innovations include short-term innovations at the project level and long-term innovations that can enhance competitive advantages. The purpose of this study is to develop an incentive mechanism for the public sector that considers short-term and long-term efforts from the private sector, aiming to promote technological innovation in major engineering projects.

This study constructs an incentive model considering the differences in short-term and long-term innovation efforts from the private sector. This model emphasizes the spillover effect of long-term efforts on current projects and the cost synergy effect between short-term and long-term efforts. It also explores the factors influencing the optimal incentive strategies for the public sector and innovation strategies for the private sector.

The results indicate that increasing the output coefficient of short-term and long-term efforts and reducing the cost coefficient not only enhance the innovation efforts of the private sector but also prompt the public sector to increase the incentive coefficient. The spillover effect of long-term innovation efforts and the synergy effect of the two efforts are positively related to the incentive coefficient for the public sector.

This research addresses the existing gap in understanding how the public sector should devise incentive mechanisms for technological innovation when contractors acting as the private sector are responsible for construction within a public-private partnership (PPP) model. In constructing the incentive mechanism model, this study incorporates the private sector's short-term efforts at the project level and their long-term efforts for sustained corporate development, thus adding considerable practical significance.

This paper aims to explore the effect of teacher–student collaboration on academic innovation in universities in different stages of collaboration.

Based on collaboration life cycle, this paper divided teacher–student collaboration into initial, growth and mature stages to explore how teacher–student collaboration affects academic innovation.

Collecting data from National Science Foundation of China, the empirical analysis found that collaboration increases the publication of local (Chinese) papers at all stages. However, teacher–student collaboration did not significantly improve the publication of international (English) papers in the initial stage. In the growth stage, teacher–student collaboration has a U-shaped effect on publishing English papers, while its relationship is positive in the mature stage.

The results offer suggestions for teachers and students to choose suitable partners and also provide some implications for improving academic innovation.

This paper constructed a model in which the effect of teacher–student collaboration on academic innovation in universities was established.

Examine the effects of sudden environmental disasters on the advancement of both renewable and conventional energy technologies.

Utilizing panel data from 31 Chinese provinces spanning 2011 to 2022, the SEM (Spatial Error Model) dual fixed model is utilized to examine the impact of sudden environmental disasters on energy technologies.

The findings reveal that: (1) Sudden environmental disasters exert a markedly positive influence on the Innovation of Renewable Energy Technologies (IRET), while their impact on conventional energy technologies is positively non-significant. (2) Sudden environmental disasters not only significantly enhance innovation in local renewable energy technologies but also extend this positive influence to neighboring regions, demonstrating a spatial spillover phenomenon. (3) Research and Development (R&D) funding serves as a partial mediator in the relationship between sudden environmental disasters and renewable ETI. In contrast, Foreign Direct Investment (FDI) exhibits a masking effect.

Consequently, the study advocates for intensified efforts in post-disaster reconstruction following abrupt environmental events, an elevation in the quality of foreign direct investments, and leveraging research funding to catalyze innovation in renewable energy technologies amid unforeseen environmental crises.

High-tech enterprises, as the national innovation powerhouses, have garnered considerable interest, particularly regarding their technological innovation capabilities. Nevertheless, prevalent research tends to spotlight the impact of individual factors on innovative behavior, with only a fraction adopting a comprehensive viewpoint, scrutinizing the causal amalgamations of precursor conditions influencing the overall innovation proficiency of high-tech enterprises.

This paper employs a hybrid approach integrating necessary condition analysis (NCA) and fuzzy-set qualitative comparative analysis (fsQCA) to examine the combinatorial effects of antecedent factors on high-tech enterprises' innovation output. Our analysis draws upon data from 46 listed Chinese high-tech enterprises. To promote technological innovation within high-tech enterprises, we introduce a novel perspective that emphasizes technological innovation networks, grounded in a network agents-structure-environment framework. These antecedents are government subsidy, tax benefits, customer concentration, purchase concentration rate, market-oriented index and innovation environment.

The findings delineate four configurational pathways leading to high innovative output and three pathways resulting in low production.

This study thereby enriches the body of knowledge around technological innovation and provides actionable policy recommendations.

This study examines the differences in the economic stimulus effects, transmission mechanisms, and output multipliers of government consumption, government traditional investment, and government science and technology investment.

This study constructs and estimates a New Keynesian model of endogenous technological progress embedded in the research and development (R&D) and technology transfer sectors. Using Chinese macroeconomic time series data from 1996 to 2019, this study calibrates and estimates the model and analyzes the impulse response function and a counterfactual simulation of expenditure structure adjustment.

The results show that compared with the traditional dynamic stochastic general equilibrium (DSGE) model, the endogenous process of technological progress amplifies the impact of government consumption shock and traditional government investment shock on the macroeconomy, leading to greater economic cycle fluctuations. As government investment in science and technology has positive external spillover effects on firm R&D activities and the application of innovation achievements, it can promote more sustainable economic growth than government consumption and traditional investment in the long run.

This study constructs an extended New Keynesian model with different types of government spending, which includes endogenous technological progress within the R&D and technology transfer sectors, thereby linking fiscal policy, business cycle fluctuations and long-term economic growth. This model can study the macroeconomic impact of fiscal expenditure structure adjustment when fiscal expansion is limited. In the Bayesian estimation of model parameters, this study not only uses macroeconomic variables but also adds a sequence of private R&D investment.

This article empirically tests the impact of risk appetite of the executive team on the re-innovation strategy after technological innovation failure using a panel regression model from the perspective of regional financial development level of enterprises.

By means of time series global principal component analysis and panel regression model method, the study validated and analyzed the impact of risk appetite of the executive team on the re-innovation strategy after enterprise technological innovation failure.

The research found that the higher the risk appetite of executive team, the more inclined the enterprise is to choose the “focusing on quantity, ignoring quality” re-innovation strategy after technological innovation failure. The better the financial development level of the region where the enterprise is located, the better it can effectively reduce the re-innovation strategy of “focusing on quantity, ignoring quality” of the enterprise due to the high risk appetite of the executive team.

The findings of this study are helpful in improving the financial development level of the region where the enterprise is located. It can help the executive team of the enterprise to more objectively choose the innovation strategy after technological innovation failure, and reduce the phenomenon that the executive team of the enterprise only pays attention to the quantity of re-innovation and underestimates the quality of re-innovation after technological innovation failure due to its high risk appetite.

This study aims to examine the complex effects of foreign direct investment (FDI) on China’s agricultural total factor productivity (TFP) from 2005 to 2020. It also explores the role of absorptive capacity as a moderating factor during this period.

Employing provincial panel data from China, this research measures agricultural TFP using the Stochastic Frontier Approach (SFA)-Malmquist method. The impact of FDI on agricultural productivity is further analyzed using a nondynamic panel threshold model.

The results highlight technological progress as the main driver of agricultural TFP growth in China. Agricultural FDI (AFDI) seems to impede TFP development, whereas nonagricultural FDI (NAFDI) shows a distinct positive spillover effect. The study reveals a threshold in absorptive capacity that affects both the direct and spillover impacts of FDI. Provinces with higher absorptive capacity are less negatively impacted by AFDI and more likely to benefit from FDI spillovers (FDISs).

This study provides new insights into the intricate relationship between FDI, absorptive capacity and agricultural productivity. It underscores the importance of optimizing technological progress and research and development (R&D) to enhance agricultural productivity in China.

The paper proposes an answer to one of the most important questions in corporate innovation management: what mechanisms of technological diversification exist within multinational companies? It is ascertained that research and development (R&D) intra-firm co-invention or co-patenting is one of those mechanisms. Co-invention implies knowledge-sharing, which should lead to unique combinations of knowledge and expertise and hence technological diversification of patent applications.

This paper offers a novel conceptual framework exploring the relationship between patents’ technological diversification and a detailed classification of different forms of international co-invention. Based on the case of Siemens’ Patent Cooperation Treaty (PCT) applications, the revealed technological advantage (RTA) index is utilized to measure the extent of the technological diversification of patent output.

The results show that patent applications generated by subsidiaries in advanced economies in cooperation with other subsidiaries feature unique technological areas that deviate from the company's overall technological specializations. These results provide a strong argument in favor of inter-subsidiary or horizontal co-patenting as a mechanism of new knowledge creation.

On the conceptual level, the results accentuate inter-subsidiary patenting being an important mechanism of knowledge meta-integration boosting technological diversification. The obvious limitation of this paper lies in exploring a single company case, which restricts the generalizability of our findings. Due to the dynamic nature of technological change, the author’s dataset also suffers from a lack of temporal external validity. Future research can expand the scope in both regards in applying our co-invention mode typology.

Based on the results, to diversify knowledge portfolio, companies should strengthen the co-patenting effort and reinforce horizontal (inter-subsidiary) R&D collaborations.

To the author’s knowledge, this is the first time when such a nuanced typology of co-invention modes is being utilized to understand the effect of different co-invention categories on knowledge diversification.

This study examines the performance of India's food processing sector by estimating its output growth, technical efficiency (TE) and input-driven growth (IDG)

This study used panel data from six food processing manufacturing industries for the period 2000–01 to 2017–18. Technical efficiency and input-driven growth was measured using the parametric half-normal stochastic frontier production function.

The findings of this study showed that the estimated average technical efficiency is 86.6%, which specifies that the Indian food processing sector is technically inefficient. In addition, the output growth rate is 5.5%, driven by high doses of inputs (5.7%), whereas there is no indication of constant returns to scale. However, the food processing sector has experienced more input-driven expansion than either technological or efficiency changes.

This study is limited to India's organized manufacturing food processing sector; the aggregate macro data at a three-digit level based on the national industrial classification (NIC) was used. This study provides robust estimates for industrialists and processors, as well as concrete policy formulations on how overdoses of inputs may lead to high exploitation of resources, whereas outputs can be augmented by implementing upgraded and new technologies.

Previous research has estimated the total factor productivity and technical efficiency only in order to analyze the food sector's performance, but none of the studies have evaluated the share of inputs in growth performance and efficiency. Therefore, this study contributes by measuring growth performance and the share of inputs in the growth performance of India's food processing sector.

A new round of technological revolution is impacting various aspects of society. However, the importance of technology adoption in fostering firm innovation is underexplored. Therefore, this study aims to investigate whether robot adoption affects technological innovation and how human capital plays a role in this relationship in the era of circular economy.

Based on the robot adoption data from the International Federation of Robotics (IFR) and panel data of China's listed manufacturing firms from 2011 to 2020, this study uses regression models to test the impact of industrial robots on firm innovation and the mediating role of human capital.

The results demonstrate that the adoption of industrial robots can significantly promote high-quality innovation. Specifically, a one-unit increase in the number of robots per 100 employees is associated with a 13.52% increase in the number of invention patent applications in the following year. The mechanism tests show that industrial robots drive firm innovation by accumulating more highly educated workers and allocating more workers to R&D jobs. The findings are more significant for firms in industries with low market concentration, in labor-intensive industries and in regions with a shortage of high-end talent.

Due to data limitations, the sample of this study is limited to listed manufacturing firms, so the impact of industrial robots on promoting innovation may be underestimated. In addition, this study cannot observe the dynamic process of human capital management by firms after adopting robots.

The Chinese government should continue to promote the intelligent upgrading of the manufacturing industry and facilitate the promotion of robots in innovation. This implication can also be applied to developing countries that hope to learn from China's experience. In addition, this study emphasizes the role of human capital in the innovation-promoting process of robots. This highlights the importance of firms to strengthen employee education and training.

The adoption of industrial robots has profoundly influenced the production and lifestyle of human society. This study finds that the adoption of robots contributes to firm innovation, which helps people gain a deeper understanding of the positive impacts brought about by industrial intelligence.

By exploring the impact of industrial robots on firm innovation, this study offers crucial evidence at the firm level to comprehend the economic implications of robot adoption based on circular economy and human perspectives. Moreover, this study reveals that human capital is an important factor in how industrial robots affect firm innovation, providing an important complement to previous studies.