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This paper aims to provide a reference for the development of digital transformation from the perspective of manufacturing process links.

This paper applies canonical correlation analysis based on digital technology patents in the key links of manufacturing industries (product design, procurement, product manufacturing, warehousing and transportation, and wholesale and retail) and the related indicators of economic benefits of regions in China.

(1) The degree of digitalization of manufacturing process links is significantly correlated with economic benefits. (2) The improvement of the degree of digitalization in the “product design” link, the “warehousing and transportation” link, the “product manufacturing” link and the “wholesale and retail” link has significant impacts on the economic benefits of manufacturing industry. (3) The digital degree of the “procurement” link has no obvious influence on the economic benefits of manufacturing industry.

The research results can provide reference for the formulation and implementation of micro policies. The strategy of improving the level of digital transformation of key links of manufacturing industry is put forward to better promote both the digital transformation of manufacturing industry and economic development.

This paper innovatively studies the relationship between digitalization of manufacturing process links and economic benefits. The findings can provide theoretical and empirical support for the digital transformation of China's manufacturing industry and high-quality development of economy.

This paper aims to explore whether firms’ performance determines innovation using a sample of Indian manufacturing firms. The impact of innovation on firms’ performance across specific countries has been discussed in the literature. However, the effect of firms’ performance on innovation output, especially for a developing country like India, remains an open question. Against this backdrop, this paper investigates whether firms’ performance determines innovation in Indian manufacturing firms.

The authors use patent filing information to instrument innovation and total factor productivity to instrument firms’ performance. The patent data are collected from the Patent Search and Analysis Software database and firm-level data from the Centre for Monitoring Indian Economy’s Prowess database. The study uses a sample of 309 Indian manufacturing firms from 2005 to 2021. Given the count nature of the data set used in this study coupled with over-dispersion issues, the authors have used the negative binomial regression to estimate the empirical specification of the models. There could be a possible problem of endogeneity due to the contemporary nature of innovation and firms’ performance. Therefore, to address the possible issues of endogeneity in the model, the authors have used the Generalized Method of Moments (GMM) estimators for more robustness checks of the empirical results.

The empirical results exhibit a positive and significant impact of firms’ performance on the innovation output, validating that firms’ performance determines innovation in Indian manufacturing firms. The posterior estimation results using GMM estimation also corroborate that firms’ productivity is a determining factor for the innovation output of Indian manufacturing firms. Furthermore, empirical results exhibit that the ex ante innovativeness of the firms substantially affects the current innovation. This validates that the firms’ prior experience, learning by doing and past innovative efforts are more likely to precipitate more innovation in the current period.

This paper’s main contribution is empirically estimating whether firms’ performance determines innovation, which is hardly discussed in the existing innovation literature, specifically using Indian manufacturing industries. Further, it adds to the existing literature in two other prominent ways. First, this paper investigates whether firms require ex ante expertise to innovate or if a firm starting from scratch can innovate significantly without any hindrances. Second, it enriches the literature by instrumenting innovation in output terms with the patent application against input measures of innovation, such as research and development expenditures, acquisition of machinery and equipment, while discussing the relationship between firms’ performance and innovation, specifically in the context of a developing economy like India.

This research outlines the technological structure of the entire Japanese manufacturing and service industry using the patent information from research and development (R&D) activities to set R&D goals.

By analyzing the technological development capability of individual companies, the direction of the companies' R&D activities and current state of technological fusion between them can be understood. A group of companies participating in a particular product/service market must have the same technological development capabilities. As a result, the ratio of patent applications by a company to the total number of applications in a technical field will be similar across companies. This study uses the inter-company correlation coefficient of the ratio of patent applications by technical field as an index of technological development capability. A total of 167 major companies covering the major industries of Japan were analyzed. The analysis period was 15 years from 2004 to 2018, and the technical fields were rearranged to 42 fields with reference to the International Patent Classification (IPC)-Technology Concordance used by the World Intellectual Property Organization (WIPO). Considering the fluctuation in patent application opportunities, the number of patent applications was collected for at least three years for the analysis of patent applications by technical field, company and industry.

Examining the entire Japanese industry, the research found that chemicals, ceramics, non-ferrous metals and electrical/electronic equipment act as intermediaries between the respective groups and are linked to the transportation equipment, electrical/electronic equipment and information and communication services industries that are currently driving the Japanese economy. However, the technical connections between these groups are relatively loose. Over the last 15 years, the propagation structure of technical knowledge information has not changed. The progress of technological fusion remains within the scope of commerce and is conditioned by commerce.

Studies focusing on the technological development capability between companies and the technological structure of the Japanese manufacturing and service industries are almost non-existent since 2000 when Japan's economic growth slowed. The analytical methods presented in this research can be applied to individual companies to gain an understanding of technical positions of companies and can be useful for planning a technical environment, business or R&D strategy.

This study aims to identify the various economic and non-economic determinants of renewable energy consumption (REC) in Brazil, Russia, India, China and South Africa (BRICS). Due to the adverse effect of carbon emission on the environment, every country is trying for a transition from fossil fuel towards renewable energy. Renewable energy plays a crucial role in reducing carbon emission and combating climate change. Understanding the determinants that influence REC helps to promote this transition.

The study is based on an unbalanced panel data over the period 2002–2019 for all five BRICS nations. The panel corrected standard error (PCSE) method has been adopted to examine the determinants of REC.

Industrialization, population growth and foreign direct investment (FDI) are found to be significant economic determinants of REC while patent on environmental technologies, political instability and industrial design are significant non-economic determinants of REC in the BRICS nations.

The findings imply that to increase REC in BRICS nations, policymakers should incentivize industries for investments in renewable energy, attract FDI aligned with environmental regulations, raise population awareness through training, enforce industrial design standards, establish fair technology transfer frameworks to overcome patent barriers and create stable, long-term renewable energy policies with risk mitigation instruments to address political instability.

The study captures the effect of patents on environmental technologies and industrial design on the consumption of renewable energy. Thus, the novelty lies in investigating unexplored variables in the previous literature likely to affect REC.

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.

Unlike traditional industries, the e-cigarette is an epoch-making innovative product originating in China and occupying an absolute competitive advantage in the international market. The traditional A-U model describes the laws and characteristics of technological innovation in developed countries. In contrast, the inverse A-U model depicts the process of “secondary innovation” in late-developing countries through digestion and absorption. This paper aims to find out that if the e-cigarette, as a “first innovation” industry in a late-developing country, conform to the A-U model or conform to the “inverse A-U model”.

This paper takes the patent data of e-cigarettes from 2004 to 2021 as the research object, and uses Python’s Jieba segment words to divide product innovation and process innovation, and then uses statistical analysis methods to conduct empirical analyses on these data.

Thus, an improved A-U model suitable for the e-cigarette industry is proposed. In this model, product innovation in the e-cigarette industry appeared earlier than process innovation, but the synchronous development of product and process innovation is not lagging. The improved A-U model in the e-cigarette industry is not only different from the traditional A-U model but also does not conform to the inverse A-U model.

It is conducive to expanding and clarifying the theoretical contribution and applicable boundaries of the A-U model and has sparked thinking and exploration of the A-U model in e-cigarettes and emerging industries.

On this basis, suggestions on the development path and countermeasures of the e-cigarette industry are put forward.

Based on the e-cigarette industry, this paper takes patents as the research object and provides the method of dividing product innovation and process innovation, and proposes an A-U model suitable for the e-cigarette industry on this basis. By comparing the traditional A-U model with the inverse A-U model in latecomer countries, the background and causes of e-cigarette A-U model heterogeneity are analyzed from different stages and overall morphology. Based on this, the heterogeneity characteristics of e-cigarette innovation are summarized and sorted out.

With the deep development of the new technological revolution and industrial transformation, the development, application, expansion and integration of digital technology provide opportunities for transforming the manufacturing industry from traditional manufacturing to intelligent manufacturing. However, little research currently focuses on analyzing the influencing factors of intelligent development in this field. There is a lack of research from the perspective of the digital innovation ecosystem to explore the intrinsic mechanism that drives intelligent development. Therefore, this article starts with high-end equipment manufacturing enterprises as the research subject to explore how their digital innovation ecosystem promotes the effectiveness of enterprise intelligent development, providing theoretical support and policy guidance for enterprises to achieve intelligent development at the current stage.

This article constructs a logical framework for the digital innovation ecosystem using a “three-layer core-periphery” structure, collects data using crawling for subsequent indicator measurement and assessment and uses the fuzzy set Qualitative Comparative Analysis method (fsQCA) to explore how the various components of the digital innovation ecosystem in high-end equipment manufacturing enterprises work together to promote the development of enterprise intelligently.

This article finds that the various components of the digital innovation ecosystem of high-end equipment manufacturing enterprises, through mutual coordination, can help improve the level of enterprise intelligence. Empirical analysis shows four specific configuration implementation paths for the digital innovation ecosystem of high-end equipment manufacturing enterprises to promote intelligent development. The core conditions and their combinations that affect the intelligent development of enterprises differ in each configuration path.

Firstly, this article discusses the practical problems of intelligent transformation and development in the manufacturing industry and focuses on the intelligent development effectiveness of various components of the digital innovation ecosystem of high-end equipment manufacturing enterprises in the context of digitalization. Secondly, this article uses crawling, text sentiment analysis and other methods to creatively collect relevant data to overcome the research dilemma of being limited to theoretical analysis due to the difficulty in obtaining data in this field. At the same time, based on the characteristics of high-end equipment manufacturing enterprises, the “three-layer core-periphery” digital innovation ecosystem framework constructed in this article helps to gain a deep understanding of the development characteristics of the industry's enterprises, provides specific indicator analysis for their intelligent development, opening the “black box” of intelligent development in the industry's enterprises and bridging the gap between theory and practice. Finally, this study uses the fsQCA research method of configuration analysis to explore the complexity of the antecedents and investigate the combined effects of multiple factors on intelligent development, providing new perspectives and rich research results for relevant literature on the intelligent development of high-end equipment manufacturing enterprises.

This study aims to analyse the intellectual property rights (INPR), foreign direct investment (FDI) inflows and technological exports of 32 developing and developed countries for the period of 2006–2020.

Diagnostic tests were used to confirm the panel least squares, fixed effect, random effect, feasible general least squares, dynamic ordinary least squares and fully modified ordinary least squares estimator results as well as to increase the robustness.

According to the findings for the developing countries, trademark, patent and industrial design applications, each had a significant positive long-run effect on FDI inflows. In addition, there was a significant positive long-run relationship between patent applications and medium- and high-technology exports. Meanwhile, trademark and industrial design applications had a significant negative long-term effect on medium- and high-technology exports. In developed countries, patent and industrial design applications each have a significant negative long-term on medium- and high-technology exports. Furthermore, patent and trademark applications each had a significant negative long-run effect on FDI inflows.

This study contributes significantly to the focus that host countries evaluate the technology gaps between domestic and foreign investors at different industry levels to select the best INPR rules and innovation process by increasing international cooperation. Furthermore, the host countries should follow the structure–conduct–performance paradigm based on analysis of the market structure, strategic firms and industrial dynamics systems.

Previous studies have mostly discussed the impact of environmental policy on enterprise innovation, but the discussion on how turbulence in environmental policy may affect firms' green innovation has been insufficient. This paper explores the effect of environmental policy uncertainty on corporate green innovation in the turnover of environmental protection officials (EPOT) context.

The authors manually collected the data on the EPOT of 280 Chinese prefecture-level cities, and used the Poisson regression model to conduct empirical analyses based on the panel data of 1472 Chinese listed manufacturing firms from 2008 to 2017.

The results show that environmental policy uncertainty leads firms to reduce their green patent applications only for green invention patent applications. Such an effect is more pronounced in non-state-owned enterprises (non-SOEs). In addition, when the new directors of the Ecology and Environmental Bureau take office through promotions or are no more than 55 years old, the negative effect is more obvious, but there is no significant difference regardless of whether new directors have worked in environmental protection departments.

First, this paper supplements the research on the antecedents of corporate green innovation from the perspective of environmental policy uncertainty and extends the applications of real options theory. Second, this paper expands the research on the government–business relationship from the EPOT perspective.

This study aims to explore the role of international ambidexterity (IA) in improving the innovation capability of emerging market multinationals. In particular, the main purpose of this research is to study the relationship amongst digitalisation, IA and innovation performance (IP) amongst multinational enterprises in China’s healthcare industry.

The data for this investigation were collected from 134 listed companies in China’s healthcare industry during the study period. This study tested the hypotheses by constructing a two-way fixed-effects model.

The results show that both the balance dimension and the combined dimension of IA have significant positive effects on IP. Digitalisation not only has a direct positive effect on IP but also positively moderates the positive correlation between IA and IP.

Previous studies have not captured the relationship between ambidexterity, digitalisation and IP, and this study helps to fill in the gap and examine these associations in China’s healthcare industry. The results of this study provide valuable insights for healthcare industry managers to understand the role of ambidexterity and digitalisation in innovation in the context of internationalisation.