China Research Institute Research Articles

Origin matters: organisational imprinting and innovation of enterprises from restructured research institutes in China’s transition economies

ABSTRACT Based on imprinting theory, this study examines how organisational imprinting, i.e. imprinting with the research institute background, impacts innovation of enterprises from restructured research institutes in subsequent periods. Using a sample of Chinese listed companies during 2000–2020, this study uses firm random effects and industry / year fixed effects models to test the hypotheses. Our study finds that organisational imprinting positively influences corporate innovation. Furthermore, CEO’s ideological imprinting weakens the impact of organisational imprinting on corporate innovation. Finally, as the institutional development improves and equity checks and balances strengthen, the negative impact of CEO’s ideological imprinting on the relationship between organisational imprinting and corporate innovation will be weakened. This study contributes to the literature on the imprinting theory by predicting the impact of organisational imprinting on innovation using a consistent and integrative framework.

"Curriculum-project-supervisor" interactively enhance the innovation capability of postgraduates majoring in bioengineering

According to the Bio-economy Development Plan during the 14th Five-Year Plan period, biotechnology has become an effective force to promote future development. More than 220 universities and research institutes in China have got the right to confer master's degrees in bioengineering. Great attention has been paid to the cultivation of top innovative talents that can serve the innovation-driven development of the national bio-economy. In the last 15 years, the Research Center of Microbial-Manufacturing Engineering in Jiangnan University has built a diversified education platform, recruited high-level faculty members, and innovated the scientific research management. The new concept and method for cultivating the innovation capabilities of postgraduates in a multi-dimension and whole-process manner have been formed, which involved building a curriculum, focusing on major projects, and establishing a supervisor team. This cultivation mode has comprehensively improved the engineering and academic innovation capabilities of postgraduates majoring in bioengineering.

A Data Analytics and Machine Learning Approach to Develop a Technology Roadmap for Next-Generation Logistics Utilizing Underground Systems

The increasing density of urban populations has spurred interest in utilizing underground space. Underground logistics systems (ULS) are gaining traction due to their effective utilization of this space to enhance urban spatial efficiency. However, research on technological advancements in related fields remains limited. To address this gap, we applied a data-driven approach using patent data related to the ULS to develop a technology roadmap for the field. We employed Latent Dirichlet Allocation (LDA), a machine learning-based topic modeling technique, to categorize and identify six specific technology areas within the ULS domain. Subsequently, we conducted portfolio analytics to pinpoint technology areas with high technological value and to identify the major patent applicants in these areas. Finally, we assessed the technology market potential by mapping the technology life cycle for the identified high-value areas. Among the six technology areas identified, Topic 1 (Underground Material Handling System) and Topic 4 (Underground Transportation System) showed significant patent activity from companies and research institutions in China, the United States, South Korea, and Germany compared to other countries. These areas have the top 10 patent applicants, accounting for 20.8% and 13.6% of all patent applications, respectively. Additionally, technology life cycle analytics revealed a growth trajectory for these identified areas, indicating their rapid expansion and high innovation potential. This study provides a data-driven methodology to develop a technology roadmap that offers valuable insights for researchers, engineers, and policymakers in the ULS industry and supports informed decision-making regarding the field’s future direction.

International Development Trends in the Field of Agricultural Resources and the Environment

The development trends and research layout of agricultural resources and the environment (ARE) are the focus of global attention. In this study, we compiled a data set of SCI papers published in the ARE field since the 13th Five-Year Plan. Thereafter, the topic extraction model of Latent Dirichlet Allocation (LDA) was used to mine the text content so as to explore the research layout of global ARE. The results show that, between 2016 and the time of this study, 31,559 articles were published in the ARE field, exhibiting an overall upward trend. During this time, China and the United States were the main forces in paper publishing. The Chinese Academy of Sciences (CAS), Northwest Agriculture and Forestry University, and the US Department of Agriculture were the top three publishing institutions. Research institutes in China, the United States, Russia, Sweden, the Netherlands, Brazil, and Australia cooperated closely, and the CAS was at the center of the cooperation network. The clustering results of text topics based on the LDA model show that three topics—namely, the interaction mechanisms of plants, the rhizosphere, and microorganisms; the mechanisms and predictive evaluation of soil landslides or erosion; and the decomposition and interaction response of organic matter in agroforestry ecosystems—have been the hot research areas in this field since 2016. The management and efficient utilization of farmland nutrients, and the technology and mechanisms of agricultural waste resource utilization have become key research directions since 2019. The research layouts of China and the United States in this field were analyzed, and the similarities and differences were compared. In addition, suggestions for the future development of ARE are proposed. This study is of great significance for the overall development trend of ARE, the timely tracking of scientific research hotspots, outlining key research and development directions, and promoting scientific and technological innovation.

Assessing efficiency in sustainable allocation of agricultural scientific and technological talent: a spatial-temporal analysis in China

Efficient allocation of agricultural scientific and technological talents (ASTTs) is crucial for agricultural innovation and economic development. This study aims to systematically evaluate ASTTs’ allocation efficiency in provincial agricultural research institutions in China, aiding decision-making for local governments and research bodies. Utilizing data from 2009 to 2019 across 31 provinces, an output-oriented data envelopment analysis model measures ASTTs’ allocation efficiency and analyzes its trends, regional differences, and spatial characteristics. Results show: (1) Provincial ASTTs’ mean comprehensive technical efficiency (CTE) in China was 0.786, with room for improvement. (2) Enhanced CTE was driven by scale efficiency improvements, while pure technical efficiency declined, indicating a need for better management systems and technology applications. (3) Disparities in ASTTs’ allocation efficiency among provinces decreased, with higher efficiencies in the East and Central-Southern China regions. At the provincial level, areas like Jiangsu, Shandong, Henan, and Sichuan demonstrated relatively high ASTTs allocation efficiencies. (4) Spatial agglomeration of ASTTs’ allocation efficiency was localized in a few major agricultural provinces without a significant overall effect. These findings advocate for further optimization of ASTTs’ regional layout and management mechanisms in China.

How do institutional dimensions of open government data affect innovation? Evidence from research institutes in China

PurposeThis study investigates the individual and binary (i.e. combined) effects of institutional dimensions of open government data (which include instructional, structural and accessible rules) on scientific research innovation, as well as the mediating roles that researchers' perceived data usefulness and data capability play in between.Design/methodology/approachBased on a sample of 1,092 respondents, this study uses partial least squares structural equation modeling (PLS-SEM) and polynomial regression with response surface analysis to evaluate the direct and indirect effects of individual and binary institutional dimensions on scientific research innovation.FindingsThe findings demonstrate that instructional, structural and restricted access data have a positive effect on scientific research innovation in the individual effect. While the binary effect of institutional dimensions produces varying degrees of scientific research innovation. Furthermore, this study discovers that the perceived usefulness and data capability of researchers differ in the mediating effect of institutional dimensions on scientific research innovation.Originality/valueTheoretically, this study contributes new knowledge on the causal links between data publication institutions and innovation. Practically, the research findings offer government data managers timely suggestions on how to build up institutions to foster greater data usage.

Global status and trends in hepatitis B virus DNA mutation research from 2003 to 2022: A comprehensive bibliometric view

AbstractAimsHepatitis B virus (HBV) is an enveloped DNA virus belonging to the Hepadnaviridae family. It is a significant contributor to the prevalence of chronic liver disease on a global scale. Mutations in HBV DNA can affect the diagnosis, treatment, and prognosis of patients with chronic hepatitis B. Although numerous studies on HBV DNA mutations have been conducted, few bibliometric studies have been performed to date. This study aimed to analyze changes in scientific output to gain a better understanding of the current research status and identify potential new research directions in the field of HBV DNA mutation.MethodsRelevant studies published between 2003 and 2022 were retrieved from the Web of Science Core Collection database. CiteSpace version 6.1.R6 was used to construct network maps to evaluate collaborations among countries, institutions, authors, journals, and keywords.ResultsIn total, 1508 publications over the past 20 years were obtained, mainly from China, the United States, and Japan. The major research institutions in China included the University of Hong Kong, Fudan University, and Peking University. Lai Ching Lung was the most productive author, whereas Anna Suk‐Fong Lok was the most frequently co‐cited author. The keyword “HBsAg” was the strongest burst keyword in recent years, indicating possible future study trends.ConclusionsThis bibliometric analysis intuitively revealed the overall research status of HBV DNA mutations, providing valuable information for researchers, funding agencies, and policymakers to explore the current research status, research hotspots, and new directions for future research.

Promoting or Prohibiting? Investigating How Time Pressure Influences Innovative Behavior under Stress-Mindset Conditions.

The existing empirical evidence on the relationship between time pressure and innovative behavior is paradoxical. An intriguing yet unresolved question is "When does time pressure promote or prohibit innovative behavior, and how?" We theorize that the paradoxical effect of time pressure on innovative behavior can be elucidated by the moderating role of stress mindset, and we also explore the mediating role of thriving at work. Our research involved a field study of 390 research and development personnel from eight enterprises and research institutes in China to test our proposed model. Results indicated that the stress-is-debilitating mindset negatively moderated the association between time pressure and thriving at work, while the stress-is-enhancing mindset positively moderated the link between time pressure and thriving at work. Furthermore, the findings demonstrated that the stress-is-debilitating mindset negatively moderated the indirect impact of time pressure on employees' innovative behavior through thriving at work, while the stress-is-enhancing mindset positively moderated the indirect effect of time pressure on employees' innovative behavior through thriving at work. The theoretical and practical implications of these findings are also discussed.

The effects of creative personality on scientist creativity

Does a creative personality make scientists more creative? Previous research on creative personality, creative behavior, and performance has been mixed, with not all studies reporting a consistent positive relationship. We propose that this is, in part, because creativity is not solely determined by traits; instead, creative individuals require a specific context to fully realize their potential to create. Using trait activation theory, we show that, as scientific creativity is becoming more dependent on teamwork, creative personality interacts with scientists’ social identities and collaborative behavior to affect scientists’ creativity. Multisource data collected from 547 scientists from 36 research institutes in China revealed that two dimensions of scientists’ creative personality (research ability and uniqueness) exhibited a positive effect on their creativity whereas two other dimensions of their creative personality had a negative effect (self-discipline) or no effect (self-verification) on their creativity. In addition, the breadth of research communication, expertise identity, and organizational identity exhibited positive moderating effects on creative personality and scientist creativity. Specifically, when research communication breadth was high, self-discipline boosted scientist creativity; when expertise identity was high, self-verification boosted scientist creativity; and when organizational identity was high, the uniqueness dimension of scientist personality boosted scientist creativity. We discuss implications for theory and practice.

Searching across-cohort relatives in 54,092 GWAS samples via encrypted genotype regression.

Explicitly sharing individual level data in genomics studies has many merits comparing to sharing summary statistics, including more strict QCs, common statistical analyses, relative identification and improved statistical power in GWAS, but it is hampered by privacy or ethical constraints. In this study, we developed encG-reg, a regression approach that can detect relatives of various degrees based on encrypted genomic data, which is immune of ethical constraints. The encryption properties of encG-reg are based on the random matrix theory by masking the original genotypic matrix without sacrificing precision of individual-level genotype data. We established a connection between the dimension of a random matrix, which masked genotype matrices, and the required precision of a study for encrypted genotype data. encG-reg has false positive and false negative rates equivalent to sharing original individual level data, and is computationally efficient when searching relatives. We split the UK Biobank into their respective centers, and then encrypted the genotype data. We observed that the relatives estimated using encG-reg was equivalently accurate with the estimation by KING, which is a widely used software but requires original genotype data. In a more complex application, we launched a finely devised multi-center collaboration across 5 research institutes in China, covering 9 cohorts of 54,092 GWAS samples. encG-reg again identified true relatives existing across the cohorts with even different ethnic backgrounds and genotypic qualities. Our study clearly demonstrates that encrypted genomic data can be used for data sharing without loss of information or data sharing barrier.

Science and technology resource allocation evaluation of research institutions considering the risk preference of decision makers

With the increasing emphasis on research institutions in China, there is a growing imperative to evaluate the efficacy of resource allocation in science and technology (S&T) investments. However, limited research has been conducted on the efficacy of resource allocation in research institutions while considering decision makers’ risk preference behavior. In order to address this issue, the present study employs a prospective cross-efficiency evaluation model that incorporates decision makers’ risk preferences. This model is utilized to assess the efficiency of S&T resource allocation in research institutions across 13 industries in China. After conducting empirical analysis, it has been observed that decision makers’ risk preferences significantly influence the efficiency outcomes of S&T resource allocation in research institutions. Finally, this study proposes appropriate measures and recommendations for S&T resource allocation in research institutions.

The status quo and development trend of talent cultivation in academic journal studies in China: A case study of Master's and Doctoral theses

Background: This paper aims to investigate the situation of discipline construction in the field of academic publishing in China, including the transfer of talents to the academic publishing and its impact on the research and development of the academic publishing in the future. Methods: This research conducted a comparative analysis of the quantity of Master’s and Doctoral theses on academic journal studies in terms of relevant educational institutes, disciplines, high-frequency keywords, and research hotspots. Result: It was found that the number of Master’s and Doctoral theses on academic journal studies has increased since 2004 and remained steady during 2016–2021; among the 26 research institutes in which more than 10 Master’s and Doctoral theses in this field were published, information science, library science, communication, and journalism constituted the main disciplines of academic journal studies in China, and the theses published in these disciplines accounted for 34.4% of the total. The research hotspots included open access, editing and publishing, bibliometrics, etc. until 2015, and bibliometric analysis from 2016–2021. With bibliometric analysis, in the past ten and more years, 849 excellent graduates have been engaged in the academic journal studies. Some research institutes have made significant contributions to this field, cultivated a great number of talents, and introduced them to this field, promoting the development of the academic publishing. Conclusion: The number of Master’s and Doctoral theses focusing on academic journal studies has grown rapidly, leading to a picture of diversified prosperity in schools, majors, and fields of study. Universities and research institutes in China should establish a special research direction of academic journal studies to give impetus to the academic publishing industry.

How organizations can use carbon emissions data to achieve carbon neutrality

To help China achieve the "dual carbon" goal at an early date and mitigate global climate change brought about by the greenhouse effect, people are increasingly interested in topics such as carbon neutrality, sustainability, and energy, which has led to apparent changes in many cross-disciplinary disciplines. While upgrading the organization industry, people often neglect to implement measures from the energy side. The study is based on 492 articles retrieved from the Web of Science (WoS) database between 1900 and 2023, and conducted comprehensive data and visual analysis of keywords such as "carbon neutrality", "carbon emissions", and "carbon footprint". Bibliometrics show that publications have proliferated since 2008 and also involve multiple disciplines, such as management, sustainable development studies, public health, energy, and other related major fields. At the same time, research institutions in China, the United Kingdom, and Singapore have excelled in such disciplines. The authors use big data analysis technology and visualization software to comprehensively analyze the research results in the form of charts from the aspects of literature publication and citation trends, top research institutions, and keyword clustering, which provides research ideas for research organizations to use carbon emission data to achieve carbon neutrality.

Assay of sodium carbonate

Main textThe CCQM-K173 Assay of Sodium Carbonate key comparison was jointly organized by the Inorganic Analysis (IAWG) and Electrochemical Analysis and Classical Chemical Methods (EAWG) working groups of CCQM to test the abilities of the national metrology institutes (NMIs) to measure the purity or amount content of solid bases. They are important challenges for reference material producers, providers of other measurement services, such as proficiency testing schemes. Evidence of successful participation in formal, relevant international comparisons are needed to support calibration and measurement capability claims (CMCs) made by NMIs and designated institutes (DIs).Nine NMIs participated in this key comparison CCQM-K173. National Institute of Metrology P. R. China (NIM) and Ural Research Institute for Metrology - Affiliated Branch of the D.I. Mendeleyev Institute for Metrology (VNIIM-UNIIM), Russian Federation, acted as the coordinating laboratories of the comparison.The measurement methods used by the participants for measuring the amount content of bases expressed as sodium carbonate were coulometry and titrimetry.In general, good overlap of results was observed, the suitability of coulometry and titrimetry for assay of high purity materials was demonstrated. The majority of results were split in two groups differing from each. This bias was however covered by the stated uncertainty estimates. Various effects have been evaluated that may cause it. However, the reason for the bias has not been identified clearly.To reach the main text of this paper, click on Final Report. Note that this text is that which appears in Appendix B of the BIPM key comparison database https://www.bipm.org/kcdb/.The final report has been peer-reviewed and approved for publication by the CCQM, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).

Analysis of Bilibili's Competitive Strategy in the New Trends

According to the "2022-2027 China Internet Video Industry Market Depth Research and Investment Strategy Forecast Report" published by the China Research Institute of Industry, as of the end of June 2021, the size of China's Internet users broke one billion, reaching 1.011 billion people, an increase of 0.22 billion people compared to the end of December 2020, the massive size of Internet users to promote the development of China's online video industry. The size of the short video market will increase more quickly between 2020 and 2022, with a compound annual growth rate of about 44%. The market size will grow at a slower rate during 2023-2025, but will still maintain a CAGR of 16%. China's short video market is expected to reach nearly 600 billion yuan in 2025 [1]. More than a quarter of a day is spent watching short videos on mobile devices in China. Along with visuals and audio, short video has emerged as the "third language" of the mobile Internet. Short-form video has rapidly increased in the new Internet economy. Bilibili's future development has attracted much attention. With the development of the Internet economy and the increase in significant video websites, whether Bilibili can continue its competitive advantage and successfully achieve business transformation has become controversial. This research will analyse Bilibili's business model through a SWOT analysis and make feasible suggestions for its future development.

Research Progress and Hotspot Evolution Analysis of Landscape Microclimate: Visual Analysis Based on CNKI and WOS.

With the increasing requirements of healthy habitat environments, landscape microclimates have been widely concerned. To comprehensively grasp the development history and research status of the landscape microclimates in China and other countries, CiteSpace software was used to comparatively analyze and visually present the literature related to landscape microclimates in CNKI and WOS databases for the past 20 years. The results show that: (1) The number of publications on landscape microclimate research shows an increasing trend in China and other countries, and the number of publications increased significantly after 2016. Although the number of publications by Chinese scholars is less than that of foreign scholars, they have developed rapidly in recent years, and their international influence has increased significantly. (2) A positive exchange has been formed among international scholars, and the number of collaborative studies has been increasing. China's microclimate research has formed relatively stable teams that have conducted numerous studies in the fields of urban communities, street greening, and plant communities, respectively. Although the links between research teams and institutions in China and other countries are relatively close, they still need to be further strengthened. (3) In the past decade, the theoretical system of landscape microclimates has been improved, and the research themes have become more concentrated, but it still has remained close to the early basic research. (4) Future research will remain centered on "mitigating the urban heat island effect" and "optimizing human heat perception". New topics such as "biodiversity", "carbon cycle", and "climate change" have been added. In conclusion, the research needs to continue to explore the evaluation system of microclimates and verify the evaluation index of outdoor thermal comfort for human thermal adaptation in different regions. The standards and systems of human habitat adapted to different regional characteristics should be formed.

Risk Constraints in the Context of Big Data and Optimization of a Virtuous Interaction Mechanism for Insurance Management Based on Distorted Risk Metrics.

A relevant definition of big data technology has been released by relevant research institutes in China. Big data technology is defined as follows: big data technology is a digitally important productivity factor in the network era; big data technology is based on network technology, statistical technology, and mathematical technology; big data technology, as a carrier of network technology, should be integrated with various industries and technologies other than contemporary ones in order to achieve better informatization and industrialization. In the context of the new crown epidemic in 2020, China's big data technology continues to grow steadily, with a total value of 39.2 trillion yuan, accounting for 38.6% of GDP. This paper investigates the risk constraint in the context of big data and optimizes the benign interaction mechanism of insurance management based on distorted risk metrics in this context. The current situation of insurance risk is investigated through a profound discussion of theories in this paper and through research methods such as distortion risk metrics and SQL Server database management. It is found that the overall situation of insurance industry development in the era of digital economy is not optimistic and so on. And relevant suggestions are made in the conclusion.

A bibliometric analysis of the application of artificial intelligence to advance individualized diagnosis and treatment of critical illness.

BackgroundArtificial intelligence (AI) has been extensively applied in the individualized diagnosis and treatment of critical illness, and numerous studies have been published on this topic. Therefore, a bibliometric analysis of these publications should be performed to provide a direction of hot topics and future research trends.MethodsA bibliometric analysis was performed on the research articles to identify the hot topics and any unsolved issues regarding the use of AI in individualized diagnosis and treatment of critical illness. Articles published from January 2011 to December 2021 were retrieved from the Web of Science (WOS) core collection database for bibliometric analysis, and a cross-sectional analysis of the relevant studies that had been registered at ClinicalTrials.gov was also conducted.ResultsThe number of articles published showed an annually increasing trend, with a worldwide geographic distribution over the past decade. Ultimately, 427 research articles were included in the bibliometric analysis. The relevant articles were divided into four separate clusters that focused on AI application aspects, prediction model establishment, coronavirus disease 2019 (COVID-19) treatment and outcome assessments, respectively. “Machine learning” was the most frequent keyword (147 occurrences, 165 links, and 395 total link strengths) followed by “risk”, “models”, and “mortality”. With 205 articles, the United States of America (USA) had interacted the most with other countries (20 links, and 94 total link strength), while the domestic research institutes in China had infrequently collaborated with others. Approximately 130 trials focusing on the application of AI in the intensive care unit (ICU) and emergency department (ED) had been registered at ClinicalTrial.gov, and most of them (n=71, 54.6%) were interventional. The main research objectives of these trials were to provide decision making assistance and establish prediction models. However, only 3.8% (5 trials) of them had reached exact conclusions which favored the application of AI.ConclusionsThe application of AI has raised great interest in critical illness and has mainly been focused on decision making assistance and prediction model establishment. Cooperation between agencies engaged in AI research needs to be strengthened. An increasing number of trials have been registered at ClinicalTrial.gov, and the results of them are promising.KeywordsBibliometric analysis; artificial intelligence (AI); individualized diagnosis; critical care medicine; emergency department (ED)

Antecedents and performance outcomes of employees’ data analytics skills: an adaptation structuration theory-based empirical investigation

ABSTRACT How do organizations develop and manage employees’ data analytics skills to create business value and enhance organizational competitive advantage? In order to address this prominent and critical research question for IS research, we conceptualize and operationalize data analytics skills at the individual level and develop a nomological network model to examine its critical antecedents and outcomes from the lens of adaptation structuration theory. We test our core proposition and research model using survey data collected from 258 frontline employees of three data-intensive research institutes in China. We discover that data-driven culture, data analytics affordance, and individual absorptive capacity are positively associated with employees’ data analytics skills, which in turn, have positive influences on their task and innovative performance. We classify the employees into digital immigrants and digital natives based on age and examine the different influences of three salient antecedents on data analytics skills between the two groups. The research findings suggest that data-driven culture plays a more significant role in driving data analytics skills for digital immigrants, while data analytics affordance exhibits a stronger influence on data analytics skills for digital natives.

Visualization of energy-environment-economy system research characteristics and hotspots evolution trends based on CiteSpace

With the recovery of the world economy, the increase in energy demand, global environmental pollution, climate change, and many other contradictory issues, after China proposed the Double-Carbon goal, research on the Energy-Environment-Economy system has attracted much attention. Through the visual analysis of Energy-Environment-Economy research literature, this paper provides scholars committed to this field with research characteristics and hotspots evolution trends in this field, and provides a direction guidance and practical method reference for subsequent research on Energy-Environment-Economy system-related issues. This study selected the literature from the Energy-Environment-Economy system field of Web of Science and CNKI database for nearly 20 years, uses CiteSpace to conduct research bibliometrics and visual analysis, and explores the main characteristics, research hotspots, and evolution trends of Energy-Environment-Economy system in Chinese and English. The research found that: ① there are many research results on Energy-Environment-Economy systems, and the research interest in the field of Energy-Environment-Economy systems continues to rise; ② the number of documents and research institutions in China ranks first in the world, and the influence of research results needs to be improved; ③ the hot topics of research topics in each period are bright, research hotspots evolve from single subsystems to systemic issues; ④ the attention to environment and climate change has increased significantly, and the evolutionary characteristics of research hotspots are in line with the development trend in reality.