Category Archives: Research Examples

How does Regulatory Uncertainty Shape the Innovation Process? Evidence from the Case of Nanomedicine

Research Examples, ST&I policy

This study investigates the effect of regulatory uncertainty on the translation of scientific discovery on emerging research topics to technical applications in science-driven industry. Our empirical analysis using the case of the US Federal Drug and Food Administration’s release of the report on the regulatory approach to nanomedicine in 2007 shows that; (1) the regulatory uncertainty decelerated the translation of nanomedicine research to technical applications, (2) this effect was particular for the nanomedicine research on emerging topics in the field. Our further analysis suggested that the effect of the regulatory uncertainty originated from the suppressed business activities in the field where the regulatory uncertainty presents. Contributions to the literature on the relationship between governmental regulation and innovation and the implication for science policymakers are discussed.

https://doi.org/10.5465/AMBPP.2022.62

Author(s): Seokbeom Kwon, Jan Youtie, Alan Porter, Nils Newman
Organization(s): Sungkyunkwan University, Georgia Institute of Technology, Search Technology
Source: Academy of Management Proceedings Volume 2022 [Best Papers], [Journal of Technology Transfer (submitted)]
Year: 2022

A hybrid approach to identify and forecast technological opportunities based on topic modeling and sentiment analysis (Full-Text)

Future-oriented technology analysis, Research Examples, ST&I indicators

The spring up of new & emerging technologies brings a lot of innovation opportunities for society, which enables technology opportunities analysis attracts increasing attention by both industry and academia recently. This study proposes a hybrid approach which integrates topic modeling, sentiment analysis, patent mining and expert judgment to identify technological topics and the potential development opportunities. In order to illustrate how the approach is validated and optimized, and to present its potential to contribute technical intelligence for research and development management, we apply the hybrid approach to analyze a set of 9883 DII records that involved dye sensitized solar cell research. The main contributions of this study include three-fold. First, we distinguished the terms in the different parts of DII patent documents when utilizing them to recognize technical topics. Second, we utilized the terms extracted from the Advantage and the Use part to identify topics on technical problems and applications, and proposed a probability-based topic relation measurement method to identify the relationships of the technical problems and applications with the core sub-technologies. Third, we introduced both topic modeling and sentiment analysis to support technical topic analysis.

For FULL-TEXT of manuscript https://eeke-workshop.github.io/2022/submissions/EEKE2022_paper_8.pdf

Author(s): Tingting Ma, Ruiping Cheng, Hongshu Chen, XiaoZhou
Organization(s): Beijing Wuzi University, Beijing Institute of Technology, Xidian University
Source: 3rd Workshop on Extraction and Evaluation of Knowledge Entities from Scientific Documents (EEKE2022)
Year: 2022

Technological Emergence and Military Technology Innovation

Research Examples, ST&I indicators

To what extent is military technology innovation emergent? This study answers this question by applying an emergence detection algorithm to roughly 300,000 technical terms extracted from military technology patents granted from 1980 to 2019. Emergence – instances of sudden and rapid growth of a technical term within the military patent corpus – is found to vary greatly over time. Military technology innovation during the period of 1996-2008 is found to be highly emergent. This period was found to be characterized by high organization-type diversity; non-traditional vendors, traditional defense contractors, large civilian-facing firms, and individuals generated military patents containing many novel emergent technical terms. However, in recent years, military technology innovation has exhibited markedly less emergence. The period of low emergence is characterized by reduced contributions by non-traditional vendors, defense prime contractors, and individual inventors to military patents containing emergent terms. These observations suggest that policies attempting to ensure a healthy defense innovation ecosystem should seek organization-type diversity and may benefit from employing promotion strategies targeted at distinct organization types.

https://doi.org/10.1080/10242694.2022.2076339

Author(s): Jon Schmid
Organization(s): RAND
Source: Defense and Peace Economics
Year: 2022

Future of genetic therapies for rare genetic diseases: what to expect for the next 15 years? (Full-Text)

Research Examples

Rare genetic diseases affect millions of people worldwide. Most of them are caused by defective genes that impair quality of life and can lead to premature death. As genetic therapies aim to fix or replace defective genes, they are considered the most promising treatment for rare genetic diseases. Yet, as these therapies are still under development, it is still unclear whether they will be successful in treating these diseases. This study aims to address this gap by assessing researchers’ opinions on the future of genetic therapies for the treatment of rare genetic diseases. We conducted a global cross-sectional web-based survey of researchers who recently authored peer-reviewed articles related to rare genetic diseases. We assessed the opinions of 1430 researchers with high and good knowledge about genetic therapies for the treatment of rare genetic diseases. Overall, the respondents believed that genetic therapies would be the standard of care for rare genetic diseases before 2036, leading to cures after this period. CRISPR-Cas9 was considered the most likely approach to fixing or replacing defective genes in the next 15 years. The respondents with good knowledge believed that genetic therapies would only have long-lasting effects after 2036, while those with high knowledge were divided on this issue. The respondents with good knowledge on the subject believed that non-viral vectors are more likely to be successful in fixing or replacing defective genes in the next 15 years, while most of the respondents with high knowledge believed viral vectors would be more successful. Overall, the researchers who participated in this study expect that in the future genetic therapies will greatly benefit the treatment of patients with rare genetic diseases.

For FULL-TEXT https://journals.sagepub.com/doi/pdf/10.1177/26330040221100840

Author(s): Luiza Amara Maciel Braga , Carlos Gilbert Conte Filho, Fabio Batista Mota
Organization(s): Oswaldo Cruz Foundation, Fluminense Federal University, Federal University of Santa Maria
Source: Therapeutic Advances in Rare Disease
Year: 2022

Scientific Trends in Artificial Neural Networks for Management Science

Research Examples

The use of artificial neural network (ANN) is growing significantly, and their areas of application are varied. In this case, the main aim of the study is to present an overall view of trends and research carried out in ANNs specifically in management science. To this aim, the data of publications about ANN in the field of management through Scopus database have been analyzed. Documents in the field of management science composed by: Business, Management and Accounting; Decision Sciences; Econometrics and Finance; and Social Sciences published from 2000 to 2019 have been obtained and downloaded. Then, text-mining and network analysis software have been applied to gather, clean, analyze and visualize article data. Thus, it has been found that the pioneer country in this research area is China, followed by the USA and India. The study allows to conclude that in the field of management science, ANNs are mostly used for: logistic regression, prediction, classification, forecasting, modelling, data mining and clustering, among others. In addition, it has also been found that the most used neural network is the convolutional neural network (CNN).

https://link.springer.com/chapter/10.1007/978-3-030-95967-8_18

Author(s): M. Jaca-Madariaga, E. Zarrabeitia, R.M. Rio-Belver, I. Álvare
Organization(s): University of the Basque Country (UPV/EHU)
Source: Ensuring Sustainability: Lecture Notes in Management and Industrial Engineering (Springer)
Year: 2022

One-Year In: COVID-19 Research at the International Level in CORD-19 Data (Full-Text)

Research Examples

The appearance of a novel coronavirus in late 2019 radically changed the community of researchers working on coronaviruses since the 2002 SARS epidemic. In 2020, coronavirus-related publications grew by 20 times over the previous two years, with 130,000 more researchers publishing on related topics. The United States, the United Kingdom and China led dozens of nations working on coronavirus prior to the pandemic, but leadership consolidated among these three nations in 2020, which collectively accounted for 50% of all papers, garnering well more than 60% of citations. China took an early lead on COVID-19 research, but dropped rapidly in production and international participation through the year. Europe showed an opposite pattern, beginning slowly in publications but growing in contributions during the year. The share of internationally collaborative publications dropped from pre-pandemic rates; single-authored publications grew. For all nations, including China, the number of publications about COVID track closely with the outbreak of COVID-19 cases. Lower-income nations participate very little in COVID-19 research in 2020. Topic maps of internationally collaborative work show the rise of patient care and public health clusters-two topics that were largely absent from coronavirus research in the two Forthcoming, PLoS One 2 years prior to 2020. Findings are consistent with global science as a self-organizing system operating on a reputation-based dynamic.

For FULL-TEXT https://doi.org/10.1371/journal.pone.0261624

Author(s): Caroline Wagner, Xiaojing Cai, Yi Zhang, Caroline Fry
Organization(s): The Ohio State University, Zhejiang University, University of Technology Sydney, University of Hawai’i at Mānoa
Source: PLoS One
Year: 2022

Exploring funding patterns with word embedding-enhanced organization–topic networks: a case study on big data

Funding, Research Examples

Understanding the complex patterns in research funding plays a fundamental role in comprehensively revealing funding preferences and informing ideas for future strategic innovation. This is especially true when the funding policies need to be constantly shifted to accommodate highly complex and ever-changing demands for technological, economic, and social development. To this end, we investigate the associations between funding agencies and the topics they fund in an attempt to understand funding patterns at both an organizational level and a topic level. In this paper, the links between heterogeneous nodes, organizations and topics, are mapped to a two-mode organization–topic network. The collaborative interactions formed by funding organizations and the semantic networks constituted by word embedding-enhanced topics are revealed and analyzed simultaneously. The methodology is demonstrated through a case study on big data research involving 9882 articles from the Web of Science over the period 2010 to 2019. The result shows a comprehensive picture of the topics that governments, academic institutions, and industrial funding organizations prefer to fund, which provide potential decision support for agencies and organizations who are exploring funding patterns, estimating funding trends, and updating their funding strategies.

https://doi.org/10.1007/s11192-021-04253-x

Author(s): Qianqian Jin, Hongshu Chen, Ximeng Wang, Tingting Ma, Fei Xiong
Organization(s): Beijing Institute of Technology, Beijing Wuzi University, Beijing Jiaotong University
Source: Scientometrics
Year: 2022

Identification of factors affecting the reduction of VOC emissions in the paint industry: Systematic literature review – SLR (Full-Text)

Research Examples, Research profiling

In recent years, the interest of the manufacturing industry in new perspectives aimed at sustainability has increased, driving the manufacture of paints to use alternatives that help to reduce the impacts generated by the emission of Volatile Organic Compounds (VOCs); these impacts are especially negative since they have environmental consequences related to atmospheric pollution and social consequences due to their harmful effects on human health. The economic field has a positive effect due to the performance and varied application of the coatings in different industries. A systematic literature review (SLR) was conducted to identify factors that contribute to the reduction of VOCs emissions in the coating industry. Search equations were proposed with selected keywords, reading and selection of articles, analysis and classification of the information and finally plotting the data with the use of VantagePoint software. A total of 51 articles were selected for analysis, finding three key factors. (1) green chemistry, based on the formulation of paints from the use and/or synthesis of raw materials with renewable materials; (2) incorporation of cleaner production strategies which involves the change of paint technology, such as water-based, powdered, or with variation of the solids content; and (3) regulations and standards that must be complied with regarding VOCs emissions and that depend on the type of industries and the country where they are applied. Polyurethane resins were identified as the most studied for VOCs abatement. The paints market has focused on new sources to develop environmentally friendly materials, introducing manufacturing processes and technologies that combine sustainability and performance in this industry.

For FULL-TEXT see https://doi.org/10.1016/j.porgcoat.2022.106945

Author(s): Ana María Jiménez-López, Gustavo Adolfo Hincapié-Llanos
Organization(s): Universidad Pontificia Bolivariana
Source: Progress in Organic Coatings
Year: 2022

Medicine-Engineering Interdisciplinary Research Based on Bibliometric Analysis: A Case Study on Medicine-Engineering Institutional Cooperation of Shanghai Jiao Tong University (Full-Text)

Citation analysis, Interdisciplinarity, Research Examples, Research profiling, VOSViewer

This article aims to provide reference for medicine-engineering interdisciplinary research. Targeted at
the scientific literature and patent literature published by Shanghai Jiao Tong University, this article attempts to set up co-occurrence matrix of medicine-engineering institutional information which was extracted from address fields of the papers, so as to construct the medicine-engineering intersection datasets. The dataset of scientific literature was analyzed using bibliometrics and visualization methods from multiple dimensions, and the most active factors, such as trends of output, journal and subject distribution, were identified from the indicators of category normalized citation impact (CNCI), times cited, keywords, citation topics and the degree of medicine engineering interdisplinarity. Research on hotspots and trends was discussed in detail. Analyses of the dataset of patent literature showed research themes and measured the degree for technology convergence of medicine engineering.

For FULL-TEXT https://doi.org/10.1007/s12204-022-2418-5

Author(s): WANG Qingwen, CUI Tingting, DENG Peiwen
Organization(s): Shanghai Jiao Tong University
Source: Journal of Shanghai Jiao Tong University
Year: 2022

Global scientific production, international cooperation, and knowledge evolution of public administration

Research Examples, Research profiling

Public administration is a discipline with considerable history, and is also a diverse, interdisciplinary field in social science. To analyze its evolution, discover the present research foci, and predict future development trends, this study applied scientometrics visualization technology to evaluate over 72,000 scientific articles from the 1920s to 2020s. This research referred to the SSCI and JCR databases to gather scientific data of the discipline and the journals’ impact factor. Consequently, paper citations, cited journals, journal co-citations, author co-citations, authoritative papers, top countries, productive institutes, average references, and research collaboration trends were analyzed on the bases of the published literature. This study found top productive journals in the discipline, discovered productive countries and institutes, present the research foci, and predicted future development trends. Through this study, scientific production, international cooperation, and knowledge evolution mode of public administration research offers a clear knowledge map of the public administration discipline.

https://doi.org/10.1111/padm.12853

Author(s): Peng-Hui Lyu, Ming-Ze Zhang, Chuan-Jun Liu, Eric W. T. Ngai
Organization(s): Sichuan University,The Hong Kong Polytechnic University
Source: Public Administration
Year: 2022