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This study specifically seeks to investigate the strategic implementation of machine learning (ML) algorithms and techniques in healthcare institutions to enhance innovation management in healthcare settings.

The papers from 2011 to 2021 were considered following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. First, relevant keywords were identified, and screening was performed. Bibliometric analysis was performed. One hundred twenty-three relevant documents that passed the eligibility criteria were finalized.

Overall, the annual scientific production section results reveal that ML in the healthcare sector is growing significantly. Performing bibliometric analysis has helped find unexplored areas; understand the trend of scientific publication; and categorize topics based on emerging, trending and essential. The paper discovers the influential authors, sources, countries and ML and healthcare management keywords.

The study helps understand various applications of ML in healthcare institutions, such as the use of Internet of Things in healthcare, the prediction of disease, finding the seriousness of a case, natural language processing, speech and language-based classification, etc. This analysis would help future researchers and developers target the healthcare sector areas that are likely to grow in the coming future.

The study highlights the potential for ML to enhance medical support within healthcare institutions. It suggests that regression algorithms are particularly promising for this purpose. Hospital management can leverage time series ML algorithms to estimate the number of incoming patients, thus increasing hospital availability and optimizing resource allocation. ML has been instrumental in the development of these systems. By embracing telemedicine and remote monitoring, healthcare management can facilitate the creation of online patient surveillance and monitoring systems, allowing for early medical intervention and ultimately improving the efficiency and effectiveness of medical services.

By offering a comprehensive panorama of ML's integration within healthcare institutions, this study underscores the pivotal role of innovation management in healthcare. The findings contribute to a holistic understanding of ML's applications in healthcare and emphasize their potential to transform and optimize healthcare delivery.

The study aims to evaluate PubMed publications on ChatGPT or artificial intelligence (AI) involvement in scientific or medical writing and investigate whether ChatGPT or AI was used to create these articles or listed as authors.

This scoping review was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. A PubMed database search was performed for articles published between January 1 and November 29, 2023, using appropriate search terms; both authors performed screening and selection independently.

From the initial search results of 127 articles, 41 were eligible for final analysis. Articles were published in 34 journals. Editorials were the most common article type, with 15 (36.6%) articles. Authors originated from 27 countries, and authors from the USA contributed the most, with 14 (34.1%) articles. The most discussed topic was AI tools and writing capabilities in 19 (46.3%) articles. AI or ChatGPT was involved in manuscript preparation in 31 (75.6%) articles. None of the articles listed AI or ChatGPT as an author, and in 19 (46.3%) articles, the authors acknowledged utilizing AI or ChatGPT.

Researchers worldwide are concerned with AI or ChatGPT involvement in scientific research, specifically the writing process. The authors believe that precise and mature regulations will be developed soon by journals, publishers and editors, which will pave the way for the best usage of these tools.

This scoping review expressed data published on using AI or ChatGPT in various scientific research and writing aspects, besides alluding to the advantages, disadvantages and implications of their usage.

The recent proliferation of preprints could be a way for researchers worldwide to increase the availability and visibility of their research findings. Against the background of rising publication costs caused by the increasing prevalence of article processing fees, the search for other ways to publish research results besides traditional journal publication may increase. This could be especially true for lower-income countries.

Therefore, the authors are interested in the experiences and attitudes towards posting and using preprints in the Global South as opposed to the Global North. To explore whether motivations and concerns about posting preprints differ, the authors adopted a mixed-methods approach, combining a quantitative survey of researchers with focus group interviews.

The authors found that respondents from the Global South were more likely to agree to adhere to policies and to emphasise that mandates could change publishing behaviour towards open access. They were also more likely to agree posting preprints has a positive impact. Respondents from the Global South and the Global North emphasised the importance of peer-reviewed research for career advancement.

The study has identified a wide range of experiences with and attitudes towards posting preprints among researchers in the Global South and the Global North. To the authors' knowledge, this has hardly been studied before, which is also because preprints only have emerged lately in many disciplines and countries.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2023-0181

The key aim of this study is to highlight current financial technology (FinTech) trends by conducting a bibliometric review of literature derived from the Scopus database.

A bibliometric analysis was conducted on articles gathered from the Scopus database. Microsoft Excel was used to perform the frequency analysis, VOSviewer for visualising the data, and Harzing’s Publish or Perish for the metrics citation.

According to this investigation, research into FinTech has been consistently increasing since 2008. The results indicate that the most active publisher of FinTech literature is Bina Nusantara University in Indonesia. In terms of country of publication, China is identified as the most active. The most cited author is Buckley, R.P., with Rabbani, M.R., having the most publications. It was also identified that FinTech researches come under three primary domains namely business management, computer science and economics.

The primary limitation of this current study is that it only relied on one data source, i.e. Scopus. Implications wise, researchers and practitioners can gain a deeper understanding of FinTech from this study, which also describes the trend in related publications on the concept. Future studies could significantly benefit from the findings of the present paper.

The outcomes of this study can assist researchers in better comprehending and summarising the key drivers of FinTech. In addition, the findings can help new researchers identify the starting point for their research on FinTech.

As far as the authors are aware, this is the first study that reviews FinTech publications derived from Scopus from 2008 to 2022. Hence, it is a pioneering study into FinTech bibliometric analysis, providing an understanding of the structural knowledge by reviewing the timeline of academic progression in FinTech.

This study aims to provide the main contents of the revision of the 2023 OECD Guidelines for Multinational Enterprises and suggest implications for the Korean government and multinational enterprises.

Following the brief history of the revision of OECD Guidelines for Multinational Enterprises, this study reviews and evaluates major substantive and procedural revisions of the 2023 OECD Guidelines, and then suggests countermeasures for Korean government and businesses.

The most significant substantive change of the 2023 revision is that expectations for environmental due diligence and disclosure obligations, including climate change and biodiversity, for multinational enterprises have been expanded and strengthened. Regarding procedural changes, the biggest change is the introduction of a basis rule for the National Contact Points for Responsible Business Conduct (NCPs for RBC) to judge each issue and a rule that the final statement must include follow-up details and deadlines, which is expected to strengthen the effectiveness of the NCP dispute resolution mechanism.

This study is the first academic paper to introduce major substantive and procedural revisions to the 2023 OECD Guidelines for Multinational Enterprises in Korea. This study also provides implications for the Korean government and companies following the 2023 revised OECD Guidelines for Multinational Enterprises as follows. First, the Korean government must establish a public–private partnership to closely communicate to prevent Korean companies from being harmed by failing to meet strengthening international Environment, Social and Governance (ESG) standards. In addition, Korean government should actively participate in ESG-related international forums, including the OECD, and strive to reflect the needs and interests of Korean companies. Second, the Korean NCP should strengthen its activities to prevent potential damage by expanding education and promotions for Korean businesses on related overseas legislative trends and NCP dispute case studies so that Korean companies can effectively deal with the strengthened ESG standards. Third, Korean multinational enterprises should preemptively establish an advanced ESG management system to seize new opportunities in the global supply chain previously concentrated in China and India in the process of reorganizing global supply chains according to the trend of strengthening ESG standards and the US value alliance strategy.

The ranking of universities and other research-intensive institutions in global ranking systems is based on numerous indicators, including number of articles with external collaboration, number of articles with international collaboration, number of articles with industry collaboration as well as co-patents with industry. The purpose of this paper is to examine university–industry research collaboration in Ghana, with the aim of exploring the relationship between the research output collaborations in the top four universities in Ghana and industry across different geographical scales.

This study’s data was obtained from the SciVal database, which drawn its data from the Scopus bibliographic and citation database. The bibliographic and citation data were extracted using a search of the publications affiliated to the University of Ghana, for the period 2011–2020.

Key findings demonstrate a constant rise in the number of research publications by the selected universities over time. Research collaboration intensity in the selected universities in terms of co-authored publications was higher as compared to single-authored publications. University–industry research co-authorships were, however, lower when compared to university–university research co-authorships. The university–industry research co-authorships occurred mostly with Europe, Asia-Pacific and North American-based institutions as opposed to African-based institutions. In Ghana, four industry-based institutions were engaged in intensive research with the selected universities.

This study demonstrates that, for each selected university, it is possible to measure the performance of individual universities in both intra-regional and international collaboration. Such results may be useful in informing policy as well as merit-based public funding of universities in Ghana.

This paper aims to promote the higher quality development of high-tech enterprises in China. While science and technology have greatly promoted human civilization, resources have been excessively consumed and the environment has been sharply polluted. Therefore, it is particularly important for current enterprises to make use of scientific and technological innovation to maximize the benefits of mankind, minimize the loss of nature, and promote the sustainable development of our country.

By using DEA-Banker-Charnes-Cooper (BCC) model and DEA-Malmquist model, this paper comprehensively examines the innovation efficiency of high-tech enterprises from both static and dynamic perspectives, and conducts a provincial comparative study with the panel data of ten representative provinces from 2011 to 2020.

The research findings are as follows: the rapid number increase of high-tech enterprises in most provinces (cities) is accompanied by an ineffective input–output efficiency; the quality of high-tech enterprises needs to comprehensively examine both input–output efficiency and total factor productivity; and there is not a positive correlation between element investment and innovation performance.

Because the DEA model used in this paper assumes that the improvement direction of invalid units is to ensure that the input ratio of various production factors remains unchanged but sometimes the proportion of scientific and technological activities personnel and the total research and development investment is not constant. In the future, the nonradial DEA model can be considered for further research. Due to historical data statistics, more provinces, cities and longer panel data are difficult to obtain. The samples studied in this paper mainly refer to the provinces and cities that ranked first in the number of national high-tech enterprises in 2020. Limited by the number of samples, DEA analysis failed to select more input and output indicators. In the future, with the accumulation of statistical data, the existing efficiency analysis will be further optimized.

Aiming at the misunderstanding of emphasizing quantity and neglecting quality in the cultivation of high-tech enterprises, this paper comprehensively uses DEA-BCC model and DEA Malmquist index decomposition method to make a comprehensive comparative study on the development of high-tech enterprises in ten representative provinces (cities) from two aspects of static efficiency evaluation and dynamic efficiency evaluation.

The purpose of this paper is to develop a software-library in the R programming language that implements the concepts of the interpretive coordinate, interpretive axis and interpretive plane. This allows for the automatic and reliable interpretation of results from the multiple correspondence analysis (MCA) as previously proposed and published. Consequently, the users can seamlessly apply these concepts to their data, both via R commands and a corresponding graphical interface.

Within the context of this study, and through extensive literature review, the advantages of developing software using the Shiny library were examined. This library allows for the development of full-stack applications for R users without the need for knowledge of the corresponding technologies required for the development of complex applications. Additionally, the structural components of a Shiny application were presented, leading ultimately to the proposed software application.

Software utilizing the Shiny library enables nonexpert developers to rapidly develop specialized applications, either to present or to assist in the understanding of objects or concepts that are scientifically intriguing and complex. Specifically, with this proposed application, the users can promptly and effectively apply the scientific concepts addressed in this study to their data. Additionally, they can dynamically generate charts and reports that are readily available for download and sharing.

The proposed package is an implementation of the fundamental concepts of the exploratory MCA method. In the next step, discoveries from the geometric data analysis will be added as features to provide more comprehensive information to the users.

The practical implications of this work include the dissemination of the method’s use to a broader audience. Additionally, the decision to implement it with open-source code will result in the integration of the package’s functions by other third-party user packages.

The proposed software introduces the initial implementation of concepts such as interpretive coordination, the interpretive axis and the interpretive plane. This package aims to broaden and simplify the application of these concepts to benefit stakeholders in scientific research. The software can be accessed for free in a code repository, the link to which is provided in the full text of the study.

Coastal zone ecological restoration project is of great significance to alleviate marine ecological degradation. Evaluating the effect of coastal ecological restoration projects and identifying the obstacle factors affecting their restoration level can provide an empirical basis for future Marine ecological restoration projects.

However, due to the initial stage of coastal zone ecological restoration projects, the actual monitoring data of coastal zone ecological restoration is relatively lacking. Based on the CRITIC-TOPSIS (combination of CRITIC method and TOPSIS method) method, combined with the subjective perception of the public and the actual data of the restoration project, this paper proposes an evaluation method of the coastal zone ecological restoration effect to obtain the specific implementation effect of the coastal zone ecological restoration project. The main obstacle factors affecting the evaluation of coastal ecological restoration effect are identified by using the obstacle degree model.

This paper conducted an empirical study on the restoration of sandy shoreline and coastal wetland in Qinhuangdao city. Based on the data of restoration projects and the subjective perception of ecological restoration by the public in Qinhuangdao city, the research results showed that the coastal zone ecological restoration effect of Qinhuangdao city was general. The quality of the restoration project and the public perception have an important influence on the evaluation of the restoration effect. Improving the quality of the restoration project, strengthening the public's participation in ecological restoration and allowing the public to better participate in the ecological restoration of the coastal zone can improve the effect of ecological restoration of the coastal zone in an all-round way.

The research results of this paper have a guiding role in the ecological restoration of coastal cities in the future, and also have a demonstration and reference role for the assessment of the effect of ecological restoration of coastal zones.

This article summarizes the international scientific research output of global forest product models, infers future research trends and provides reference for quantitative analysis and mathematical modeling of Chinese forest product problems, with the aim of contributing to promoting domestic production of Chinese forest products and strengthening international trade competitiveness of forest products.

In 1999, Joseph Buongiorno, a scholar at the University of Wisconsin in the United States of America, proposed the global forest products model (GFPM), which was first applied to research in the global forestry sector. GFPM is a recursive dynamic model based on five assumptions: macroeconomics, local equilibrium, dynamic equilibrium, forest product conversion flow and trade inertia. Using a certain year from 1992 to present as the base period, it simulates and predicts changes in prices, production and import and export trade indicators of 14 forest products in 180 countries (regions) through computer programs. Its advantages lie in covering a wide range of countries and a wide variety of forest products. The data mainly include forest resource data, forest product trade data, and other economic data required by the model, sourced from the Food and Agriculture Organization (FAO) of the United Nations and the World Bank, respectively.

Compared to international quantitative and modeling research in the field of forest product production and trade, China's related research is not comprehensive and in-depth, and there is not much quantitative and mathematical modeling research, resulting in a significant gap. This article summarizes the international scientific research output of global forest product models, infers future research trends, and provides reference for quantitative analysis and mathematical modeling of Chinese forest product problems, with the aim of contributing to promoting domestic production of Chinese forest products and strengthening international trade competitiveness of forest products.

On the basis of summarizing and analyzing the international scientific research output of GFPM, sorting out the current research status and progress at home and abroad, this article discusses potential research expansion directions in 10 aspects, including the types, yield and quality of domestic forest product production, international trade of forest products, and external impacts on the forestry system, in order to provide new ideas for global forest product model research in China.