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Textile waste water contains dyes and chemicals that produce harmful vapors and exhaust gases, which is hazardous to the environment and public health. Therefore, it must be carefully treated before discharged. To understand the research evolution in the research area of textile waste water treatment, based on bibliometrics, an in-depth analysis of the publications and hotspots in this area was presented in this paper. For the analysis, totally 6774 papers related to the research area that are published between the year 1964 and 2023 were collected from the Web of Science Core Collection. Using CiteSpace and VOSviewer as bibliometric analysis tools, the collaboration of countries, regions, and organizations was investigated. Besides, an analysis for citation and co-citation of journals, authors, references, and co-occurrence of keywords was performed. The evolution of research hotspots in the three major research directions related to degradation, oxidation, and adsorption is also analyzed in this paper. The analysis results show that researches related to oxidation and adsorption are active in recent years, while nanocomposite adsorbents and graphene oxide are the current research hotspots. [ABSTRACT FROM AUTHOR]

This paper reports a bibliometric analysis of two small data sets: a set of 34 papers that make up The Routledge handbook of vocabulary studies (Webb, 2020) and a set of papers dealing with second language (L2) vocabulary research taken from a single journal Frontiers in Psychology. Bibliometric maps based on author co-citations in these two data sets are presented and compared. Although the two data sets are comparable in terms of size, they appear to be very divergent. In particular, the significant sources identified in The Handbook map seem to play a relatively minor role in the Frontiers map. The obvious conclusion is that The Handbook is not as representative of L2 vocabulary research as its title might lead us to believe. The paper argues that micro-bibliometric studies like this one can sometimes highlight features that are lost in the more traditional large-scale bibliometric approach. [ABSTRACT FROM AUTHOR]

Purpose: In parallel with the rising importance of innovation and digital technologies, research on platform ecosystems is rapidly accumulating. This study aims to summarize the current research published in leading technical and innovation management (TIM) journals and provide recommendation for future research. Design/methodology/approach: In this paper, the authors combine multiple quantitative literature review methods (social network analysis, citation analysis and co-citation analysis) with a systematic literature review of articles published in the 13 most influential TIM journals over a 15-year period. Findings: The citation network of 168 selected papers is sparse, with low network density indicating the emerging nature of the field. Using network centrality values, the authors identified the 33 most influential articles. The review of methodologies in these articles found that conceptual and case study research dominate, suggesting the need for additional confirmatory and quantitative analysis. Co-citation analysis of the references identified six research clusters: foundation, network-centric innovation, complementor management, platform competition, ecosystemsand product platforms. Originality/value: This analysis is among the first to examine the knowledge structure of platform ecosystem research in the TIM domain by using multiple evidence-based analysis methods. The authors also apply the theory, context, characteristics and methodology framework to suggest areas for future research. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to uncover and develop explicit knowledge of existing smart communities (SCs) to guide services and business solutions for enterprises and serve community users in a well-thought-out manner. These sagacious frameworks will assist in analyzing trends and reaching out to pre-existing setups with different degrees of expertise. Design/methodology/approach: A systematic overview is provided in this paper to unify insights and competencies toward building SCs; a hybrid analytical approach is used consisting of machine learning and bibliometric analysis. Scopus and Web of Science (WoS) are the primary databases for this purpose. Findings: SCs implement cutting-edge technologies to enhance mobility, elevating information and communication technology (ICT) skills and data awareness while improving business processes and efficiency. This system of SC is an evolution of the conventional method. It provides a foundation for intelligent community services based on individual users and technologies such as the Internet of Things (IoT), artificial intelligence, cloud computing and big data. Manufacturing-based, service-based, retail-based, resource management and infrastructure-based SCs exist in the literature. Originality/value: The paper summarizes a conceptual framework of SCs based on existing works around SCs. To the best of the authors' knowledge, this is the first systematic literature review that uses a hybrid approach of topic modeling and bibliometric analysis to understand SCs better. [ABSTRACT FROM AUTHOR]

The International Journal of Advances in Soft Computing and its Applications (IJASCA) is a rapidly growing academic journal published by Al-Zaythoonah University of Jordan (ZUJ). IJASCA publishes original contributions on soft computing, machine learning and artificial intelligence, cloud computing, big data and other current science and technological trends. This study uses different bibliometric analysis tools to analyze the IJASCA published research papers between 2009 and 2021. The analysis includes annual publication growth, citation patterns, most prolific authors, institutions and countries, co-citation and co-occurrence networks analysis. A total of 317 published papers have been studied. The results show that IJASCA has grown in research contributions from 12 papers in 2009 to 40 papers in 2021, and citations have grown drastically to 2253. Universiti Teknologi Malaysia, Johor, is the institution that contributed the most to IJASCA publications with 109 papers. Malaysia is the country that was cited the most with 794 citations. Thematic analysis shows that the most important author keywords are soft computing, optimization, machine learning, big data and cloud computing. Overall, the findings are beneficial to the IJASCA editorial board. Its retrospective review will most likely encourage journal readers and assist the editorial team in developing research strategies that will allow research scientists to contribute high-quality research papers to the IJASCA. [ABSTRACT FROM AUTHOR]

Summary: Advances in computer networking, storage technologies, and high‐performance computing are helping global communities of researchers to address increasingly ambitious problems in Science collaboratively. E‐Science is the "science of this age"; it is realized through collaborative scientific enquiry which requires the utilization of non‐trivial amounts of computing resources and massive data sets. Core to this is the integrated set of technologies collectively known as e‐Infrastructures. In this paper, we explore the e‐Science and the e‐Infrastructure knowledge base through co‐citation analysis of existing literature. The dataset for this analysis is downloaded from the ISI Web of Science and includes over 12,000 articles. We identify prominent articles, authors, and articles with citation bursts. The detection of research clusters and the underlying seminal papers provide further insights. Our analysis is an important source of reference for academics, researchers, and students starting research in this field. [ABSTRACT FROM AUTHOR]

The paper aims to build the genealogy of the literature on Internationalization of Family Business (IFB) identifying the 'fathers' (the most important papers in the field), 'ancestors' (those on which the fathers have been built on), and 'descendants' (the most recent publications and topics) of the research field. Results originate from a complete database on which we used a bibliometric mixed-method approach by sequentially employing citation analysis, co-citation analysis, and bibliographic coupling. Our analysis enriches the academic debate on IFB with more understanding of the field's knowledge structure (origins, theoretical roots, and developments), meanwhile proposing a sequential use of different bibliometric methods and providing a methodological contribution. [ABSTRACT FROM AUTHOR]

Despite the long-recognised importance of data-driven operations and supply chain management (OSCM) scholarship and practice, and the impressive development of big data analytics (BDA), research finds that firms struggle with BDA adoption, which suggests the existence of gaps in the literature. Therefore, we conduct this systematic literature review of journal articles on data-driven OSCM from 2000 to early 2020 to ascertain established research clusters and literature lacunae. Using co-citation analysis software and double-checking the results with factor analysis and multidimensional-scaling-based k-means clustering, we find six clusters of studies on data-driven OSCM, whose primary topics are identified by keyword co-occurrence analysis. Five of these clusters relate directly to manufacturing, which, in line with the existing literature, indicates the crucial role of production in OSCM. We highlight the evolution of these research clusters and propose how the literature on data-driven OSCM can support BDA in OSCM. We synthesise what has been studied in the literature as points of reference for practitioners and researchers and identify what necessitates further exploration. In addition to the insights contributed to the literature, our study is amongst the first efforts to deploy multiple clustering techniques to undertake a rigorous data-driven systematic literature review (SLR) of data-driven OSCM. [ABSTRACT FROM AUTHOR]

This study explores the classics that significantly contribute to the research of construction engineering and management (CEM). Previous studies usually simply applied the number of citation to identify the classics, causing some flaws. To overcome the flaws, an advanced approach is developed by integrating scientometric methods (G-index and co-citation analysis) and a social network analysis (SNA) technique (modularity optimization algorithm), thus providing more precise and persuasive results that denote what academic works have made significant inspirations and illuminations on CEM research. This study retrieves 13,273 CEM literature and extracts 336,129 bibliographies from these literature. Based on the G-index, a total of 67 publications are identified as CEM classics. Moreover, this paper measures and maps the structure of the classics by using co-citation analysis and modularity optimization algorithm. The results provide a basic source of academic information representing the foundation of CEM and draw a big picture of CEM to show the underlying associations between the identified classics. This can help researchers recognize the key scientific contributions for improving the academy progress [ABSTRACT FROM AUTHOR]

Regional foresight is one of the regional planning approaches that increase the ability to deal with uncertainty and changes. This study aims to provide an overview of regional foresight studies and domain map to evaluate their merits and defects and direct future studies in this field. For this purpose, 111 papers related to regional foresight were identified at the "Web of Science" in the period 2000 to 2019 and used as the basis for further analysis. These papers have been reviewed in various aspects. In addition, the domain map of regional foresight and its intellectual bases was drawn based on co-citation analysis of these papers and their 4194 references. The domain map includes five main clusters of research areas or intellectual bases for regional foresight: normative forecasting, participation, foresight in policy and strategy, innovation systems, and multi-level governance. Finally, the merits and defects of regional foresight studies are evaluated based on research results and some suggestions are provided for future studies. [ABSTRACT FROM AUTHOR]

Shared micromobility services have become increasingly prevalent and indispensable as a means of transportation across diverse geographical regions. Integrating shared micromobility with public transit offers opportunities to complement fixed-route transit networks and address first- and last-mile issues. To explore this topic, a systematic literature review was conducted to consolidate knowledge, analyze research achievements and best practices, and provide future research recommendations. This study examined 108 journal papers from the Web of Science (WoS) core collection from 2016 to 2022, along with grey literature. Citation and co-citation analyses were performed to build and illustrate the literature's bibliometric networks. This analysis categorized the literature into four major study themes: policy, sustainability, the interaction between shared micromobility and public transportation, and infrastructure. The implementation approaches of integrating shared micromobility and public transportation in different cities were classified into four categories: physical integration, payment and fee integration, informational integration, and institutional integration. The findings indicate that the relationship between shared micromobility and public transportation varies with spatial–temporal conditions and the population density of the city. Overall, integrating micromobility into public transit can offer faster and more cost-effective mobility options for most trips, contributing to urban resilience, a better air quality, lower greenhouse gas emissions, and livable communities. Based on these insights, further research is recommended to explore dynamic and context-specific strategies for successful shared micromobility and public transit integration, considering diverse urban settings and demographic factors. [ABSTRACT FROM AUTHOR]

Purpose: Due to the financial disturbances created by the COVID-19 pandemic and the burden on the government exchequer, it is expected to see a rise in the knowledge base of the research corpus so far as the government's fiscal sustainability is concerned. Therefore, the present research examines a systematic quantitative analysis of public debt sustainability research by applying a bibliometric approach. Research also analyzes journals, institutions, countries and authors contributing to public debt sustainability. Design/methodology/approach: This paper scrutinizes the published scientific research on public debt sustainability based on the dataset of 535 articles from 1991 to 2021 obtained from the Scopus database. Biblioshiny (R-based application) and VoSviewer software were used to perform bibliometric analysis through Performance analysis and science mapping techniques. The authors combined co-citation analysis (CCA), bibliometric analysis, keyword co-occurrence analysis (KCA) and a conceptual thematic map of the most cited articles to find the intellectual structure. Findings: The research identified three dominating clusters, e.g. fiscal sustainability and policy rules, empirical sustainability testing and debt and growth dynamics. Another finding was that most articles were analytical and empirical and few descriptive articles were found. Owing to the empirical nature of the domain, the issues concerning public debt sustainability have continued to change over the past decades for different economies, reflecting the complexity and diversity of economic structures of different economies at different times. Originality/value: The insight of this article provides academicians and researchers with a more refined comprehension of the conceptual and intellectual structure of the research corpus. The present research complements the existing literature review studies by pushing the research towards emerging or less developed issues such as financial and debt crises. [ABSTRACT FROM AUTHOR]