USAGE OF VOICE ASSISTANT: A BIBLIOMETRIC LITERATURE REVIEW AND RESEARCH AGENDA

Purpose The purpose of this article is to explore how FinTech is transforming financial inclusion in emerging markets through a detailed bibliometric analysis. The study identifies key research trends, themes and gaps, providing both theoretical insights and practical recommendations for policymakers and financial institutions. By integrating FinTech into established models of financial inclusion, the article highlights its potential to drive inclusive economic growth. Additionally, it proposes a future research agenda to address emerging challenges and opportunities, ensuring that the impact of FinTech on financial inclusion in developing regions is fully realized. Design/methodology/approach The study employs a bibliometric analysis to examine the existing literature on FinTech and financial inclusion in emerging markets. Using data from the Scopus database, the analysis focuses on identifying key trends, research themes and gaps within the field. The methodology includes performance analysis to determine the most prolific authors, institutions and countries, as well as science mapping to visualize the intellectual structure and thematic evolution. The combination of quantitative bibliometric techniques and qualitative content analysis provides a comprehensive overview of the research landscape and informs the proposed future research agenda. Findings The findings reveal that FinTech is significantly advancing financial inclusion in emerging markets, with research increasingly focusing on mobile banking, peer-to-peer lending and blockchain technologies. The analysis identifies China, the USA and the UK as leading contributors to this research. Key themes include the role of FinTech in reducing financial barriers and promoting economic development. However, gaps remain in understanding the long-term impacts of FinTech on financial stability and the specific needs of marginalized populations. The study highlights the need for more targeted research to fully leverage FinTech’s potential in driving inclusive growth in developing regions. Originality/value This study provides a unique contribution by offering a comprehensive bibliometric analysis of FinTech’s role in financial inclusion, specifically within the context of emerging markets. Unlike previous studies that focus on individual technologies or regions, this research systematically maps the global research landscape, identifying key trends, gaps and future research opportunities. The involvement of Dr Petterson Ozili, an expert from the Central Bank of Nigeria, adds significant value, ensuring the analysis is both relevant and informed by practical expertise. This paper serves as a valuable resource for researchers, policymakers and practitioners aiming to leverage FinTech for inclusive economic growth.

Industry 4.0 (I4.0) technologies have improved operations in health care facilities by optimizing processes, leading to efficient systems and tools to assist health care personnel and patients. This study investigates the current implementation and impact of I4.0 technologies within maternal health care, explicitly focusing on transforming care processes, treatment methods, and automated pregnancy monitoring. Additionally, it conducts a thematic landscape mapping, offering a nuanced understanding of this emerging field. Building on this analysis, a future research agenda is proposed, highlighting critical areas for future investigations. A bibliometric analysis of publications retrieved from the Scopus database was conducted to examine how the research into I4.0 technologies in maternal health care evolved from 1985 to 2022. A search strategy was used to screen the eligible publications using the abstract and full-text reading. The most productive and influential journals; authors', institutions', and countries' influence on maternal health care; and current trends and thematic evolution were computed using the Bibliometrix R package (R Core Team). A total of 1003 unique papers in English were retrieved using the search string, and 136 papers were retained after the inclusion and exclusion criteria were implemented, covering 37 years from 1985 to 2022. The annual growth rate of publications was 9.53%, with 88.9% (n=121) of the publications observed in 2016-2022. In the thematic analysis, 4 clusters were identified-artificial neural networks, data mining, machine learning, and the Internet of Things. Artificial intelligence, deep learning, risk prediction, digital health, telemedicine, wearable devices, mobile health care, and cloud computing remained the dominant research themes in 2016-2022. This bibliometric analysis reviews the state of the art in the evolution and structure of I4.0 technologies in maternal health care and how they may be used to optimize the operational processes. A conceptual framework with 4 performance factors-risk prediction, hospital care, health record management, and self-care-is suggested for process improvement. a research agenda is also proposed for governance, adoption, infrastructure, privacy, and security.

BackgroundRobotic and artificial intelligence (AI)-assisted neurorehabilitation has emerged as a rapidly growing interdisciplinary field, integrating engineering innovations with clinical practice to enhance motor and cognitive recovery in neurological disorders. While research in this domain has expanded substantially over the last two decades, only a few bibliometric studies have examined related topics (e.g., new technologies in neurorehabilitation, rehabilitation robotics after stroke, AI in stroke care), and, to our knowledge, no study has provided a comprehensive bibliometric mapping specifically focused on robotics and artificial intelligence applications in neurorehabilitation. This study aimed to analyse the global trends, influential contributors, thematic evolution, and collaborative networks in robotic and AI-assisted neurorehabilitation.MethodsA bibliometric analysis was conducted using the Web of Science Core Collection. A comprehensive search covering 2003–2025 identified relevant articles using controlled terms for neurorehabilitation, robotics, and AI. Data were exported as plain text files (savedrecs.txt) from the Web of Science Core Collection and processed using the Bibliometrix R package via the Biblioshiny interface. Analyses included annual growth, citation performance, authorship patterns, journal impact, keyword co-occurrence, thematic mapping, and international collaboration networks.ResultsA total of 468 articles were retrieved from 191 sources, showing a rapid annual growth rate of 19.57%. The average citation per article was 24.22, with 17,792 references cited overall. Authorship analysis revealed contributions from 1,972 authors, with an average of 5.49 co-authors per paper and 32.05% international collaboration. The Journal of NeuroEngineering and Rehabilitation (h-index = 15, 1,740 citations) and Sensors (m-index = 1.714) were identified as the leading journals. The most prolific authors included Aiguo Song (8 publications) and Robert Riener (6 publications), while Marchal-Crespo L. and Reinkensmeyer D.J. were the most locally cited. Keyword analysis highlighted “stroke” (n = 93), “rehabilitation” (n = 82), “design” (n = 58), “recovery” (n = 53), and “exoskeleton” (n = 49) as dominant themes, with stroke rehabilitation and robotic exoskeletons representing core research foci. China (n = 697) and the USA (n = 251) emerged as the most productive countries, with strong collaborative ties.ConclusionRobotic and AI-assisted neurorehabilitation has demonstrated exponential growth, reflecting both technological innovation and clinical translation. Stroke rehabilitation and gait training remain central themes, while emerging areas such as AI-based assessment systems, wearable sensors, and tele-rehabilitation suggest future directions. To our knowledge, this study provides a comprehensive bibliometric overview specifically centred on robotics and artificial intelligence applications in neurorehabilitation, offering strategic insights for guiding future research and clinical integration.

Artificial intelligence has ushered in a new era of technological innovation, fundamentally transforming various sectors, including accounting. As businesses increasingly operate in a data-driven environment, the demand for real-time financial analysis and predictive insights has surged. This study aims to perform a bibliometric literature analysis focusing on significant literature, countries, authors, keywords, thematic evolution, citations, and documents, which researchers can reference in future studies related to the implementation of AI in accounting. VOSviewer software tool is used to create various maps based on the bibliographic data. The dataset was extracted from the Scopus database. Citation analysis, bibliographic coupling analysis, co-citation analysis, and co-occurrence analysis of author keywords are conducted. The analysis shows a sharp increase in research on AI and accounting from 2019, with 72 publications in 2023 and 66 publications by mid-2024, indicating rising interest and progress in this field. The bibliometric analysis reveals the dominant role of the United States in AI and accounting research. The co-occurrence analysis of author keywords shows important themes and their connections in AI and accounting, with artificial intelligence being the central theme, closely linked with other key concepts like accounting, machine learning, and big data. The findings underscore the practical implications of AI integration in accounting, emphasizing its potential to enhance efficiency, accuracy, and strategic decision-making in financial practices.

PurposeRobotic Process Automation (RPA) is a digital transformation tool that demonstrated tremendous growth in research output as well as its application in the past decade. This study attempts to identify essential research gaps and proposes future research agendas in the field by analyzing publishing trends, major stakeholders (authors, countries, affiliations, journals), key clusters and evolving themes by mapping the most recent research (2016–2022) in the field.Design/methodology/approachPreferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) was used to retrieve a total of 244 publications from the Web of Science (WOS) database for this analysis. The study then uses the open-source R program bibliometrix to conduct bibliometric analysis. A variety of tools and methods including collaboration network, word dynamics, co-occurrence network, thematic map and strategy map were utilized.FindingsThe analysis reveals the most influential stakeholders (country: the USA, author: Arai K, affiliation: Christ Deemed University), main clusters of intellectual structure (process mining, digital transformation, blockchain, information systems) and the evolution of themes (model innovation, artificial intelligence, big-data, design science and user acceptance) in the subject.Originality/valueThis study uses bibliometric analysis to provide a comprehensive overview of RPA literature which unravels the conceptual structure of the stream and proposes a research agenda for the future. Based on the growth of themes and the strategy map, this study may assist entrepreneurs and practitioners in determining field priorities for strategizing process innovation.

This study examines the intellectual development and thematic evolution of research connecting intellectual property and environmental issues. Using bibliometric analysis, it explores how the discourse has expanded from normative legal studies toward interdisciplinary research integrating innovation, policy, and technology. The dataset was collected from the Scopus database using the search query (“intellectual property”) AND (“environmental”), yielding 268 valid documents published between 1989 and 2025. Data were analyzed using the Bibliophagy package in R to identify publication trends, citation patterns, productive authors, influential journals, and conceptual structures based on co-occurrence, co-authorship, and thematic evolution mapping. The findings reveal a significant growth of publications in recent years, particularly after 2015, with China, the United States, and the United Kingdom emerging as the leading contributors. Prominent journals such as Technological Forecasting and Social Change and Journal of Environmental Economics and Management serve as key outlets for this domain. The thematic structure identifies intellectual property rights, innovation, and China as the dominant motor themes, reflecting the growing importance of intellectual property in driving environmental innovation and governance. Earlier themes, such as environmental protection, industrial economics, and biodiversity have evolved into broader frameworks incorporating patents, technological development, and green innovation policies.

This study investigates the scientific development of Artificial Intelligence (AI) and Machine Learning (ML) applications in Human Resource Management (HRM) through bibliometric analysis. To this end, 522 academic publications indexed in the Scopus database between 2020 and 2024 were analyzed using the R-based bibliometrix package and VOSviewer software. Descriptive analysis, scientific productivity metrics, and content analysis techniques were employed. The findings revealed three main patterns. First, research on AI and ML applications in HRM has grown significantly—particularly between 2022 and 2024—driven by post-pandemic digital transformation. Second, India, China, and the USA lead in research output, while the UK and France demonstrate strong citation impact, indicating a globally expanding research ecosystem. Third, the thematic focus of research is shifting from technical infrastructure toward more human-centered and ethical dimensions. Additionally, keyword co-occurrence network analysis identified three major thematic clusters: HRM functions, AI applications, and machine learning analytics, highlighting the field’s interdisciplinary nature. Compared to the previous studies, this research provides a more comprehensive bibliometric analysis of AI and ML applications in HRM. It is the first extensive study to map the intellectual evolution of the field from a multidisciplinary perspective. Furthermore, it charts research trends and collaboration networks, revealing a shift from technical implementations to strategic integration. In conclusion, this analysis offers new insights to the literature by illustrating the technological evolution in HRM and highlighting the growing significance of cutting-edge approaches such as AI and ML, reaffirming the field as a timely and impactful area of research.

Co-authorship network analysis of AI applications in sustainable supply chains: Key players and themes

The growing application of artificial intelligence (AI), especially technologies based on machine learning, has changed how academics write in higher education and raised issues with academic honesty. This study looks at current research trends on the application of AI to thesis writing worldwide and the ways in which scholarly literature addresses academic integrity. A systematic literature review and bibliometric analysis were performed following the PRISMA framework. The Scopus database was used to gather data using with the keywords “machine learning” and “academic integrity.” A total of 52 peer-reviewed journal articles published between 2016 and September 2025 were examined to determine publication trends, key research themes, and the distribution of scholarly contributions across countries and institutions. The results show that since 2022, publications have significantly increased, demonstrating the increasing scholarly focus on ethical concerns in AI-assisted thesis writing. Themes that predominate include plagiarism detection, authorship and originality, ethical AI use, and pedagogical strategies to preserve academic integrity. The study highlights the necessity of institutional regulations and explicit ethical standards to promote ethical and long-term AI integration in higher education.

In recent years increasing attention has been paid to theory building and empirical research that explore the links between the business model and open innovation (BM&OI). Nevertheless, studies presenting the results of bibliometric analyses merging these two terms are still scarce. Therefore, the main aim of this paper was to present the results of a comprehensive bibliometric analysis focused on the determination and mapping of the evolving cognitive and social structures in the BM&OI literature to set proposals for directions of future research. Our research was based on the dataset obtained from the Scopus database and made use of the Biblioshiny and the VOSviewer software. Descriptive and network analyses were conducted to demonstrate an overview of the scientific field under consideration. We identified the leading authors, sources, countries and institutions in the BM&OI literature. The most influential publications on the BM&OI and the most cited references by documents covering the BM&OI research were indicated. Based on the thematic evolution and thematic maps, the evolving structures of key sub-fields of the BM&OI research were determined and discussed. Moreover, the major clusters and the specificity of scientific collaboration in the analyzed research domain were identified and described. Our intention was to demonstrate to both scholars and practitioners the wide-ranging landscape of multifaceted research on the BM&OI.

This study investigates the application of Artificial Intelligence (AI) in academic libraries through a bibliometric analysis of 354 publications indexed in Scopus from 2010 to 2023. Using Bibliometrix, VOS Viewer, and MS Excel, it examines publication trends, influential authors, sources, geographical contributions, and thematic evolution. The findings reveal a surge in AI-related research in recent years, with significant contributions from scholars like Cox AM and Stock WG. Collaborative research accounts for 75% of publications, indicating its high impact. The Journal of Academic Librarianship emerges as the leading source, while China, the USA, and India lead in publication output. Key research themes include AI integration in library services, data mining, and user personalization, with notable collaborations between Indonesia-Malaysia and Nigeria-South Africa. A thematic analysis identifies artificial intelligence, machine learning, and chatbots as pivotal areas. The study underscores the interdisciplinary nature of AI research in academic libraries and its growing influence on enhancing library operations, resource discovery, and user experiences. By identifying research gaps and future directions, the findings provide valuable insights for library professionals and policymakers, enabling more strategic AI adoption to improve efficiency, automate routine tasks, and enhance decision-making in library management.

A simplified bibliometric mapping and analysis about sustainable polymers

PurposeThe purpose of this study is to map the research landscape on the topic of waste management in the business and management domain, with a particular emphasis on pro-environmental ethical behaviour. The objective is to evaluate publication performance, identify key stakeholders, investigate major clusters, recognise the evolution of themes and offer a research agenda for the future based on bibliometric reflection.Design/methodology/approachPreferred Reporting Items for Systematic Review and Meta-Analysis procedure was used to extract and choose a total of 609 publications from the Scopus database from 1985 to 2022. The research then does bibliometric analysis with the open-source R application bibliometrics. The authors used a number of tools and techniques, including a collaboration network, word dynamics, co-occurrence network, thematic map and strategy map.FindingsThe analysis identifies most prolific stakeholders, key clusters and evolving themes in the field. The motor themes, niche themes, basic themes and emerging themes of the field were identified, and future research agenda is proposed based on such identification.Originality/valueTo the best of the authors’ knowledge, this is the first bibliometric study in the field of waste reduction, providing a comprehensive view of the research landscape by analysing more than 50 years of literature focussing on behavioural aspects. These findings could assist policymakers in identifying waste management/reduction priority regions and developing policy guidelines for a more sustainable waste practise. In addition to providing recommendations and future directions for academic research, this report also includes these elements.

PurposeWith the rise in adverse impact of excessive technology use, such as smartphone; the issue of smartphone addiction has gained the attention of researchers in recent years. Therefore, this study undertakes to review the literature on smartphone addiction research by identifying the current state of research in this domain and the future avenues that need to be addressed.Design/methodology/approachA comprehensive bibliometric analysis was conducted on 652 articles extracted from SCOPUS database. Publications were extracted from Scopus by performing a keyword search of “Smartphone Addiction” OR “Problematic smartphone use”. Bibliometric methods such as performance analysis and science mapping were used to perform the overview of smartphone addiction research. In addition, VOSviewer software was used to organise, analyse and present the data. This study identifies the most prolific authors, journals, documents, collaborative work, major research themes, potential research avenues in this field of research.FindingsThe result shows that the research on smartphone addiction has increased recently, the dominance of research is found in few countries only. There is preponderance of research in this domain in Asian countries, particularly South Korea and still there is a significant scope for future research in this area, which is presented in detail in this study. The research on smartphone addiction has been mainly conducted in the field of medicine and psychology; the other subjects lack behind by a significant margin in terms of research publications in this domain. The findings suggest Elhai (US) is the most influential researcher in this field, and US has shown high collaboration in smartphone addiction research with other countries as well as with authors within its domestic territory. Thematic map obtained from R software presents the evolution of themes. It shows that quality of life, social support, self-efficacy, anxiety and depression are major variables studied over the period. Respondents in most of the studies were university students, as the young generation is technology-savvy and is more attracted to gadgets such as smartphones.Research limitations/implicationsThis study provides an overview of research on smartphone addiction through an exhaustive bibliometric analysis to organise the fragmented literature on smartphone addiction and provide structure for future research in the domain. This is the first study of its kind on the subject. This study has found important future research avenues in the domain, which need to be addressed. Also, it will provide guidance to stakeholders from different backgrounds like, manufacturers, marketers, regulators, policymakers, consumers and academicians to contribute in controlling this problem as a part of their social responsibility.Originality/valueThis paper is unique in the sense that it, for the first time, attempts to provides valuable insights on the current status of research on smartphone addiction and also provides guidance for potential future agenda through bibliometric and content analysis techniques.

IntroductionThe integration of artificial intelligence (AI) into bacteriology has marked a pivotal advancement by enabling the analysis of large-scale microbiological datasets. Despite growing adoption, significant research gaps persist, hindering the full exploitation of AI’s potential in bacterial research and diagnostics.ObjectiveTo analyze global scientific production on the application of AI techniques in bacteriology and propose a future research agenda based on bibliometric trends.MethodsThis study conducts a bibliometric analysis of artificial intelligence (AI) applications in bacteriology, explicitly guided by the PRISMA 2020 framework. Unlike traditional reviews, this approach combines PRISMA’s methodological rigor with bibliometric techniques to map scientific production. Metadata were retrieved from Scopus and Web of Science using predefined search strategies. Quantitative indicators, co-occurrence networks, and thematic mapping were applied to examine the field’s temporal evolution and conceptual structure. The findings provide an evidence-based overview of research trends and gaps, supporting the design of a future research agenda on AI integration in bacteriology.ResultsThe findings reveal exponential growth in scientific output, especially between 2022 and 2024. Leading authors include Singh and Waegeman, with high-impact journals such as Frontiers in Microbiology and MSystems. The United States and China are the most productive countries. Thematic evolution shows a shift from early topics like microbial spoilage toward advanced applications including bacterial classification and diagnostic modeling. Key conceptual clusters were identified around microbiomes, classification, and bioinformatics. Emerging terms such as “diagnosis,” “metagenomics,” and “transfer learning” indicate future research directions.ConclusionAI applications in bacteriology are expanding rapidly yet still rely heavily on traditional machine learning methods. There is a need to incorporate advanced approaches such as deep learning and transformer-based models. The findings support a strategic agenda for promoting interdisciplinary collaboration and technological innovation in bacteriological research.