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This paper aims to examine the current practice of reporting on translation issues in qualitative, interdisciplinary accounting research. Based on an analysis of the methodological consideration of the translation of quotations from non-English interviews and additional interviews with experienced researchers, the authors aim to develop recommendations for the reporting on such translation procedures in future accounting research relying on interviews not conducted in English.

The analysis is based on papers published in four highly ranked interdisciplinary accounting journals: Accounting, Auditing and Accountability Journal (AAAJ), Accounting, Organizations and Society (AOS), Critical Perspectives on Accounting (CPA) and Qualitative Research in Accounting and Management (QRAM). The subjects of the analysis are publications of non-English-speaking researchers who conducted non-English interviews and therefore were confronted with translation issues when attempting to get published in these English-language journals. Additionally, to gain deeper insights into reporting decisions on language and translation issues, the authors conducted interviews with experienced researchers in the field of qualitative, interdisciplinary accounting research whose mother tongue is not English. The authors combine these empirical insights with current developments in translation studies.

As suggested by translation studies, translation is an act of sense making and reconstruction of meaning, and therefore is a complex task that needs to be carried out with caution. However, the findings suggest that in current interdisciplinary, qualitative accounting research, the reporting of language and translation issues, especially with regards to the translation of quotations from interview data, have so far received only limited attention. The authors therefore call for more awareness of and sensibility toward dealing with language and translation issues, which should be reflected in more transparent reporting on translation processes to support the credibility and authenticity of qualitative accounting studies based on non-English interviews.

This paper is limited to the reporting on the methodological consideration of translating quotations from non-English interviews in papers published in AAAJ, AOS, CPA and QRAM between 2004 and 2015. For future accounting research that relies on such interviews, the authors call for more transparency and provide specific recommendations. This in turn should strengthen the awareness that language and translation are factors to be considered and reported.

This paper is the first to develop recommendations for the reporting of translation processes in accounting research studies, which are based on interviews not led in the English language.

The study aims to analyze the synergy of artificial intelligence (AI), with scientometrics, webometrics and bibliometrics to unlock and to emphasize the potential of the applications and benefits of AI algorithms in these fields.

By conducting a systematic literature review, our aim is to explore the potential of AI in revolutionizing the methods used to measure and analyze scholarly communication, identify emerging research trends and evaluate the impact of scientific publications. To achieve this, we implemented a comprehensive search strategy across reputable databases such as ProQuest, IEEE Explore, EBSCO, Web of Science and Scopus. Our search encompassed articles published from January 1, 2000, to September 2022, resulting in a thorough review of 61 relevant articles.

(1) Regarding scientometrics, the application of AI yields various distinct advantages, such as conducting analyses of publications, citations, research impact prediction, collaboration, research trend analysis and knowledge mapping, in a more objective and reliable framework. (2) In terms of webometrics, AI algorithms are able to enhance web crawling and data collection, web link analysis, web content analysis, social media analysis, web impact analysis and recommender systems. (3) Moreover, automation of data collection, analysis of citations, disambiguation of authors, analysis of co-authorship networks, assessment of research impact, text mining and recommender systems are considered as the potential of AI integration in the field of bibliometrics.

This study covers the particularly new benefits and potential of AI-enhanced scientometrics, webometrics and bibliometrics to highlight the significant prospects of the synergy of this integration through AI.

This paper aims to describe the popular trends and methods and ICT tools used for mapping and visualization of scientific domains as a research methodology which is attracting more and more interest from scientific information and science studies professionals. Science mapping or bibliometric mapping is a spatial representation of how disciplines, fields, specialties and individual documents or authors. The researchers analysed Bibexel, Pajek, VOSViewer, programmes used for processing and visualization of bibliographic and bibliometric data, within the framework of the implementation of IRNet research network project and activities and presented several examples of visualisation.

Among the main indicators of research effectiveness – bibliometric indicators – is a powerful information tool to support the development of science. Practical research methods were prepared using the programs: Bibexel (metadata analyses), program Pajek: graph editing and visualization of the graph structure of co-authorship – using the method of Kamada-Kawai and VOSviewer: a computer program for creating maps based on network data and for visualizing and exploring these maps. The main features of VOSviewer can be summarized for creating maps based on network data, visualizing and exploring maps.

The cited references and keywords are used to analyse in particular the intellectual base used by the research field or to analyse documents that cite the same references.

The authors of the paper, who are researchers of the European IRNet project and international research network, described and analysed certain bibliographic results of these activities using methods of mapping and visualization of scientific domains.

This paper aims to provide an overview of research topics and publications produced by Islamic Finance scholars in Malaysia, focusing on six research domains (Shariah-based, Islamic Finance, Islamic Economics, Islamic Accounting, Islamic Management and Halal Management) in five public universities in Malaysia.

This study seeks to analyse the research gaps and recommend future research based on publications produced by Islamic Finance scholars from five public universities in Malaysia. Data on talents were collected from the MIFER report 2016 and each universities’ website, while research and publications of the talents were collected from Google Scholar and the Scopus database. The extracted data were analysed using bibliometric analysis in VOSviewer version 1.6.15.

The results show that the five selected universities talents have different research strengths according to six research domains highlighted in MIFER 2021 and Beyond Report. All five universities are found to contribute the least research in Halal Management domain. In view of the increasing prominence of this area of research in the national and international levels, these universities and other universities in Malaysia can generate more research in Halal Economy, Halal Management, and other related areas within this domain. The finding indicated that each university tends to have a strength according to the different domains, and 2019 is the most productive year for Islamic Finance publications. Analysis from productive scholars and co-citations shows that the authors collaborate within the same university to create a different topic for each research cluster.

The bibliometric analysis only captures the general keyword terms, which may be limited to the only generalised sub-research areas.

This bibliometric study, which is based on the expertise of researchers, complements meta-analysis, and qualitative structured literature reviews aid researchers or talents in developing future research directions such as Green economy, Cryptocurrency, Fintech, Halal Management and others. In addition, this is a case study in nature and can serve to enhance understanding the landscape of Islamic finance education and as a reference for practices in institutions of higher learning from around the world.

To the best of the authors’ knowledge, this study is a new research initiative comparing five top programme providers in the field of Islamic finance using a bibliometric approach to enhance talent development and capacity building. With the government’s efforts to further promote Malaysia as the Islamic Financial Hub, this study highlights the research gap in Islamic finance based on scholars’ publications from selected five Malaysian universities and potential topics for future research. This study focuses on the research domain for each university, the trend of publishing, the number of journals published by academics, productive scholars and citations by Malaysian universities, and examines if the publications align with current industry needs.

This paper aims to present a comprehensive overview of the patterns and trends of publications on artificial intelligence (AI) in personalised learning. It addresses the need to investigate the intellectual structure and development of this area in view of the growing amount of related research and practices.

A bibliometric analysis was conducted to cover publications on AI in personalised learning published from 2000 to 2022, including a total of 1,005 publications collected from the Web of Science and Scopus. The patterns and trends in terms of sources of publications, intellectual structure and major topics were analysed.

Research on AI in personalised learning has been widely published in various sources. The intellectual bases of related work were mostly on studies on the application of AI technologies in education and personalised learning. The relevant research covered mainly AI technologies and techniques, as well as the design and development of AI systems to support personalised learning. The emerging topics have addressed areas such as big data, learning analytics and deep learning.

This study depicted the research hotspots of personalisation in learning with the support of AI and illustrated the evolution and emerging trends in the field. The results highlight its latest developments and the need for future work on diverse means to support personalised learning with AI, the pedagogical issues, as well as teachers’ roles and teaching strategies.

Librarians are challenged by technology transformations in research data metrics to re-position in the evolving cycles of research production, communication and evaluation. They are also are challenged by the new and emerging strategic needs of universities for data-driven research intelligence that provides a comparative edge in the global world of higher education. This paper aims to discover how libraries can support universities in applying international assessment standards by delivering new reference and information services based on data analytics.

This paper focuses on a new reference and information service Research Data Analytics in China that was launched in 2017. There is a discussion of how new university needs have provided the strategic drive for librarians to develop skills.

The Research Data Analytics service development is significant for showcasing a new role for librarians in relation to research data evaluation metrics linked to publication strategies for authors and strategic intelligence for institutions. This has driven the development of the library’s position and influence in strategic research intelligence services.

The future for librarians is about being adventurous. Librarians are experts who will sparkle and shine beyond the bubble of the library walls. They can demonstrate a commitment to supporting university colleagues in professional services and academia to blossom and flourish. Distinctive visual technologies can be adopted for exploring research data which transform research production, communication and evaluation and therefore transform our position and future through technology-enabled innovations.

Image Processing is an emerging field that is used to extract information from images. In recent years, this field has received immense attention from researchers, especially in the research domains of object detection, Biomedical Imaging and Semantic segmentation. In this study, a bibliometric analysis of publications related to image processing in the Science Expanded Index Extended (SCI-Expanded) has been performed. Several parameters have been analyzed such as annual scientific production, citations per article, most cited documents, top 20 articles, most relevant authors, authors evaluation using y-index, top and most relevant sources (journals) and hot topics.

The Bibliographic data has been extracted from the Web of Science which is well known and the world's top database of bibliographic citations of multidisciplinary areas that covers the various journals of computer science, engineering, medical and social sciences.

The research work in image processing is meager in the past decade, however, from 2014 to 2019, it increases dramatically. Recently, the IEEE Access journal is the most relevant source with an average of 115 publications per year. The USA is most productive and its publications are highly cited while China comes in second place. Image Segmentation, Feature Extraction and Medical Image Processing are hot topics in recent years. The National Natural Science Foundation of China provides 8% of all funds for Image Processing. As Image Processing is now becoming one of the most critical fields, the research productivity has enhanced during the past five years and more work is done while the era of 2005–2013 was the area with the least amount of work in this area.

This research is novel in this regard that no previous research focuses on Bibliometric Analysis in the Image Processing domain, which is one of the hot research areas in computer science and engineering.

This paper aims to conduct a review of the literature published, between 2006 and 2018, that used search engine data on tourism and hospitality research, namely, Google Insights for Search and Google Trends. More specifically, it intends to identify the purpose and context of the data use, ascertaining the main findings and reviewing the methodological approaches.

A systematic literature review of Scopus indexed research has been carried out. Given the novelty of search engine data use in tourism and hospitality research and the relatively low number of search results in Scopus, other databases were used to broaden the scope of analysis, namely, EBSCO and Google Scholar. The papers selected were subjected to content and statistical analyses.

Google Trends data use in tourism and hospitality research has increased significantly from 2012 to 2017, mainly for tourism forecasting/nowcasting; knowing the interest of users’ searches for tourist attractions or destinations; showing the relationship between the official tourism statistics and the search volume index of Google Trends; and estimating the effect of one event on tourism demand. The categories and search terms used vary with the purpose of the study; however, they mostly focus on the travel category and use the country as the search term.

Google Trends has been increasingly used in research publications in tourism and hospitality, but the range of its applications and methods used has not yet been reviewed. Therefore, a systematic review of the existing literature increases awareness of its potential uses in tourism and hospitality research and facilitates a better understanding of its strengths and weaknesses as a research tool.

本文回顾2006年至2018年发表文献使用酒店旅游相关的搜索引擎数据, 即Google Insights for Search 以及Google Trends。确切地说, 本文旨在研究数据使用目的和背景, 归纳主要研究成果和研究方法。

本文采用Scopus索引, 由于旅游酒店领域使用搜索引擎数据的文献较少, Scopus搜索结果样本量较低, 本文扩展到其他数据库, 即EBSCO以及Google Scholar。选定的样本文献采用文本分析和统计分析法。

旅游酒店领域中对Google Trends数据使用的增加主要集中在2012年到2017年, 主要研究领域有（1）旅游预测/即时预报；（2）了解用户搜索旅游景点或目的地的需求；（3）官方旅游数据和Google Trends搜索量索引之间的关系；以及（4）评估大事件对旅游需求的影响。文献归类和搜索名词根据研究目的而不同。然而, 大多数文章使用‘旅游’归类以及使用国家作为搜索关键词。

Google Trends在酒店旅游领域研究中的使用逐渐增加, 但是据作者所知, 其应用的范畴和方法仍处在起步阶段。因此, 对现有文献的系统回顾可以提高对其在旅游酒店领域中应用的认知, 并且本文结果使其作为研究工具的优劣分析更深理解。

Google Trends, Google insights for search, 搜索引擎数据, 旅游酒店研究, 系统文献回顾

International political economy is an emerging yet specialized field that combines political analysis with the study of markets, trade, and development. With the global economy having an interdependent effect on politics, environment, and society, and with several major economic events of the last 20 years, the authors perceived a need to provide a guide to the sources in this field. This paper seeks to address this issue.

The authors identified resources using WorldCat and standard reference sources, such as American Library Association's Guide to Reference Books; the annual American Libraries’ “Outstanding Reference Sources” articles; American Reference Books Annual (ARBA) volumes; Booklist's Editor's Choices articles; and Choice's “Outstanding Academic Titles”. Sources were selected from 2000 to the present, concomitant with development of the global economy in the twenty‐first century.

This guide contains reference works and internet resources that include or provide access to primary source documentation and statistical studies and tables, as well as handbooks, guides, encyclopedias and dictionaries that place the field in context.

Given the interdisciplinary nature of the field, focus was placed on sources that emphasize the core focus of international political economy. Related fields of study, including globalization, development, environmentalism and social movements, were largely excluded.

The authors found no other comprehensive bibliographies containing reference, primary and statistical sources that cover the field in its breadth during this time period.

The purpose of this study is to comprehend how AI aids marketers in engaging customers and generating value for the company by way of customer engagement (CE). CE is a popular area of research for scholars and practitioners. One area of research that could have far-reaching ramifications with regard to strengthening CE is artificial intelligence (AI). Consequently, it becomes extremely important to understand how AI is helping the marketer reach customers and create value for the firm via CE.

A detailed approach using both systematic review and bibliometric analysis was used. It involved identifying key research areas, the most influential authors, studies, journals, countries and organisations. Then, a comprehensive analysis of 50 papers was carried out in the four identified clusters through co-citation analysis. Furthermore, a content analysis of 42 articles for the past six years was also conducted.

Emerging themes explored through cluster analysis are CE concepts and value creation, social media strategies, big data innovation and significance of AI in tertiary industry. Identified themes for content analysis are CE conceptualisation, CE behaviour in social media, CE role in value co-creation and CE via AI.

CE has emerged as a topic of great interest for marketers in recent years. With the rapid growth of digital media and the spread of social media, firms are now embarking on new online strategies to promote CE (Javornik and Mandelli, 2012). In this review, the authors have thoroughly assessed multiple facets of prior research papers focused on the utilisation of AI in the context of CE. The existing research papers highlighted that AI-powered chatbots and virtual assistants offer real-time interaction capabilities, swiftly addressing inquiries, delivering assistance and navigating customers through their experiences (Cheng and Jiang, 2022; Naqvi et al., 2023). This rapid and responsive engagement serves to enrich the customer’s overall interaction with the business. Consequently, this research can contribute to a comprehensive knowledge of how AI is assisting marketers to reach customers and create value for the firm via CE. This study also sheds light on both the attitudinal and behavioural aspects of CE on social media. While existing CE literature highlights the motivating factors driving engagement, the study underscores the significance of behavioural engagement in enhancing firm performance. It emphasises the need for researchers to understand the intricate dynamics of engagement in the context of hedonic products compared to utilitarian ones (Wongkitrungrueng and Assarut, 2020). CEs on social media assist firms in using their customers as advocates and value co-creators (Prahalad and Ramaswamy, 2004; Sawhney et al., 2005). A few of the CE themes are conceptual in nature; hence, there is an opportunity for scholarly research in CE to examine the ways in which AI-driven platforms can effectively gather customer insights. As per the prior relationship marketing studies, it is evident that building relationships reduces customer uncertainty (Barari et al., 2020). Therefore, by using data analysis, businesses can extract valuable insights into customer preferences and behaviour, equipping them to engage with customers more effectively.

The rapid growth of social media has enabled individuals to articulate their thoughts, opinions and emotions related to a brand, which creates a large amount of data for VCC. Meanwhile, AI has emerged as a radical way of providing value content to users. It expands on a broader concept of how software and algorithms work like human beings. Data collected from customer interactions are a major prerequisite for efficiently using AI for enhancing CE. AI not only reduces error rates but, at the same time, helps human beings in decision-making during complex situations. Owing to built-in algorithms that analyse large amounts of data, companies can inspect areas that require improvement in real time. Time and resources can also be saved by automating tasks contingent on customer responses and insights. AI enables the analysis of customer data to create highly personalised experiences. It can also forecast customer behaviour and trends, helping businesses anticipate needs and preferences. This enables proactive CE strategies, such as targeted offers or timely outreach. Furthermore, AI tools can analyse customer feedback and sentiment across various channels. This feedback can be used to make necessary improvements and address concerns promptly, ultimately fostering stronger customer relationships. AI can facilitate seamless engagement across multiple digital channels, ensuring that customers can interact with a brand through their preferred means, be it social media, email, or chat. Consequently, this research proposes that practitioners and companies can use analysis performed by AI-enabled systems on CEB, which can assist companies in exploring the extent to which each product influences CE. Understanding the importance of these attributes would assist companies in developing more memorable CE features.

This study examines how prominent CE and AI are in academic research on social media by identifying research gaps and future developments. This research provides an overview of CE research and will assist academicians, regulators and policymakers in identifying the important topics that require investigation.