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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.

The purpose of this study is to identify the main perspectives and trends in educational data mining (EDM) in the e-learning environment from a managerial perspective.

This paper proposes a systematic literature review to identify the main perspectives and trends in EDM in the e-learning environment from a managerial perspective. The study domain of this review is restricted by the educational concepts of e-learning and management. The search for bibliographic material considered articles published in journals and papers published in conferences from 1994 to 2023, totaling 30 years of research in EDM.

From this review, it was observed that managers have been concerned about the effectiveness of the platform used by students as it contains the entire learning process and all the interactions performed, which enable the generation of information. From the data collected on these platforms, there are improvements and inferences that can be made about the actions of educators and human tutors (or automatic tutoring systems), curricular optimization or changes related to course content, proposal of evaluation criteria and also increase the understanding of different learning styles.

This review was conducted from the perspective of the manager, who is responsible for the direction of an institution of higher education, to assist the administration in creating strategies for the use of data mining to improve the learning process. To the best of the authors’ knowledge, this review is original because other contributions do not focus on the manager.

The purpose of this study was to analyze importance of artificial intelligence (AI) in education and its emphasis on assessment and adversity quotient (AQ).

The study utilizes a systematic literature review of over 141 journal papers and psychometric tests to evaluate AQ. Thematic analysis of quantitative and qualitative studies explores domains of AI in education.

Results suggest that assessing the AQ of students with the help of AI techniques is necessary. Education is a vital tool to develop and improve natural intelligence, and this survey presents the discourse use of AI techniques and behavioral strategies in the education sector of the recent era. The study proposes a conceptual framework of AQ with the help of assessment style for higher education undergraduates.

Research on AQ evaluation in the Indian context is still emerging, presenting a potential avenue for future research. Investigating the relationship between AQ and academic performance among Indian students is a crucial area of research. This can provide insights into the role of AQ in academic motivation, persistence and success in different academic disciplines and levels of education. AQ evaluation offers valuable insights into how individuals deal with and overcome challenges. The findings of this study have implications for higher education institutions to prepare for future challenges and better equip students with necessary skills for success. The papers reviewed related to AI for education opens research opportunities in the field of psychometrics, educational assessment and the evaluation of AQ.

Fish quarantine is a measure to prevent the entry and spread of quarantine fish pests and diseases abroad and from one area to another within Indonesia's territory. Based on these backgrounds, this study aims to identify the knowledge, knowledge management (KM) processes and knowledge management system (KMS) priority needs for quarantine fish and other fishery products measures (QMFFP) and then develop a classification model and web-based decision support system (DSS) for QMFFP decisions.

This research methodology uses combination approaches, namely, contingency factor analysis (CFA), the cross-industry standard process for data mining (CRISP-DM) and knowledge management system development life cycle (KMSDLC). The CFA for KM solution design is performed by identifying KM processes and KMS priorities. The CRISP-DM for decision classification model is done by using a decision tree algorithm. The KMSDLC is used to develop a web-based DSS.

The highest priority requirements of KM technology for QMFFP are data mining and DSS with predictive features. The main finding of this study is to show that web-based DSS (functions and outputs) can support and accelerate QMFFP decisions by regulations and field practice needs. The DSS was developed using the CTree algorithm model, which has six main attributes and eight rules.

This study proposes a novel comprehensive framework for developing DSS (combination of CFA, CRISP-DM and KMSDLC), a novel classification model resulting from comparing two decision tree algorithms and a novel web-based DSS for QMFFP.

The purpose of this study/paper is conducting a Systematic mapping review, as a systematic literature review method for reviewing the literature of new product development by textmining and mapping the results of this review.

This research has been conducted with the aim of systematically reviewing the literature on the field of design and development of products based on textual data. This research wants to know, how text data and text mining methods, can use for the design and development of new products.

This review finds out what are the most popular algorithms in this field? What are the most popular areas in using these approaches? What types of data are used in this area? What software is used in this regard? And what are the research gaps in this area?

The contribution of this review is creating a macro and comprehensive map for research in this field of study from various aspects and identifying the pros and cons of this field of study by systematic mapping review.

This research aims to contribute to the literature on sustainable hospitality and tourism by applying social network analysis to identify sustainable tourism business networks and untangle the role of cognitive and geographical proximity in their formation.

Data mining and machine learning techniques were applied to data collected from the websites of tourism companies located in northeastern Italy, namely, the Veneto region. Specifically, the authors used Web scraping to extract relevant information from the internet.

The results support the existence of geographical clusters of tourist accommodation providers that are linked by strong cognitive proximity based on sustainability principles that are well communicated via their websites. This does not appear to be greenwashing because companies that have agreed on sustainability principles have also implemented concrete actions and tend to signal these actions through a variety of sustainability certifications.

The results may guide tourism managers and policymakers in developing tourism initiatives directed at the creation of fruitful collaborations between similarly oriented organizations and methods to support clusters of sustainable tourism accommodation. Identifying sustainable tourism networks may assist in the identification of potential actors of change, fueling a widespread transition toward sustainability.

In this study, the authors adopted an innovative methodology to detect sustainability-oriented tourism business networks. Additionally, to the best of the authors’ knowledge, this study is one of the first to simultaneously explore the cognitive and geographical connections between tourism businesses.

This bibliometric study aims to analyze the latest trends and patterns of big data applications in librarianship from 2000 to 2022. By conducting a comprehensive examination of the existing literature, this study aims to provide valuable insights into the emerging field of big data in librarianship and its potential impact on the future of libraries.

This study employed a rigorous four-stage process of identification, screening, eligibility and inclusion to filter and select the most relevant documents for analysis. The Scopus database was utilized to retrieve pertinent data related to big data applications in librarianship. The dataset comprised 430 documents, including journal articles, conference papers, book chapters, reviews and books. Through bibliometric analysis, the study examined the effectiveness of different publication types and identified the main topics and themes within the field.

The study found that the field of big data in librarianship is growing rapidly, with a significant increase in publications and citations over the past few years. China is the leading country in terms of publication output, followed by the United States of America. The most influential journals in the field are Library Hi Tech and the ACM International Conference Proceeding Series. The top authors in the field are Minami T, Wu J, Fox EA and Giles CL. The most common keywords in the literature are big data, librarianship, data mining, information retrieval, machine learning and webometrics.

This bibliometric study contributes to the existing body of literature by comprehensively analyzing the latest trends and patterns in big data applications within librarianship. It offers a systematic approach to understanding the state of the field and highlights the unique contributions made by various types of publications. The study’s findings and insights contribute to the originality of this research, providing a foundation for further exploration and advancement in the field of big data in librarianship.

Sustainability is a major challenge for India’s (Bharat’s) coal mining industry. The government has prioritized sustainable growth in the coal mining industry. It is putting forth multifaceted economic, environmental and social efforts to accomplish the Sustainable Development Goals (SDGs). This research aims to identify the factors for sustainable improvements in coal mining operations. Secondly, this study examines the intensity of causal relations among the factors. Thirdly, this study examines whether causal relations exist among the factors to be considered for sustainable improvement in coal mining operations. Lastly, the study aims to understand how the factors ensure sustainable improvement in coal mining operations.

An integrated three-phase methodology was applied to identify the critical factors related to coal mining and explore the contextual relationships among the identified factors. Fifteen critical factors were selected based on the Delphi technique. Subsequently, the fifteen factors were analyzed to determine the contextual and causal relationships using the total interpretive structural modelling (TISM) and DEMATEL methods.

The study identified “Extraction of Coal and Overburden” as the leading factor for sustainable improvement in coal mining operations, because it directly or indirectly influences the overall mining operation, environmental impact and resource utilization. Hence, strict control measures are necessary in “Extraction of Coal and Overburden” to ensure sustainable coal mining. Conversely, “Health Impact” is the lagging factor as it has very low or no impact on the system. Therefore, it requires fewer control mechanisms. Nevertheless, control measures for the remaining factors must be decided on a priority basis.

The proposed structural model can serve as a framework for enhancing sustainability in India’s (Bharat’s) coal mining operations. This framework can also be applied to other developing nations with similar sustainability concerns, providing valuable guidance for sustainable operations.

The current study highlights the significance of logical links and dependencies between several parameters essential to coal mining sustainability. Furthermore, it leads to the development of a well-defined control sequence that identifies the causal linkages between numerous components needed to achieve real progress towards sustainability.

The paper examines whether, along with the financial performance, the disclosure of research and development (R&D) expenses, patent portfolios, patent citations and innovation activities affect the market capitalization of Russian companies.

The paper opted for a set of techniques including bag-of-words (BoW) to retrieve additional innovation-related data from companies' annual reports, self-organizing maps (SOM) to perform visual exploratory analysis and panel data regression (PDR) to conduct confirmatory analysis using data on 74 Russian publicly traded companies for the period 2013–2019.

The paper observes that the disclosure of nonfinancial data on R&D, patents and primarily product and marketing innovations positively affects the market capitalization of the largest Russian companies, which are mainly focused on energy, raw materials and utilities and are operating on international markets. The study suggests that these companies are financially well-resourced to innovate at risk and thus to provide positive signals to stakeholders and external agents.

Our findings are important to management, investors, financial analysts, regulators and various agencies providing guidance on corporate governance and sustainability reporting. However, the authors acknowledge that the research results may lack generalizability due to the sample covering a single national context. Researchers are encouraged to test the proposed approach further on other countries' data by using the compiled lexicons.

The study aims to expand the domains of signaling theory and market valuation by providing new insights into the impact that companies' reporting on R&D, patents and innovation activities has on market capitalization. New nonfinancial factors that previous research does not investigate – innovation disclosure indicators (IDI) – are tested.

The identification of network user relationship in Fancircle contributes to quantifying the violence index of user text, mining the internal correlation of network behaviors among users, which provides necessary data support for the construction of knowledge graph.

A correlation identification method based on sentiment analysis (CRDM-SA) is put forward by extracting user semantic information, as well as introducing violent sentiment membership. To be specific, the topic of the implementation of topology mapping in the community can be obtained based on self-built field of violent sentiment dictionary (VSD) by extracting user text information. Afterward, the violence index of the user text is calculated to quantify the fuzzy sentiment representation between the user and the topic. Finally, the multi-granularity violence association rules mining of user text is realized by constructing violence fuzzy concept lattice.

It is helpful to reveal the internal relationship of online violence under complex network environment. In that case, the sentiment dependence of users can be characterized from a granular perspective.

The membership degree of violent sentiment into user relationship recognition in Fancircle community is introduced, and a text sentiment association recognition method based on VSD is proposed. By calculating the value of violent sentiment in the user text, the annotation of violent sentiment in the topic dimension of the text is achieved, and the partial order relation between fuzzy concepts of violence under the effective confidence threshold is utilized to obtain the association relation.