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This study aims to investigate the impact of ResearchGate (RG) scores on the scientific indicators of Qur’anic productions in Scopus.

This research is fundamental in method, created in a descriptive method and a scientometric approach. The statistical society includes 4,105 records produced by writers in the field of Qur’an during the time period of 2011–2020 indexed in the Scopus database. In this study, Pearson correlation coefficient test is used with aid of Excel and SPSS software.

Countries such as Malaysia, the USA and Indonesia, respectively, hold the highest number of scientific productions in the field of Qur’an. Iran holds the fourth place with a 544 record difference in comparison to Malaysia (866 documents). There is a positive and meaningful relation between measures of RG, readers, followers, citations and the H-index score regarding writers with received citations and the H-index score of scientific productions in the field of Qur’an in Scopus. However, no relation can be observed between the mentioned measures and indicators of the number of documents with cowriting.

Because of some authors not using the same name in their articles, it was not possible to access their profile in RG. For this reason, the information of these authors was not accessible in RG.

The activity of researchers in social networks will receive feedback from readers. This will result in more citations. Contrary to expectations, the research results showed that Iran does not have a good position in publishing Qur’anic works. Therefore, researchers should publish their findings in international journals.

Iranian writers should try to publish their scientific productions in international publications and also widely spread their content and research results across social networks to increase feedback and gain the attention of readers. This process leads to receiving reader feedback and publication of the Islamic teachings.

The relationship between the number of citations of Qur’anic works in Scopus and altmetric indicators in RG has not been investigated in previous researches. Therefore, this research is innovative in this field.

The purpose of this article is to theoretically outline and empirically test two contribution-based indicators: (1) the scholars' annual contribution-based measurement and (2) the annual contribution modified h-index, computing six criteria: total number of papers, computed SCImago Journal Rank values, total number of authors, total number of citations of a scholar’s work, number of years since paper publication and number of annual paper citations.

Despite widespread scholarly agreement about the relevance of research production in evaluation and recruitment processes, the proposed mechanisms for gauging publication output are still rather elementary, consequently obscuring each individual scholar’s contributions. This study utilised the Technique for Order of Preference by Similarity to Ideal Solution method, and the authors built two indicators to value author's contribution.

To test both indicators, this study focussed on the most productive scholars in communication during a specific time period (2017–2020), ranking their annual research contribution and testing it against standard productivity measures (i.e. number of papers and h-index).

This article contributes to current scientometric studies by addressing some of the limitations of aggregate-level measurements of research production, providing a much-needed understanding of scholarly productivity based on scholars' actual contribution to research.

Gamification is an emerging technique of applying game elements to difficult and tedious learning activities to make them fun and exciting. This study aims to review the scientific landscape of the library’s readiness to adopt gamification with context to application in teaching and learning purposes based on computational tools. The present research also aims to study the growth of literature on gamification, to identify the most contributing authors, countries, affiliations and journals and collaboration status with different geographical settings. The study will also identify the most influential paper on the area with the highest citation and Altmetric Attention Score (AAS) as well as analyzing the keywords for locating the research trend in the subject area.

The study has adopted Scientometric and Altmetric approach by considering the research outputs of a decade (2013–2022) from Scopus database. First, the required data has been searched using appropriate keywords forming the search strategy by running title–abstract–keywords considering the limitation in the system. The exported data is systematically visualized for performing science mapping like the collaboration of authors, countries, organizations and co-occurrence of keywords using VOSviewer. For finding the Altmetrics score and Mendeley readership of the influential research works, the system Dimension.ai is further used.

The study found 928 records indicating an exponential growth over the years with total 2,750 authors. Samuel Kai Wah Chu from the University of Hong Kong, China, is the most contributed author. Spain and the USA are highly productive countries, but there needs to be a strong collaboration pattern among authors. It is found that gamification is widely applied in education discipline than any other. Some of the libraries have already implemented gamification tools for learning purposes in their services. The research on gamification still lacks social media attention and needs to be promoted more through various social media platforms for greater visibility.

The study explores the global scientific literature to identify the library’s awareness of implementing gamification tools in their services for teaching and learning purposes. As per the author’s knowledge, no such study has been conducted until date with such aims and objectives through the application of both Scientometrics and Altmetrics approaches.

Adopting and implementing robotic technology applications in the library is a significant technological up-gradation today. The purpose of this study was to evaluate selected literature focused mainly on robotics technology applications in the field of libraries and to assess the online social attention to research publications.

The study employed Scientometric and altmetric tools to evaluate the research publications. The bibliographic data of research publications were downloaded from Scopus database and scrutinized one by one and 71 articles were selected which mainly focused on robotic technology in libraries. Altmetric data were collected from the Dimensions.ai database. The analysis was performed using MS Excel, Tableau, Biblioshiny, VOSviewer and SPSS software.

Research on robotic technology in the field of libraries has been experiencing a gradual increase, marked by an annual growth rate of 12.93%. The United States has prominently led the way as the most active participant and collaborator in this advancement. Among the various journals, Library Hi Tech has notably stood out as a significant contributor to this field. However, the research articles have garnered limited social attention and impact. Furthermore, the patterns of authorship collaboration have demonstrated relatively modest levels within the field, and a weak correlation has been observed between the social attention received and the Scopus citation metrics of the publications.

The research needs to be disseminated more through various social media platforms to increase its visibility. Sharing research information through social media can bridge the gap between academia and society. The findings of this study can serve as a valuable reference for researchers and policymakers.

This study presents a Scientometric analysis of the selected published literature on robotics technology applications in the field of libraries, highlighting the progress and development of worldwide research in this area.

Given the magnitude of the literature, a researcher must be selective of research papers and publications in general. In other words, only papers that meet strict standards of academic integrity and adhere to reliable and credible sources should be referenced. The purpose of this paper is to approach this issue from the prism of scientometrics according to the following research questions: Is it necessary to judge the quality of scientific production? How do we evaluate scientific production? What are the tools to be used in evaluation?

This paper presents a comparative study of scientometric evaluation practices and tools. A systematic literature review is conducted based on articles published in the field of scientometrics between 1951 and 2022. To analyze data, the authors performed three different aspects of analysis: usage analysis based on classification and comparison between the different scientific evaluation practices, type and level analysis based on classifying different scientometric indicators according to their types and application levels and similarity analysis based on studying the correlation between different quantitative metrics to identify similarity between them.

This comparative study leads to classify different scientific evaluation practices into externalist and internalist approaches. The authors categorized the different quantitative metrics according to their types (impact, production and composite indicators), their levels of application (micro, meso and macro) and their use (internalist and externalist). Moreover, the similarity analysis has revealed a high correlation between several scientometric indicators such as author h-index, author publications, citations and journal citations.

The interest in this study lies deeply in identifying the strengths and weaknesses of research groups and guides their actions. This evaluation contributes to the advancement of scientific research and to the motivation of researchers. Moreover, this paper can be applied as a complete in-depth guide to help new researchers select appropriate measurements to evaluate scientific production. The selection of evaluation measures is made according to their types, usage and levels of application. Furthermore, our analysis shows the similarity between the different indicators which can limit the overuse of similar measures.

This study aims to examine the research productivity of Pakistan Agricultural Research Council’s (PARC’s) researchers published during 2001–2020 by using scientometric indicators. The study explored the growth and collaborative trends along with authorship and institutional collaborative patterns at the national and international levels.

The study was conducted in four phases. Firstly, a search strategy was designed to retrieve reliable data sets. During the second phase, data from PARC research was retrieved from Scopus and Web of Science (WoS). In the third phase, the data were combined, and duplications were removed. Finally, the data were analysed using RStudio and VOSviewer.

The study identified 2,868 research publications from 16 communication channels spanning over the period of 2001–2020. The growth rate varied during the study period and the year 2020 was the most productive year of the organization. Most of the research was produced in multi-authorship and five authors were dominant. Pakistan Journal of Botany was the most preferred and cited source. Moreover, PARC research collaboration with Pakistani researchers was more than their international counterparts.

Like other research, this research has some limitations. For example, this research is based on secondary data extracted from WoS and Scopus databases, world-renowned online academic. However, researchers should keep in mind while interpreting the results of this study. Secondly, the research publications published by PARC researchers during 2001–2020 were considered. Finally, this research considered English language literature only.

The study’s key theoretical contribution is its strategy for merging WoS and Scopus in RStudio, while its findings could assist agriculture research stakeholders in identifying new areas of research, awards, promotions and identification of research gaps.

To the best of the author’s knowledge, this study is the first to use scientometric indicators to evaluate PARC’s research productivity. This detailed analysis provides a deeper understanding of PARC’s contribution to agriculture research and its potential implications.

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 analyze the relationships between discourse leading indicators and citations from perspectives of integrating altmetrics indicators and tries to provide references for comprehending the quantitative indicators of scientific communication in the era of open science, constructing the evaluation indicator system of the discourse leading for academic journals and then improving the discourse leading of academic journals.

Based on the theory of communication and the new pattern of scientific communication, this paper explores the formation process of academic journals' discourse leading. This paper obtains 874,119 citations and 6,378,843 altmetrics indicators data from 65 international multidisciplinary academic journals. The relationships between indicators of discourse leading (altmetrics) and citations are studied by using descriptive statistical analysis, correlation analysis, principal component analysis, negative binomial regression analysis and marginal effects analysis. Meanwhile, the connotation and essential characteristics of the indicators, the strength and influence of the relationships are further analyzed and explored. It is proposed that academic journals' discourse leading is composed of news discourse leading, social media discourse leading, peer review discourse leading, encyclopedic discourse leading, video discourse leading and policy discourse leading.

It is discovered that the 15 altmetrics indicators data have a low degree of centralization to the center and a high degree of polarization dispersion overall; their distribution patterns do not follow the normal distributions, and their distributions have the characteristics of long-tailed right-peaked curves. Overall, 15 indicators show positive correlations and wide gaps exist in the number of mentions and coverage. The academic journals' discourse leading significantly affects total cites. When altmetrics indicators of international mainstream academic and social media platforms are used to explore the connotation and characteristics of academic journals' discourse leading, the influence or contribution of social media discourse, news discourse, video discourse, policy discourse, peer review discourse and encyclopedia discourse on the citations decreases in turn.

This study is innovative from the academic journal level to analyze the deep relationships between altmetrics indicators and citations from the perspective of correlation. First, this paper explores the formation process of academic journals' discourse leading. Second, this paper integrates altmetrics indicators to study the correlation between discourse leading indicators and citations. This study will help to enrich and improve basic theoretical issues and indicators’ composition, provide theoretical support for the construction of the discourse leading evaluation system for academic journals and provide ideas for the evaluation practice activities.

In the era of artificial intelligence (AI), algorithms have gained unprecedented importance. Scientific studies have shown that algorithms are frequently mentioned in papers, making mention frequency a classical indicator of their popularity and influence. However, contemporary methods for evaluating influence tend to focus solely on individual algorithms, disregarding the collective impact resulting from the interconnectedness of these algorithms, which can provide a new way to reveal their roles and importance within algorithm clusters. This paper aims to build the co-occurrence network of algorithms in the natural language processing field based on the full-text content of academic papers and analyze the academic influence of algorithms in the group based on the features of the network.

We use deep learning models to extract algorithm entities from articles and construct the whole, cumulative and annual co-occurrence networks. We first analyze the characteristics of algorithm networks and then use various centrality metrics to obtain the score and ranking of group influence for each algorithm in the whole domain and each year. Finally, we analyze the influence evolution of different representative algorithms.

The results indicate that algorithm networks also have the characteristics of complex networks, with tight connections between nodes developing over approximately four decades. For different algorithms, algorithms that are classic, high-performing and appear at the junctions of different eras can possess high popularity, control, central position and balanced influence in the network. As an algorithm gradually diminishes its sway within the group, it typically loses its core position first, followed by a dwindling association with other algorithms.

To the best of the authors’ knowledge, this paper is the first large-scale analysis of algorithm networks. The extensive temporal coverage, spanning over four decades of academic publications, ensures the depth and integrity of the network. Our results serve as a cornerstone for constructing multifaceted networks interlinking algorithms, scholars and tasks, facilitating future exploration of their scientific roles and semantic relations.

The COVID-19 crisis has adversely affected entrepreneurs, innovators and their ventures and, arguably, entrepreneurship research. This study aims to map the knowledge of entrepreneurship research during the COVID-19 pandemic to provide evidence of literature evolution in the field with the purpose of supporting future decision-making for policymakers, academics and practitioners in the post-COVID-19 era.

The study examines various bibliometric and scientometric indicators of entrepreneurship research in the Web of Science database using bibliometric techniques and visualization tools. Using the information gained, the scientometrics of entrepreneurship research during the COVID-19 time slice (2020–02-12 to 2022–10-15) are synthesized and comprehensively presented, and future research avenues for the post-COVID-19 era are suggested.

The results of rigorous quantitative analyses show that entrepreneurship research activities were not disrupted by COVID-19, although entrepreneurial activities themselves were impacted worldwide. In addition to providing key insights into the research field, including the most relevant keywords, keyword co-occurrences, publication sources, countries' contribution and collaboration, and source co-citations, the conceptual structural analysis separates the current trends (hotspots) into ten themes. Based on the evolution of author keywords and research themes, the study identified numerous future research directions, including 1) entrepreneurship in emerging countries, 2) firm performance in different categories of enterprises, 3) immigrants and transnational entrepreneurs, 4) technology in entrepreneurship education and 5) the impact of COVID-19 on the entrepreneurial ecosystem and entrepreneurship.

By building firm foundations for advancing the field in innovative and systematic ways, this timely study contributes to entrepreneurship literature and facilitates the understanding of the features and structures of entrepreneurship research towards the end of the pandemic. The research also has important implications for research management and entrepreneurship policymaking. The study's main limitation is that the results can only represent the time slice between 2020-02-12 and 2022-10-15.

Policymakers and managers of research and development can utilize this research to prepare a crisis-related minimization handbook in advance.

This first data mapping and thematic analysis research for entrepreneurship during the period of COVID-19 provides the latest knowledge in the field at the beginning of the end of the pandemic. It empowers scholars by 1) providing a one-stop literature overview for this global crisis time slice, 2) identifying research focuses and gaps, 3) developing new research avenues for investigation and 4) contributing conceptual structure for specific entrepreneurship research projects.