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The use of structured literature review methods like bibliometric analysis is growing in the management fields, but there is limited knowledge on how they can be facilitated by technology. Hence, we conducted a broad overview of software tools, their roles, and limitations in structured (bibliometric) literature reviewing activities. Subsequently, we show that several software tools are freely available to aid in searching the literature, identifying/ extracting relevant publications, screening/assessing quality of the extracted data, and performing analyses to generate insights from the literature. However, their applications may be confronted with several challenges such as limited analytical and functional capabilities, inadequate technological skills of researchers, and the fact that the researcher's insights are still needed to generate compelling conclusions from the results produced by software tools. Consequently, we contribute toward advancing the methodologies for performing structured reviews by providing a comprehensive and updated overview of the knowledge base of key technological software tools and the conduct of structured or bibliometric literature reviews.

Motivated with the ever growing number of bibliometric trend extrapolation studies, the purpose of this paper is to demonstrate through two technologies how the selection of an upper limit of growth affects the correlation and causality of technology development measured with bibliometric data.

The paper uses Gompertz and Fisher-Pry curves to model the technological development of white light emitting diodes and flash memory, and show with extrapolation results from several bibliometric sources how a typical bias is caused in trend extrapolations.

The paper shows how drastic an effect the decision to set an upper bound has on trend extrapolations, to be used as a reference for applications. The paper recommends carefully examining the interconnection of actual development and bibliometric activity.

Despite increasing interest in modelling technological data using this method, reports rarely discuss basic assumptions and their effects on outcomes. Since trend extrapolations are applied more widely in different disciplines, the basic limitations of methods should be explicitly expressed.

The purpose of this paper is to predict bibliometric indicators based on ARIMA models and to study the short-term trends of bibliometric indicators.

This paper establishes a non-stationary time series ARIMA (p, d, q) model for forecasting based on the bibliometric index data of 13 journals in the library intelligence category selected from the Chinese Social Sciences Citation Index (CSSCI) as the data source database for the period 1998–2018, and uses ACF and PACF methods for parameter estimation to predict the development trend of the bibliometric index in the next 5 years. The predicted model was also subjected to error analysis.

ARIMA models are feasible for predicting bibliometric indicators. The model predicted the trend of the four bibliometric indicators in the next 5 years, in which the number of publications showed a decreasing trend and the H-value, average citations and citations showed an increasing trend. Error analysis of the model data showed that the average absolute percentage error of the four bibliometric indicators was within 5%, indicating that the model predicted well.

This study has some limitations. 13 Chinese journals were selected in the field of Library and Information Science as the research objects. However, the scope of research based on bibliometric indicators of Chinese journals is relatively small and cannot represent the evolution trend of the entire discipline. Therefore, in the future, the authors will select different fields and different sources for further research.

This study predicts the trend changes of bibliometric indicators in the next 5 years to understand the trend of bibliometric indicators, which is beneficial for further in-depth research. At the same time, it provides a new and effective method for predicting bibliometric indicators.

How academic libraries support the research of their parent institutions has changed as a result of forces such as changing scholarly communication practices, technological developments, reduced purchasing power and changes in academic culture. The purpose of this paper is to examine the professional and educational implications of current and emerging research support environments for academic libraries, particularly with regard to research data management and bibliometrics and discuss how do professionals and educators “make space” as new service demands arise?

The present paper uses data from a recent survey of research support provision by academic libraries in Australia, New Zealand, the UK and Ireland, (authors 2013), and provides additional in depth analysis of the textual responses to extend the analysis in the light of forces for change in higher education. The original online questionnaire surveyed current and planned research support in academic libraries, and constraints or support needs related to service developments. It was distributed to 219 institutions in Australia, New Zealand, the UK, and Ireland, and obtained 140 valid responses (response rate of 63.9 percent). Results were analyzed using descriptive statistics with thematic categorization and coding for the textual responses.

Most academic libraries surveyed are already providing or planning services in the focal areas of bibliometrics and data management. There was also increasing demand for other research support services, not the focus of the study, such as eresearch support, journal publishing platforms, and grant writing support. The authors found that while many academic libraries perceive increasing research support services as a “huge opportunity” they were constrained by gaps in staff skills, knowledge, and confidence and resourcing issues. With regard to staff education and training, it was reported they require a broader understanding of the changing research and scholarly landscape, the research cultures of different disciplines, and technological change. There was a near-universal support for development of more comprehensive, specialized, LIS education to prepare professionals for broader research support roles.

This further analysis of the implications of our survey in relation to influences such as economics, academic culture, technology, raises questions for both educators and practitioners about the future direction of the profession and how the authors collectively “make space” as new potential services arise.

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 study aims to map the “Desktop Research” (DR) output in Pakistan, as part of the growing field of research globally. It also ascertains the productive institutions and prolific authors along with their collaboration patterns.

Bibliometric techniques were used to quantitatively analyze the DR published in Pakistan. The publications from 1981 to 2021 were retrieved from Scopus. A total of 1,802 publications were retrieved and used for analysis.

Results indicated an unpredictable increase in DR output from approximately 100 to 400 records during the past five years. The year 2020 was most productive in DR research showing the excess use of secondary data by researchers in COVID-19. The focus of researchers towards DR was consistently rising. Medical journals were found to publish DR extensively. Majority of the publications were contributed by collaborative work and researchers of the USA were found as the most collaborative with Pakistani authors. Publications of single category journals, open access journals and international collaboration get more citations.

The results of the analysis rely only on a single database, Scopus, for retrieving the publication data.

The study has practical implications for the policymakers and higher education development organizations to introduce the DR as a course in academic schools.

To the best of the authors’ knowledge, this study is the first to review DR in the context of Pakistan through bibliometric analysis. This comprehensive overview provides a better understanding of the development of the field and possible practice implications.

Using analytics for firms' competitiveness is a vital component of a company's strategic planning and management process. In recent years, organizations have started to capitalize on the significant use of big data for analyses to gain valuable insights to improve decision-making processes. In this regard, leveraging and unleashing the potential of big data has become a significant success factor for steering firms' competitiveness, and the related literature is increasing at a very high pace. Thus, the authors propose a bibliometric study to understand the most important insights from these studies and enrich existing conceptual models.

In this study, the authors use a bibliometric review on articles related to the use of big data for firms' competitiveness. The authors examine the contributions of research constituents (authors, institutions, countries and journals) and their structural and thematic relationships (collaborations, co-citations networks, co-word networks, thematic trends and thematic map). The most important insights are used to enrich a conceptual model.

Based on the performance analysis results, the authors found that China is by far the most productive country in this research field. However, in terms of influence (by the number of citations per article), the most influential countries are the UK, Australia and the USA, respectively. Based on the science mapping analysis results, the most important findings are projected in the common phases of competitive intelligence processes and include planning and directions concepts, data collection concepts, data analysis concepts, dissemination concepts and feedback concepts. This projection is supplemented by cross-cutting themes such as digital transformation, cloud computing, privacy, data science and competition law. Three main future research directions are identified: the broadening of the scope of application fields, the specific case of managing or anticipating the consequences of pandemics or high disruptive events such as COVID-19 and the improvement of connection between firms' competitiveness and innovation practices in a big data context.

The findings of this study show that the most important research axis in the existing literature on big data and firms' competitiveness are mostly related to common phases of competitive intelligence processes. However, concepts in these phases are strongly related to the most important dimensions intrinsic to big data. The use of a single database (Scopus) or the selected keywords can lead to bias in this study. Therefore, to address these limitations, future studies could combine different databases (i.e. Web of Science and Scopus) or different sets of keywords.

This study can provide to practitioners the most important concepts and future directions to deal with for using big data analytics to improve their competitiveness.

This study can help researchers or practitioners to identify potential research collaborators or identify suitable sources of publications in the context of big data for firms' competitiveness.

The authors propose a conceptual model related to big data and firms' competitiveness from the outputs of a bibliometric study.

The purpose of this paper is to describe the methods and findings of a pilot study which applied bibliometrics and webometrics to examine collaboration in Malaysian biotechnology.

The research applied bibliometric and webometric methods to publications and web sites affiliated with Malaysian institutions. The bibliometric analysis focused on biotechnology‐related journal articles indexed in Web of Knowledge. The webometric analysis examined the web sites of top biotechnology institutions generated in the bibliometric analysis. Collaboration behaviour was assessed in three ways: intra‐institutional versus inter‐institutional; national versus international collaboration; and by type of institution collaboration according to the triple helix model.

Findings of the pilot study, which applied bibliometric and webometric analyses to a limited sample, indicate that the methodologies will collect the desired data for a more extensive study.

The quantitative research results describe the collaboration evident in publications and web sites, but not why it has happened in such a way.

The methodologies provide a framework for similar research exploring the impacts of collaboration in an e‐research environment. The methodology is innovative and practical in terms of the combined use of bibliometric and webometric analyses.

This is one of few studies that has examined collaboration using both bibliometric and webometric methods, and elements of the methodology appear to be unique to the study. The methodologies will contribute to an emerging body of literature that explores the nature of research productivity and research collaboration.

The proliferation of bibliometric review articles is a true reflection of how bibliometrics is gaining popularity and has been widely adopted in various disciplines. The growing interest of scholars has encouraged us to dwell upon the what, why, when, how, and where of bibliometric literature reviews. The study explained the bibliometric review with the standpoint that it can be considered a strong review method for analyzing a large volume of data and scholars can supplement their traditional reviews with bibliometric reviews to strengthen their knowledge. This will help researchers to justify the (a) need for a study on the particular topic; (b) type or method of review chosen; (c) number of articles selected; (d) inclusion and exclusion criterion; (e) method of analysis; and (f) presentation of the findings.

The aim of this paper is to explore the benefit of forecasting emerging biomedical therapy technologies as well as the rate of diffusion of resultant biomedical products in the context of management of technology.

The research method is exploratory using a case study approach. Techniques such as bibliometric analysis and the Bass diffusion model are utilized to assess the growth rate and market penetration of pulsed electromagnetic field therapy (PEMF) as a technology.

The penetration and growth rate of user acceptance of the technology in a global context are simulated across a 15‐year period. The technology forecasting model is also used in a case study to simulate the penetration of a product using ten years' medical application data of a patented pulsed electromagnetic field for biomedical therapy application in the global context. Useful correlation between bibliometric data for PEMF and real data for the case study is illustrated.

The research is limited to the case of PEMF data presented. Further research may be done on other PEMF technology applications.

Aspects of a holistic management model that was developed for high technology companies are invoked in the practical realisation of the professional paradigm shift required when an emerging biomedical therapy technology is in the process of becoming mainstream.

It has been shown that technology diffusion traces exist for PEMF therapy technology as is evident from the bibliometric PEMF global data presented. Furthermore it is possible to simulate the PEMF therapy technology diffusion process with a Bass diffusion model incorporating innovation imitation and market size coefficients.