Bibliometrics, reference enhanced databases and research evaluation

This study presents the panoramic view of the described issues related to coverage, services and bibliometrics for Research Evaluation (RE) purposes by three reference enhanced databases. The researchers’ viewpoint is based on the relevant literature and data accessed from most preferred citation sources: Web of Science, Scopus and Google Scholar.The study seeks the worldview challenges, highlights and theorizes the core issues for those regions and disciplines that have more challenges and fewer opportunities in getting publishing, citing and cited by. It discusses the new insights and directs the stakeholders to explore other possible sources, metrics and evaluation techniques for RE

Citation Analysis: A Comparison of Google Scholar, Scopus, and Web of Science

When faculty members are evaluated, they are judged in part by the impact and quality of their scholarly publications. While all academic institutions look to publication counts and venues as well as the subjective opinions of peers, many hiring, tenure, and promotion committees also rely on citation analysis to obtain a more objective assessment of an author’s work. Consequently, faculty members try to identify as many citations to their published works as possible to provide a comprehensive assessment of their publication impact on the scholarly and professional communities. The Institute for Scientific Information’s (ISI) citation databases, which are widely used as a starting point if not the only source for locating citations, have several limitations that may leave gaps in the coverage of citations to an author’s work. This paper presents a case study comparing citations found in Scopus and Google Scholar with those found in Web of Science (the portal used to search the three ISI citation databases) for items published by two Library and Information Science full-time faculty members. In addition, the paper presents a brief overview of a prototype system called CiteSearch, which analyzes combined data from multiple citation databases to produce citation-based quality evaluation measures

Revisiting h measured on UK LIS and IR academics

A brief communication appearing in this journal ranked UK LIS and (some) IR academics by their h-index using data derived from Web of Science. In this brief communication, the same academics were re-ranked, using other popular citation databases. It was found that for academics who publish more in computer science forums, their h was significantly different due to highly cited papers missed by Web of Science; consequently their rank changed substantially. The study was widened to a broader set of UK LIS and IR academics where results showed similar statistically significant differences. A variant of h, hmx, was introduced that allowed a ranking of the academics using all citation databases together

Adoção de Cloud Computing nas organizações: uma análise bibliométrica desta tecnologia no contexto da transformação digital

Purpose: The purpose of this study is identify and analyze articles on the adoption of Cloud Computing services in the business field. The study intends to demonstrate the increasing relevance and impact of Cloud Computing technology on business processes.Design/methodology/approach: The study adopted a Bibliometric Review methodology to collect and analyze data. A total of 1,330 articles were collected from the Scopus (Elsevier) database, and various aspects such as authors, journals, and countries were considered. The analysis includes the use of maps to visualize the co-occurrence of terms, co-citation of references, and bibliographic coupling.Findings: The investigation reveals that the adoption of Cloud Computing services in the business environment is a rapidly growing area of research. The study provides an overview of the theme and highlights the significance of Cloud Computing technology in enhancing business processes’ efficiency.Research limitations/implications: The study’s limitations include relying solely on articles available in the Scopus (Elsevier) database and focusing on the period between 2008 and 2020. Future research can expand the analysis by including a broader range of databases and considering a more recent timeframe.Practical implications: The findings of this study have practical implications for businesses, as they highlight the benefits of adopting Cloud Computing services. The technology offers low cost and flexible use, contributing to increased efficiency in business processes.Social implications: The adoption of Cloud Computing services can have significant social impacts by enabling businesses to provide enhanced value to their clients. The technology’s efficiency and flexibility contribute to improved service delivery and customer satisfaction.Originality/value: This study contributes to the advancement of knowledge in the field of Cloud Computing adoption in the business field. The bibliometric analysis provides a comprehensive overview of the research landscape and highlights the key contributions and trends in this area.Finalidade: O objetivo deste estudo é identificar e analisar artigos sobre a adoção de serviços de computação em nuvem na área empresarial. O estudo pretende demonstrar a crescente relevância e impacto da tecnologia Cloud Computing nos processos de negócio. Desenho/metodologia/abordagem: O estudo adotou uma metodologia de Revisão Bibliométrica para coleta e análise de dados. Um total de 1.330 artigos foram coletados da base de dados Scopus (Elsevier), e vários aspectos como autores, periódicos e países foram considerados. A análise inclui o uso de mapas para visualizar a coocorrência de termos, cocitação de referências e acoplamento bibliográfico. Constatações: A investigação revela que a adoção de serviços de computação em nuvem no ambiente de negócios é uma área de pesquisa em rápido crescimento. O estudo oferece uma visão geral sobre o tema e destaca a importância da tecnologia Cloud Computing na melhoria da eficiência dos processos de negócios. Limitações/implicações de pesquisa: As limitações do estudo se apresentam na utilização de somente artigos disponíveis na base de dados Scopus (Elsevier) e em focar no período entre 2008 e 2020. Pesquisas futuras podem expandir a análise incluindo uma gama mais ampla de bases de dados e considerar um período de tempo mais recente. Implicações práticas: Os achados deste estudo têm implicações práticas para as empresas, pois destacam os benefícios da adoção de serviços de computação em nuvem. A tecnologia oferece baixo custo e flexibilidade de uso, contribuindo para o aumento da eficiência nos processos de negócios. Implicações sociais: A adoção de serviços de computação em nuvem pode ter impactos sociais significativos, permitindo que as empresas forneçam maior valor aos seus clientes. A eficiência e a flexibilidade da tecnologia contribuem para melhorar a prestação de serviços e a satisfação do cliente. Originalidade/valor: Este estudo contribui para o avanço do conhecimento na área de adoção de computação em nuvem na área empresarial. A análise bibliométrica fornece uma visão abrangente do cenário de pesquisa e destaca as principais contribuições e tendências nesta área

Can we use Google Scholar to identify highly-cited documents?

The main objective of this paper is to empirically test whether the identification of highly-cited documents through Google Scholar is feasible and reliable. To this end, we carried out a longitudinal analysis (1950 to 2013), running a generic query (filtered only by year of publication) to minimise the effects of academic search engine optimisation. This gave us a final sample of 64,000 documents (1,000 per year). The strong correlation between a document’s citations and its position in the search results (r= -0.67) led us to conclude that Google Scholar is able to identify highly-cited papers effectively. This, combined with Google Scholar’s unique coverage (no restrictions on document type and source), makes the academic search engine an invaluable tool for bibliometric research relating to the identification of the most influential scientific documents. We find evidence, however, that Google Scholar ranks those documents whose language (or geographical web domain) matches with the user’s interface language higher than could be expected based on citations. Nonetheless, this language effect and other factors related to the Google Scholar’s operation, i.e. the proper identification of versions and the date of publication, only have an incidental impact. They do not compromise the ability of Google Scholar to identify the highly-cited papers

Can we use Google Scholar to identify highly-cited documents?

The main objective of this paper is to empirically test whether the identification of highly-cited documents through Google Scholar is feasible and reliable. To this end, we carried out a longitudinal analysis (1950 to 2013), running a generic query (filtered only by year of publication) to minimise the effects of academic search engine optimisation. This gave us a final sample of 64,000 documents (1,000 per year). The strong correlation between a document’s citations and its position in the search results (r= -0.67) led us to conclude that Google Scholar is able to identify highly-cited papers effectively. This, combined with Google Scholar’s unique coverage (no restrictions on document type and source), makes the academic search engine an invaluable tool for bibliometric research relating to the identification of the most influential scientific documents. We find evidence, however, that Google Scholar ranks those documents whose language (or geographical web domain) matches with the user’s interface language higher than could be expected based on citations. Nonetheless, this language effect and other factors related to the Google Scholar’s operation, i.e. the proper identification of versions and the date of publication, only have an incidental impact. They do not compromise the ability of Google Scholar to identify the highly-cited papers