Implications of ambiguity for scientometric measurement

Information Systems Management Tools: An Application of Bibliometrics to CSR in the Tourism Sector

Increasingly, the literature highlights the importance of implementing and developing socially responsible behaviours by all stakeholders in the tourism sector. This paper describes the evolution of research on social responsibility in the tourism sector until July 2020. We compiled a database of 846 articles focused on the field and published in academic journals in the ISI WoS database. Bibliometric methods and techniques were used to describe the evolution of scientific activity, countries and active institutions, most productive authors, most relevant sources, most influential documents, trend topics, and social structure researched. This determined the state of the art and described the evolution of the literature in this field, and will help scholars refine existing and initiate new research agendas. A total of 846 papers were identified and the results showed an upward trend in scientific production relating corporate social responsibility (CSR) to tourism. Based on these analyses, possible forms of future research are proposed to advance towards the consolidation of this scientific discipline.

Bibliometric study of social network analysis literature

Purpose

The purpose of this paper is to use bibliometric analysis to identify the current state of the academic literature regarding social network analysis (SNA) and analyze its knowledge base such as research authors, research countries, document type, keyword analysis and subject areas.

Design/methodology/approach

Bibliometric analysis is used and furthermore, Lotka’s and Bradford’s law is applied to perform author productivity analyses in this field during 1999 and 2018, respectively, in turn, discovering historical vein and research tendency in the future.

Findings

It appears that the research on SNA has been very popular and still in the highly mature period. So far, the USA takes the lead among the published paper. The top 2 subject areas are “Computer Science” and “Business Economics.” The primary journal that SNA articles were published is Computers in Human Behavior. SNA has been related to many research areas, such as “Social network analysis,” “Computer-mediated communication,” “Online learning,” “Social Network” and “Community of inquiry.” Finally, Kolmogorov–Smirnov (K-S) test proved that the frequency indexes of author productivity distribution certainly followed Lotka’s law.

Research limitations/implications

First, the productivity distribution may inform researchers and scholars of current issues and development of SNA. Second, the study proposed a theoretical model, based on Lotka’s law, for author productivity analysis of SNA, which can serve as reference for different areas of study in the evaluation of author productivity models. Also, in order to allow researchers to gain in-depth insights, this study aimed to report the most published institutions and keep track of the growth and trend of author productivity, by which scholars in related fields are provided with more opportunities for academic communication and technological cooperation.

Originality/value

This research on the productivity distribution of SNA may inform researchers and scholars of current issues and development of SNA. The findings report the major publication outlets and related discussion issues about SNA. Such information would be valuable for related authors, who are writing the manuscript on SNA, and also for practitioners, who may be interested in applying the theory or ideas of SNA.

A produtividade dos autores sobre a Lei de Lotka

Estuda a produção estratificada dos autores produtores da literatura sobre a Lei de Lotka de 1922 a 2003 e analisa essa produtividade através dos modelos Poisson lognormal e Gauss Poisson inversa generalizada. Para tanto, faz uso dos três tipos de contagem da literatura produzida: contagem direta, contagem completa e contagem fracionada. Os dados da pesquisa são avaliados usando o teste qui-quadrado ao 0.05 nível de significância. Ambos os modelos ajustam-se muito bem à distribuição da literatura produzida, porém a distribuição Poisson Gauss inversa generalizada produz um chi-quadrado menor e prediz melhor o total de autores do que a distribuição Poisson Lognormal.