Analysis of scientific information from a bibliometric approach between Chat GPT and Scopus: A comparative study

Abstract

One of the main challenges faced by teachers, researchers, and students today is efficiently filtering reliable and useful information available on the internet, as well as in scientific academic databases. To address this phenomenon, the bibliometrics tool is used, which involves understanding the number of publications, analyzing them, and determining their trend based on the application of filters and relationships of scientific concepts in specialized topics. There are other technological tools that allow finding bibliographic information on the internet, such as artificial intelligence (AI) specifically through the ChatGPT chatbot (Generative Pre-trained transformer). The objective of this article is to identify the differences between the results of a bibliometric analysis from Scopus and ChatGPT; the research type is documentary; the search strategy for the bibliometric analysis was "Dynamic Capabilities." Findings show that there are differences between the data obtained from the two bibliometric analyses, including authors, subject areas, affiliations, and keywords; it should be noted that the use of ChatGPT is a basic and simple tool that complements the bibliometric analysis provided by an academic database like Scopus; it is suggested to compare the results analytically and manually at all times, which is of interest to academia and the development of theoretical frameworks in research work