Sustainable smart digital library (SSDL) ecosystems in the digital age: a text mining analysis of emerging trends and research gaps

Purpose This study aims to focus on understanding the research progress in “Sustainable Smart Digital Library (SSDL)” ecosystems. The main aim is to identify the major research areas and trends using topic modeling and highlight which topics are well explored and which need more attention. Design/methodology/approach The authors collected 3,704 articles from the Scopus database using keywords like “Digital Library,” “Sustainable Library,” “E-Library” and “Information Discovery.” After filtering, 3,378 articles with abstracts were selected. The data was imported into R and RStudio software, where the Abstract column was extracted into a data frame. After text cleaning and pre-processing, latent Dirichlet allocation (LDA) was used to identify key research topics. Findings The LDA model identified 15 core research areas. Popular topics include “Digital Library Usage,” “Digital Literacy Training,” “Sustainable Library Development,” “Digital Medical Library” and “Semantic Library Networks.” Topics like “Information Discovery Model,” “Digital Library Analysis,” “Digital Archive Access,” “Digital Library Systems,” “Copyright in Digital Libraries” and “Digital Library Services” also received considerable attention. On the other hand, areas such as “Digital Information Retrieval,” “Sustainable Digital Library,” “Library Management System” and “Digital Resource Access” are still less explored. However, all these topics have shown rapid growth in research publications over the past decade. Research limitations/implications As the study is based only on abstracts from Scopus, it may not capture the full depth of the research. However, it provides useful direction to researchers, academicians and editors for identifying gaps and future focus areas. This is the first known study to use an intelligent algorithm to map topic clusters in SSDL. Originality/value By applying LDA topic modeling on a large data set spanning decades, this study offers fresh insights into ongoing and under-researched topics in smart and sustainable digital library systems.