Development of the Diversity Concept for the Construction Sector: A Bibliometric Analysis

Abstract

The significance of a diverse workforce in organizations has been increasingly recognized over the past two decades due to its substantial impact on both organizational and employee performance. This study conducted a bibliometric analysis of research articles on workforce diversity in the construction sector using the Scopus and Web of Science databases. A total of 197 articles were included in the review, revealing a marked increase in research on diversity in the construction industry, with 147 articles published between 2011–2023 compared to 50 articles between 2000–2010. The University of New South Wales emerged as the most productive institution with eleven articles, followed by Loughborough University with eight articles and Universidad De Sevilla with seven articles. This study found that 459 authors contributed to the production of 197 articles. This study underscores the critical importance of diversity in the construction sector and calls for further research to devise effective diversity management strategies. It contributes to the literature by being the first bibliometric review of its kind in this sector, offering valuable insights for researchers, policymakers, and industry practitioners. It enhances our understanding of the existing literature and helps identify potential avenues for future research. This original contribution to the field is expected to stimulate further scholarly discourse and practical advancements in managing workforce diversity in the construction industry.

Study Trends and Core Content Trends of Research on Enhancing Computational Thinking: An Incorporated Bibliometric and Content Analysis Based on the Scopus Database

“…The efficiency of implementing manual technology is typically constrained by the ongoing growth of literary data with the spread of "big data", while bibliometric analysis is particularly ideal for dealing with a large-scale dataset. Furthermore, since bibliometric analysis is predominantly quantitative-based [46], its findings are typically generated more objectively and reliably than those obtained via content analysis research methods, which often rely on prioritizing conceptual categories [47]. This analysis [48] provides advantages over experimentally based research approaches in terms of the quantity of data acquired and a more objective examination, allowing for the discovery of novel research patterns and topics.…”