Research on sport marketing and sustainability: an integrated bibliometric machine learning approach

Purpose: There has been a noticeable increase in review studies exploring the relationship between sport and sustainability; however, these studies significantly overlook the marketing function, creating a critical gap in understanding how sustainable practices can be promoted within the sports industry. The purpose of this study is to build a research agenda of the sport–sustainability domain within the marketing field by using an integrated bibliometric and unsupervised machine learning approach. Design/methodology/approach: Bibliometric analysis, along with Latent Dirichlet Allocation (LDA) for topic modeling, enabled us to identify key trends and themes in the sport–sustainability domain. The study uses the Scopus and Web of Science (WoS) databases to extract a final dataset of 929 texts (titles, abstracts and keywords) from published research on sport–sustainability domain within the marketing field. Findings: We decipher the key trends in the literature and segregate them into four broad topics – places, consumers, markets and strategies – to enhance the understanding of this field of inquiry. This study is the first in the sport–sustainability domain to use this integrated approach to review the literature, and the findings lay the groundwork for future research. Originality/value: This study uses a combined methodology thereby offering distinct advantages over other review approaches.