Empirical modeling approaches in sustainable supply chain management: A review with bibliometric and network analyses

Abstract This paper aims to perform a hybrid study with a focus on empirical modeling (EM) and its applications in sustainable supply chain management (SSCM) concepts using bibliometric, network, thematic, and content analyses. The outcomes provide a viewpoint on literature categorization and several innovative insights and perspectives into contemporary research trends in this field. The review is undertaken using 2192 out of more than 24,000 research papers that are extracted and sifted from the Scopus and WoS databases. The results indicate an annual growth rate of 17.65% for the subject under consideration, which holds considerable significance and portends a promising outlook for the field. Seven clusters of papers are identified: (1) SSCM and design using versatile empirical approaches; (2) socially and environmentally responsible SCM and design; (3) new theories and impacts of SSCM integration; (4) data science, predictive analytics, and big data for SC sustainability; (5) closed loop SC roles in bringing sustainability; (6) SSCM during disasters with respect to humanitarian aspects; and (7) linkages between Industry 5.0, digital technologies, circular economy, and SSCM. In this review, fundamental ideas are established, research gaps are identified, and multiple future research subjects are proposed. In addition, important topics are presented for managers and practitioners to consider when adopting EM principles in SSCM. The results contribute to both theoretical and practical management elements and give a solid road map for future study in this sector. This study provides an overview of the evolution of EM and its applications in SSCM concepts; it also particularly elaborates on general pertinent research dimensions, SSCM, sustainability practices, and EM aspects.