Recent trends in supply chain management of business-to-business firms: a review and future research directions

Abstract

PurposeThis study aims to identify the trending topics, emerging themes and future research directions in supply chain management (SCM) through multiple source of data. The insights would be of use to academics, practitioners and policymakers to leverage latest developments in addressing current and future challenges.Design/methodology/approachThis study uses a multiple source of data such as published literature and social media data including supply chain blogs and forums contents on business-to-business (B2B) firms to identify trending topics, emerging themes and future research directions in SCM. Topic modeling, a machine learning technique, is used to derive the topics and themes. Examining supply chain blogs and forums offer a valuable perspective on current issues and challenges faced by B2B firms. By analyzing the content of these online discussions, the study identifies emerging themes and topics of interest to practitioners and researchers.FindingsThe study synthesizes 1,648 published articles and more than 1.3 lakh tweets, discussions and expert views from social media, including various blogs and supply chain forums, and identifies six themes, of which three are trending, and the other three are emerging themes in the supply chain. Rather than aggregate implications, the study integrates findings from two databases and proposes a framework encompassing the drivers, processes and impacts on each theme and derives promising avenues for future research.Originality/valuePrior literature has majorly used published research articles and reports as a primary source of information to identify the trending theme and emerging topics. To the best of the authors’ knowledge, this is the first study of its kind to examine the potential value of information from social media, such as blogs, websites, forums and published literature to discover new supply chain trends and themes related to B2B firms and derive encouraging possibilities for future research.