Researching Like a Master Chef: An Expansion of the Quantitative “Kitchen Tools” in Supply Chain Management Research

World-renowned chefs achieve culinary excellence by mastering diverse cooking techniques and specialized tools. Similarly, supply chain management (SCM) faces complex and dynamic research phenomena that defy simple methods. This editorial argues that SCM researchers need to expand their methodological toolkit of quantitative data collection and analysis approaches. Although traditional quantitative data collection and analysis methods have advanced SCM theory, they impose limitations on capturing real-world complexities. Issues like retrospective bias, the cross-sectional nature of data, the inability to replicate managerial dynamics, and constraints in network-level analysis hinder theoretical development. Moreover, dominant data analysis techniques struggle to accommodate temporal dynamics, multilevel interactions, and causal inferences. To overcome these constraints, this editorial advocates the need for promising but underutilized research methods: field experiments, neuroscience methods, agent-based modeling, SIENA, dynamic SEM, multilevel models, QCA, and AI-based methods. By expanding the methodological “kitchen tools,” researchers can generate more powerful, convincing, and comprehensive theories about supply chain decision-making and performance.