A journey from normative to behavioral operations in supply chain management: A review using Latent Semantic Analysis

• Systematic interdisciplinary literature review on behavioral operations.
• Latent Semantic Analysis applied for review and knowledge extraction methodology.
• Text analysis and mining to combine statistical methods and expert judgment.
• Knowledge extracted in the form of key latent factors.
• Text mining approach aids taxonomy of research on power influences in supply chain.

This study aims to systematically review the cross disciplinary literature covering the time period from 1934 to January 2013 on behavioral operations in supply chain in order to identify and define the taxonomy of the research on power influences in supply chain. A list of noted journals and search results from Science Direct and Web of Knowledge, IEEE Xplore, and INFORMS (approximately 11,000 journal articles) is used to prepare content collection. Latent Semantic Analysis (LSA) is applied as the review and knowledge extraction methodology. Using the text analysis and mining method we can combine statistical methods and expert human judgment to extract knowledge in the form of key latent factors. The LSA based analysis gives the study a scientific grounding which helps to overcome the subjectivity of collective opinion about the trends. This approach allows proposing taxonomy of the research on power influences in supply chain. The adopted systems approach is used to find research gaps in each class of taxonomy. An emerging trend is noticed in the research of behavioral operations in supply chain. Understanding such a scholarly structure and future trends will assist researchers to assimilate the divergent developments of this multidisciplinary research in one place. This review will be beneficial for practitioners as they consider behavioral aspects in decision making. We have also studied articles related to supply chain published in Expert Systems with Applications (ESWA) journal. We have speculated what an ESWA-related community would like to see in future publications. This will encourage researchers to explore the recommended areas and publish to these outlets.