Saljoughian, Mike, Kelly Hewett, Harald van Heerde, and Bill Rand (2025), "How Can Firms Steer Social Media Conversations?" Journal of Marketing Research.
Mike Saljoughian
Mike Saljoughian is an Assistant Professor of Marketing at the Robert J. Trulaske, Sr. College of Business, University of Missouri. His research focuses on digital marketing, social media, customer experience, and the customer journey. Methodologically, he specializes in artificial intelligence, machine learning, deep learning, natural language processing, and causal inference, bringing a distinctive quantitative lens to marketing problems.
Dr. Saljoughian's research has been published in the Journal of Marketing Research and the Journal of the Academy of Marketing Science. He currently serves on the Editorial Review Board of the Journal of the Academy of Marketing Science.
He teaches Digital Customer Experience, Advanced Professional Selling, and Applied Statistics in Marketing at the undergraduate and MBA levels. Prior to his doctoral studies, he worked in international business development conducting market research and feasibility studies.
Education
PhD, Business Administration – Marketing, University of Tennessee, 2022; MS, Statistics and Data Science, University of Tennessee, 2022; MS, Business Analytics, University of Tennessee; MBA, University of Isfahan, 2014
Awards
2023 Haslam College of Business Post-Doctoral Research Award; 2022 AMA-Sheth Doctoral Consortium Fellow; 2022 Research Competition First Runner-Up, Southeastern Marketing Symposium
Publications
Smith, Leah, Randy Rose, Mike Saljoughian, Alex Zablah, and Heath McCullough (2023), "Examining Post-Purchase Consumer Responses to Product Automation," Journal of the Academy of Marketing Science.
Aghaie, Sina, Mike Saljoughian, and Omid Kamran Disfani (2024), "Incumbent's Deterrence Strategies and Potential Entrant's Time-to-Entry: Evidence from the U.S. Airline Industry," European Journal of Marketing.
McCullough, Rose, Saljoughian, Smith, and Zablah. “Examining Post-Purchase Consumer Responses to Product Automation.” Journal of the Academy of Marketing Science, 2022, doi:10.1007/s11747-022-00900-8.