Bio
Seung-Wook Kim is an Assistant Professor of Marketing at Bentley University. He is an empirical modeler interested in marketing analytics, digital marketing, and unstructured data (image and text) analysis with strong technical foundations in machine learning and statistics. His research goal has been to utilize recent advances in machine learning to explore marketing-related problems that cannot be solved through traditional models and provide novel information that generates insights for managers and consumers. He has published articles in Journal of Retailing. He previously taught Marketing Analytics and Statistics for Business at the University of Iowa.
Awards and Honors
2024, FAC Research Grant, Bentley University2024, Summer Research Grant, Bentley University2022, Tippie College of Business Research Excellence Grant, University of Iowa2021, Graduate College Post-Comprehensive Research Award, University of Iowa2021, Graduate College Summer Research Award, University of Iowa2019, ALLEN T. CRAIG Award for Teaching Excellence, University of IowaPublications
Journal Articles
Moon, S., Kim, S., Iacobucci, D. (2024). Dynamic Relationship Changes between Reviewers and Consumers in Online Product Reviews. Journal of Retailing.
Presentations
Moon, S., Kim, S., Kim, M., Iacobucci, D. (2025). “Why Do Koreans Respond to US Movies Differently from Americans?: The Psychological Distance Discount (PDD) Phenomenon” Presented at theTippie Ph.D. Alumni ConferenceKim, S., Gruca, T., Lee, H. (2024). “Transformative Consumer Research in the Age of Algorithms: Consumer Credit Score Insights from Interpretable Machine Learning” Presented at the Bentley Research Council 10th Annual Poster SymposiumKim, S., Russell, G. (2024). “Uncovering Consumer Heterogeneity in Big Data: A Hybrid Marketing Science – Deep Learning Approach” Presented at the 46th ISMS Marketing Science ConferenceKim, S., Russell, G. (2024). “Uncovering Consumer Heterogeneity in Big Data: A Hybrid Marketing Science – Deep Learning Approach” Presented at the Boston Area Marketing Scholars Research ConferenceKim, S., Russell, G. (2023). “Uncovering Consumer Heterogeneity in Big Data: A Hybrid Marketing Science – Deep Learning Approach” Presented at the State University of New York- New Paltz SeminarMoon, S., Kim, S., Iacobucci, D. (2023). “Do the Relationships between Product Reviewers and Consumers Change with Time?: The Evolving Impacts of Reviewers” Presented at the 45th ISMS Marketing Science ConferenceKim, S., Gruca, T., Lee, H. (2023). “Transformative Consumer Research in the Age of Algorithms: Consumer Credit Score Insights from Interpretable Machine Learning” Presented at the Seoul National University SeminarKim, S., Russell, G. (2023). “Uncovering Consumer Heterogeneity in Big Data: A Hybrid Marketing Science – Deep Learning Approach” Presented at the Bentley Research ShowcaseKim, S., Russell, G. (2023). “Uncovering Consumer Heterogeneity in Big Data: A Hybrid Marketing Science – Deep Learning Approach” Presented at the 45th ISMS Marketing Science ConferenceKim, S., Russell, G. (2022). “Uncovering Consumer Heterogeneity in Big Data: A Hybrid Marketing Science – Deep Learning Approach” Presented at the Seoul National University SeminarKim, S., Gruca, T., Lee, H. (2022). “Transformative Consumer Research in the Age of Algorithms: Consumer Credit Score Insights from Interpretable Machine Learning” Presented at the Boulder Summer Conference on Consumer Financial Decision MakingKim, S., Gruca, T., Lee, H. (2022). “Transformative Consumer Research in the Age of Algorithms: Consumer Credit Score Insights from Interpretable Machine Learning” Presented at the INFORMS Iowa Knowledge WorkshopKim, S., Russell, G. (2022). “Deep Learning Based Choice Model for Market Segmentation” Presented at the Seoul National University SeminarKim, S., Gruca, T., Lee, H. (2021). “Using Interpretable Machine Learning to Understand Consumer Credit Scores” Presented at the 43rd ISMS Marketing Science ConferenceKim, S. (2021). “Application of Machine Learning Algorithms to Marketing Research” Presented at the State University of New York- New Paltz Machine Learning SeminarKim, S., Gruca, T., Lee, H. (2021). “Using interpretable machine learning to understand consumer credit score” Presented at the State University of New York- New Paltz Machine Learning SeminarKim, S., Gruca, T., Lee, H. (2020). “Using Interpretable Machine Learning to Understand Consumer Credit Scores” Presented at the NYU-Temple-CMU Conference on Artificial Intelligence, Machine Learning, and Business AnalyticsService
Department Service
Marketing Department Research Committee 2023 - PresentProfessional Service
Reviewer, Ad Hoc Reviewer for Journal of Retailing 2024 - PresentReviewer, Ad Hoc Reviewer for Service Science 2023 - Present
