Optimization of dynamic product offerings on online marketplaces: A network theory perspective

Zuo, M.; Angelopoulos, S.; Ou, C.X.J.; Liu, H.; Liang, Z.

Abstract

The fierce competition amongst brands on online marketplaces makes the optimization of offerings within this context a significant challenge. To address this challenge, we draw upon network theory and model the degree of competition through consumers’ consideration sets. We use a large empirical dataset from one of the biggest online marketplaces to explore the dynamic relationship between network position and the degree of competition, and we depict the redistribution of market share of related offerings after adjusting their array. In doing so, we provide a theoretical reference on when and how brands should optimize their product offerings on online marketplaces. We further demonstrate that intra-brand cannibalization relations have a significantly greater impact on the degree of competition compared to inter-brand ones, while intra-brand cannibalization relations represent the main reason for fluctuations in the degree of competition. Hence, contrary to existing theoretical insights and practical intuitions, our findings demonstrate that brands should minimize the number and heterogeneity of their offerings within a market segment to increase their sales on online marketplaces.

Citation

Zuo, M., Angelopoulos, S., Ou, C., Liu, H., & Liang, Z. (2023). Optimization of dynamic product offerings on online marketplaces: A network theory perspective. Journal of Management Information Systems, 40(4), 1328-1357. https://doi.org/10.1080/07421222.2023.2267314

• Journal Article Type: Article
• Acceptance Date: Jul 13, 2023
• Online Publication Date: Dec 11, 2023
• Publication Date: 2023
• Deposit Date: Jul 13, 2023
• Publicly Available Date: Jan 11, 2024
• Journal: Journal of Management Information Systems
• Print ISSN: 0742-1222
• Electronic ISSN: 1557-928X
• Publisher: Taylor and Francis Group
• Peer Reviewed: Peer Reviewed
• Volume: 40
• Issue: 4
• Pages: 1328-1357
• DOI: https://doi.org/10.1080/07421222.2023.2267314
• Public URL: https://durham-repository.worktribe.com/output/1170028