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Open AccessArticle
Optimal Service Strategies of Online Platform Based on Purchase Behavior
by
Xudong Lin
Xudong Lin 1,
Tingyi Shi
Tingyi Shi 2,*,
Hanyang Luo
Hanyang Luo
Prof. Dr. Hanyang Luo is an Associate Professor at the College of Management, Shenzhen University. a [...]
Prof. Dr. Hanyang Luo is an Associate Professor at the College of Management, Shenzhen University. He received a Ph.D. degree in management from the Harbin Institute of Technology, Shenzhen, China, in 2013. Prof. Dr. Hanyang Luo’s research results have been published in refereed journals and conference proceedings, including Information Fusion, Knowledge-Based Systems, Computers & Industrial Engineering, Expert Systems, Journal of Intelligent & Fuzzy Systems, and Computational and Applied Mathematics, among others. His research interests include decision theory and application, electronic commerce, marketing, and information systems. Prof. Dr. Hanyang Luo is also a Member of the Institute of Big Data Intelligent Management and Decision, at Shenzhen University.
1 and
Hao Zhu
Hao Zhu 2
1
Institute of Big Data Intelligent Management and Decision, College of Management, Shenzhen University, Shenzhen 518060, China
2
College of Management, Shenzhen University, Shenzhen 518060, China
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(19), 8545; https://doi.org/10.3390/su16198545 (registering DOI)
Submission received: 9 August 2024
/
Revised: 19 September 2024
/
Accepted: 24 September 2024
/
Published: 30 September 2024
Abstract
In the rapidly evolving platform economy, online platforms have emerged as pivotal providers of digital services to sellers. The paper investigates how online platforms optimize service strategies based on consumers’ purchase behavior, influencing sellers’ pricing and social welfare. Using a two-period Hotelling model and a cooperative game framework, we discover that the optimal service strategies of a platform with data collecting capabilities are collaborating with two sellers to offer to extend services to new consumers in the second period, maximizing profits for all sellers and platform. Applying Shapley value analysis, we determine the platform’s equitable service charge strategies. When sellers adopt behavior-based pricing (BBP), the pricing escalates in the first period, and the platform’s optimal service strategies also enhance the pricing of sellers. However, in the second period, BBP intensifies competition, leading to generally lower pricing. Our findings suggest that optimal pricing in the second period for new consumers should increase with enhanced quality perception, which is provided by the platform’s digital services and heightened by consumers’ privacy concerns, while decreasing for regular consumers. Lastly, we offer policy recommendations, exploring optimal regulatory scenarios—limiting or not limiting data collection—to maximize social welfare or consumer surplus, and the Mathematica software is used to identify distinct optimal policy intervals.
Share and Cite
MDPI and ACS Style
Lin, X.; Shi, T.; Luo, H.; Zhu, H.
Optimal Service Strategies of Online Platform Based on Purchase Behavior. Sustainability 2024, 16, 8545.
https://doi.org/10.3390/su16198545
AMA Style
Lin X, Shi T, Luo H, Zhu H.
Optimal Service Strategies of Online Platform Based on Purchase Behavior. Sustainability. 2024; 16(19):8545.
https://doi.org/10.3390/su16198545
Chicago/Turabian Style
Lin, Xudong, Tingyi Shi, Hanyang Luo, and Hao Zhu.
2024. "Optimal Service Strategies of Online Platform Based on Purchase Behavior" Sustainability 16, no. 19: 8545.
https://doi.org/10.3390/su16198545
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