Paper Info
Title
A multi-stage hidden Markov model of customer repurchase motivation in online shopping.
Abstract
Product promotions and liberal return policies are two effective signals that can increase customers' repurchase behavior when online shopping, and how these signals work at different stages of market growth for various customer groups is an important topic for research and applications. Thus, to help online merchants make more effective decisions regarding the use of such signals, this paper proposes a multi-stage hidden Markov model (MS-HMM) to explore the motivational process behind customer repurchase behavior through the lens of the Signaling Theory. The customer-merchant relationship (CMR) is represented as the latent state in the hidden Markov model and is coupled with stage-heterogeneity in terms of state transition probabilities and state-dependent choice probabilities. Moreover, extensive experiments with real-world data are conducted to validate the effectiveness of the MS-HMM.
Year
DOI
Venue
2019
10.1016/j.dss.2019.03.012
Decision Support Systems
Keywords
Field
DocType
Multi-stage,Repurchase behavior,Signaling theory,Hidden Markov model
Computer science,Knowledge management,Artificial intelligence,Through-the-lens metering,Hidden Markov model,Machine learning
Journal
Volume
ISSN
Citations 
120
0167-9236
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Xiao-Lin Li18916.69
Yuan Zhuang265.84
Benjiang Lu381.10
Guoqing Chen491271.58