Hidden Semi-Markov Model-Based Reputation Management System for Online to Offline (O2O) E-Commerce Markets

Abstract

The rapid development of information technology enables an increasing number of consumers to search and book products/services online first and then to consume them in brick-and-mortar stores. This new e-commerce model is called Online to Offline (O2O) e-commerce and has received significant managerial and academic attention. Compared with many extant e-commerce models (i.e., B2B, B2C and C2C), reputation management in this emerging model needs some improvement. It has to collect more raw reputation-related data, consider more reputation-related factors and show more comprehensive reputation evaluation results. As a stepping-stone in the research in O2O e-commerce, a new reputation management system (HSMM-RMS) has been developed based on a probabilistic model called the Hidden Semi-Markov Model. By combining observable online and offline raw reputation information, the proposed system can accurately, promptly and dynamically provide O2O e-commerce participants with offline merchants’ historical and predictive reputation information. Our Monte-Carlo simulation experiments indicate that the proposed system performs significantly better than the extant Hidden Markov Model-based reputation management system. A case study based on a real O2O e-commerce platform demonstrates the real application of HSMM-RMS. It also shows that the proposed system can provide a realistic solution for reputation management in the O2O e-commerce market.

Keywords: Online to Offline E-commerce; Reputation Management System; Hidden Semi-Markov Model