مدل نیمه مارکوف پنهان مبتنی بر سیستم مدیریت اعتبار آنلاین به آفلاین (O2O) بازار تجارت الکترونیک
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|44131||2015||13 صفحه PDF||سفارش دهید||9280 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Decision Support Systems, Volume 77, September 2015, Pages 87–99
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.