Consumers' Continued Intention to Use Online-to-Offline (O2O) Services in Omnichannel Retail: Differences between To-Shop and To-Home Models

Online-to-offline (O2O) commerce is a specific form of omnichannel retailing, wherein consumers search and purchase online and then consume offline. There are many different O2O models, and new O2O businesses are emerging during the COVID-19 pandemic;they can be cate-gorized into two types of O2O services: to-shop and to-home. However, few studies have focused on consumer behavior in a comprehensive O2O scenario, and no study has attempted to compare the differences between to-shop and to-home consumers. Therefore, this study aimed to propose a universal model to predict consumers' continued intention to use O2O services and to compare the differences between to-shop and to-home O2O in terms of factors influencing consumer behavior. A cross-sectional survey was conducted, and the PLS-SEM was used for data analysis. The basic SEM results indicated that habit, performance expectancy, confirmation, and offline facilitating conditions are the main predictors. The multigroup analysis showed differences between to-shop and to-home consumers regarding hedonic motivation, price value, and perceived risk. The study suggests that marketers and designers in various O2O scenarios can use the framework to build their business plans and develop different marketing strategies or sub-platforms for to-shop and to-home consumers. © 2023 by the authors.

A Multi-modal Attention-based Seq2eq Model for Predicting Real-estate Prices

Some studies show that the closure and reopening orders brought by covid-19 have had a negative impact on the residential real estate market. Generally speaking, real estate sales decreased significantly during this period, such as office buildings, shopping centers and family houses. Although the overall situation is declining, there are also some new situations. For example, people's desire for spacious family space caused by home office leads to an increase in the demand for large houses in the suburbs. This paper mainly compares the sales differences between suburban family houses and urban family houses in San Francisco and New York in the real estate market during covid-19. The data come from multiple dimensions such as house listing price on the real estate sales website, Machine learning methods could be used for analysis. This paper proposed a multi-modal joint attention seq2seq method to analyze these differences and the reasons for the differences. The experimental results show that one of the possible reasons the house price change in San Francisco is that there are more high-tech job position and their family income is higher than the average level of other regions. © 2021 IEEE.