Xiucun Wang's scientific contributions
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Publications (6)
It is becoming increasingly important for service managers and marketers to understand motivations for posting online reviews. Previous studies mainly focused on factors influencing consumers' willingness to post online reviews, but paid little attention to why posters contribute different content in their reviews. This study aimed to identify vari...
Instance message intelligence, which is deemed as a part of intelligent service system, could provide e-shoppers with help in the service delivery by automatically answering e-customer questions. As such, more interests are aroused in developing more effective and efficient instance message intelligence management practices. To investigate the effe...
The study aims to investigate the influencing mechanism of customer orientation on e-customer engagement relationship outcomes through perceived support for customer. A total of 525 responses to a survey were collected from e-shoppers in china. The results showed that customer focus and customer feedback have a positive effect on perceived rapport...
Citations
... Online reviews shape consumers' perceptions of products by providing relevant information about products and services (Yan & Wang, 2018). Such reviews have become a decisive factor affecting product sales on e-commerce platforms (Zhao et al., 2018). ...
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... Sensory marketing manages the communication of the brand toward the senses [60], analyzing the perception and behavior of consumers [61] as a complement to rational marketing [62]. "Sensory pleasure" appeals to the client's senses, creating unforgettable experiences [19,63], facilitating the client's reaction to emotional and creative stimulation, and is connected to certain lifestyles [64]. The human brain regulates the level of the emotion, reaching balance and tranquility (concept of emotional evanescence) after a greater or lesser period of time. ...
... The aim of user recommendation is to seek a group of users who are interested in an item (e.g., customer recommendation (Li & Wang, 2017;Liu et al., 2015) and news recommendation (Chen, Meng, Xu & Lukasiewicz, 2017;Okura, Tagami, Ono & Tajima, 2017)) or have some certain relationships with target users (e.g., friend recommendation (Bagci & Karagoz, 2016;Chen, Shih & Lee, 2016;Huang, Zhang, Schonfeld, Wang & Hua, 2017Yu, Che, Li, Li & Jiang, 2017)). Generally speaking, based on whether to consider users' location information or not, user recommendation can be divided into two categories: location-free user recommendation and location-aware user recommendation. ...
