The development of mobile social networking service (SNS) triggers the growth of social commerce industry. Customers rely considerably on electronic word of mouth (eWOM) to make purchasing decisions. Thus, SNS is an important commercial platform that offers attractive opportunities and challenges to firms. This study sheds light on the role of SNS as a social commerce platform by focusing on WeChat, the most popular SNS in China. This study identifies three different types of trust based on SNS that customers perceive in the context of social commerce. These types of trust are contents trust, source trust, and platform trust. This study suggests the antecedents and consequences of each trust. Our results prove that eWOM intention relies on contents trust and source trust, whereas purchase intention depends on contents trust, source trust, and platform trust. This study also finds that contents trust is positively influenced by source trust and platform trust. Finally, the result verifies the key antecedents of each trust, namely, vividness and timeliness for contents trust, competence, benevolence, and integrity for source trust, and instrumental need and social need for platform trust. The discussion and implications on the findings are provided.

In this study, we propose an operations model to automate a home energy management system (HEMS) that utilizes an energy storage system (ESS) in consideration of consumer utility. Most previous studies focused on the system for the profits obtained from trading charged energy using large-scale ESS. By contrast, the present study focuses on constructing a home-level energy management system that considers consumer's utility over energy consumption. Depending on personal preference, some residential consumers may prefer consuming additional energy to earn increased profits through price arbitrage and vice versa. However, the current system could not yet reflect on this aspect. Thus, we develop an operations model for HEMS that could automatically control energy consumption while considering the level of consumer's preference and the economic benefits of using an ESS. The results of simulations using a dataset of the Korean market show that an operations policy of charging and discharging can be changed depending on consumer's utility. The impact of this policy is not ignorable. Moreover, the technical specifications of ESS, such as self-discharge rate and round-trip efficiency, can affect the operations policy and automation of HEMS.

The proliferation of items increased the difficulty of customers in finding the specific items they want to purchase. To solve this problem, companies adopted recommender systems, such as collaborative filtering systems, to provide personalization services. However, companies use only meaningful and essential data given the explosive growth of data. Some customers are concerned that their private information may be exposed because CF systems necessarily deal with personal information. Based on these concerns, we analyze the effects of the amount of information on recommendation performance. We assume that a customer could choose to provide overall information or partial information. Experimental results indicate that customers who provided overall information generally demonstrated high performance, but differences exist according to the characteristics of products. Our study can provide companies with insights concerning the efficient utilization of data.