With the growing use of social media in the world, a wide variety of social media applications and services have been produced. Popular social media platforms leverage their unique functions to persuade their users to adopt such applications and services, and many enterprises have begun to consider social media operations as a crucial aspect. Thus, since the gradual influence of social media on consumer behavior, a new social commerce model has begun to develop. This research examined both business and social aspects of social commerce sites to establish an evaluation model for measuring their quality and effectiveness. We collected 468 valid samples of online users who had used social commerce site to browse or purchase products and were willing to use again. We used EFA and CFA to confirm the model we constructed, and Partial least squares regression was used to analyze the relationship among the antecedents and consequences of quality evaluation in social commerce sites. According to the result, the consumers' behaviors were most likely affected by functionality, enjoyment, process, reliability, presence, and identity with a social commerce site. This study not only provided a credible social commerce quality measurement for other scholars to conduct Contemporary Management Research 70 relevant research but also provide conclusions for the marketing strategy of social commerce sites and products as a reference.
In recent years, people have begun to use sharing economy platforms such as Airbnb and Uber. The rapid development of such sharing economy platforms has thus become an important topic. Studies regarding the sharing economy have discussed resource providers but not users. Therefore, this study constructs a model to measure the components of sharing economy drivers and the correlation between those drivers and usage intention, in addition to exploring the differences in the composition of drivers and usage intention between Airbnb and Uber. The survey method was an online questionnaire. The sample analysis uses partial least squares regression to verify the hypothesis and analyze the components that form the sharing economy for drivers. According to the results, sharing economy drivers─Societal drivers, Economic drivers, Technological drivers, affect usage intention, and different combinations of sharing economy components, such as enjoyment, network externalities, perceived quality, cost saving, and efficiency, exist in Airbnb and Uber. For the reference of relevant academic research and practical operation in the future.
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