A Study on Impact to Customer''s Purchase Intention in Live-Stream Commerce

• Main Authors: Chun-Hung Hsieh, 謝均鴻
• Other Authors: 陳佳楨
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/9te64d

碩士 === 國立中興大學 === 資訊管理學系所 === 106 === With the rapid development of E-commerce, consumers are able to shop online without restriction on time and place. Although the total transaction amount of E-commerce is increasing, the growth rate is gradually declining. It forces online retailers to find other ways to attract customers. Since live-stream is rising recently, it shortens the distance between buyers and sellers in online environment. Customers can acquire the information about the commodity in real-time by using live-stream and buy it immediately. Compared with shopping in traditional E-commerce, consumers no longer need to spend time waiting sellers to reply the messages. Likewise, sellers can utilize live-stream to grasp the customers who interest in their commodities. Our study aims to understand the key factors that influence consumers’ purchase intentions in live-stream commerce, which is the combination of live-stream and E-commerce. Our framework bases on Social Support Theory and Social Presence Theory, combines the consumer''s trust toward live-streamer and discusses the key factors that ultimately affect consumers'' purchase intentions. Besides, we adopt online survey to collect data and use Structural Equation Modeling (SEM) to verify our research model and hypotheses. The results indicate that informational support has a positive effect on social support. Both perceived sociability and media richness have positive effects on social presence. Therefore, social support and social presence will directly affect purchase intention or indirectly affect purchase intention through trust. However, emotional support in our study didn’t significantly affect social support. Purchase experience didn’t have a significant effect on purchase intention as well. In addition, gender and monthly disposable income category are used to classify the samples to know whether there is difference between these classifications. Last but not least, we expect our study to be helpful for subsequent researchers and live-stream commerce vendors.