Applying the Technology Acceptance Model to Explore User Intentions with Online Repast Reservation in China: A Case of Eleme

• Main Authors: Yue Huang, 黃躍
• Other Authors: Chu-Ching Wang
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/pk984j

碩士 === 淡江大學 === 企業管理學系碩士班 === 106 === With the continuous development of the Internet, O2O business model has being entered a stage of rapid development. After the online shopping, the platform for online reservation shows has gradually become a hot spot for O2O business model in recent years. Online repast reservation not only for people having meal more convenience, but also for traditional food industry broadening the scope of business. For the moment, there are 300 million online repast reservation users in Mainland China, and online repast reservation model is popular with consumers in China. However, there are many people still refuse to use this kind of consumption model, so this study intends to explore what causes Chinese consumer to adopt or reject this model. The main purpose of this study takes the case of Eleme to apply the technology acceptance model to explore online service quality, perceived risk, network externalities and user intentions with online repast reservation in China. Based on the 333 valid samples, this study conducted descriptive statistical analysis, reliability and validity analysis, correlation analysis, and structural equations modeling analysis. Finally, the study has following important findings: 1. Perceived usefulness of online repast reservation has a significant positive impact on user intentions. 2. Perceived ease of use of online repast reservation has a significant positive impact on perceived usefulness. 3. Online service quality and network externalities of online repast reservation both have a significant impact on perceived usefulness. 4. Online service quality, perceived risk, and network externalities have a significant impact on perceived ease of use. 5. Online ordering frequency of consumer demographic variables has a significant impact on user intentions.