Using Means-End Chain Model to Analyze the User Willingness of the Mobile Health APP

• Main Authors: HUANG, MEI-CHIEN, 黃玫蒨
• Other Authors: LIU, SHU-FAN
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/69950878895087252712

碩士 === 元培醫事科技大學 === 資訊管理系數位創新管理碩士班 === 105 === Population aging is a global trend nowadays; and the growing popularity of smartphone and tablets usage causes a crazy amount of mobile apps in the market. The mobile health applications (apps) industry has exploded because of this trend of aging population and low fertility. Accompanied with complete legislations and multiple smart advices, these services are going to be the biggest profit in mobile applications market. The policy framework of Active Aging was developed in WHO 2002, is intended to inform discussion and the formulation of action plans that promote healthy and active aging in three dimensions: health status, participation and safe. Not only taking care of the health status from body, soul and social life, we upgrade the living quality of aging population. However, wheter the smartphone users are wiling to download these health mobile apps and whetere the users trust the apps or understand the functions of these apps are the main effects on their usage will of these health apps. Therefore, the purpose of the research is to explore and analyze the usage intention of the health mobile apps. Through the model of Means-end chains, the category and the values which the users are considering when they are downloading the apps are extracted. This is how to figure out the deepest needs and the values of the app users. Also, a complete motivation of downloading the apps would be constructed and provided and the functions and values would be properly provided to the users in the future. Through the analysis of the users’ intention of downloading, a much more efficient managing policy can be properly provided to create the whole benefit of the apps. The research uses in-depth interviews, focusing on the function category of apps to find out the connection between the users’ intention of downloading and the apps’ values. These results can be provided to the health mobile apps developers for their future use.