Research On Social Networking Site’s Long-term Stickiness Based On User Experience

The Social Networking Site（SNS）is widely popular with people because of that itis convenient, fast, not restricted by space and time. However, in recent years,plagiarism and serious homogeneity phenomenon is happened in almost each lagerwebsite since the rapid development of SNS. It makes website become boring andlead to stickiness decrease of websites. Therefore, user stickiness has become one ofthe focuses of people’s attention.At present, the previous studies about this problem base on the perspective angleswere not enough in both domestic and foreign scholars. Therefore, this articlediscusses the effect on SNS user long-term stickiness by the perspective angles. Thisresearch is not only enriches the theoretical system of the SNS user experience anduser stickiness, but also helps the administrator of website to improve its design forattracting and retaining users, which is valuable in technology and business of SNS.The following is the results obtained from this research.Firstly, it summarizes the elements of the SNS user experience levels by combiningthe user experience factor with the feature of SNS. It also investigates the sensoryexperience, the interactive experience, the content experience, the social experienceand the entertainment experience of SNS.Secondly, it builds a long-term stickiness formation model of SNS based onexperience by referencing the IS Continuance Model which is based onExpectation-Confirmation, and then combining it with user experience elements. Itprovides a theoretical guidance for the following research and analysis.Thirdly, it constructs the typical event task model of SNS by observation. Startingwith typical event, it summarizes the long-term user·s experience elements throughthe depth interview with users.Finally, it verifies how the user experience affects long-term stickiness and theinfluence degree. By using questionnaire survey and quantitative analysis of thesample, it comes to the user experience that affects different level of user stickiness,and then analyzes the influence degree. It provides a theoretical support foroptimization and improving of the SNS.