Grounded Theory-Based User Needs Mining and Its Impact on APP Downloads: Exampled With WeChat APP

Tinggui Chen; Chu Zhang; Jianjun Yang; Guodong Cong

doi:10.3389/fpsyg.2022.875310

Grounded Theory-Based User Needs Mining and Its Impact on APP Downloads: Exampled With WeChat APP

Front Psychol. 2022 Jun 14:13:875310. doi: 10.3389/fpsyg.2022.875310. eCollection 2022.

Authors

Tinggui Chen^{1

2}, Chu Zhang¹, Jianjun Yang³, Guodong Cong⁴

Affiliations

¹ School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, China.
² Academy of Zhejiang Culture Industry Innovation & Development, Zhejiang Gongshang University, Hangzhou, China.
³ Department of Computer Science and Information Systems, University of North Georgia, Oakwood, GA, United States.
⁴ School of Tourism and Urban-Rural Planning, Zhejiang Gongshang University, Hangzhou, China.

Abstract

Software development is an iterative process from designing to implementation, and to testing, in which product development staff should be closely integrated with users. Satisfying user needs effectively is often the pain point for developers. In order to alleviate this, this paper manages to establish the quantitative connection between users' online reviews and APP (Application Program) downloads. By analyzing user online comments, companies can dig out user needs and preferences. This could benefit them by making accurate market positioning of their APP products, and therefore iteratively innovating products based on user needs, which hopefully will increase the volume of APP downloads. This paper regards WeChat APP during 47 updates periods as the research object. Based on Grounded Theory, user needs are extracted after data cleaning. Next, by using semantic analysis and word frequency analysis, we are able to obtain the implicit feedbacks such as emotion tendency, satisfaction and requirements lie under online reviews. Then, we construct a quantile regression model to study the impact of users' online reviews on downloads based on the influencing factors we extracted so as to provide a decision basis for enterprises to iteratively update their products. Results show that: (1) Generally speaking, needs of WeChat users mainly focus on performance, reliability, usability, functional deficiency, functional insufficiency, and system adaptability; (2) For those APP versions with relatively fewer downloads, user needs are mostly about functional deficiency, followed by functional insufficiency, performance, usability, and system adaptability. At this stage, it is found out that users' emotion tendency and user satisfaction significantly affect the volume of downloads; (3) When the volume of APP downloads is moderate, the user needs are functional deficiency, functional insufficiency, and system adaptability. While under this circumstances, users' star ratings have a significant impact on downloads; (4) In addition, when the volume of App downloads is high, user needs are performance, usability, and system adaptability. Our methods effectively extract users' requirements from online reviews and then successfully build up the quantitative connection between the implicit feedbacks from those requirements and APP downloads.

Keywords: APP downloads; Grounded Theory; online reviews; quantile regression; user demand mining.