Adoption of Recurrent Innovations: A Large-Scale Case Study on Mobile App Updates
Authors: 
Fuqi Lin, Xuan Lu, Wei Ai, Huoran Li, Yun Ma, Yulian Yang, Hongfei Deng, Qingxiang Wang, Qiaozhu Mei, 
Xuanzhe LiuAuthors Info & Claims
Fuqi Lin, Xuan Lu, Wei Ai, Huoran Li, Yun Ma, Yulian Yang, Hongfei Deng, Qingxiang Wang, Qiaozhu Mei, Xuanzhe LiuAuthors Info & ClaimsArticle No.: 13, Pages 1 - 26
Abstract
Modern technology innovations feature a successive and even recurrent procedure. Intervals between old and new generations of technology are shrinking, and the Internet and Web services have facilitated the fast adoption of an innovation even before the convergence of its predecessor. While the adoption and diffusion of innovations have been studied for decades, most theories and analyses focus on single and one-time innovations. Meanwhile, limited work has investigated successive innovations while lacking user-level analysis, possibly due to the unavailability of fine-grained adoption behavior data. In this study, we present the first large-scale analysis of the adoption of recurrent innovations in the context of mobile app updates, investigating how millions of users consume various versions of thousands of apps on their mobile devices. Our analysis reveals novel patterns of crowd and individual adoption behaviors, which suggest the need for new categories of adopters to be added on top of the Rogers model of innovation diffusion. We show that standard machine learning models are able to pick up various sources of signals to predict whether users in these different categories will adopt a new version of an app and how soon they will adopt it.
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Publication History
Published: 14 November 2023
Online AM: 10 October 2023
Accepted: 04 September 2023
Revised: 19 July 2023
Received: 18 May 2022
Published in TWEB Volume 18, Issue 1
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- National Natural Science Foundation of China
- Beijing Outstanding Young Scientist Program
- Center for Data Space Technology and System, Peking University, and the Alibaba Group’s University Joint Research Program
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