Understanding Diffusion of Recurrent Innovations
Authors: Fuqi Lin
AI-generated Key Points
- The diffusion of innovations theory has been extensively studied, with a focus on key elements and adopter categories in the process of innovation diffusion.
- Previous research primarily concentrated on single innovations, but recurrent innovations have gained prominence in today's rapidly evolving technological landscape.
- Fuqi Lin conducted a groundbreaking large-scale analysis on the adoption of recurrent innovations within mobile app updates.
- The study delves into adoption behavior and identifies new adopter categories specific to recurrent innovations, as well as highlights key features that influence the adoption process.
- Examining how recurrent innovations are embraced within mobile app updates provides valuable insights into the dynamics of innovation diffusion in today's digital age.
- Understanding recurrent innovations not only expands our knowledge of innovation adoption but also emphasizes their significance in shaping technological advancements and user behavior patterns.
- Fuqi Lin's research contributes to advancing knowledge in this field and offers a nuanced perspective on how recurrent innovations impact the broader landscape of innovation diffusion.
Abstract: The diffusion of innovations theory has been studied for years. Previous research efforts mainly focus on key elements, adopter categories, and the process of innovation diffusion. However, most of them only consider single innovations. With the development of modern technology, recurrent innovations gradually come into vogue. In order to reveal the characteristics of recurrent innovations, we present the first large-scale analysis of the adoption of recurrent innovations in the context of mobile app updates. Our analysis reveals the adoption behavior and new adopter categories of recurrent innovations as well as the features that have impact on the process of adoption.
Tools for this paper
Ask questions about this paper to our AI assistant
You can also chat with multiple papers at once here.
⚠The license of the paper does not allow us to build upon its content and the AI assistant only knows about the paper metadata rather than the full article.
The previous summary was created more than a year ago and can be re-run (if necessary) by clicking on the Run button below.
⚠The license of this specific paper does not allow us to build upon its content and the summarizing tools will be run using the paper metadata rather than the full article. However, it still does a good job, and you can also try our tools on papers with more open licenses.
Similar papers summarized with our AI tools
Navigate through even more similar papers through a
tree representationLook for similar papers (in beta version)
We compare the title and abstract with papers indexed in our database. For arXiv papers, we search within the same category; for DOI and other sources, we search across sources and subjects. You can rerun the search as our database grows.
Disclaimer: The AI-based summarization tool and virtual assistant provided on this website may not always provide accurate and complete summaries or responses. We encourage you to carefully review and evaluate the generated content to ensure its quality and relevance to your needs.