Smartphone intention behavior gaps shaped by gender time app and input
To Stop or Not to Stop: Exploring the Intention-Behavior Gaps in Smartphone Usage
Human-Computer Interaction
Summary
Many people plan to stop using their phones but keep using them anyway, creating a gap between intentions and actions. The authors studied this gap by tracking 37 people’s phone use and their plans to stop over two weeks. They found that gender, time of day, app type, and how people interact with their phones affect this gap. They also built computer models that can predict when people might struggle to stop using their phones. This knowledge can help build better tools that remind or help people at the right moment and with the right intensity.
What this means in practice
- •For mobile app developers: Create adaptive apps that predict when users intend but fail to stop phone usage and provide timely personalized interventions.$Commercial implications: Enables development of commercial self control or wellbeing apps that optimize intervention timing and intensity using real-time prediction models.
- •For user experience designers: Design user interfaces that incorporate timing and context to reduce intention-behavior gaps in smartphone usage.
Authors
Jian Zheng, Eun Kyoung Choe
Abstract
As smartphones become integral to daily life, researchers have sought to identify when the use becomes problematic. Previous studies have operationalized problematic smartphone usage (PSU) from either an intention or a behavior perspective. Both risk delivering interventions not welcomed by users. We propose a novel approach to operationalizing PSU as the intention-behavior gap (IBG). We collected self-reported data on intentions to stop phone usage, alongside usage behavior data, from 37 participants over two weeks. We calculated IBG, examined effects of demographic and contextual variables, and developed machine learning models to predict IBG in real time. We found that IBG was explained by gender, time, app, and input interactions, among other factors. Intention was predicted most accurately with only personal data, whereas behavior and IBG were predicted most accurately with both personal and global data. Our findings can inform the design of future intervention tools optimized for timing and adaptive intensity.