Problem
Research questions and friction points this paper is trying to address.
smartphone usage
intention-behavior gap
problematic smartphone usage
To Stop or Not to Stop: Exploring the Intention-Behavior Gaps in Smartphone Usage
🤖 AI Summary
研究探索了智能手机使用中的意图-行为差距，通过收集用户数据并利用机器学习模型预测该差距，以优化干预工具的设计。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
intention-behavior gap
machine learning models
real-time prediction
problematic smartphone usage
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