Smartphone intention behavior gaps influenced by gender time and apps
To Stop or Not to Stop: Exploring the Intention-Behavior Gaps in Smartphone Usage
Human-Computer Interaction
Summary
People often want to stop using their smartphones but end up using them anyway. This study looked at the difference between what people intended to do and what they actually did with their phones, calling this the intention-behavior gap. The authors found that factors like gender, time of day, and which apps were used affected how big this gap was. They also used computer models to predict when someone might struggle to follow their intention, which could help create better tools to support people in controlling their phone use.
smartphone usageproblematic smartphone usageintention-behavior gapself-reported datamachine learning modelsreal-time predictiondemographic variablescontextual variablesbehavior predictionintervention design
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.