Community centered factors drive crime report app use in cities
Mixed-Methods Exploration of Sociotechnical Factors to Improve Adoption of Crime-Reporting Apps in Urban Communities
Artificial IntelligenceComputers and Society
Summary
Crime-reporting apps can help make cities safer, but not many people keep using them. The authors found that what really matters is how valuable people feel these apps are over time. This value comes from feeling safe, staying anonymous, having control, and sharing a sense of teamwork. These feelings predict whether people will keep using the app more than how easy or fast it seems to use.
What this means in practice
- •For mobile app developers: Design crime-reporting apps that prioritize user feelings of safety, anonymity, autonomy, and camaraderie to increase sustained usage.
- •For community safety teams: Use insights on key social and technical factors to promote and support adoption of crime-reporting apps in urban neighborhoods.
Authors
Olalekan David-Banjo, Chinazunwa Uwaoma, Paul Witman
Abstract
Crime-reporting mobile applications can enhance community safety and engagement in urban areas, yet their adoption remains low, partly because developers prioritize technological features over residents' social realities. This study used an exploratory sequential mixed-methods design to identify and validate the sociotechnical factors that drive adoption. Two focus groups and an analysis of 660 app reviews revealed that users' initial sense of usefulness fades, whereas sustained engagement depends on perceived value (PV), comprising six dimensions: safety, anonymity, recency, autonomy, camaraderie, and accessibility. A survey of 505 urban residents, analyzed with PLS-SEM, validated PV as a formative higher-order construct formed by four of these dimensions (safety, anonymity, autonomy, and camaraderie). PV was the strongest predictor of behavioral intention, ahead of effort expectancy, facilitating conditions, and performance expectancy, and behavioral intention strongly predicted actual use. The model explained 75% of the variance in behavioral intention and 51% in actual use. The findings offer design principles for community-centered crime-reporting technologies.