A Design-Oriented Pre-Deployment Evaluation Framework for Citizen Adoption of Crowdsourced Non-Emergency Reporting Apps

Citizen-sourced non-emergency reporting is an increasingly important component of smart-city governance, yet cities often lack systematic methods for evaluating alternative participation designs before deployment. This study develops a design-oriented pre-deployment evaluation framework for assessing citizen adoption of a proposed reporting application and comparing alternative incentive mechanisms. Drawing on an extended UTAUT framework, the study introduces perceived value (PV) as a mediating mechanism linking incentive framings to behavioral intention. A scenario-based survey experiment with 580 participants in the Greater Toronto Area compared the following four conditions: control, monetary incentive, recognition, and donation-based community-benefit incentive. Data were analyzed using PLS-SEM and bootstrapped mediation analysis. Results indicate that performance expectancy and attitude are the strongest predictors of behavioral intention, while trust was significantly associated with both cognitive and affective evaluations of the reporting system. Social influence becomes non-significant once these evaluations are included. Recognition and donation-based community-benefit incentives significantly increased perceived value and showed both direct and indirect effects on performance expectancy and behavioral intention. At the behavioral-intention stage, indirect effects accounted for approximately 80% of their respective total effects, indicating complementary mediation with indirect effects predominating. By contrast, the specific $2 city-credit condition showed no significant direct, indirect, or total effect. Rather than proposing a new acceptance theory, the study demonstrates how technology acceptance modeling can be repurposed as a practical decision-support framework for evaluating alternative civic-technology designs before system launch. Limitations related to the pre-deployment setting and intention-based measures are acknowledged, and future research directions involving field deployment and longitudinal participation analysis are discussed.