As smart dynamic ecosystems, represented e.g. by (semi-) autonomous vehicles in smart city settings, become more prevalent, determining liability and accountability for these systems becomes increasingly complex. This raises serious concerns about the course of the investigation and fair judgments in case of incidents such as rule violations or accidents, which directly impact humans affected by these systems. The current research gap lies in distinguishing between intentional and unintentional misbehavior within these ecosystems, which depend on interpreting human action and user interactions with the autonomous system. This paper addresses this multidisciplinary challenge through three main contributions. First, we consider both technological and human factors for intent classification in post-incident investigations. Second, we extend human-like analysis to autonomous systems to evaluate their intent in a manner consistent with legal reasoning. Third, we propose a comprehensive, context-aware conceptual framework grounded in legal theory, which integrates trust modeling and social metrics to support the identification of intent for investigating misbehavior within smart dynamic ecosystems.