Growing acceptance of applied behavioral science in public policy and organizational decision-making has resulted in ambitions to go beyond nudging, particularly following developments in new technologies. To support these ambitions, this paper presents the GAP framework. Building from established behavioral science concepts (General Tools), GAP incorporates new technologies like artificial intelligence (AI) into the behavioral science toolkit (Algorithms), as well as practical considerations for implementing behavioral science within organizational settings (Practical Considerations). The framework unifies diagnostic, design, and scalability considerations to support practitioners in tailoring behavioral capacities to their organizational contexts. GAP is developed as a ‘modular’ framework, allowing practitioners to draw on its insights to supplement existing knowledge and adapt knowledge to organizational demands.
Costa et al. (Mon,) studied this question.