Digital Cognition in Predictive Marketing Personalization: A Conceptual Framework
本文基于文献综述提出一个概念框架，将预测性个性化重新定义为算法、社交媒体和AI驱动的递归认知过程，解释个性化如何改变消费者的注意力、记忆和决策，并引入认知均衡营销视角，为管理者设计AI个性化系统提供指导。
ABSTRACT This study introduces a conceptual framework that reframes predictive personalization as a recursive cognitive process shaped by algorithms, social media platforms, and artificial intelligence (AI), based on a systematic literature review. Although predictive personalization has been widely examined as a tactical tool to increase engagement, existing research lacks an integrative theoretical framework explaining how it recursively interacts with consumer cognition across fragmented literatures. In this model, digital cognition interprets behavioral traces as data‐driven representations of cognitive processes. Through AI‐driven algorithmic prediction, behavioral data are used to personalize digital exposure that influence users' attention, memory, and decision processes, gradually reshaping how cognition operates within digital environments. As a result, predictive marketing personalization enables the modeling of a closed‐loop system that may support more effective digital marketing strategies. What begins as digitally extended cognitive activity is rendered into streams of behavioral data. These data fuel predictive models, which generate outputs that subsequently reshape mental states and influence future behaviors. The outcome is a self‐modifying system in which human cognition and machine logic evolve in tandem. The loop then restarts. The study relies on qualitative synthesis and theory mapping rather than on primary empirical data analysis. Positioned within the predictive marketing personalization and algorithmic decision‐making research stream, the study theoretically maps this loop across two interdependent layers (behavioral traces and algorithmic interventions) and integrates them with established theories. It further introduces the concept of cognitive equilibrium marketing as a conceptual perspective that aligns algorithmic precision with cognitive and ethical balance. The results analyze how personalization not only reacts to user behavior but transforms how individuals attend, remember, decide, and learn in digital environments. The study concludes with 7 research propositions linked to boundary conditions and 30 future research questions, highlighting the psychological, ethical, and managerial implications of cognition‐aware personalization systems in contemporary digital marketing. From a practical perspective, the framework offers actionable guidance for managers and practitioners designing AI‐driven personalization systems, helping them align algorithmic precision with users' cognitive capacities, ethical constraints, and long‐term engagement outcomes.