AI-driven personalization has transformed consumer engagement, yet existing research offers no systematic way to determine when personalization shifts from helpful to intrusive. Current research and models explain privacy concerns or expectancy violations but do not measure the point at which personalization triggers user discomfort, creating the personalization paradox. This study introduces the Discomfort Threshold Assessment & Measurement (DTAM) framework, a conceptual model that identifies and evaluates this threshold by integrating Dual Process Theory, Expectation Violation Theory, and Privacy Calculus Theory. DTAM distinguishes between affective discomfort reactance and cognitive discomfort reactance and outlines how these constructs could, in future empirical studies, be operationalized using neurophysiological signals (e.g., EEG, eye-tracking) or behavioral indicators (e.g., ad avoidance, sentiment shifts). Using real-world cases, the framework provides a structured approach for reasoning discomfort in AI-driven personalization and provides. a foundation to balance personalization with trust, ethical design, and sustained consumer engagement.
Chalakudi et al. (Wed,) studied this question.