Abstract: The rapid proliferation of Generative Artificial Intelligence (GenAI) is fundamentally reshaping consumer landscapes. Yet its profound implications for consumer cognition and subsequent behavioral outcomes remain insufficiently explored. Prior research has predominantly focused on short-term drivers of technology adoption, such as perceived usefulness and trust, while largely neglecting the deeper cognitive consequences of generative AI use. In particular, little is known about how generative AI affects memory efficacy and metamemory monitoring. Moreover, existing studies rarely connect the distinctive technological affordances of generative AI (e.g., content generation and natural-language interaction) to their downstream behavioral consequences, resulting in a fragmented understanding of how technology use shapes consumer behavior through cognitive pathways.
To address these gaps, this research develops an interdisciplinary “technology-cognition-behavior” framework to systematically examine the dynamic, dual-edged effects of generative AI product usage on consumer memory efficacy and behavior. We propose that the impact of generative AI usage is temporally contingent. In the short term, it enhances memory efficacy and judgment confidence through cognitive offloading, yielding positive or neutral behavioral outcomes; over time, however, prolonged reliance may undermine individuals’ memory processing capabilities and impair metamemory calibration, producing negative behavioral consequences. Building on this premise, the research addresses four research questions: (1) whether and how generative AI usage affects consumer memory efficacy, and how such effects evolve over time; (2) how short-term improvements in memory efficacy translate into positive or neutral consumer behaviors; (3) how long-term impairments in memory efficacy generate negative behavioral outcomes; and (4) which marketing interventions can effectively mitigate these adverse effects.
To empirically investigate these questions, the research is organized into four sequential studies. Study 1 adopts a longitudinal and exploratory approach, combining machine learning-based text analysis with survey and interview data to uncover the non-linear relationship between generative AI usage and memory efficacy. We anticipate an inverted U-shaped trajectory, moderated by factors such as cognitive autonomy and usage patterns. Building on these findings, Studies 2 and 3 examine the downstream effects of memory efficacy changes. Study 2 focuses on the positive and neutral behavioral consequences arising from short-term cognitive gains, whereas Study 3 investigates the negative outcomes associated with long-term cognitive inhibition. Finally, Study 4 identifies and experimentally evaluates marketing interventions —specifically, “assistant role positioning” and “cyclical temporal interfaces”—designed to mitigate the negative effects identified in Study 3.
This work offers three primary theoretical contributions. First, by integrating insights from information systems (IS), cognitive psychology, and marketing, it proposes a cross-layer framework that elucidates how generative AI alters memory processing, judgment confidence, and choice. By synthesizing cognitive offloading theory and metamemory monitoring theory, it introduces memory efficacy as a critical cognitive construct within consumer decision-making, thereby extending information processing and cognitive decision theories in technology-mediated contexts. Second, it advances the literature on the double-edged sword effect by incorporating a temporal dimension, revealing the dynamic tension between immediate benefits (e.g., increased decision confidence) and cumulative costs (e.g., diminished future self-continuity) of generative AI usage. Moving beyond the fragmented treatment of positive and negative effects in prior studies, the framework provides a more coherent account of the interplay between technological dependence and cognitive autonomy. Third, the study extends metamemory theory into real-world consumption settings through field experiments and longitudinal tracking, offering new insights into how consumers form judgment confidence, calibrate memory accuracy, and make behavioral choices in technology-augmented environments.
From a practical standpoint, the findings will inform the design and development of generative AI products by helping firms balance efficiency gains with potential cognitive risks. The research proposes actionable intervention strategies, such as framing AI systems as collaborative assistants and implementing “cyclical temporal interfaces,” to reduce long-term cognitive dependency and its associated negative consequences. Additionally, these insights carry implications for consumer cognitive protection and policy-making, contributing to the responsible development and deployment of generative AI technologies and, ultimately, promoting long-term societal welfare.

Key words: generative AI products, memory efficacy, consumer behavior, metamemory, cognitive offloading