Abstract

This paper aims to extend continuance intention theory by introducing a goal-oriented view of perceived usefulness (PU) of emerging technologies, operationalized here in the context of large language models (LLMs). Building on the Technology Acceptance Model (TAM) and the IS Continuance framework, we argue that PU is not a static belief but a dynamic cognitive evaluation shaped by users’ motivational orientations and feedback from use. Users with a learning goal orientation (LGO) pursue mastery and transfer of knowledge, leading to cognitive reappraisal that can reduce satisfaction, and consequently PU, as they recognize system limitations or their overreliance. In contrast, performance goal orientation (PGO) emphasizes outcome validation and efficiency, producing habitual reinforcement loops that increase satisfaction through repeated success. When both orientations coexist, PGO’s reinforcement mechanism and increased satisfaction would trigger recognition of dependence for LGO, causing cognitive dissonance. Further, it would attenuate the negative reappraisal effect of LGO on satisfaction, revealing an interaction effect between the two motivational pathways. Together, these mechanisms position PU as a function of cognitive goal pursuit and fulfillment, not merely perceived system efficacy. This framework explains why continuance intention can either increase through reinforcement or decline through reflective evaluation. This study contributes a cognitive–motivational adaptation layer to continuance theory and offers a foundation for examining how evolving user goals, feedback environments, and adaptive system features jointly shape long-term engagement with intelligent technologies.

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