Seeing Less, Engaging More: Rethinking Early User Experience on GenAI Co-Creation Platforms–Findings from a Field Experiment

Generative AI content-generation (GCG) platforms enable users to co-create personalized content with remarkable speed. Yet recent research suggests that such immediacy may undermine early engagement: when content appears instantly, users may not realize sufficient value to register on the platform. We address this challenge by introducing fulfillment, i.e., the extent to which co-created content is revealed prior to registration on GCG platforms, as an experiential design lever that shapes value realization in initial interactions. Drawing on value co-creation literature, we suggest that fulfillment operates through two motivational pathways: value-in-use, reflecting users’ recognition that their input meaningfully shaped the output, and curiosity, reflecting anticipatory motivation when the experience remains perceptually open. Using a randomized field experiment on a GCG platform, complemented by a follow-up online experiment, we show that partial fulfillment, which reveals some but not all generated output, outperforms both full and no fulfillment in driving registration. This effect is also conditioned by the framing of the registration message. While loss-framed messages that emphasize the cost of inaction increase registration on average, this effect attenuates under full fulfillment, suggesting a substitution relationship. Formal mediation analyses indicate that although both full and partial fulfillment enhance value-in-use, only partial fulfillment sustains curiosity, and this dual activation explains its effectiveness. Additional analyses delineate the scope of these effects, which persist beyond registration to shape subsequent engagement and return behavior, but arise only when users meaningfully co-produce content and are enhanced by better quality outputs. Together, these findings suggest that registration on GCG platforms depends not on maximizing disclosure or curiosity alone, but on structuring interactions to preserve users’ involvement in shaping generated outputs. In doing so, they highlight how effective design on GCG platforms supports engagement that emerges from complementary human and GenAI contributions, rather than from automation alone.