Disentangling the collective motivations for user innovation in a 3D printing community

Abstract

Online innovation communities encourage innovators to build upon others' prior work (i.e., remixing). This generative user innovation necessitates new theorization to better understand the interplay between characteristics of the source innovation and the community's collective motivation. Motivation is a heightened concern in online communities where contributors often select which problems warrant their effort. Two studies improve our understanding of how the community's motivations compel remixing and impact two aspects of the depth of these remixes (improvement and differentness). First, hypotheses regarding the community's collective remix response are developed and tested. After this, an exploratory (fsQCA) study seeks out configurations of these motivations that consistently result in improved and different remixes. Using data from thingiverse.com, we show that established motivations for user innovation (enjoyment, learning, use-value) motivate remixing, but learning and use-value's effects are moderated by source innovation quality. Learning and use-value are only impactful for high quality source objects. We also demonstrate that originality of the source object has an inverted-U relationship with remixing; innovations need to be novel, but not drastically different from expectations to generate a remix response from the community.