Improving Makers’ Remix Contribution Behavior in the Open Innovation Communities——An Example of Thingiverse

Abstract

Using existing innovations as raw materials or inspiration to promote further innovation is becoming a new way of knowledge reuse and contribution in open innovation community (OIC). Study on makers' remix contribution behavior in the open innovation communities will help to promote the development of community innovation based on remix. Based on the theories of social capital, pro-social behavior and psychological ownership, this paper studies how social network relationship, norm of reciprocity, self-rated expertise, peer recognization about remix and psychological ownership influence makers' remix contribution behavior in OIC. We conduct a negative binominal regression by collecting a large-scale data set mining from Thingiverse, the typical open innovation community. Social network relationship, norm of reciprocity, peer recognization about remix and psychological ownership have a significant positive impact on makers' remix contribution behavior, while the positive influence of self-rated expertise on remix contribution behavior exists only among novice and intermediate makers.

Understanding Nonlinear Collaboration between Human and AI Agents: A Co-design Framework for Creative Design

“…We bridge this gap by formulating a nonlinear human-AI co-design framework featuring flexible communication and selective choices. Following the nonlinear framework, Opti-Muse enables parallel iteration [21] and remixing [7,46,58,63]. Similar to Rezwana's finding in Penpal [53], we observe enhanced user engagement in OptiMuse, where AI was perceived as more collaborative than a mere tool.…”

“…Many design tools allow designers to merge their human collaborators' ideas into their creative output iteratively [5,12,39]. Also, there has been a surge in research interest in exploring methods to foster innovative remixing [7,46,58,63]. For instance, through an empirical study on the Thingiverse (a 3D printing community), Tan et al [58] revealed the contradictory impact of knowledge endowment heterogeneity on users' quality and quantity of remixing.…”

“…Designers derive inspiration when navigating among these design choices. Navigating, summarizing, and discarding among the design choices are central to the nonlinear process of collective creativity [7,29,46,58,63] In alignment with the fourth and fifth design requirements addressing the cons of human-human collaboration, two required actions of AI agents are notable for outputs: A9 Clarify operational strategies. (DR4) A10 Preview the visual effect of operational strategies.…”