A better understanding of how information in networks is reused or mixed, has the potential to significantly contribute to the way value is exchanged under a market- or commons-based paradigm. Data as collaborative commons, distributed under creative commons licenses, can generate novel business models and significantly spur the continuing development of the knowledge society. However, looking at data reuse in a large 3d-printing community, we show that the remixing of existing 3d models is substantially influenced by bots, customizers and self-referential designs. Linking these phenomena to a more fine-grained understanding of the process and product dimensions of innovations, we conclude that remix- ing patterns cannot be taken as direct indicators of innovative behavior on sharing platforms. A further exploration of remixing networks in terms of their topological characteristics is suggested as a way forward. For the empirical underpinning of our arguments, we analyzed 893,383 three-dimensional designs shared by 193,254 members.

1. Not Every Remix is an Innovation:
A Network Perspective
on the 3D-Printing Community
Christian Voigt (ZSI, Vienna)

2. The underlying paper can be
found at:
https://www.researchgate.net/p
ublication/325089934_Not_Ever
y_Remix_is_an_Innovation_A_N
etwork_Perspective_on_the_3D
-Printing_Community
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3. Relevance / Motivation
• Sharing is a fundamental part of the maker ethos
• Data, designs and software are often referred to as ‘digital commons’
• Related to Rifkin’s (2014) ‘Zero Marginal Cost Society’
... or simply a form of prosumerism
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4. Platform Context
Thingiverse, as of November 2017
• 893,383 publicly accessible design pages
provided by 193,254 community members
• 499,750 connections between designs
• Designs included formats from Blender,
OpenSCAD, Fusion360 or plain STL
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5. Research Questions
• Short term:
Does remixing (co-creating) on Thingiverse lead to innovation?
• Long term:
Is there a pedagogical value in remixing?
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6. Concepts and Method
• Innovation:
a form of disruption, discontinuity
through changes in product features or value creating processes
• Network perspectives:
Authors (e.g. massive remixers, customizers, occasional tinkerers)
Things (e.g. designs reusing previous designs)
• Keychain effect (next slide)
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7. Keychain Effect
• Few remixes resulted in a
substantially novel product which got
remixed itself
• Shows 7,376 remixes versus 6
designs who went beyond
customizing
• Keychain effect (Blikstein 2013)
High product value &
Low investment in learning
• Number of designs tripled after
customizers were introduced
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8. Network Characteristics
• Long-tailed distribution of remixes:
A few attractive designs are responsible for
most of the remixing activities, ‘preferential
attachment’)
• Fits best a log-normal distribution
(see Probability Density Function)
• Network of 403,096 sparsely connected
components (sub-groups of designs)
• Mean clustering coefficient: 0.0057
(e,g. Star has coefficient zero)
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PDF(n)

9. Innovation chains: visualizing
mathematics with 3d printing
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(Segerman 2016)

10. Summary and Outlook
So, where again is the innovation ... ?
• key-chain effected turns ‘number of remixes’ into a popularity indicator,
remixed remixes can be a proxy
• Additional analysis on tag or content level needed, to identify
multidisciplinary nature of innovation
Outlook:
• Differentiate between structure (branching behaviour) and content (diffs
between versions)
• Learn from existing (recommendable) seeds for innovation
(i.e. how to reuse designs?)
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