Redistributing Leadership in Online Creative Collaboration

Online creative collaboration is complex, and leaders frequently become overwhelmed, causing their projects to fail. We introduce Pipeline, a collaboration tool designed to ease the burden on leaders, and describe how Pipeline helped redistribute leadership in a successful 28-person artistic collaboration.

For the Holiday Flood, 28 artists from around the world used Pipeline to create 24 artworks and release them in the days leading up to Christmas.

Leadership is important in many types of online creative collaboration, from writing encyclopedias to developing software to proving mathematical theorems. In previous work, we studied leaders of online animation projects, called collabs, organized on websites like Newgrounds. These leaders take on a huge variety of responsibilities, and many become desperately overwhelmed. They also struggle with poor technological support, relying on discussion forums designed for conversation, not complex multimedia collaboration. To manage these challenges, leaders attempt less ambitious projects and embrace top-down leadership styles. Still, less than 20% of collabs result in a finished product, like a movie, video game, or artwork.

Our goal was to encourage complex, creative, and successful collabs by designing a technology to ease the burden on leaders. Two theories guided our approach:

  • Distributed cognition holds that cognitive processes can be distributed across people, objects, and time.
  • Distributed leadership suggests that leadership roles can be decoupled from leadership behaviors, which could be performed by any member of a group.

We integrated these theories and used them to design a system which helps leaders distribute their efforts across both people and technology.

The result is Pipeline, a free, open-source collaboration tool. Pipeline enables redistributed leadership through the notion of “trust.” Projects have two types of members:

  • Trusted members, who can create and lead tasks (among other privileges)
  • Regular members, whose privileges are limited to working on existing tasks

At one extreme, creators can replicate the old “benevolent dictator” model popular on Newgrounds by trusting only themselves. At the other extreme, creators can trust every member of their projects, creating an open, wiki-like environment. Most Pipeline users will opt for something in between, making real-time adjustments as needed.

The Pipeline tasks system. In this example, Spagneti posts a new version of a work-in-progress, and RAMATSU provides feedback. The right column includes information about the task, links to other versions of this work, and a recent activity feed.

We launched Pipeline in 2011 and have seen users organize a variety of creative projects, includi moviesvideo games, and even a global scavenger hunt. Our paper focuses on one case study, an artistic collaboration called Holiday Flood. Over six weeks, 28 artists from at least 12 countries used Pipeline to create a digital Advent calendar with 24 original Christmas-themed artworks, along with an interactive Flash gallery. Every aspect of the project was completed on schedule, and the Newgrounds community responded with high ratings and a staff award.

The main menu of the interactive Flash gallery for Operation Holiday Flood. Clicking any of the square thumbnails reveals one of 24 Christmas-themed artworks.

Our research suggests that Pipeline contributed to Holiday Flood’s success in several key ways. It emboldened the project creators to attempt something more complex and ambitious than anything they had tried previously. Pipeline also helped members perform leadership behaviors previously reserved for leaders, like planning, problem solving, and providing feedback.

For more, see our full paper, Redistributing Leadership in Online Creative Collaboration.
Kurt Luther, Carnegie Mellon University
Casey FieslerGeorgia Institute of Technology
Amy Bruckman, Georgia Institute of Technology

This entry was posted in CSCW 2013 by Kurt Luther. Bookmark the permalink.

About Kurt Luther

Kurt Luther is a postdoctoral fellow in the Social Computing Lab at Carnegie Mellon University. His research focuses on how social computing systems can support creative collaboration. He recently completed his Ph.D. in Human-Centered Computing at Georgia Tech.

9 thoughts on “Redistributing Leadership in Online Creative Collaboration

  1. Very interesting work, Kurt. Quickly looking through the paper, I think my favorite examples of the art were collaborations between artist on a single submission (such as the physical-digital art one in Fig 3). Did these artists know each other before they started? Had they previously worked together?

    • Hi Lana, thank you! We too were intrigued by the single-artwork collaborations. We didn’t find any evidence that the artists involved in partnerships knew each other beforehand. For the Season’s Greeting poster (created by three artists working in sequence, a process we called “collaborative iteration”), two of the artists, Renae and Robert, were well-known Newgrounds moderators who also co-founded the Holiday Flood.

  2. I love this paper (all 16 pages of it!!!!) It jives with my personal philosophy of crowdsourcing and that is that ANYTHING can be broken down. It’s cool to see that even something abstract like “leadership” can be broken into parts and distributed across people, given the right tools.

    • Hi Lydia, thanks for making it through all 16 pages! As you know, brevity is not my strong suit. ;-) But at least we include some fun images.

      Distributed leadership is a fascinating theory and we were lucky to draw on some very useful previous work. DL originates in management and education research, and Haiyi Zhu brought it to the CHI community with a couple of great papers about Wikipedia. With this paper, we wanted to explore how DL could inform the design of a new social computing system.

  3. Agreed, this is terrific work! It’s been a goal of many folks in the crowdsourcing community to figure out a framework that can reliably crowdsource “anything” (ie, complex open-ended projects), and it looks like Pipeline has pulled it off, to an extent.

    Part of the reason for the project’s success seems to be that the amount of decomposition was limited, and the participants were trusted members of the community working on macro-activities rather than microworkers (as some of the crowd literature has examined in the past).

    Does Pipeline’s model have anything to say about activities where hundreds of contributors might contribute in smaller components across more complex decompositions — would it still succeed? I think this model may still work, as seen in Github and Wikipedia.

    • Hi Anand, thanks for your comments! You raise two major issues that we wrestled with while designing Pipeline. The first was how general the system should be–earlier versions were very specific to animation, while the current Pipeline tries to be more widely applicable. We’ve found that Pipeline tends to be most helpful in larger collaborations (for small groups, existing tools like Skype/Dropbox often do the job) and for projects with a significant multimedia component.

      The second issue relates to your question about the granularity of contributions. I think Yochai Benkler was really onto something when he wrote that peer production thrives when tasks aren’t just granular, but heterogeneously granular. We don’t just want to offer microtasks, but microtasks and medium-sized tasks and even large tasks. This helps workers self-select for the tasks most appropriate to their expertise and available time, and avoids unnecessary decomposition/integration–two results that decrease transaction costs and make crowdsourcing efficient.

      The projects you mention, Wikipedia and open-source software, typically do a great job of offering this heterogeneous granularity. I can fix a typo or report a bug, or I can write an entire article or module. In my opinion, both Pipeline and most crowdsourcing platforms could do better in this area. Pipeline allowed for significant decomposition by Newgrounds standards (most animations have one contributor; Holiday Flood had nearly 30), and even some emergent decomposition (e.g. partnerships), but the more fine-grained the task, the less support Pipeline provided. On the other hand, crowdsourcing platforms (and requesters) rarely give workers with more time/expertise the option to do more complex, fulfilling tasks.

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