Improving Crowd Innovation with Expert Facilitation

Chan, Joel, Steven Dang, and Steven P. Dow. “Improving Crowd Innovation with Expert Facilitation.”

Discussion Leader: Shiwani

Summary:

As the title suggests, this paper studies whether crowd innovation can be improved through expert facilitation. The authors created a new system which builds on the strategies used during face-to-face brainstorming and uses this to provide high-level “inspirations” to crowd-workers to improve their ideation process.

The first study compared the creativity levels of ideas generated by a “guided” crowd with ideas generated without any facilitation. The study showed that the ideators in the condition with expert facilitators generated more ideas, generated more creative ideas and exhibited more divergent thinking. The second study focused on the abilities of the facilitator and involved novice facilitators, keeping all other constraints same. Surprisingly, the facilitation seemed to negatively influence the ideation process.

Reflections:

This paper touches on and build on many things we have been talking about this semester. One of the key ideas behind <SystemName> is feedback, and its role in improving creativity.

I really liked the paper as a whole. Their approach of adapting expert facilitation strategies from face-to-face brainstorming to a crowd-sourcing application was quite novel and interesting. They took special efforts to make the feedback synchronous in order to “guide” the ideation, as with real-time brainstorming.

They make a strong case for the need for something such as <SystemName>. Their first point centers around the fact that the crowd-workers may be hampered because of inadequate feedback (as we have discussed before in the “feedback” papers). And the second point is that the existing systems were not built to scale. With <SystemName> the authors created a system to provide expert feedback while also ensuring it could scale by keeping the feedback at a higher level, rather than individualized.

The authors mention that a good system requires divergent thinking as well as convergence of ideas. The divergence prevents local minima in the ideation, and the convergence allows for growth of promising solutions into better ideas. This was an interesting way of looking at the creative process. And this situates their choice of  using a skilled facilitator as a tool.

The study was quite well-designed with clear use-cases. On one-hand they wished to study the effect of having a facilitator guide the ideation. And a second study captured the effect of the skill-level of the facilitator. The interface design was simplistic, both for the ideators and the facilitators. I liked the word-cloud idea for the facilitators- it is a neat way to present an overview/insight at such a scale. I also liked the “pull” model for inspiration, where the ideators were empowered to ask for inspiration whenever they felt the need for it as opposed to pre-determined check points. This deviates somewhat from the traditional brainstorming where experts choose when to intervene, but again, for the scale of the system and the fact that the feedback was not individualized, it makes sense.

 The authors do mention that their chosen use-case may limit the generalization of their findings, but the situational, social case was a good choice for an explorative study.

As with a previous paper we read, creativity was qualified by the authors, due to its subjective nature. Using novelty and value as evaluative aspects of creativity seems like a good approach, and I liked that the creativity score was a multiplication of these two to reflect their interactive effect.

Questions:

  1. A previous paper defined creativity in terms of novelty and practical (being of use to the user, and it being practically feasible to manufacture in today’s age), whereas this paper focused only on the “of value” aspect in addition to novelty. Do you think either definition is better than the other?
  2. The paper brings forth the interesting notion that “just being watched” is not sufficient to improve worker output. Do you think this is specific to creativity, and the nature of the feedback the workers received?
  3. For the purpose of scale, the authors gave “inspirations” as opposed to individualized feedback (like Shepherd did). Do you think the more granular, personalized feedback would be helpful in addition to this? In fact, would you consider the inspirations as “feedback”?

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