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Konstantin Bork:

Investigating Idea Quality in Crowdsourced Ideation when using Exemplars Based on People's Curiosity

Requirements

  • User Studies
  • Data Analysis
  • Web Applications
Academic Advisor
Discipline
Crowdsourcing, Collaborative Ideation, Electronic Brainstorming
Degree
Master of Science (M.Sc.)

Contents

Context

Crowdsourcing has a big potential as an innovation method (1). Several platforms benefit from the idea generation of the crowd to create innovative products or solve problems. Within the Ideas to Market project, the crowd is used to create new ideas based on new research results.

Problem

Participants of crowdsourcing ideation often create simple and repetitive ideas when they do not get any help (2). Research shows that showing ideas of other users has in general positive effects on idea generation (3). The timing of such inspirations (4) and the influence of the semantical distance (5) have been researched already. The results point towards the benefits of personalized inspirations in crowdsourced ideation. One way to personalize inspiration is to choose inspiration from people’s curiosity using the Wundt Curve. This approach already showed a positive effect in a classic recommender system (6).

Objectives

It is the goal of this thesis to answer the question if personalized inspirations using the Wundt Curve can improve the idea quality in crowdsourced ideation. To answer this question, a system should be built which selects inspirations using the Wundt Curve and a study should be conducted to test the system.

Possible Procedure

  • Build an application which can select inspirations using the Wundt Curve and randomly
  • Setup and run a study using the application
  • Analyze the data from the study to answer the research question

References

(1) A. Kittur. Crowdsourcing, collaboration and creativity. XRDS: crossroads, the ACM magazine for students, 17(2):22-26, 2010.

(2) P. Siangliulue, J. Chan, K. C. Arnold, B. Huber, S. P. Dow, and K. Z. Gajos. Largescale collaborative innovation: Challenges, visions and approaches. In 2016 AAAI Spring Symposium Series, 2016.

(3) B. A. Nijstad, W. Stroebe, and H. F. Lodewijkx. Cognitive stimulation and interference in groups: Exposure effects in an idea generation task. Journal of experimental social psychology, 38(6):535-544, 2002.

(4) P. Siangliulue, J. Chan, K. Z. Gajos, and S. P. Dow. Providing timely examples improves the quantity and quality of generated ideas. In Proceedings of the 2015 ACM SIGCHI Conference on Creativity and Cognition, pages 83-92. ACM, 2015.

(5) J. Chan, P. Siangliulue, D. Qori McDonald, R. Liu, R. Moradinezhad, S. Aman, E. T. Solovey, K. Z. Gajos, and S. P. Dow. Semantically far inspirations considered harmful? accounting for cognitive states in collaborative ideation. In Proceedings of the 2017 ACM SIGCHI Conference on Creativity and Cognition, pages 93-105, 2017.

(6)  F. Abbas and X. Niu. One size does not fit all: Modeling users’ personal curiosity in recommender systems. arXiv preprint arXiv:1907.00119, 2019