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Linking solver characteristics, solving processes and solution attributes: A data explainer for an open innovation

Zoe Szajnfarber¹, Anthony Hennig¹, Suparna Mukherjee¹

¹The George Washington University, 800 22 St NW, Washington DC 20052, USA.

|September 27, 2023

Summary

This study explored how problem formulation affects solution quality in open innovation challenges. It found that understanding solver characteristics is key to generating diverse and feasible technical solutions.

Keywords:

Crowdsourcing Design data Feasibility Novelty Robotics Solver attributes

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Area of Science:

Engineering and Technology
Social Sciences
Computer Science

Background:

Open innovation platforms facilitate problem-solving by a diverse range of individuals.
Understanding the link between problem formulation, solver characteristics, and solution quality is crucial for effective innovation.

Purpose of the Study:

To characterize the relationship between technical problem formulation, solver attributes, and the quality of generated solutions.
To create a unique dataset capturing solver demographics, expertise, and solution outcomes in a large-scale field experiment.

Main Methods:

Conducted the "Astrobee Challenge Series," a 17-contest field experiment over 12 months.
Tracked 16,249 potential solvers, with 6,219 initiating and 147 submitting 263 solutions.
Collected rich data on solver demographics, expertise, problem-solving processes, and solution outcomes.

Main Results:

The dataset uniquely links solver data to their problem-solving activities and solution quality.
Analysis includes not only winning designs but also all submitted solutions, enabling study of solution variety.
The data allows for examination of novelty, variety, and feasibility of solutions in relation to solver expertise and diversity.

Conclusions:

The Astrobee Challenge Series generated a unique dataset for studying the dynamics of open innovation and problem-solving.
This data is valuable for researchers investigating the impact of problem design on solution diversity and quality.
Findings can inform strategies for optimizing crowdsourcing and co-creation processes in technical fields.