Short answer
When initiating an open innovation process, carefully consider how the problem is framed, as this will shape the pool of solvers and the nature of the solutions you receive.
- Field
- Innovation & Design
- Source
- Data in Brief (2023)
- Method
- Data analysis of an open innovation challenge dataset
- Sample
- 16,249 potential solvers, 6,219 initiated solving, 147 unique solvers submitted 263 solutions.
- Evidence
- Strong effect
The way a technical problem is defined and presented in an open innovation context directly influences the range and caliber of solutions generated by participants. This innovation & design research insight is drawn from a 2023 study published in Data in Brief. Using Data analysis of an open innovation challenge dataset with 16,249 potential solvers, 6,219 initiated solving, 147 unique solvers submitted 263 solutions., researchers explored how this design variable affects real-world outcomes. The key design takeaway: When initiating an open innovation process, carefully consider how the problem is framed, as this will shape the pool of solvers and the nature of the solutions you receive.
Problem framing significantly impacts solution diversity and quality in open innovation challenges.
The way a technical problem is defined and presented in an open innovation context directly influences the range and caliber of solutions generated by participants.
Data in Brief · 2023
Key Findings
- 01The structure of the posed problem influences solver engagement and the diversity of solutions.
- 02Rich data can be collected on solver demographics, expertise, processes, and solution outcomes in open innovation.
- 03Analysis of all submitted solutions, not just winning ones, reveals a broader spectrum of design possibilities.
Application
Design takeaway
When initiating an open innovation process, carefully consider how the problem is framed, as this will shape the pool of solvers and the nature of the solutions you receive.
How to apply
When setting up a design challenge or seeking external innovation, spend time defining and refining the problem statement to guide participants towards desired outcomes.
Project actions
- 01When designing a user research study, think about how your questions might influence the responses you receive.
- 02Consider how to frame a design brief to encourage a wide range of ideas or specific types of solutions.
Method & Evidence
Variables
Strengths & Limitations
Strengths
- +Large dataset capturing a wide range of potential solvers and outcomes.
- +Rich data linking solver characteristics to solution attributes.
- +Analysis of all submitted solutions, not just winners.
Limitations
The specific tools and platforms used in the 'Astrobee Challenge' might not be available in all contexts. The types of problems posed were specific to robotics.
Reliability & validity
The study's validity is supported by the large sample size and the detailed data collection linking solver attributes to solution outcomes. Reliability would depend on the consistency of the data collection and analysis methods across the different contests.
Think critically
To what extent can problem framing be used to 'engineer' specific types of innovation, and what are the ethical considerations of such manipulation?
Design Principles
"Problem framing is a critical upstream variable in open innovation that dictates downstream solution diversity and quality."
Understanding this relationship allows design teams to strategically frame challenges to elicit specific types of innovation, whether it's novel approaches, diverse solutions, or highly feasible designs. This can optimize the outcomes of crowdsourced design efforts and accelerate product development.
What This Means for Your Design
How you ask a design question in a competition can change the kinds of answers you get and who tries to answer it.
How to use in your project
- 1.Reference this study when discussing how the framing of a design brief or problem statement influenced the outcomes of your design process or a comparative analysis of different approaches.
Add to My Project
Quick Cite
Paragraph starter
The 'Astrobee Challenge Series' data explainer (Szajnfarber et al., 2023) highlights that the architectural framing of a technical problem in an open innovation context is a significant determinant of solver engagement and solution attributes. This suggests that design practitioners should carefully consider how problem statements are formulated to elicit desired innovation outcomes, such as novelty or diversity.
Source
Data in Brief
Linking solver characteristics, solving processes and solution attributes: A data explainer for an open innovation generated robotic design dataset
journal · 2023
View sourceQuestions About This Research
- What does the research say about problem framing significantly impacts solution diversity and quality in open innovation challenges?
- When initiating an open innovation process, carefully consider how the problem is framed, as this will shape the pool of solvers and the nature of the solutions you receive. Evidence: Data in Brief (2023).
- Why does "Problem framing significantly impacts solution diversity and quality in open innovation challenges." matter for design?
- Understanding this relationship allows design teams to strategically frame challenges to elicit specific types of innovation, whether it's novel approaches, diverse solutions, or highly feasible designs. This can optimize the outcomes of crowdsourced design efforts and accelerate product development.
- How can designers apply this research?
- When initiating an open innovation process, carefully consider how the problem is framed, as this will shape the pool of solvers and the nature of the solutions you receive.
- What were the main findings?
- The structure of the posed problem influences solver engagement and the diversity of solutions.. Rich data can be collected on solver demographics, expertise, processes, and solution outcomes in open innovation.. Analysis of all submitted solutions, not just winning ones, reveals a broader spectrum of design possibilities.
- What research method was used?
- Data analysis of an open innovation challenge dataset with 16,249 potential solvers, 6,219 initiated solving, 147 unique solvers submitted 263 solutions..
- How strong is the evidence?
- Evidence strength is rated Strong effect, based on a 2023 journal from Data in Brief.
- What should I do differently in my next project?
- When setting up a design challenge or seeking external innovation, spend time defining and refining the problem statement to guide participants towards desired outcomes.
- What are the limitations?
- The specific context of a robotic design challenge might not generalize perfectly to all design domains. The dataset is specific to the 'Astrobee Challenge Series'.