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Open2012 measuring-what-matters-unger
1. Measuring what matters
in teaching innovation
March 2012
Darian Unger, Ph.D.
Associate Professor
Howard University School of Business
dwunger@howard.edu
202-806-1656
2. Research Topic
• Purpose
– To better assess whether students
understand and can apply key
innovation concepts and skills
• Methodology
– Comparison of course objectives to
performance on actual innovation and
commercialization projects
– Feedback from inventors, professors,
and innovation-oriented MBA students
3. Research Topic
• Literature review
– Technological innovation as
“introducing a new device, method, or
material for application to commercial
or practical objectives (Schilling, 2010)
– Assessment as the “new reality” at
colleges and universities (Pelimeni and
Iorgulescu, 2009)
• Assessments are helpful in facilitating
replicable models, but should not be an
exclusive focus because of several
difficulties (Klein, 2005; Schmoker, 2002)
4. Research Topic
• Findings
– Assessing innovation education is harder
than it looks (and it never looked easy)
– Traditional assessments are insufficient
– Most promising assessments include
• evaluations of project based innovation and
commercialization plans
• innovator-based evaluations of utility
5. Drivers for this work
• Necessity may be the mother of
invention, but in this case…
• Inventions themselves drove the need
for an innovation & tech. mgmt.
course
6. Drivers for this work
• Development of new courses on
innovation management, technology
strategy, and sustainable business at
Howard University
7. Key course skills and lessons:
commercializing innovation
• Creating value through innovation
• Technology & market adoption S-curves
• Categories and patterns of innovation
• (Sources of) creativity
• New product design and development
• Tech strategies to protect & exploit
innovation
– Dominant designs, patents, and licensing
– 1st and 2nd mover advantages
8. …along with other skills
(and traits)
• Ability to communicate
Evaluated,
– Writing (business plans) but also
– Group dynamics common to
other
– Listening (customer needs) subjects
– Speaking (persuasion)
• Tenacity
– Edison’s “stick-to-it-iveness” Not
evaluated
• Propensity for risk
9. Assessable methods or
demonstrations
1) Concept identification
2) Recognition of a historical parallel or
previous application
3) Evaluation of performance on specific
exercises or problems
4) Actual project synthesis – case
10. Ability to assess
Value Type
S- (Sources New Prod.
of of
Curves of) Creativity Design/Dev.
innov innov
Concept ID
Historic or
prior apps.
Exercise/
Problem
Project
synthesis
11. Example
• Technology and adoption S-curves
– Objective assessment of how they can be
identified and distinguished (Exams)
– Many prior examples in different industries
available (Discussions)
– Gradable time-series problems and numerical
exercises
– But since S-curves are often descriptive
rather than prescriptive, S-curve skills or
knowledge are more difficult to assess on
actual, ongoing projects
13. How to assess these
individually?
• Hard quantities
– #’s of patents and licenses
– % scores on exams
• Softer quantitative measures
– Innovator assessments of student
assistance and insight
• Quantified subjective measures
– Student evaluations
• Creativity and tenacity are subjective,
unquantified, and unassessed
14. Findings and Implications
• Findings
– Assessing innovation education is difficult,
because traditional assessments are
insufficient
– Categorization helps (i.e. between skills and
traits)
– Multiple forms of assessment necessary,
including
• evaluations of project based innovation and
commercialization plans
• innovator-based evaluations of utility
16. Innovation models
Sales
Time
Adoption
Time
S-curve and adoption models (1965 and 1986)
17. Innovation models
Creates new markets and/or breaks down
existing market linkages
Niche Architectural
creation innovation
Reinforces existing Makes existing
competence within competence within
technology technology obsolete
Regular Revolutionary
innovation Innovation
Reinforces existing market linkages
Abernathy and Clark model (1985)
18. Innovation models
Great impacts on links
between components
Architectural Radical
innovation innovation
Little impact on Great impact on
components components
Incremental Modular
innovation Innovation
Little impact on links
between components
Henderson model (1990)
