Blue Sky 3 - Poster Gallery

1. • Higher amount of product than process indicators, higher amount of
quantitative than qualitative indicators, nearly the same for process
indicators, concept phase and market launch show highest amount
of indicators (1.)
• Increasing interest in the innovation indicator field (2.)
• Indicators are often named as “indicator” although they are not actual
indicators but rather factors
• Regression analysis is the most used statistical and econometric method
to study innovation
• Availability might be an explanation for why some indicators are applied more frequently than others: accessible data like financial or patent
data is used more often than information that is difficult to obtain, the further along the process of innovation,
the more indicators are presented in relevant publications
• The extensive literature review of innovation indicators allows policy makers and companies to improve decision processes
Innovation indicators throughout the innovation process:
An extensive literature analysis
Relevance of the study
Theoretical contribution
Methodology
Conclusion, implications and future research
Results
Framework to categorize indicators
Marisa Dziallas, Chair of Innovation Economics, Technical University of Berlin, Germany
marisa.dziallas@volkswagen.de
Environment (41 = 4.1%)
Innovation culture (83 = 8.3%)
Strategy
(37 =
3.7%)
Competence and
knowledge
(84 = 8.5%)
Structure
(109 = 11.03%)
Internationalization (12 = 1.2%)
Resourceanddevelopment
activitiesandinput
(128=12.96%)
Process
(103 =
10.43%)
Product
(173 =
17.51%)
Market
(116 =
11.74%)
Financial performance (70 = 7.09%)
100% = 988
Network
(32 = 3.24%)
0% 5% 10% 15% 20% 25% 30% 35%
Project
definition
Project concept
Validation
phase
Production
Market launch
Product innovation indicators Indicators for innovation process
100% = 253
5.93%
7.91%
30.04%
2,77%
2%
8%
31.62%
12.25%
How can we measure innovation and determine which indicators best
estimate future success of innovative products?
• These questions are continuously asked by policy makers, managers and
academics (e.g. Becheikh et al., 2006; Dewangan & Godse, 2014)
• Need to measure performance of innovation projects: High development
investments for innovation projects (Dewangan & Godse, 2014), success
rate of new product developments is generally below 25% (Evanschitzky
et al., 2012)
• Common indicators for measurements are missing (Bloch & Bugge,
2013), it is challenging to measure new evolving ideas due to the fact
that it is not clear how and what to measure (Kirchhoff et al., 2013)
• Immense growth in the use of science, technology and innovation
indicators (Freeman & Soete, 2009) reflects the demand for answers to
these questions
• Ex-ante evaluation, i.e. foresight assessment of innovation projects
in order to take decisions, is particularly challenging
1. An extensive overview of innovation indicators and publication
development over time
2. Presenting the complete innovation process with indicators and a
forward-looking approach
• Keyword search: Science Direct, Web of Science, Scopus
• Peer-reviewed journal publications between 1980-2016
• Indicators analyzed in terms of qualitative and quantitative nature
(Freudenberg, 2003), direct and indirect influence on innovation success
and phase of innovation process based on an independent three-stage
evaluation process by experts working in the innovation
management field
Becheikh, N., Landry, R. & Amara, N. 2006. Lessons from innovation empirical studies in the manufacturing sector: A syste-
matic review of the literature from 1993–2003. Technovation 26(5-6): 644-664.
Bloch, C. & Bugge, M. M. 2013. Public sector innovation—From theory to measurement. Structural Change and Economic
Dynamics 27: 133-145.
Dewangan, V. & Godse, M. 2014. Towards a holistic enterprise innovation performance measurement system. Technovation
34(9): 536-545.
Freudenberg, M. 2003. Composite Indicators of Country Performance: A Critical Assessment. OECD Science Technology and
Industry Working Papers 16, OECD Publishing.
Freeman, C. & Soete, L. 2009. Developing science, technology and innovation indicators: What we can learn from the past.
Research Policy 38(4): 583-589.
Kirchhoff, B.A., Linton, J.D., Walsh, S.T., 2013. Neo-Marshellian Equilibrium versus Schumpeterian Creative Destruction:
Its Impact on Business Research and Economic Policy.J.SmallBus.Manage.51(2),159–166.
Evanschitzky, H., Eisend, M., Cat alone, R. & Jiang, Y. 2012. Success Factors of Product Innovation: An Updated Me-
ta-Analysis. Journal of Product Innovation Managenet 29(S1): 21-37.
1. Indicators per stage of innovation process
2. Chronological development of publications
0
5
10
15
20
25
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Publications per year
Publications per year Linear (Publications per year)
Selected references