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Demand and Design Demand and Design Choices in an Open Choices in an Open Innovation system:Innovation system:
The case for CoPS and The case for CoPS and B2BB2BVirginia AchaCoPS Centre, SPRU & CENTRIM (Us of Sussex &Brighton)
Presentation to the CIS User GroupPresentation to the CIS User GroupDTI Innovation Economics ConferenceDTI Innovation Economics ConferenceNovember 17, 2006November 17, 2006
Work in ProgressWork in Progress
Project Team
Centre for Complex Products and Systems (CoPS)
- Virginia Acha- Mike Hobday- Howard Rush
CENTRIM, University of Brighton
Overview
Demand and Design Choices in an Open Innovation System Research Aims Project Methodology
Descriptive findings - B2B and CoPS Characteristics in the UK population Innovative profile
Open Innovation in CIS4 Through the B2B and CoPS lenses
Drivers for Open Innovation patterns Role of design and market dynamics Models and preliminary results
Limitations and Conclusions
Demand and Design Choices in an Open Innovation system Research Aims
Open innovation models used to describe increasingly complex and distributed patterns of innovation (Chesbrough, 2003; von Hippel and von Krogh, 2003; Coombs, Harvey and Tether, 2001)
Pattern that has been core to the development of Complex Products and Systems (CoPS)
o Emergence of systems integration and integrated solutions in response
o Core role of design and customer engagement in these responses
Complex Products and Systems (CoPS) High value, engineering-intensive customised capital goods Produced in one-off projects or small batches (Hobday, 1998) Decade of empirical, largely case study research (ESRC Centre,
www.cops.ac.uk) Economic impact and classification (Acha et al, 2004)
Extend to B2B CoPS as a leading sector of B2B innovation Can open innovation patterns be found in both
o CoPSo B2B
Drivers for Open Innovation Characteristics of CoPS innovation and production patterns
lend themselves to open structures Design, customer engagement, undefined markets as drivers
Test these questions using the CIS4 Define B2B (Q3) Define CoPS
o Classification system (Acha et al, 2004)o CoPS-based Services classification also done in previous study,
now applicable
Demand and Design Choices in an Open Innovation system
Overview
Demand and Design Choices in an Open Innovation System Research Aims Project Methodology
Descriptive findings - B2B and CoPS Characteristics in the UK population Innovative profile
Open Innovation in CIS4 Through the B2B and CoPS lenses
Drivers for Open Innovation patterns Role of design and market dynamics Models and preliminary results
Limitations and Conclusions
11721
4723
Key Descriptives: B2B
Firms by main customers - B2B* 71% of all respondents
B2B or B2G
B2C188
152
60
BothB2B & B2G
Both B2B & B2C All
Question 3 is open to some interpretation, as some firms
recorded mixed markets.
Key Descriptives: CoPS are B2B
By definition, CoPS are B2B, aren’t they? Based on established classification (Industrial and Corporate Change,
2004)
Addition of CoPS-based services CoPS classified respondents cross-checked by main
customers 356 firms identified as CoPS but with B2C markets, of which
o 1717 also have B2B customers, ando 1111 have B2G customers
Seven SIC codes account for 78% of this crossovero In services and constructiono 3 dropped from the CoPS filter - ambiguouso 4 retained: queried share small in comparison, clear CoPS
relevance (e.g. telecommunications, engineering consultancy)
14100
986
1359
2345
Key Descriptives: CoPS
Manufacturing & Construction
ServicesCoPS
CoPS firms represent 14% of the survey population.
Patterns in Innovation
0.13
0.22
0.27
0.30
0.25
0.14
0.20
0.29
0.18
0.37
0.20
0.33
0.40
0.36
0.43
0.14
0.23
0.25
0.22
0.28
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
B2C B2B CoPS all CoPS Production CoPS Services
Shar
e of
Pop
ulat
ion
Goods Innov Services Innov Product Innov Process Innovator
B2B - greater innovators
CoPS even more so
Product, Process Innovation in CoPS Services-Integrated Solutions
CoPS Services leading in service innovations
0.53
0.60
0.660.68
0.65
0.90
0.850.82
0.78
0.85
0.26
0.310.34 0.33 0.34
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
B2C B2B CoPS all CoPS Production CoPS Services
Sh
are
of
Pro
du
ct o
r P
roce
ss I
nn
ovato
rs
Product New to Market Product New to Enterprise Process New to Industry
Degree of novelty in Innovation
Greater novelty in B2B and CoPS
Overview
Demand and Design Choices in an Open Innovation System Research Aims Project Methodology
Descriptive findings - B2B and CoPS Characteristics in the UK population Innovative profile
Open Innovation in CIS4 Through the B2B and CoPS lenses
Drivers for Open Innovation patterns Role of design and market dynamics Models and preliminary results
Limitations and Conclusions
Relative openness in Product Innovation
B2C relatively more open to external sources
CoPS more collaborative
But 2/3rds still mainly done in-house
0.61
0.69 0.69 0.69 0.69
0.22 0.22 0.24 0.24 0.23
0.16
0.08 0.07 0.07 0.08
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
B2C B2B CoPS all CoPSProduction
CoPSServices
Sh
are
of
Pro
du
ct I
nn
ovato
rs
Internal Development (Share of Total)Collaborative Development (Share of Total)External Development (Share of Total)
0.52
0.60
0.66
0.61
0.68
0.34
0.280.26
0.33
0.22
0.140.12
0.08
0.05
0.10
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
B2C B2B CoPS all CoPS Production CoPS Services
Sh
are
of
Pro
cess I
nn
ovato
rs
Internal Development Collaborative Development External Development
Relative openness in Process Innovation
AgainB2C relatively more open to external sources
CoPS do more in-house
Openness in Innovation Activities
0.23
0.35
0.43
0.39
0.45
0.10
0.13
0.17 0.170.17
0.12
0.15
0.19
0.16
0.21
0.14
0.20
0.26
0.29
0.23
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
B2C B2B CoPS all CoPS Production CoPS Services
Sh
are
of
po
pu
lati
on
Intramural R&D Extramural R&D Acquisition of external knowledge Design
** significance, except for CoPS production, Acquisition of External Knowledge
B2B more open in unlinked innovation activities
CoPS more open and do more in-house
Design features prominently
Breadth and Depth of Information Sources
4.27
5.86
6.39
6.04
6.65
2.90
3.57
4.13
3.78
4.38
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
B2C B2B CoPS all CoPS Production CoPS Services
Avera
ge n
um
ber
of
so
urc
es
All sources Breadth & Depth
** significance for all Laursen & Salter (2006)
All sources - count
Breadth & Depth - count where Medium or High
B2B and CoPS more intensive users of sources of information
Overview
Demand and Design Choices in an Open Innovation System Research Aims Project Methodology
Descriptive findings - B2B and CoPS Characteristics in the UK population Innovative profile
Open Innovation in CIS4 Through the B2B and CoPS lenses
Drivers for Open Innovation patterns Role of design and market dynamics Models and preliminary results
Limitations and Conclusions
What leads to Open Innovation? Descriptive evidence shows B2B and CoPS firms as
relatively open innovation systems Collaboration Drawing externally for innovation
o More for CoPS than B2B for innovationso Both in innovation activities
More intensive users of informationo Breadth and Depth (Laursen & Salter, 2006)
What drives the process to open innovation? Chesbrough (2003) reflects upon
o Markets for ideas and technologyo Availability and mobility of human capitalo Distributed and enhanced capability across the value chain
What leads to Open Innovation? Tendency to open innovation patterns may also be related to:
Nature of innovation in the firm Market dynamics for the firm
Nature of innovation in the firm Partitioning of the innovation process Design as a translator, bridge across stages, sectors,
specialisationso Role of design (Tether, DTI Presentation, 2006; Whyte, Bessant
and Neely, 2005)o In partitioning (von Hippel, 1990)
Market dynamics for the firm Uncertainty, unknown markets Engagement with the consumer
Testing for Open Characteristics Openness
How to construct a variable? Characteristics of openness across questionnaire
o How innovations are developed (Q6, Q10) Innovation through collaboration Innovation through others
o Innovation activities (Q13) and values (Q14) Acquisition of R&D or Acquisition of External Knowledge
o Sources of information (Q16) Breadth and depth
o Co-operation (Q17, Q18) Correlations show interesting distinctions
Open Activities - selected as a proxy
Correlations
Open activities
Open Sources Collaborate
Innovation through
Collaboratio
Innovation through others
Open activities
Pearson Correlation 1.00 0.36 0.30 0.25 0.07Sig. (2-tailed) . 0.00 0.00 0.00 0.00N 16445.00 15698.00 15753.00 16445.00 16445.00
Open Sources
Pearson Correlation 0.36 1.00 0.29 0.21 0.08Sig. (2-tailed) 0.00. 0.00 0.00 0.00N 15698.00 15698.00 15692.00 15698.00 15698.00
CollaboratePearson Correlation 0.30 0.29 1.00 0.27 0.06Sig. (2-tailed) 0.00 0.00. 0.00 0.00N 15753.00 15692.00 15753.00 15753.00 15753.00
Innovation through
Collaboration
Pearson Correlation 0.25 0.21 0.27 1.00 -0.01Sig. (2-tailed) 0.00 0.00 0.00. 0.14N 16445.00 15698.00 15753.00 16445.00 16445.00
Innovation through
othersPearson Correlation 0.07 0.08 0.06 -0.01 1.00Sig. (2-tailed) 0.00 0.00 0.00 0.14.N 16445.00 15698.00 15753.00 16445.00 16445.00
**Correlation is significant at the 0.01 level (2-tailed).
Measures of Openness
Testing the sources: Design
Openness = ƒ(importance of design) Proxies
Design activities (Q13) Registration of design (Q21) Complexity of design (Q21)
Predicted positive relationships with open activities
Logistic regression 15,699 used in analysis Size (employment), B2B, CoPS
Design model results
Proxies are positively related, as predicted. Design activities and Design complexity influential and positively
correlated with open activities. Registration less influential
CoPS and size weakly positive; B2B weakly negative
B SE Sig. Exp(B)Design activities 1.511 0.049 0.000 4.53Design Registration 0.000
Low 0.188 0.075 0.012 1.207Medium 0.289 0.08 0.000 1.335
High 0.382 0.086 0.000 1.466Complexity of Design 0.000
Low 0.84 0.065 0.000 2.315Medium 0.891 0.069 0.000 2.439
High 1.113 0.089 0.000 3.042Size (SME, Large) 0.299 0.051 0.000 1.349B2B -0.074 0.052 0.155 0.929CoPS all 0.168 0.06 0.005 1.182Constant -2.168 0.047 0.000 0.114Note: Pseudo R2=.23 (Nagelkerke). Model Chi-square (1)= 2531, p<.000
Testing the sources: Market Dynamics Openness = ƒ(Importance of customer
engagement, ‘cloudy’ markets) Proxies
Clients as a source of information (Q16) Clients as collaborators (Q18) Lack of information on markets (Q19) Uncertain demand (Q19)
Predicted positive relationships with open activities
Logistic regression 15,684 used in analysis Size (employment), B2B, CoPS
Market Dynamics model results
Proxies are positively related, as predicted.
Client engagement positive and influential
‘Cloudy markets’ positive but only weakly influential
CoPS and size weakly positive; B2B weakly negative
B SE Sig. Exp(B)Clients as a Source 0.000
Low 1.337 0.095 0.000 3.809Medium 1.524 0.086 0.000 4.59
High 1.714 0.085 0.000 5.552Client Collaborators 1.085 0.056 0.000 2.958Uncertain Demand
Low 0.45 0.066 0.000 1.569Medium 0.468 0.069 0.000 1.596
High 0.365 0.088 0.000 1.441Lack of market info
Low 0.32 0.061 0.000 1.377Medium 0.58 0.071 0.000 1.786
High 0.451 0.123 0.000 1.571Size (SME, Large) 0.409 0.049 0.000 1.505B2B -0.059 0.051 0.251 0.943CoPS all 0.244 0.058 0.000 1.276Constant -3.393 0.081 0.000 0.034Note: Pseudo R2=.227 (Nagelkerke). Model Chi-square (1)= 2469, p<.000
Limitations and ‘To Do’
Fine tune the individual models Some noise in the results Comparison of B2B, CoPS, CoPS production, CoPS services
sub-groups Link the models
Only a partial answer to the question, ‘What drives open innovation processes?’ Design, Market dynamics +… Proxies are limited
o Time dimension to demonstrate dynamics Causality cannot be proven
Only correlation Other research methods needed to establish direction of link
Conclusions
CoPS are relatively more open in innovation B2B show more open innovation practices (activities,
practices) B2C innovations acquired more externally Measures of ‘openness’ show interesting distinctions
o Choice is meaningful
Features that have contributed to open structures in CoPS may be drivers in all sectors
Evidence from design and market models Needs confirmation of a linked, more fully specified model Needs further qualitative search to establish process,
causality
Conclusions
Policy implications Systems Integrators, design play important
connecting roles in an ‘open innovation’ environment Drivers for openness beyond opportunity
o Beyond availability of resources and markets for knowledge
o Perhaps more structural features which will shape the degree to which innovation systems become open
Role for policy
Questions and Discussion