The Transforma,on of Innova&on Ecosystems
in Global Metropolitan Areas A Data-‐Driven Perspec,ve
Martha G Russell, Jukka Huhtamäki, Kaisa S,ll Innova,on Ecosystems Network
TUT eMBA Visit to Stanford University Martha Russell, Rahul C. Basole, Neil Rubens, Jukka Huhtamäki, Kaisa S,ll
Transforming Innova,on Ecosystems Through Network
Orchestra,on: Case EIT ICT Labs Dr. Kaisa S,ll, VTT Technical Research Centre of Finland
In collabora,on with Marko Turpeinen and others at EIT ICT Labs Helsinki
Need for innova,on indicators:
tradi,onal measures and metrics are limited
Innova,on ac,vi,es rarely carried out within a single organiza,on: Network approach to
understand the complex systems of innova,on
Unprecedented amount of data about
the complex innova,on system and its actors:
Social media, socially constructed data
Possibili,es of SNA and visualiza,ons
Computer power
• EIT ICT Labs aims “to build European trust based on mobility of people across countries, disciplines and organiza,on”
• People, their knowledge and the financial flows are networked, all contribu,ng toward poten,al of innova,on -‐> Analysis should not be limited to labor mobility
• How to measure, analyze and visualize mobility of people, money and technology in the European ICT innova,on ecosystem?
EIT ICT Labs’ mission is to turn Europe into a global leader in ICT Innova,on
Mobility is a central theme
5 nodes working together
Student and teacher mobility, Doctoral
School, Mobility programs
Ini,al analysis of mobility (S#ll et al 2010) for baseline:
with geospa,al representa,ons of networks and a metric of betweenness
Highligh,ng few individuals, more investors, less so of universi,es, and the role of Silicon Valley as connectorà
(1) new ”requirements” for data/ process of next network visualiza,on, and (2) ini,al insights for network
orchestra,on
Two Studies
§ Using IEN Dataset § Betweenness Centrality
§ number of ,mes that a given node is included in the shortest path between any two nodes in the network (Wasserman and Faust, 1994)
§ point out investors, individuals and educa,onal ins,tu,ons that operate in between the six EIT ICT Labs Nodes
§ Coupled with the modeling applied, can be used as a metric for actor mobility in an innova,on ecosystem
§ Note: analysis does not show the mobility of people within individual companies § Two consecu,ve analysis: first in 2011 and the second in 2012, with refined segng and updated data
(Gray, 2012)
S,ll, Russell, Huhtamäki, Turpeinen, Rubens (2011). Explaining innova#on with indicators of mobility and networks: Insights into central innova#on nodes in Europe
Mobility and Educa,onal Ins,tu,ons 2011
S,ll, Russell, Huhtamäki, Turpeinen, Rubens (2011). Explaining innova#on with indicators of mobility and networks: Insights into central innova#on nodes in Europe
Mobility and Financial Flows 2011
Analysis round #2: Trento included as the sixth node, more ci,es connected to coloca,on centers, updated data and transforma,on in place
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S,ll, Huhtamäki, Russell, Rubens (2012). Transforming Innova#on Ecosystems Through Network Orchestra#on: Case EIT ICT Labs
Finally, adding San Francisco Bay Area as “the seventh EIT ICT Labs node” for contrast, interconnec,ons, comparison and benchmark
S,ll, Huhtamäki, Russell, Rubens (2012). Transforming Innova#on Ecosystems Through Network Orchestra#on: Case EIT ICT Labs
Conclusions § Geospa,al social network visualiza,on make it possible to share and show special characteris,cs, significant actors and connenc,ons in the innova,on ecosystem § Betweenness centrality (how central a node is within a network) can be used to measure innova,on poten,al of an ecosystem § Our framework can be used for understanding the transforma,on and for bringing transparency § At the same ,me, when interpreted in the context, our approach can be used to suggest possibili,es for network orchestra,on
Networks of innova&on rela&onships: mul&scopic
views on Finland
Presented at ISPIM Helsinki 2012 Kaisa S,ll, VTT Jukka Huhtamäki, TUT Martha G. Russell, Stanford mediaX Rahul C. Basole, Georgia Tech Jaakko, Salonen, TUT Neil Rubens, University of Electro-‐Communica,ons
Jukka Huhtamäki, Tampere University of Technology
Networks of innova,on
Approach By whom
The shik of innova,on from a single firm toward an increasingly network-‐centric ac,vity
Chesbrough 2003
Importance of collabora,on and value co-‐crea,on
Ramaswamy and Goullart 2010
Resul,ng networks of rela,onships between individual and organiza,onal en,,es
Kogut and Zander 1996, Vargo 2009
Studies of innova,on ecosystems Iansi, and Levien 2004, Russell et al. 2011, Basole et al. 2012, Hwang and Horowio 2012, Marts et al. 2012
From data with visualiza,on to insights
Sense-‐making and storytelling Boundary specifica,on
Computa,on, analysis and visualiza,on Metrics iden,fica,on
Analysing a business ecosystem
Boundary specifica,on: nodes and edges
Metrics: for descrip,ons
Visualiza,on 1: Highligh,ng enterprise level rela,onships
Visualiza,on 2: Highligh,ng growth companies
Visualiza,on 3: Highligh,ng start-‐up companies
Visualiza,on 4: Mul,scope with aggregated data
Sense-‐making and storytelling: So what?
• Visualiza,ons of metrics and networks can be seen to model the skeleton of an ecosystem
• Tacit knowledge about networks (and the roles of certain actors) becomes explicit and shared
Visualizing an Open Innovation Platform: The structure and
dynamics of Demola
Huhtamäki, Luotonen, Kairamo, Still, Russell TUT // New Factory // VTT // Stanford
Academic MindTrek 2013: "Making Sense of Converging Media”
http://bit.ly/mt2013visualizingdemola // @jnkka
In this presentation
• Case context description: what is Demola? • Challenges in measuring Demola & open
innovation • Use case examples • Method: data-driven network animation • Results • Discussion • Critique • Wrap up and future work
What is Demola?
• Open innovation platform & ecosystem engager established in 2008 in Tampere
• By 2013, 86 companies and 1200 students from 3 universities have participated in 250+ projects
• The Demola network is expanding internationally
• This study focuses in Demola Tampere
Open innovation platform & ecosystem engager established in 2008 in Tampere By 2013, 86 companies and 1200 students from 3 universities have participated in 250+ projects The Demola network is expanding internationally; this study focuses in Demola Tampere
Challenges in measuring Demola
…and open innovation in general: • Tradition: A linear view on innovation; • Measuring inputs (money) and outputs (patents, products, new companies); • Survey-based methods, aggregate measures How does one measure the performance of an ecosystem engager?
Still, K., Huhtamäki, J., Russell, M. & Rubens, N. 2012. Paradigm shift in innovation indicators—from analog to digital. Proceedings of the 5th ISPIM Innovation Forum, 9-12 December, Seoul, Korea.
Use case examples Who wants to measure?
Why do they want to measure? What will the do with the measurement insights?
Policy makers Interested in the impact that Demola has had to the surrounding ecosystem
Evaluate the utility of the platform for future investments and the applicability of the approach
Company representatives
Utility of Demola engament Decide whether to engage or not; Select an approach suitable for their portfolio
Demola operators Activity in general; Companies with changing (increasing/decreasing) Demola engagement; Ecosystem Structure
General Demola introductions, marketing & sales; Demola key area development
University students Reviewing opportunities that participating in a Demola project would open
Decide whether to participate or not
University decision-makers
Impact, new developments in the ecosystem
Add initiatives for students to get involved
International actors Impact, engagement, transformation To evaluate the utility of the process for deciding the applicability of the approach
Method: data-driven network analysis (& action research)
(Hansen et al., 2009)
Project Detail Example
Project Id Project 115
Name Koukkuniemi 2020
Started 2010-05-04
Ended 2010-10-31
Status Completed
Collaboration Partner City of Tampere
Type of Partner Public
Project Domain Non-profit
Location Tampere
Key Areas well-being, knowledge management, regional studies
Project Team Members uta, uta, tut, tut
Result 1: Project network
Nodes represent projects and companies
Company nodes are light green; other colors indicate cluster membership
Node size shows its betweenness value
Force-driven layout
Result 2: Project domain network
Nodes represent project domains
Nodes are connected through domain co-occurence
Colors show cluster membership
Node size shows its betweenness
Result 3: Project sphere animation
Discussion
• Technical challenges exist when using internally collected data for network visualization and animation
• Visualization development challenges data-collection procedures and can add value to existing data
• Demola operators find value particularly in the animation of the project sphere; international collaborators have also expressed an interest in them
Critique
• Method? • Results? • Validation? • NAV model vs.
visual analytics
Acknowledgements & thank you
Time for your comments and questions.
Jukka Huhtamäki <jukka.huhtamaki @tut.fi> Ville Luotonen Ville Kairamo Kaisa Still Martha G. Russell Acknowledgements Ville Ilkkala, Meanfish Ltd, supported animation development. Heikki Ilvespakka took care of exporting the data from the Demola platform
Innovation ecosystem Context
Data-driven visualization
Process
Availability of relational data about innovation activities (free, easily available public data) Can be studied as networks (SNA)
Application arena Supporting insights on Highlighting
Network visualization Innovation indicators Indicator ”osoitin”
Network dynamics Relational capital (Ecosystemic relational capital)
metrics
Various levels: International
National Local/regional Organizational
Questions What would your ecosystem look like based on the publicly available data?
§ What info is there about you, your organization, your stakeholders– and the connections between all these?
à Is this relevant for you? Could this have implications for some action? Would the visualization of your ecosystem be valuable for you?
§ How? § What could you do better with that? § What could you do that you cannot do now?
Would knowing about your relational capital be valuable for you? § How? § What could you do better with that? § What could you do that you cannot do now? Where could we find more relational data (easily available public data, almost free)?
Measuring Rela,onal Capital – work on progress
Dr. Kaisa S,ll
• Sindi 2010-‐2012 • Reino 2013-‐2014 • Entegrow? 2014-‐2015 • SPEED 2014-‐2015
Kuvalähde: Laihonen et al. (2013)
Suhdepääoma & verkostot
Framework of network dynamics (Ahuja et al 2009):
Operate via the mechanisms of: • Homophily • Heterophily • Prominence aorac,on • Brokerage • Closure
Microdynamics of networks
Network Architecture Dimension
Network primi,ves
Micro-‐founda,ons of networks:
Basic factors that drive or shape the forma,on and content of ,es in the network: • Agency • Opportunity • Iner,a • Random & Exogenous
Causing changes in network membership (through dissolu,on or forma,on of ,es, changes in ,e content, strength and mul,plexity)
Structure -‐ Ego network
• Centrality • Contraint
-‐ Whole network • Degree distribu,on • Connec,vity • Clustering • Density • Degree assorta,vity
Content • Types of flows • Number of dis,nct flows
(mul,plexity)
Architecture of any network can be conceptualized in terms of: • Nodes (that comprise the network) • Ties (that connect the nodes) • Structure (the paoerns of structure that
result from these connec,ons)
Dimensions of dynamics
Descrip&on Meaning
Network Architecture Dimension for structure
Ego network
Centrality has been associated with a wide variety of poten,al benefits such as access to diverse informa,on and higher status or pres,ge (Brass 1985)
Constraint The presence of structural hole is commonly related to brokerage possibili,es (Burt 1992, Zaheer and Soda 2009)
Whole network
Degree Distribu,on
reflects the rela,ve frequency of the occurrence of ,es across nodes or the variance in the distribu,on of ,es (Jackson 2008
has been used to signify the dis,bu,on of status, power or pres,ge across organiza,ons (Gula, and Caguilo, 1999; Ahuja, Polidoro and Mitchell 2009); may be reflec,ve of changes in the status hierarchy of the observed system (Ahuja et al 2009)
Connec,vity Is captured in the diameter of a network which in turn reflects the largest path-‐distance between any two nodes of the network (Jackson 2008)
The average path length connec,ng any two nodes in the ntework is an indicator of the connec,vity or ”small-‐wordness” of the network; as network becomes more ”small-‐wordly” informa,on can diffuse more quickly fostering outcomes such as inova,on or crea,vity (Schilling 2005, Schilling and Phelps 2007); as the path length between any two nodes of a network diminishes, it is possible that informa,on can become more decomra,zed and result in a reduc,on in the informa,onal advantage of any single player (Ahuja et al 2009)
Clustering The degree to which the network is formed of ,ghtly interconnected cliques (Ahuja et al 2009)
The emergence of inter-‐connected subgroups or cliques suggests that the network is being differen,ated into a variety of dis,nct sub-‐networks or communi,es (Ahuja et al 2009); at inter-‐organiza,onal level this may represent the reclustering of clusters or constella,ons of firms that may be compe,ng against each other as ’alliance network’ (Gomes-‐Cassares 1994); clique instability maybe a precursor of a significant technological discon,nuity if the network is an interorganiza,onal technology network, or perhaps portend an imminent change in the power structure of an organiza,on in an intraorganiza,onal employee network (Ahuja et al 2009)
Density The propor,on of ,es that are realized in the network rela,ve to the hypothe,cal maximum possible (Ahuja et al 2009)
In organiza,onal segngs, higher network density may be reflec,ve of network closure, a condi,on that in turn may be associated with the development of norms; increasing density could be reflec,ng in a reduc,on of diversity of perspec,ves and choice within the network as the high propor,on of realized ,es provide a hologenizing influnce across actors , and thus results in increasing reifica,on of ideas (Ahuja et al 2009)
Degree Assorta,vity
The degree to which nodes with similar degrees connect to each other (Waos, 2004)
Posi,ve assorta,vity implies that high-‐degree nodes connect to other high degree nodes etc. ; in an intra-‐organiza,onal segng, assorta,vity could be driven by homophily processes and disassorta,vy by complimentary needs (Ahuja et al 2009; assorta,vity can be associated with the emergence of a core-‐periphery structure (Borgag and Evereo 1999) where a set of densely connected actors cons,tute a core of an industry while many of other low degree actors cons,tute a periphery. Changes might signal a shik in the resource requirements for success in the industry (Powell, Packalen and Whigngton ????)
Microfounda&ons– d
Agency Agency behavior, choosing or not choosing to establish connec,ons; The focal actor’s mo,va,on and ability to shape rela,ons, and create a beneficial link or dissolve an unprofitable one or shape an advantageous structure (Sewell 1992; Emirbayer and Goodwin 1994; Emirbayer and Mische 1998)
As actors deliberately seek to create social structures, which is in line iwth Burt’s idea of structural holes as socfial capital, highligh,ng the entrepreneurial role in the crea,on of this valuable form os social structure (Burt 1992) à Network structures emerbe as a result of self-‐seeking ac,ons by focal nodes and their connec,ons, no,ng that actors can devise unique responses to imporve their own situa,ons in the network (Ahuja et al 2009)
Opportunity Reflects the structural context of ac,on (Blau 1994) and includes the argument that actors tend to prefer linking within groups rather than across them (Li and Rowley 2002)
Iner,a Includes the pressures for persistence and change (Giddens 1984, Portes and Sensenbrenner 1993, Coleman 1988) and refers to the durability of social structures as well as the social processes by which the focal actor’s ac,ons are influenced, directed and constrained by norms and ins,tu,onal perssures
Random & exogenous
Exogenous factors that can have an impact that emanate from beyond the network or from simply random processes, whether generated inside or outside (Mizruchi 1989)
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