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Evaluating the human element in clusters 
Emily Wise 
Parallel 4.1 New Cluster Policies and Cluster Evaluation 
12 November 2014
EVALUATING THE HUMAN ELEMENT IN CLUSTERS 
EMILY WISE, PHD MADELINE SMITH 
RESEARCH FELLOWAND CONSULTANT HEAD OF STRATEGY, INSTITUTE OF DESIGN INNOVATION 
LUND UNIVERSITY AND IEC GLASGOW SCHOOL OF ART
CLUSTER POLICY ADDRESSES SYSTEM 
(OR COORDINATION) FAILURES 
The stated objective of cluster programmes (examples): 
• Denmark: The objectives innovation networks are to strengthen 
public-private collaboration and knowledge transfer between 
public universities and private companies on research and 
innovation, thus promoting knowledge-based growth in business 
and industry. 
• France: The goal of competitiveness clusters is to build on 
synergies and innovative, collaborative projects in order to give 
partner firms the chance to become first in their fields, both in 
France and abroad. 
• Germany: The Leading-Edge Cluster competition supports high-performance 
clusters formed by business and science that enter 
into strategic partnerships which boost Germany's innovative 
strengths and economic success. 
• Sweden: VINNVÄXT’s mission is to promote sustainable regional 
growth by developing internationally competitive research and 
innovation milieus in specific growth fields. Effective regional 
innovation systems speed up industrial and business renewal 
towards innovation-led sustainable growth. 
Clusters address seven ”innovation gaps” 
Source: Lindqvist, Ketels and Sölvell (2013), The Cluster 
Initiative Greenbook 2.0
A GENERALLY ACCEPTED EFFECT 
LOGIC FOR CLUSTERS? 
Norwegian Innovation Clusters – programme effect logic 
TACTICS handbook on impact evaluation of cluster-based policies 
Evaluation Framework for US Regional Innovation Clusters
THIS LOGIC HIGHLIGHTS THE ROLE OF 
COLLABORATION IN DRIVING 
LONGER-TERM ECONOMIC EFFECTS 
Input/Resources Activities Results/Outcomes Effects 
...contribute to increased interactive 
learning and collaborative R&I 
projects 
...which contributes to increased 
innovation, international 
attractiveness, productivity and 
growth 
Activities to strengthen or 
upgrade a cluster/ innovation 
environment... 
3-10 years >10 years
NEED TO EVALUATE IMPACT ON 
BOTH SYSTEM COORDINATION AND 
ECONOMIC PERFORMANCE 
Data/Indicators: 
? Number/type/strength of engagement of actors 
in cluster initiative 
? Number/type/strength of 
alliances/collaborations among cluster 
participants 
? Number/type/strength of 
alliances/collaborations with related actors 
outside the cluster 
Methods of data collection/analysis: 
- Surveys 
- Interviews 
- Social network analysis 
Data/Indicators: 
- Number/quality of publications and patents 
(and other IP) 
- Number of new products/processes/ services 
- Number of new firms/firm growth 
- Level of investments attracted (VC, FDI) 
- Firm-level revenue/growth; export/growth; 
employment/growth; and wages/growth 
Methods of data collection: 
- Surveys 
- Interviews 
- Business registers/national statistics 
Impacts on System Coordination (aka the human element) 
(engagement, linkages/interaction, collaboration) 
Impacts on Economic Performance 
(intermediate outcomes and productivity) 
Note: see Giuliani et al. (2014) for additional elaboration on the ”two-stage” evaluation process
EVALUATING IMPACT OF THE HUMAN 
ELEMENT MORE OF A “BLACK BOX” 
CURRENT ISSUES 
• Perspectives on what is included 
in the human element vary 
• Types of data collected and from 
whom (cluster manager or firms) 
differ 
• Little possibility to benchmark 
and learn across geographies 
THE TASK AFTER BELFAST 
• Shared perspective on the key 
dimensions of “the human 
element” 
• Consensus on what should (and 
could) be measured 
• A systematic and easily-implementable 
approach to data 
collection
WHAT IS THE HUMAN ELEMENT?
KEY DIMENSIONS OF THE HUMAN 
ELEMENT 
DIMENSIONS 
• Engagement/mobilisation 
• Internal collaboration 
• External interaction 
CHARACTERISTICS (EXAMPLES) 
• Key actors are engaged and guide the strategic 
direction of activities 
• A high percentage of cluster stakeholders 
participate in activities 
• There is regular interaction and dynamic 
collaborative links between actors within the cluster 
• There are perceived benefits and commitment to 
pursuing joint activities (addressing common goals) 
• The cluster has a clear value proposition and 
positions itself in relation to a broader market 
• The cluster proactively seeks ways to strengthen its 
value proposition through external linkages
ADDITIONAL DIMENSIONS OF THE 
HUMAN ELEMENT (?) 
DIMENSIONS 
• Orchestration 
• Strategic orientation 
CHARACTERISTICS (EXAMPLES) 
• Leadership/management is familiar with the various 
actors in the cluster 
• There is a proactive attitude and effective approach 
to identifying and acting on opportunities 
• Leadership/management is knowledgeable of the 
business area and its market 
• Longer-term plans for cluster development are 
anchored with the local/regional government
HOW TO MEASURE – FIRM LEVEL 
Target Group Data/indicators Method of data collection 
Core Cluster 
Firms 
• Strength of engagement in cluster 
initiative 
• Type of activity (networking, workshop, 
project) 
• Type of engagement (time, financial 
investment) 
• New cooperative activities with 
cluster participants 
• Type of partner (company, university, 
research organisation, investor) 
• Type of activity (networking, workshop, 
project) 
• New cooperative activities with 
actors outside of the cluster 
• Type of partner (company in same or 
different sector, university, research 
organisation, investor) 
• Type of activity (networking, workshop, 
project) 
• Standard survey 
questions 
• Regular reporting 
intervals
HOW TO MEASURE – CLUSTER 
LEVEL 
Target Group Data/indicators Method of data collection 
Cluster 
manager 
Description of characteristics at 
(four) levels of development 
• Engagement/mobilisation 
• Internal collaboration 
• External interaction 
• Orchestration 
• Strategic orientation 
• Self-assessment tool 
rating perceived level of 
development in relation 
to standard descriptions 
• Regular reporting 
intervals
WHAT ARE THE STRENGTHS AND 
BENEFITS OF THIS APPROACH? 
• Evaluation of the human element 
• Is an integral part of the ”generally accepted” effect logic for cluster policy 
• Provides evidence of the impacts we already see (and explain qualitatively) 
• Can be used to explain results (or lack of results) 
• Can be used to examine the relationship between ”the human element” and 
economic performance 
• The proposal from members of the TCI working group 
• Establishes an initial set of key dimensions 
• Suggests a simple approach for measuring these dimensions at firm and cluster 
level, which could be easily integrated into (existing) regular assessments 
• Sets the stage for international peer learning, benchmarking and more in-depth 
analysis
NEXT STEPS 
• Innovation Norway and VINNOVA will pursue data collection on the 
human element as part of their existing monitoring/evaluation 
processes...others interested? 
• Discussion of results from these national efforts in forthcoming 
meeting of TCI evaluation working group 
• As a core group develops, could pursue: 
• Common portals for cluster-level self-assessments 
• Approaches for sharing/benchmarking firm-level data 
• Further joint development of frameworks – including analysis of relation between 
human element and economic performance 
• International peer learning and benchmarking

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TCI 2014 Evaluating the human element in clusters

  • 1. Evaluating the human element in clusters Emily Wise Parallel 4.1 New Cluster Policies and Cluster Evaluation 12 November 2014
  • 2. EVALUATING THE HUMAN ELEMENT IN CLUSTERS EMILY WISE, PHD MADELINE SMITH RESEARCH FELLOWAND CONSULTANT HEAD OF STRATEGY, INSTITUTE OF DESIGN INNOVATION LUND UNIVERSITY AND IEC GLASGOW SCHOOL OF ART
  • 3. CLUSTER POLICY ADDRESSES SYSTEM (OR COORDINATION) FAILURES The stated objective of cluster programmes (examples): • Denmark: The objectives innovation networks are to strengthen public-private collaboration and knowledge transfer between public universities and private companies on research and innovation, thus promoting knowledge-based growth in business and industry. • France: The goal of competitiveness clusters is to build on synergies and innovative, collaborative projects in order to give partner firms the chance to become first in their fields, both in France and abroad. • Germany: The Leading-Edge Cluster competition supports high-performance clusters formed by business and science that enter into strategic partnerships which boost Germany's innovative strengths and economic success. • Sweden: VINNVÄXT’s mission is to promote sustainable regional growth by developing internationally competitive research and innovation milieus in specific growth fields. Effective regional innovation systems speed up industrial and business renewal towards innovation-led sustainable growth. Clusters address seven ”innovation gaps” Source: Lindqvist, Ketels and Sölvell (2013), The Cluster Initiative Greenbook 2.0
  • 4. A GENERALLY ACCEPTED EFFECT LOGIC FOR CLUSTERS? Norwegian Innovation Clusters – programme effect logic TACTICS handbook on impact evaluation of cluster-based policies Evaluation Framework for US Regional Innovation Clusters
  • 5. THIS LOGIC HIGHLIGHTS THE ROLE OF COLLABORATION IN DRIVING LONGER-TERM ECONOMIC EFFECTS Input/Resources Activities Results/Outcomes Effects ...contribute to increased interactive learning and collaborative R&I projects ...which contributes to increased innovation, international attractiveness, productivity and growth Activities to strengthen or upgrade a cluster/ innovation environment... 3-10 years >10 years
  • 6. NEED TO EVALUATE IMPACT ON BOTH SYSTEM COORDINATION AND ECONOMIC PERFORMANCE Data/Indicators: ? Number/type/strength of engagement of actors in cluster initiative ? Number/type/strength of alliances/collaborations among cluster participants ? Number/type/strength of alliances/collaborations with related actors outside the cluster Methods of data collection/analysis: - Surveys - Interviews - Social network analysis Data/Indicators: - Number/quality of publications and patents (and other IP) - Number of new products/processes/ services - Number of new firms/firm growth - Level of investments attracted (VC, FDI) - Firm-level revenue/growth; export/growth; employment/growth; and wages/growth Methods of data collection: - Surveys - Interviews - Business registers/national statistics Impacts on System Coordination (aka the human element) (engagement, linkages/interaction, collaboration) Impacts on Economic Performance (intermediate outcomes and productivity) Note: see Giuliani et al. (2014) for additional elaboration on the ”two-stage” evaluation process
  • 7. EVALUATING IMPACT OF THE HUMAN ELEMENT MORE OF A “BLACK BOX” CURRENT ISSUES • Perspectives on what is included in the human element vary • Types of data collected and from whom (cluster manager or firms) differ • Little possibility to benchmark and learn across geographies THE TASK AFTER BELFAST • Shared perspective on the key dimensions of “the human element” • Consensus on what should (and could) be measured • A systematic and easily-implementable approach to data collection
  • 8. WHAT IS THE HUMAN ELEMENT?
  • 9. KEY DIMENSIONS OF THE HUMAN ELEMENT DIMENSIONS • Engagement/mobilisation • Internal collaboration • External interaction CHARACTERISTICS (EXAMPLES) • Key actors are engaged and guide the strategic direction of activities • A high percentage of cluster stakeholders participate in activities • There is regular interaction and dynamic collaborative links between actors within the cluster • There are perceived benefits and commitment to pursuing joint activities (addressing common goals) • The cluster has a clear value proposition and positions itself in relation to a broader market • The cluster proactively seeks ways to strengthen its value proposition through external linkages
  • 10. ADDITIONAL DIMENSIONS OF THE HUMAN ELEMENT (?) DIMENSIONS • Orchestration • Strategic orientation CHARACTERISTICS (EXAMPLES) • Leadership/management is familiar with the various actors in the cluster • There is a proactive attitude and effective approach to identifying and acting on opportunities • Leadership/management is knowledgeable of the business area and its market • Longer-term plans for cluster development are anchored with the local/regional government
  • 11. HOW TO MEASURE – FIRM LEVEL Target Group Data/indicators Method of data collection Core Cluster Firms • Strength of engagement in cluster initiative • Type of activity (networking, workshop, project) • Type of engagement (time, financial investment) • New cooperative activities with cluster participants • Type of partner (company, university, research organisation, investor) • Type of activity (networking, workshop, project) • New cooperative activities with actors outside of the cluster • Type of partner (company in same or different sector, university, research organisation, investor) • Type of activity (networking, workshop, project) • Standard survey questions • Regular reporting intervals
  • 12. HOW TO MEASURE – CLUSTER LEVEL Target Group Data/indicators Method of data collection Cluster manager Description of characteristics at (four) levels of development • Engagement/mobilisation • Internal collaboration • External interaction • Orchestration • Strategic orientation • Self-assessment tool rating perceived level of development in relation to standard descriptions • Regular reporting intervals
  • 13. WHAT ARE THE STRENGTHS AND BENEFITS OF THIS APPROACH? • Evaluation of the human element • Is an integral part of the ”generally accepted” effect logic for cluster policy • Provides evidence of the impacts we already see (and explain qualitatively) • Can be used to explain results (or lack of results) • Can be used to examine the relationship between ”the human element” and economic performance • The proposal from members of the TCI working group • Establishes an initial set of key dimensions • Suggests a simple approach for measuring these dimensions at firm and cluster level, which could be easily integrated into (existing) regular assessments • Sets the stage for international peer learning, benchmarking and more in-depth analysis
  • 14. NEXT STEPS • Innovation Norway and VINNOVA will pursue data collection on the human element as part of their existing monitoring/evaluation processes...others interested? • Discussion of results from these national efforts in forthcoming meeting of TCI evaluation working group • As a core group develops, could pursue: • Common portals for cluster-level self-assessments • Approaches for sharing/benchmarking firm-level data • Further joint development of frameworks – including analysis of relation between human element and economic performance • International peer learning and benchmarking

Hinweis der Redaktion

  1. The two of us are presenting a summary of discussions and proposals from several members of the TCI working group on cluster evaluation Mention Olav and Knut at Innovation Norway, and Göran Andersson at VINNOVA
  2. There is a general acceptance that social capital and trust is critical for effective cluster development, “building bridges” between different actor groups in the innovation ecosystem – mobilizing engagement and developing linkages that lead to new knowledge, strengthened capacity and collaborative innovation projects to deliver concrete economic advantages. The main differentiator between cluster programmes and other innovation and business development is the relationship building, and internal/external behaviours allowing the group to do together what they could not do alone.
  3. After talking A LOT about evaluation over the years in various European projects and TCI working groups, in academic publications, and in the context of national/regional evaluations, it seems that ”great minds think alike”... We begin to see the emergence of a generally-accepted effect logic (or theory of change) for cluster initiatives. A general understanding that activities of cluster initiatives yield short-term results that, in turn, contribute to strengthened innovation capacity and economic performance in the longer-term.
  4. Simplified logic: Cluster initiatives aim at building on various types of input factors (both the tangible ”structural capital” including people, money and various types of infrastructure, and the intangible ”social/relational capital”) to upgrade the cluster/innovation environment. The direct results – experienced in the near(er) term – are in the form of strengthened linkages/interactive learning processes...and collaborative R&I projects. (This makes sense given that clusters are primarily focused on addressing coordination/system failures.) These direct results (a more efficient/coordinated innovation system) then enhance longer-term outcomes/impacts on economic performance.
  5. As with the overall ”generally accepted” effect logic, we start to see a ”generally accepted” set of indicators for evaluating impacts on economic performance, and typical approaches to collecting and analysing data (including increasing use of firm-level data and data on ”control groups”). However, there are no standard approaches to evaluating the impacts on system coordination/interactive learning processes/collaboration...what we call the human element.
  6. Current evaluation practices do collect data on both the “human element” and economic performance. However, the types of data collected (and from whom) vary…making it difficult to benchmark across geographies, or to conduct more comprehensive analyses (to better understand the relation between the human element and economic performance). To advance understanding of cluster initiatives’ impact economic performance and competitiveness, we need to understand the human element. To accomplish this, we need a shared perspective on the key dimensions of “the human element”, and a systematic and easily-implementable approach to data collection. This was where we left off in Belfast earlier this year.