The Cluster ObservatoryEvaluation Model

Regional Programmes & Cluster Programmes

Coaching &Implementation

Learning AcrossRegions and Clusters in Europe

Evaluation Model

Simpler

Surveys

TM

Surveys1. Surveys of member firms/organisations in cluster2. Surveys of cluster organisations3. Surveys of social media (text analysis)

1. Interviews(process tracing/confirmation stats)A Member firms/organisationsB Cluster LeaderC Cluster Organisation Board

2. Participatory observation

1. Benchmarking with otherA RegionsB ClustersC Cluster organisations

2. Peer Evaluation Teams

1. Company Financial Performance(Collection/Cluster definition/Control groups)A Value addedB WagesC Profitability

2. Statistical Analysis

Benchmarking&

Peer Evaluation

The Evaluation Model - Four Complementary Components

Level of Data and Variables

I

IIIII

IV

Method Data point Variables

SIMPLER Firm Value AddedProfitabilityWagesJobs

Survey Firm General performance: Innovation gaps:Cluster networking Firm-to-firmCluster identity Firm- researchTrust Firm - educationInnovation performance Firm- capitalBusiness development Firm - publicSustainability Firm - other clusters

Firm - globalCluster organisation Internal performance (memberships, workshops, projects etc.)

General external performanceInnovation gap performance

Interview Firm Participation within cluster activitiesEffects from activities (intended and unintended)Pros and cons being a memberExpectations from being a memberRecommended actions to cluster leader

Cluster leader & ObjectivesCluster board Financing

Internal and external performance

Benchmarking Region Up to around 50 variables measuring regional quality and attractivenessCluster Cluster observatory data rankings: size, specialisation and focus of cluster employmentCluster organisation Size, objectives, financing, performance of peers

I

II

III IV1. A Member firms/organisations B Cluster Leader

C Cluster Organisation Board

SIMPLERresults

Surveyresults

Interviewresults

Fullevaluation

The Full Evaluation Model - Timing

III 2. Participatory observation

II

3. Social media/Email server/text analysis

1.2.

3.

4.

Component Strengths Weaknesses

I SIMPLER 1. and 4. 2. and 3.

II Survey tools 1. 2. 3. and 4.

III Interviews 2. and 3. 1. and 4.

IV Benchmarking 1. and 4. 2. and 3.

Cluster Observatory Model 1. 2. 3. 4.

The Model is Designed to Capture Unintended Effects andControlling for Outside Explanations

TM

2012-06-12 © Grufman Reje 2012

All companies in X region 2010

2012-06-12 © Grufman Reje 2012

All companies in cluster X 2010

Cluster X

Cluster XRegion

Cluster X outperforms its peer cluster and the region in general

Change in value added among member firms in a cluster, as compared to

A) Firms in an unorganized peer cluster andB) All firms within the region within the cluster category

Peer Group

% Change in Value Added

IResults

Performance measurement over 5 years based on 3 indicators

European Cluster Observatory SIMPLER AnalysisComparison of 12 Clusters in a European Region 2006 - 2010 (unidentified names)

Value Added Growth Profitability % of Value Added Wages per employee

Total Rank Cluster Cluster Peer Diff Rank Cluster Peer Diff Rank Cluster Peer Diff Rank SUM1 A 38% 21% 17% 3 5% -13% 18% 2 17% 7% 10% 1 62 B 27% -1% 28% 2 12% 7% 5% 6 16% 8% 8% 2 103 C 8% -6% 14% 4 12% -4% 16% 3 3% -2% 5% 4 114 D 67% 21% 46% 1 12% 7% 5% 6 13% 11% 2% 6 135 E -11% -8% -3% 12 10% -16% 26% 1 12% 9% 3% 5 186 F 16% 4% 12% 6 -2% 1% -3% 11 17% 9% 8% 2 197 G 40% 28% 12% 6 3% -2% 5% 6 11% 10% 1% 8 208 H 31% 18% 13% 5 5% 5% 0% 10 13% 11% 2% 6 219 I 18% 11% 7% 9 0% -15% 15% 4 11% 11% 0% 9 22

10 J 29% 20% 9% 8 7% 1% 6% 5 8% 15% -7% 12 2511 K 33% 26% 7% 9 9% 8% 1% 9 13% 13% 0% 9 2712 L 45% 38% 7% 9 -4% 12% -16% 12 10% 14% -4% 11 32

Results

10 out of 12 Clusters perform better than their peer groupsin terms of financial results and wage development (productivity)

I

$

Companies

Research organisations

Education organisations

Government

Capital

The dream of dynamic clusters

II

Measured the degree of networking across innovation gaps:

Firm- to- firm (SMEs cooperate with large firms)Firm- to-research Firm-to-educationFirm-to-capitalFirm-to-public organisationsFirm-to- other clustersFirm-to-global markets

Results

Some clusters are much better at networking across the innovation gaps = area where SLIM helps out with learning across clustersSome clusters exhibit a negative development after the 2008 crisis

Come a long way

Large gaps still exist

IIResults – Innovation Performance

Cluster Firms have improved their innovation performance significantly by being members in clusters

Moderate effects on sales performance and very limited effects on equality and sustainability

2006 2007 2008 20090

10

20

30

40

50

60

70

80

90

Performance increases over time - by performance type

SalesCost BenefitsEmployment increaseNew investmentsBetter Products and ServicesNew Products and ServicesEqualityIntegrationSustainability

Innovation

Benchmarking regions IV

The Cluster ObservatoryEvaluation Model

Regional Programmes & Cluster Programmes

Coaching &Implementation

Learning AcrossRegions and Clusters in Europe

Evaluation Model Mats.Williams@hhs.se