Developing Smart Specialization Strategies for Cluster Improvement

Developing Smart Specialization Strategies for Cluster Improvement Francesco Molinari, mail@francescomolinari.it Budapest, 18 September 2013

Contents • ClusterPoliSEEfact sheet • Project focus • Problem(s) addressed • Specific objectives • Workflow of activities • Current status • Areas of interest • Learning mechanisms / objects • Reflective policy making patterns • The Policy Learning Space • Some interim conclusions • Future work and ways of collaboration Francesco Molinari

ClusterPoliSEE fact sheet • Strategic project, led by the Marche Region and managed by SVIM SpA (in-house company) • 24 partners (+1 associated member) from 11 SEE countries • Timeline: May 2012 – October 2014 (30 months) • Official website: www.clusterpolisee.eu • Policy learning platform (still under construction): www.clusterpoliseeS3.eu Francesco Molinari

Project focus • “(Design of) smart specialization strategies for cluster improvement” • “A methodological approach based on experiments in reflective policy making, basis for better policy formulation and implementation, foreseeing the creation of mutual learning tools” Source: ClusterPoliSEE AF Francesco Molinari

Problem(s) addressed • Regional socio-economic (and policy making capacity) disparitiesin the SEE space • Need for fact-based cluster policies addressing the framework conditions for innovation and competitiveness in a more reflective way • Lack of guidelines for the creation/support of transnational clusters with a distribution of local and foreign actors at different stages of the value chain Francesco Molinari

Specific objectives • Setup and population of aPolicy Learning Platform • National/regional Cluster Policy Assessment within the partnership • Development and pilot testing of new Policy Learning Mechanisms • Capitalisation of results in terms of a SEE level Cluster Cooperation Initiative Francesco Molinari

Workflow of activities Permanent Heritage 1.1. Platform design 1.2. Setup 1.3. Population 2.2. Policy assessment 2.1 Evidence gathering Lessons learnt Practical examples 3.1 Pilot experiments 3.2. Learning mechanisms 4. SEE Cluster Initiative Immediate capitalisation Francesco Molinari

Current status Finalised Ongoing Work done Planned 1.1. Platform design 1.2. Setup 1.3. Population Clustering with other projects 2.2. Policy assessment 2.1 Evidence gathering Lessons learnt Practical examples 3.1 Pilot experiments 3.2. Learning mechanisms 4. SEE Cluster Initiative Immediate capitalisation Francesco Molinari

Welcome page of the PLP • To be added Francesco Molinari

Six areas of interest Francesco Molinari

Six internal Working Groups Francesco Molinari

Logical flow (others are possible) Francesco Molinari

Learning Mechanisms  Not a linear, but a recursive process! Francesco Molinari

Learning Objects • Annotated collections of: • Scientific / policy documents • Case study descriptions • Hands-on experience of methods and tools, such as: • Foresight experiments • SWOT analyses • Survey questionnaires • Assessment exercises • Study visits • Results of platform enabled project work, including: • Working groups / discussion forums / calls for ideas / eLearning sessions (Moodle) Francesco Molinari

Reflective policy making patterns Model Transfer Exchange Take Stock Single Loop Learning “How to do things right” Double Loop Learning “How to do the right things” Triple Loop Learning “How to decide what is right” WG activities + platform activities (coming soon) Francesco Molinari

The Policy Learning Space Who learns Community members Elected officials Colleagues (civil servants) WG members Learning objects To do things right To do the right things What for Learning mechanisms To decide what is right Reflective capacity Learns what Francesco Molinari

(Interim) Conclusions • Learning is an individual, but also a collective endeavour, and the relations between the two are still fuzzy and unexplored • In managerial theory, (organisational) learning is almost invariably associated to change • If there is no change, this is a sign that there has been no learning! • Change quite often takes place by adaptation, rather than adoption, of external knowledge • The most challenging task is to learn how to adapt pre-existing / available know-how from one to another situation • Required skills and resources for policy learning and change (learn what) are varying in dependence of the personal identity (who learns) and the purposes of learning (why, what is this for) • In ClusterPoliSEE, we are testing the potential of a suitably designed and developed platform to act as both learning source and stimulus for sustainable change Francesco Molinari

Next steps • Configure and populate thePolicy Learning Platformwith project results, being mapped and tagged in terms of relevance (pertinence) to: • The learner’s identity and purposes (Single/Double/Triple Loop Learning) • Their nature of learning objects / mechanisms / reflective skills • The six thematic domains of the project’s WGs • Make strategic alliances with current/past consortia and organisations, to the purpose of sharing knowledge and expanding the platform’s user base • Experiment on one or more applications of the Smart Specialisation concept, by “creating or enhancing cross national clusters” and activities • Elaborate the joint action plan and initiative for the SEE space, based on this experience and lessons learnt Francesco Molinari

Possible synergies European Cluster Alliance http://www.proinno-europe.eu/eca/about PRO-INNO http://www.proinno-europe.eu/eca/about European Cluster Memorandum http://www.clusterobservatory.eu/ SmartSpecialisation Strategy Platform http://s3platform.jrc.ec.europa.eu/home Francesco Molinari

Thanks for your kind attention • To join our efforts and for any further information please contact: • polcom@regione.marche.it • clusterpolisee@svimspa.it • mail@francescomolinari.it Francesco Molinari

Developing Smart Specialization Strategies for Cluster Improvement