The role of the municipalities within regional smart specialization strategies: an exploratory study

Master Program in Innovation and Technological Entrepreneurship Faculdade de Engenharia da Universidade do Porto The Role of the Municipalities within Regional Smart Specialization Strategies: an Exploratory Study Pedro Nuno Queirós Ferreira de Oliveira 2015 Supervisor: Aurora A.C. Teixeira ii Acknowledgements To Professor Aurora Teixeira, for her guidance, help and encouragement. To Faculties of Engineering and Economics of the University of Porto, the opportunity to be re-student. To Master Program in Innovation and Technological Entrepreneurship’s teachers, all teachings. To my Master’s colleagues, the good times together. To God, to my father and to my friends, always on my side. To Lídia, my wife, a partner for life. To Eduardo and Guilherme, my children and my life. My sincere thanks, Pedro Oliveira September 2015 iii Abstract In the last few years, European Union (EU)’s regional policy discourse has included the smart specialization agenda as a central part of EU cohesion policy reform, considering its fundamental influence on the allocation of funds within the upcoming new program period of the EU’s structural policy. The premise of smart specialization requires each region build on its own strengths and to manage a priority-setting process in the context of national and regional innovation strategies. In smart specialization strategy definition and implementation is important that all relevant stakeholders (entrepreneurial actors) working together. The municipalities are an important stakeholder in this process due to their competences and their role in the development and innovation of their region. Thus, the main purpose of this dissertation is to analyze the municipalities’ awareness of the importance of smart specialization to regions and their role and activities performed (if any) in this process using the Portuguese municipalities as unit of analysis. The large majority of municipalities are knowledgeable of policies of smart specialization strategy under Horizon 2020. But 50 in 110 municipalities (sample) are not involved yet in the process. Within the definition process of smart specialization strategies, municipalities develop activities such as: participation in meetings; promoter of workshops; people training; gathering of information (studies); benchmarking; and working groups to implement the strategies. The activities in which municipalities are more involved are “interlocutor with the entrepreneurs and/or business associations and/or professionals of the regions”, “supporting entrepreneurs in the management and dissemination of a network of mutual relations to boost the discovery process”, and “facilitator / promoter of incentives in terms of public place policies to the discovery of specializations”. Overall, “absence of human resources”, “need of human resources training”, and “scarce information from central government” are the constraints considered more severe in the implementation of smart specialization process. Keywords: Smart Specialization Strategy; Innovation; Portuguese Municipalities; Regional Policy JEL-Codes: O38; R11; R58 iv Resumo Nos últimos anos, o discurso da política regional da União Europeia (UE) incluiu a especialização inteligente como uma parte central da reforma da política de coesão da UE, considerando a sua influência fundamental sobre a atribuição de fundos no âmbito do próximo período do programa de política estrutural da UE. A premissa da especialização inteligente exige que cada região desenvolva os seus pontos fortes e gira um processo de definição de prioridades no contexto de estratégias nacionais e regionais de inovação. Na definição e implementação de estratégias de especialização inteligente é importante que todas as partes interessadas (atores empresariais) trabalhem juntas. Os municípios são uma importante parte interessada neste processo devido às suas competências e ao seu papel no desenvolvimento e inovação da sua região. O objetivo principal deste trabalho é analisar a consciencialização dos municípios sobre a importância da especialização inteligente das regiões e do seu papel e das atividades (se houver) nesse processo, utilizando como unidade de análise os municípios portugueses. A grande maioria dos municípios tem conhecimento das políticas da estratégia de especialização inteligente no âmbito do Horizonte 2020. Mas 50 em 110 municípios (amostra) ainda não estão envolvidos no processo. Dentro do processo de definição de estratégias de especialização inteligente, os municípios desenvolvem atividades como: participação em reuniões; promotor de workshops; formação de pessoas; recolha de informação (estudos); benchmarking; e grupos de trabalho para implementar as estratégias. As atividades nas quais os municípios estão mais envolvidos são: “interlocutor com os empreendedores e/ou associações de empresas e/ou profissionais das regiões”, “apoio aos empresários na gestão e difusão de uma rede de relações recíprocas para impulsionar o processo de descoberta” e “facilitador/promotor de incentivos em termos de políticas públicas à descoberta de especializações”. No geral, "falta de recursos humanos”, “necessidade de formação adicional e específica dos recursos humanos do município” e “escassa informação do governo central” são as restrições consideradas mais problemáticas na implementação do processo de especialização inteligente. Palavras-chave: Estratégia de especialização inteligente; Inovação; Municípios Portugueses; Política Regional. v Table of Contents Acknowledgements ....................................................................................................... ii Abstract ....................................................................................................................... iii Resumo ........................................................................................................................ iv List of Figures .............................................................................................................. vi List of Tables .............................................................................................................. vii 1. Introduction .............................................................................................................. 1 2. Literature Review ...................................................................................................... 4 2.1. The emergence and concept of (Regional) Smart Specialization ...................... 4 2.2. Smart Specialization in practice: an account of extant empirical literature ....... 9 2.3. The role of local government in Smart Specialization strategies .................... 12 3. Methodological considerations ............................................................................ 15 3.1. Research method and tools ............................................................................ 15 3.2. The questionnaire design............................................................................... 16 3.3. Data collection .............................................................................................. 17 3.4. Population and sample .................................................................................. 18 4. Results of Empirical Analysis .............................................................................. 23 4.1. Characterization of the respondents ............................................................... 23 4.2. Analysis of the responses .............................................................................. 23 5. Conclusion .......................................................................................................... 35 5.1. Main outcomes ............................................................................................. 35 5.2. Contribution, limitations and opportunities for future research ...................... 38 References .................................................................................................................. 39 Appendices ................................................................................................................. 45 Appendix 1. Questionnaire ...................................................................................... 45 Appendix 2. Presentation Letter .............................................................................. 50 vi List of Figures Figure 1. Synthesis of the development process of smart specialization and stylised facts ..........8 Figure 2. Process of data collection ......................................................................................... 18 Figure 3. Municipalities’ participation in SSS ......................................................................... 28 vii List of Tables Table 1. Findings of case studies reported in OECD (2013) ..................................................... 10 Table 2. Links between stages of the research process ............................................................. 17 Table 3. Number of Municipalities by size and NUTS II classification .................................... 19 Table 4. Respondent municipalities (sample) by size and NUTS II classification ..................... 20 Table 5. Sample versus Population by NUTS II classification .................................................. 20 Table 6. Sample versus Population by size and NUTS II classification .................................... 21 Table 7. Population versus sample by NUTS III classification ................................................. 21 Table 8. Sample Representativeness tests ................................................................................ 22 Table 9. Respondent’s function/ position in the Municipality ................................................... 23 Table 10. Municipality’s knowledge about the policies of regional smart specialization strategy under Horizon 2020 (Q1) ......................................................................................... 24 Table 11. Q1 by municipality’s size ........................................................................................ 24 Table 12. Q1 by NUTS II ........................................................................................................ 24 Table 13. Existence of key domains/areas/sectors of specialization already identified that would benefit from smart specialization strategy (Q2.1) ...................................................... 25 Table 14. Q2.1 by municipality’s size ..................................................................................... 26 Table 15. Q2.1 by NUTS II ..................................................................................................... 26 Table 16. Importance of the municipality’s key interlocutors within the regional policy implementation (Q2.2) ............................................................................................. 27 Table 17. Municipality’s key interlocutors (important and very important) .............................. 27 Table 18. Municipality’s involvement in the definition process of smart specialization strategies (Q3.1) ...................................................................................................................... 28 Table 19. Q3.1 by municipality’s size ..................................................................................... 29 Table 20. Q3.1 by NUTS II ..................................................................................................... 29 Table 21. Developed activities by Municipalities within the definition process of smart specialization strategies (Q3.2) ................................................................................. 30 Table 22. Municipality’s involvement degree in various activities of the smart specialization process (Q3.3) .......................................................................................................... 31 Table 23. Degree of involvement of each of the following entities in the smart specialization process of municipality (Q3.4) ................................................................................. 33 Table 24. Severity level of the following constraints in the implementation of smart specialization process of the municipality (Q3.5) ...................................................... 34 1 1. Introduction ‘Smart specialization’ is currently gaining importance in the European Union (EU)’s regional policy discourse, since it will have a fundamental influence on the allocation of funds within the upcoming new program period of the EU’s structural policy (Benner, 2013). The ‘smart specialization’ is a strategic approach towards economic growth through a specific support to research and innovation (R&I). This concept is based on the principle that the knowledge-resources’ concentration and its connections to a restricted number of specific economic activities will allow countries and regions to be competitive and stay competitive in the global economy (Benner, 2013). The Europe 2020 strategy focus on three growth priorities: smart (effective investments in education, R&I); sustainable (low-carbon economy); and inclusive (job creation and poverty reduction) (Carayannis and Rakhmatullin, 2014). According to these priorities, the premise of smart specialization requires each region build on its own strengths and to manage a priority-setting process in the context of national and regional innovation strategies (European Commission, 2012). Regions, by a smart specialization process, should concentrate their knowledge investments in certain areas of specialization. This applies to both economically strong and weaker regions. In the latter ones, smart specialization is a way to direct resources to areas that may produce a lasting impact on the regional economy (Foray et al., 2009). That means smart specialization is growth oriented. Instead of compensate weak regions, common practice so far, this kind of strategy promotes regional strengths within a perspective of growth of the specific chosen areas (Benner, 2013). The industries in which the regions have comparative advantages should be the target of specialized diversification and smart upgrading either through general-purpose applications technologies (GPT) or other innovation activities (Foray et al., 2012). According to McCann and Ortega-Argilés (2011: 3), The idea is that regional authorities can exploit the smart specialization logic by undertaking a rigorous self-assessment of a region’s knowledge assets, capabilities and competences and the key players between whom knowledge is transferred. This militates against recommending off-the-shelf local economic policy solutions and instead requires a careful analysis of regional. knowledge capabilities and research competences. 2 Smart specialization promotes a participatory fundamental perspective, combining topdown and bottom-up approaches and involving different regional agents (Benner, 2013). Therefore, smart specialization offers a way of ensuring both empowerment of regional and local agents and their ownership of the process. These are important prerequisites for the sustainability and the effectiveness of regional economic strategies in the long term. (Benner, 2013: 4-5) A key concept on smart specialization involves ‘an entrepreneurial discovery process’ that reveals what a country or region excel in terms of Research and Innovation (R&I) (Foray et al., 2009). The old approaches to the problem of prioritization and resource concentration involved formal exercises based on robust theories and were by their very nature largely technocratic. However, they despised the essential knowledge – the entrepreneurial knowledge, which combines and relates such science, technology, engineering, knowledge of market potential, competitors as well a whole set of inputs to a new activity (Foray et al., 2011). According to Foray et al. (2011), the entrepreneurial process leads to the emergency of new knowledge related to the relevant specializations of the regions, stimulating the development of the regional economy, being for that considered of high social value. Smart specialization should also enable potential synergies (economies of scope, spillovers) between already well-established branches of economic activity and others still underdeveloped ones, and promote emerging fields or radical foundation, as the discovery of a new niche potentially important in the region's economy (Foray et al., 2011). The process of smart specialization involves choices in the areas considered the most promising. The selection of the areas should be made by dynamic “top-down” and “bottom-up” processes. The municipalities are an important stakeholder in this process (Ortega-Argilés, 2012), due to their competences and their role in the development and innovation of their region. The proximity of the citizens, the knowledge of the past and present of their region, their strengths and their role in public policy process and in helping to build relationships with others stakeholders with ‘responsibility’ on this process are factors that justify the pertinence to study the participation of municipalities in smart specialization process. Thus, the main purpose of this dissertation is to analyze the municipalities’ awareness of the importance of smart specialization to regions and their role and activities performed (if any) in this process using the Portuguese municipalities as unit of analysis. Consequently, it intends to answer the following questions: 9 of present or future comparative advantage; and iii) governance mechanisms that assign a central role to regions, private stakeholders and entrepreneurs in the process of converting specialization strategies into economic and social outcomes. 2.2. Smart Specialization in practice: an account of extant empirical literature OECD (2013) report presents the findings of 15 case studies of country and regional experience in designing and implementing smart specialization strategies. Supported by this report, a summary on SSS in distinct regions is presented in Table 1. Its aim is to understand the chosen domains and the involved actor, particularly the role of the government. These cases are generally characterized by implementation of a vertical policy 1 with the prioritization of fields/domains (OECD, 2013). Regional, national and international policies that have been decisive for prioritization of domains were analysed in what concerns to: governance system, key policy instruments, coordination activities and measuring the effects and impacts. Future development for smart specialization and lessons learned and conclusions for political action are presented. However, horizontal policies were not forgotten, when considered as drivers of development and growth of some sectors (OECD, 2013). In these cases different stakeholders could be identified, with special role to universities (e.g., Auwera et al., 2013; Csank et al., 2013; Helleputte et al., 2013; Kardas and Mieszhowski, 2013; Lee, 2013; Seppo et al., 2013; van der Zee, 2013) and research and development institutions (e.g., Lee, 2013; Linshalm et al., 2013). It is highlighted that central (e.g., Quinn and Bampton, 2013; Seppo et al., 2013), regional (Auwera et al., 2013; Helleputte et al., 2013) and local (e.g., Csank et al., 2013; Eulenhöfer and Kopp, 2013; Quinn and Bampton, 2013; Vazquez and Ruiz, 2013) governments from the different places play an important role in the promotion and implementation of smart specialization strategy. The government participation mostly favours a hybrid policy, topdown and bottom-up. 1 A vertical policy selects projects attending to preferred fields, sectors or technologies; a horizontal policy is only responding to demands that arise spontaneously from industry (Foray, 2014). 10 Table 1. Findings of case studies reported in OECD (2013) Case Domain/field Aims Principal Stakeholders Other Stakeholders Reported the Role of the Government Type of approach (Top down/ bottom up/hybrid) Australia (Quinn and Bampton, 2013b) Grain s (Agriculture) Innovation and productivity Australian agriculture Grains research and Development corporation; Australian grain growers and Australian government Grower groups, research partners Central government Bottom up Australia, Melbourne (Quinn and Bampton, 2013a) The South East Melbourne Innovation Precinct Advanced manufacturing (networks, leading businesses and professionals hubs) Australia National Science Agency Monash University Local government Bottom up Austria, Lower Austria (Breitfuss et al., 2013) Develop Sustainable Industry Science Without lead industries Infrastructure (scientific centers); R&D in manufacturing Steering Committee RIS NÖ Companies Regional government Top-down and bottom-up Austria, Upper Austria (Linshalm et al., 2013) Technology clusters, High education and technology networks Mechatronics and process automation, innovative materials and ICT´s (Triple helix) Austrian Research & Technology Council Leading companies Regional government Bottom up Belgium, Flanders (Helleputte et al., 2013) Nanotech - for - health Cross - fertilization Flemish based health research and medical sectors IMEC (Research Institute nano electronics) and VIB (another Institute) Universities, academic hospitals, health sector and insurers Regional government Bottom up Belgium, Flanders (Auwera et al., 2013) Sustainable Chemistry Cluster initiative (match scientific base and industry needs) - greening of society FISCH (multisector business federation) and VITO (Public research Institute) Universities Regional government Top-down and bottom-up Czech Republic, South Moravia (Csank et al., 2013) Specific industries: Mechanical engineering; electronics; ICT ; Life - Sciences Shift from Foreign Direct Investments to endogenous approach: Functional priorities: Tech. Transfer; Services; Human resources; Internationalization University JIC (South Moravian Innovation Centre); Steering Committee Regional government Top-down and bottom-up Estonia – Estonian R&I Strategies (Seppo et al., 2013) Knowledge based economy; Key Technologiestraditional industry, energy, ICT´s, Biotechnologies Increase the capacity Estonian R&D on those fields Ministry of R&D; Ministry of Economic Affairs and Communication University of Tartu Central government Top-down Finland, Lathi (Hermans, 2013) From cluster to SS. Tekes (Finnish Funding Agency T&I ) Regional Authorities; Technical Research Centre of Finland Local government/ Municipalities Top-down and bottom-up Germany , Berlin and Brandenburg (Eulenhöfer and Kopp, 2013) Five clusters: Healthcare, <energy Technology, Transport and Logistics, ICT and Optics) International Competitiveness. Big Growth potential international scale Frauhhofer and Max Planck Institutes, Universities Associations and municipalities Local government/ Municipalities Top-down and bottom-up 11 Case Domain/field Aims Principal Stakeholders Other Stakeholders Reported the Role of the Government Type of approach (Top down/ bottom up/hybrid) Korea, Gwangju (Lee, 2013) Photonics Cluster Optical communications and LED Public Service agencies, Universities and Local Research Institutes Central and Local government Top-down Netherlands, brainport Eindhoven (van der Zee, 2013) Key Enabling Technologies (nano; Photonics; advanced materials and manufacturing systems) "Business-driven"- Triple Helix Industry, Knowledge institutes and government Universities and Leading Original Equipment manufactures (OEMs) Research Institutes Local government/ Municipalities Top-down and bottom-up Poland, Malopolska Region (Kardas and Mieszhowski, 2013) Key technologies - Areas: Energy, Life Science Cluster, ICT Key regional Councils Advisory bodies; Scientists and entrepreneurs Regional government Top-down and bottom-up Spain, Andalusia (Vazquez and Ruiz, 2013) Aerospace cluster Innovation infra-structure - Empower capabilities in aeronautics Regional and CATEC (Advance Centre for Aerospace Technologies) Universities Regional government Top-down Spain, Basque country (Zelaia, 2013) RDI Transition to sectors of High value added : Transport and mobility; nanoscience and advanced manufacturing University of the Basque Country Basque Council of STI´s Regional government Top-down and bottom-up Turkey, East Marmara Automotive Cluster Scientific and Tech infrastructure to work with and support automotive industry. Increase competitiveness (human resources) in all supply chain Automotive Associations and Tubitak (R&D Institute) the sector Universities Central government Top-down and bottom-up United Kingdom (Hodges, 2013) Automotive Industry Transition to a low-carbon vehicles (environment protection and safety legislation) NAITG( new automotive innovation and growth team) Industry bodies and Academia Central government Top-down and bottom-up 12 Four case studies stress the local government engagement in the smart specialization process (e.g., Csank et al., 2013; Eulenhöfer and Kopp, 2013; Vazquez and Ruiz, 2013; Zelaia, 2013). Only in 3 cases it is directly mentioned the municipalities within local government institutions, namely: in Netherlands, Brainport Eindhoven (van der Zee, 2013); in Finland, Lathi (Hermans, 2013) (it presents “city authorities” expression); and in Germany, Berlin and Brandenburg (Eulenhöfer and Kopp, 2013), as a part of a broad social dialog between different stakeholders. Other studies also present empirical cases of smart specialization strategies in different contexts, such us: Valdaliso et al. (2014) (Basque country), Georghiou et al. (2014) (Malta) and Baier et al. (2013) (Germany and Austria). 2.3. The role of local government in Smart Specialization strategies According to Foray et al. (2009), public entities can play an important infrastructural role in smart specialization process. They can provide and collate pertinent information about emerging technological and commercial opportunities and obstacles, product and process safety standards both for domestic and foreign markets, and external sources of finance and distribution entities. They can also aid local entrepreneurs to manage and spread a network of mutual relationships and share knowledge that will boost this discovery process. Foray et al. (2009: 23-24) highlight three main responsibilities of governments:  Providing incentives to drive the discovery of the regions’ specializations by entrepreneurs and other organisations (higher education institutions, research laboratories, etc.):  Monitoring the process evaluating and assessing its effectiveness preventing the waste of funding or cuts of funding too soon;  Setting up complementary investments related to the emerging specializations (educational and training institutions, for instance) in regional investing, namely in the applications’ co-invention processes of a GPT. In what concerns to local government, it has an important role in region’s level of economic, and social-cultural development, especially supported by the argument of the 13 decentralization of public policies, enabling the creation of a local economic climate and encouraging the development of the local potential (Teixeira and Barrros, 2014a, b). The structures of local or sub-regional governments play an important role in the process of smart specialization strategies primarily with respect to areas of public policies. The proximity to citizens and the necessary deepening of local and inter local autonomy generate a significant number of advantages in the decision, implementation, monitoring and evaluation of public policies, explained by the closer relationship between costs and benefits perceived by citizens (see OECD, 2013). Also the increased interest in the public domain issues, the increased possibility of participation and involvement in the sphere of political decision and the ability to see the differentiation of public policies reflect the specific characteristics and preferences of each region are key elements to take into account. It is a question of valuation of proximity instruments of definition and implementation of public policies, including through the enhancement of municipal and inter-municipal scale, recognizing the territories as a tool, as a context and as a differentiated and determinant resource for the success of a strategy of smart growth, inclusive and sustainable. The role of the government 2 described in OECD (2013) report is essentially as facilitator of the ‘discovery’ (e.g., Auwera et al., 2013; Hodges, 2013), rather than instigator of a new emerging field (e.g., Lee, 2013) and being the support by providing incentives, funding or removing regulatory constraints (e.g., Helleputte et al., 2013), creating “the necessary conditions, environment, dynamics and structures through which entrepreneurs and government learn about costs and opportunities and engage in strategic coordination” (OECD, 2013: 20). To support this assertion, Table 2 presents some extracts of the OECD (2013) case studies about the role of local government. 2 According to OECD (2013: 202), “Government may be represented by any of the three levels of government and their owned corporations: National (federal), regional (state), and local (municipal).” 14 Table 2: The role of Local Government according to case studies reported in OECD (2013) Case The role of Local Government Australia, Melbourne “The broader MSE regional economic plan has also feed into this process by aligning economic priorities among local government bodies in the region. […] Research organisations and government bodies can struggle to connect and establish working long term sustainable relationships with industry.” (Quinn and Brampton, 2013a: 98-99) Germany , Berlin and Brandenburg “The model is the result of a broad social dialog, in which a lot of citizens, associations, municipalities and politicians were involved. With this model both States agreed on a common development strategy. Representatives of the innovation and economic development agencies of Brandenburg and Berlin form the Cluster managements and coordinate the funding and support activities.” (Eulenhöfer and Kopp, 2013: 129-130) Korea, Gwangju “Photonics was the first industry promoted by the central and local governments and has strong potential to diversify and modernize local industry.” (Lee, 2013:81) Netherlands, brainport Eindhoven “The recent trend of decentralisation of government powers has resulted in a growing importance of the municipalities, especially in policy implementation (e.g. in social security and unemployment). Decentralisation has also increased the powers of the provinces, most importantly in regional-economic policy, nature management and spatial planning.” (van der Zee, 2013:73) Poland, Malopolska Region “One of the main goals of the regional authorities of the Małopolska Region is to engage citizens, especially scientists, students and entrepreneurs in the process of preparing and implementing RIS 2013-2020. The Marshal Office of the Małopolska Region delivers analytical and organisational support. Information about RIS 2013-2020 was put into local and regional newspapers as well as via Internet.” (Kardas and Mieszhowski, 2013: 138) After the literature review, the next chapter is dedicated to methodological considerations. 15 3. Methodological considerations This chapter presents and describes the methodological options, such as the research method and tools, the questionnaire design and the data collection process, and characterizes the population and sample. 3.1. Research method and tools As mentioned previously, the main purpose of this dissertation is to analyze the municipalities’ awareness of the importance of smart specialization to regions and their role and activities performed (if any) in this process using the Portuguese municipalities as unit of analysis. In line with this objective, it proposes to response the following research questions:  In what extent local government is aware of the smart specialization?  What are the perceptions of municipalities about their role in Regional Smart Specialization Strategy process and the current constraints?  What are they doing within Regional Smart Specialization Strategy process? In order to answer these questions, a quantitative methodological approach is adopted, where representativeness and reliability are essential topics when interpreting the data (Silva and Teixeira, 2012). This is an exploratory study. Based on the literature review, especially on the role of local government reported by previous empirical studies, it was constructed a quantitative questionnaire survey targeting all 308 municipalities. Given that no public information on the issue is available, direct questionnaire is the adequate source for getting relevant information. Portuguese municipalities have acquired over time a great historical, political, economic, administrative, financial and legal importance, revealing of pivotal significance in the context of local public decisions. This role of local government has known a considerable notoriety with the increasing transfer of powers and responsibilities to municipalities (Carvalho et al., 2014). Currently municipalities have competencies in the following domains as stated by Article 23 of Law No. 75/13 of 12 September): Rural and urban equipment; Energy; Transport and communications; Education; Heritage, culture and science; Free times and sports; Health; Social action; Housing; Civil protection; Environment and basic sanitation; Consumer protection; 16 Promotion of development; Territorial and urban planning; Municipal Police; and External Cooperation (Carvalho et al., 2014). This research tool – the questionnaire - is used to: obtain knowledge of a population, its opinions and actions; to analyze a social phenomenon that is thought to be better grasped through collected information about individuals of the population; in cases where it is necessary to examine a large number of people (Quivy and Campenhoudt, 2005). So, questionnaire survey serves this dissertation purposes. 3.2. The questionnaire design The questionnaire (see Appendix 1) was developed in Portuguese, considering the target respondents. The target respondents are the relevant interlocutors appointed by each municipality/ City Hall mayor. The questionnaire comprises two parts. The first part addresses the characterization of respondents: the name of the Municipality, the first and last names of the respondent, function/ position in the Municipality, the department that the respondent belongs and its contact (email or telephone). The second part consists of three major issues, which aim to assess, respectively: the municipality's knowledge about the policies of regional smart specialization strategy within the 2020 (Question 1); in the case of the municipality does not have knowledge, if it has already identified key domains, areas, sectors of specialization that benefit from smart specialization strategy and what they are, as well as the importance of the key interlocutors of the municipality (Question 2); and, in the case of the municipality has knowledge, if the municipality has been or is involved in the process of the definition of smart specialization strategies and hence what activities have been developed in this context, at what level the municipality is involved, as well as the other stakeholders, in the process and what is the severity of the constraints felt in implementing the smart specialization process (Question 3). The questionnaire presents: closed-ended questions, which can be categorized as either single (as questions related to respondent characterization, where one response is required, dichotomous (Question 1, 3.1, where two response items are provided) and multichotomous (Questions 3.2, where several alternatives are listed); scaled-response questions, such as Questions 2.2 and 3.3, which use a scale to measure the attributes of the construct; and opened-ended questions, such as questions 2.2.1, 3.2.1, 3.4. and 3.5 17 (see Frazer and Lawley, 2000). The scaled-response questions are design as a Likert scale of five categories. Based on Frazer and Lawley (2000), Table 2 presents the links between the different stages of the research process. Table 2. Links between stages of the research process Research questions Relevant questions of the questionnaire Proposed analysis technique In what extent local government is aware of the smart specialization? Q1. Q2.1 Q3.1 Frequencies, percentages and appropriated tests What are the perceptions of municipalities about their role in Regional Smart Specialization Strategy process and the current constraints? Q2.2 Q3.3 Q3.4 Q3.5 What are they doing within Regional Smart Specialization Strategy process? Q3.2 Q3.4 3.3. Data collection The questionnaire was sent to all 308 municipalities, with a cover letter (see Appendix 2), addressed to the respective mayors, by the email [email protected] exclusively created for this research. The submission of the questionnaire was preceded by the collection of institutional emails (of the mayors’ office, of the office that support the mayor or the general email) of the Portuguese municipalities, in their websites or in the website of the Ministry of Internal Affairs. In addition, it was used the Google Forms platform to insert the questionnaire. The submission was made from March 26th to 31th, 2015, requesting response via Google Forms platform, fax, email or phone until the 8th of April 2015. However, by that date, the response rate was manifestly insufficient, so it a new email request was sent from April 16th to 19th with a new deadline of April 24th. Later, during the month of May, telephone contacts were made with the municipalities that had not yet responded, reiterating the call for its collaboration and a new request was re-sent sent by email. The contacts were closed on May 31th, 2015, however it was considered the responses received until July 17th, 2015, which totaled 110 responses received. The majority of the answers (108) was received by the platform; one municipality sent the responses by 18 email and another one answered the questionnaire by telephone. Figure 2 summarizes the process of data collection. Figure 2. Process of data collection The gathered data by the questionnaire, based on pre-codified answers, was then analysed statistically by SPSS software. 3.4. Population and sample There are 308 municipalities in Portugal, including 278 in mainland and 30 in Azores and Madeira. Giving that the most often used criterion to classify municipalities' size still takes into account the number of inhabitants, Carvalho et al. (2014) grouped the Portuguese municipalities in three different categories by their size measured in number of inhabitants:  Small municipalities: population with lower or equal to 20 000 inhabitants;  Medium municipalities: with more than 20 000 inhabitants and less than or equal to 100 000 population; February-March 2015 Questionnaire construction Creation of email [email protected] Insertion of the questionnaire in Google Forms Platform March 2015 Collection of institutional municipalities’ emails Questionnaire submission (26th March) by email (First request with first deadline on 8th April) April 2015 Second request (from 16th to 19th April) by email with second deadline (24th April) May 2015 Third request: Telephone contacts and email Contacts closed on 31st May. June and July 2015 Questionnaire’s reception Platform’ closing on 17th July: 110 responses received 25 All respondent municipalities that belong to Algarve and Lisbon have knowledge about the policies of regional smart specialization strategy under Horizon 2020. However, two of the three respondent municipalities of Madeira have not knowledge about the policies of regional smart specialization strategy under Horizon 2020. This proportion is lower to NUTS II of Azores (50%); Centre (18%); Alentejo (18%); and North (10.7%). We find that there is not statistically significant relationship between municipality’s knowledge about the policies of regional smart specialization strategy under Horizon 2020 and NUTS II (Fisher’s exact= 0.176). The municipalities that have no knowledge about such policies were invited to answer the question 2. Table 13 shows the frequencies of the responses to the question 2.1 “Has municipalities identified key domains /areas/sectors of specialization that would benefit from smart specialization strategy?” (“O município tem já identificados domínios/áreas/setores de especialização chave que beneficiariam da estratégia de especialização inteligente?”). Table 13. Existence of key domains/areas/sectors of specialization already identified that would benefit from smart specialization strategy (Q2.1) Frequency Percent Percent Sample (n=110) No 13 72.2 11.8 No, but the identification is in process 5 27.8 4.5 Yes 0 0 0 Total 18 100.0 The majority of municipalities that have not knowledge about SSS policy, when asked about if they have already identified key specialization fields / areas / sectors that could benefit from smart specialization strategy, said no (13 municipalities, 72.2%, corresponding to 11, 8% of the sample). Only 5 in 18, i.e., 27.8% (corresponding to 4.5% relative to the total sample) assumed that the identification in process. Once again, Table 14 presents the answers of Q2.1 by municipality’s size, as well as the Fisher’s exact test. None of the eighteen municipalities that answered to Q2.1 are considered large. Fifty percent of small of small municipalities that answered to Q2.1 state that the identification is in process. Considering the medium size municipalities, this percentage is lower (16.7%). 26 Table 14. Q2.1 by municipality’s size Q2.1 Size Total Medium Small No, but the identification is in process 1 4 5 No 5 8 13 Total 6 12 18 Fisher’s exact= 0.615 We find that there is not statistically significant relationship between the existence of key domains/areas/sectors of specialization already identified that would benefit from smart specialization strategy and municipality’s size (Fisher’s exact= 0.615). Table 15 shows the answers to Q2.1 by NUTS II. The five municipalities that responded “No, but the identification is in process” belongs to: Alentejo (1), Centre (3) and Madeira (1). Table 15. Q2.1 by NUTS II Q 2.1 NUTS II Total Alentejo Centre North Azores Madeira No, but the identification is in process 1 3 0 0 1 5 No 3 5 3 1 1 13 Total 4 8 3 1 2 18 Fisher’s exact= 0.784 Once again, we find that there is not statistically significant relationship between the existence of key domains/areas/sectors of specialization already identified that would benefit from smart specialization strategy and NUTS II (Fisher’s exact= 0.784). Since none of these 18 municipalities claimed to have identified key areas or sectors, there was no response to question 2.1.1 “If you answered yes to Question 2.1, please specify.” Considering the question 2.2 “In the context of regional policy implementation, indicate what would be the importance of the key interlocutors of the municipality” (No âmbito da implementação da política regional, indique qual seria a importância dos interlocutors chave do município”, Table 16 summarizes the responses. 27 Table 16. Importance of the municipality’s key interlocutors within the regional policy implementation (Q2.2) CCDR Professional Associations Commercial/ industrial Research Institutes Reference Company Others Associations n % n % n % n % n % n % Nothing important 2 11.1% 3 16.7% 3 16.7% 2 11.1% 2 11.1% 12 66.7% Little important - 4 22.2% - - - 1 5.6% Some important - - 3 16.7% 2 11.1% 5 27.8% 2 11.1% Important 7 38.9% 10 55.6% 10 55.6% 8 44.4% 8 44.4% 3 16.7% Very important 9 50.0% 1 5.6% 2 11.1% 6 33.3% 3 16.7% 0 0.0% Total 18 100.0% 18 100.0% 18 100.0% 18 100.0% 18 100.0% 18 100.0% CCDR: Comissão de Coordenação e Desenvolvimento Regional (Commission for Regional Coordination and Development) In general the presented interlocutors (CCDR, professional associations, commercial/industrial associations, research institutes, and reference company) are considered as important in the context of regional policy (except for “Other”). Thus, Table 17 considers only the answers “Important” and “Very Important” in an aggregated way, with its absolute and relative frequency. Table 17. Municipality’s key interlocutors (important and very important) Important and very important CCDR Professional Associations Commercial/ industrial Associations Research Institutes Reference Company Others Number of answered 16 11 12 14 11 3 Group % (n=18) 88.9% 61.1% 66.7% 77.8% 61.1% 16.7% Among the interlocutors important, we can highlight CCDRs (88.9%), followed by Research Institutes (77.8%), Commercial/Industrial Associations (66.7%), Professional Associations and reference companies (both with 61.1%). In “Others”, 3 municipalities emphasize the importance of the following entities: municipal company; Turismo de Portugal; and Unions and Workers Associations, thus responding to the question 2.2.1. It is also noted a municipality which mentions the professional schools, but giving them little importance. The 92 municipalities that state to be knowledgeable of smart specialization strategy policies responded to Question 3, specifically, to the question 3.1 "Was/Is the municipality involved in the definition process of smart specialization strategies?" (O município esteve/está envolvido no processo de definição de estratégias de especialização inteligente?”). Table 18 shows the frequency of responses. 28 Table 18. Municipality’s involvement in the definition process of smart specialization strategies (Q3.1) Frequency Percent Cumulative Percent No 32 34.8 34.8 Yes 60 65.2 100.0 Total 92 100.0 Among municipalities who know SSS, 65.2% (60 municipalities) declare they are involved in the definition of SSS. However, more than one third of the sample (32 municipalities, which account for 34.8% of the knowledgeable group) says is not involved. Adding 18 municipalities that say ignore the SSS to these 32 municipalities, there are 50 municipalities in 110 (sample) who apparently are not linked to this process, i.e., 45.45% of municipalities do not participate in SSS processes. Combining the answers to question 2.1 with the answers to question 3.1, the following map summarizes the results (Figure 3). The municipality does not know. No, but the identification is in process. Yes, the municipality is involved. Yes, but the municipality is not involved. Not answered. Figure 3. Municipalities’ participation in SSS 29 Then we associate the answers to Q3.1 with municipality’s size and use Fisher’s exact test (see Table 19). Table 19. Q3.1 by municipality’s size Size Q3.1 Large Medium Small Total No 4 10 18 32 Yes 6 19 35 60 Total 10 29 53 92 Fisher’s exact= 0.950 The sixty municipalities that say they are involved in the definition of SSS, 6 are large, 19 are medium and 35 are small. Considering the respondent municipalities to Q3.1, 40% of large municipalities, 35% of medium municipalities and 34% of small municipalities that know SSS are not involved in that process. The results of Table 19 suggest that there is not a statistically significant relationship between the answers to Q2.1 and the size of the municipality (Fisher’s exact=0.95). Table 20 presents the answers of Q3.1 by NUTS II. Table 20. Q3.1 by NUTS II Q 3.1 NUTS II Total Alentejo Algarve Centre Lisbon North Azores Madeira No 6 2 9 2 13 0 0 32 Yes 12 4 28 2 12 1 1 60 Total 18 6 37 4 25 1 1 92 Fisher’s exact= 0.319 Once again, the results of Table 20 suggest that there is not a statistically significant relationship between the answers to Q3.1 and the NUTS II (Fisher’s exact= 0.319). Considering the question 3.2, “What activities have been developed in this area by the municipality? (Please select the relevant options)” (“Que atividades têm sido desenvolvidas neste âmbito pelo município? (Por favor marque as opções que entender relevantes))”, the summary of the answers and the results of the Independent Samples Kruskal-Wallis test (involved versus not involved) by each group of activities are presented in Table 21. 30 Table 21. Developed activities by Municipalities within the definition process of smart specialization strategies (Q3.2) Activities Total Mean (# 92) Involved group (# 60) Noninvolved group (#32) p-value (Kruskal-Wallis Test) Decision 1. Participation in meetings 57.6 78.3 18.8 0.000 Reject the null hypothesis 2. Promoter of workshops 22.8 25.0 18.8 0.490 Retain the null hypothesis 3. People training 18.0 26.0 3.0 0.008 Reject the null hypothesis 4. Gathering of information (studies) 48.9 56.7 34.4 0.043 Reject the null hypothesis 5. Benchmarking 28.3 33.3 18.8 0.141 Retain the null hypothesis 6. Working groups to implement the strategies 41.3 55.0 15.6 0.000 Reject the null hypothesis 7. Others - - - - Asymptotic significance are displayed. The significance level is 0.05. With regard to involved municipalities in the definition process of smart specialization strategies (60 municipalities), the principal activities are: participation in meetings (78.3%); gathering of information (studies) (56.7%) and working groups to implement the strategies (55%). The 32 municipalities that are not involved, as expected, have lower frequencies in the all presented activities; however, the main activity is the gathering of information (studies) (34.4%). Overall (92 municipalities), we can stand out positively the participation in meetings and the gathering of information (studies), and less positively the people training activity. Considering the p-value of Kruskal-Wallis test, whose the null hypothesis assumes that the distribution of each activity of Q3.2 is the same across categories of Q3 group (involved and non-involved), the null hypothesis is rejected to activities 1, 3, 4, and 6, and it is retained to activities 2 and 5. There are significant differences between the involved and non-involved groups in what concerns to participation in meetings, people training, information collection (studies) and strategies implementation’s groups; and there are not significant differences between the involved and non-involved groups regarding to workshop promoter and benchmarking. As responses to question 3.2.1 “If you selected ‘Participation in meetings’, please indicate with whom” (Se selecionou ‘Participação em reuniões’, por favor, indique com 31 que Entidades”), several municipalities stand out the inter-municipal Communities (CIM), the CCDR, the Secretary of State, regional development institute, business associations and research institutes. On the question 3.2.2 “If you selected ‘Other’, please specify the most relevant” (Se selecionou “Outras”, por favor, especifique as mais relevantes”, the inter-municipal communities are highlighted. Regarding the question 3.3 “Municipality’s involvement degree in various activities of the smart specialization process” (“Grau de envolvimento do município em diversas actividades do processo da especialização inteligente regional”), the summary of the answers and the results of the Independent Sample Kruskal-Wallis test (involved and non-involved groups) by each group of activities, are presented in Table 22. Considering the Likert scale used of five categories (“nothing involved”, “little involved”, “some involved”, “involved” or “too involved”), the options “involved” and “too involved” are aggregated as well as the other three options (“nothing involved”, “little involved”, “some involved”), creating dummy variables, assuming the value 1 if the involvement level is “involved” or “too involved”, and 0 if the involvement level is “nothing involved”, “little involved” or “some involved”. Table 22. Municipality’s involvement degree in various activities of the smart specialization process (Q3.3) Activities Total Mean (# 92) Involved group (# 60) Non-invol ved group (#32) p-value (KruskalWallis Test) Decision 1. Facilitator / promoter of incentives in terms of public local policies to the discovery of specializations - supporting the Horizon 2020 applications 42.39 55.00 18.75 0.001 Reject the null hypothesis 2. Interlocutor with the entrepreneurs and/or business associations and/or professionals of the regions 55.43 68.33 31.25 0.001 Reject the null hypothesis 3. Supporting entrepreneurs in the management and dissemination of a network of mutual relations to boost the discovery process 45.65 56.67 25.0 0.004 Reject the null hypothesis 4. Relevant information's agglutinant on emerging opportunities, products, and safety standards of the process 35.87 48.33 12.5 0.001 Reject the null hypothesis 5. Promoter of complementary investments related to emerging specializations, primarily training people. 35.87 45.00 18.75 0.013 Reject the null hypothesis 6. Influencer of the choice of specializations by supra-regional entities. 33.70 41.67 18.75 0.028 Reject the null hypothesis 7. Monitoring. 23.91 35.00 3.13 0.001 Reject the null hypothesis Asymptotic significances are displayed. The significance level is 0.05. 32 As expected, the percentages/frequencies for all presented activities are frankly higher for the involved group compared to the non-involved group. To the involved group, the activities “Interlocutor with the entrepreneurs and/or business associations and/or professionals of the regions” (68.33%; 41 of 60), “Supporting entrepreneurs in the management and dissemination of a network of mutual relations to boost the discovery process” (56.67%; 34 municipalities of 60) and “Facilitator / promoter of incentives in terms of public place policies to the discovery of specializations - supporting the Horizon 2020 applications” (55%; 33 municipalities of 60) present higher municipalities' involvement. The activity of lesser involvement is “Monitoring”. For the group of non-involved municipalities, the greater involvement activities and the activity of lesser involvement are the same of the involved group. Considering the p-value of Kruskal-Wallis test, whose the null hypothesis assumes that the distribution of municipality’s involvement degree in each activity of the smart specialization process of Q3.3 is the same across categories of Q3 group (involved and non-involved), the null hypothesis is rejected to all presented activities. This means that there are significant differences between the involved and non-involved groups of municipalities in what concerns to municipality’s involvement degree in the various activities of the smart specialization process. The five responses received to question 3.3.1 “If you selected ‘Other tasks’, please specify the most relevant” (“Se selecionou ‘Outras tarefas’, por favor, especifique as mais relevantes”) are mere observations, noting the availability of the municipality to join / participate in the proposed activities (open attitude). Regarding to question 3.4 “Degree of involvement of each of the following entities in the smart specialization process of municipality” (“Grau de envolvimento de cada uma das seguintes entidades no processo de especialização inteligente do município”), Table 23 presents the summary of the answers and the results of the Independent Samples Kruskal-Wallis test (involved versus not involved) by each group of activities. Once again, by each entity, the five Likert scale is reduced to two groups to create dummy variables, as described above. According to respondent municipalities, overall CCDR (63.04%), reference companies (41.3%) and universities (35.87%) are the most involved entities in the smart specialization process of municipality. Once again and as expected, the degree of 33 involvement of the different presented entities is higher in the involved group of municipalities, compared to the non-involved group. Considering the p-value of Kruskal-Wallis test, whose the null hypothesis assumes that the distribution of the degree of involvement of each entity of Q3.4 is the same across categories of Q3 group (involved and non-involved), the null hypothesis is rejected to CCDR and reference companies. This means that there are significant differences between the involved and non-involved groups of municipalities in what concerns to the degree of involvement of those entities in the smart specialization process of municipality. Table 23. Degree of involvement of each of the following entities in the smart specialization process of municipality (Q3.4) Entities Total Mean (# 92) Involved group (# 60) Noninvolved group (#32) p-value (Kruskal Walis Test) Decision Universities 35.87 36.67 34.38 0.828 Retain the null hypothesis CCDR 63.04 75.00 40.63 0.001 Reject the null hypothesis Professional Associations 22.83 25.00 18.75 0.499 Retain the null hypothesis Commercial and industrial Associations 33.70 40.00 21.88 0.081 Retain the null hypothesis Reference institutes 27.17 31.67 18.75 0.187 Retain the null hypothesis Reference companies 41.30 50.00 25.00 0.021 Reject the null hypothesis Others 2.17 1.67 3.13 0.650 Retain the null hypothesis Asymptotic significances are displayed. The significance level is 0.05. As responses to question 3.4.1 “If you selected ‘Other’, please specify the most relevant” (“Se selecionou ‘Outras’, por favor, especifique as mais relevantes:”) were highlighted the regional inter-municipal Communities, the competitiveness and technology Poles and Clusters. Finally, the answers to the question 3.5 “Severity level of the following constraints in the implementation of smart specialization process of the municipality” (“Grau de gravidade de cada um dos seguintes constrangimentos na implementação do processo de especialização inteligente do município”) are considered. Table 24 presents the summary of the answers and the results of the Independent Samples Kruskal-Wallis test (involved versus not involved) by each group of constraints. Using the same procedure above, by each constraint, the Likert scale of five categories (“no problematic”, “a bit problematic”, “some problematic”, “problematic” and “too problematic”) is reduced to 34 a dummy variable, which assumes the value 1 if the severity level is “too problematic” or “problematic”, and 0 if it is “no problematic”, “a bit problematic” or “some problematic”. Table 24. Severity level of the following constraints in the implementation of smart specialization process of the municipality (Q3.5) Constraints Total Mean (# 92) Involved Group (# 60) Non-involved group (#32) p-value (KruskalWallis Test) Decision Scarce Information from CCDR 17.39 15.00 21.88 0.410 Retain the null hypothesis Scarce Information from central government 29.35 26.67 34.38 0.442 Retain the null hypothesis Scarce EU Information 25.00 23.33 28.13 0.615 Retain the null hypothesis Poor quality of information from CCDR 17.39 16.67 18.75 0.803 Retain the null hypothesis Poor quality of information from central government 20.65 18.33 25.00 0.454 Retain the null hypothesis Poor quality of EU Information 22.83 21.67 25.00 0.718 Retain the null hypothesis Not clarifying the boundaries 23.91 23.33 25.00 0.859 Retain the null hypothesis Absence of human resources 39.13 36.67 43.75 0.510 Retain the null hypothesis Need of human resources training 37.78 35.00 43.33 0.445 Retain the null hypothesis Other constraints 3.26 3.33 3.13 0.957 Retain the null hypothesis Asymptotic significances are displayed. The significance level is 0.05. As expected the severity level of the constraints is higher for the non-involved group, compared to the involved group. Overall, “Absence of human resources”, “Need of human resources training” and “Scarce Information from central government” are the constraints considered more severe. However, considering the p-value of Kruskal-Wallis test, the null hypothesis is rejected to all presented constraints, i.e., there are not significant differences between the involved and non-involved groups of municipalities in what concerns to the severity level of the constraints in the implementation of smart specialization process of the municipality. As responses to question 3.5.1 “If you selected ‘Other constraints’, please specify the most relevant” (“Se selecionou ‘Outros constrangimentos’, por favor, especifique as mais relevantes:”) were noted constraints in recruitment process of human resources and short time to the assessment and analysis. 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Zelaia, A. (2013), “Basque Country, Spain: Smart Specialisation Strategies”, in Organisation for Economic Co-operation and Development (OECD), Innovation-driven Growth in Regions: The Role of Smart Specialisation, Preliminary version, 141-148. 45 Appendices Appendix 1. Questionnaire 46 47 48 49 50 Appendix 2. Presentation Letter De: Pedro Nuno Ferreira de Oliveira Assunto: Estratégias de especialização inteligente dos municípios portugueses Ex.mo(a). Sr(a). Presidente da Câmara Municipal No âmbito do Mestrado em Empreendedorismo e Inovação Tecnológica, em co-tutela pelas Faculdades de Engenharia e de Economia da Universidade do Porto, encontro-me a desenvolver a minha dissertação subordinada ao tema O Papel dos Municípios Portugueses na Estratégia de Especialização Inteligente Regional, orientada por Aurora Teixeira. Em virtude da escassez de informação sobre este assunto ao nível municipal, a inquirição direta aos municípios releva-se como a única fonte de informação relevante para elaboração de estudos de natureza científica. Assim, elaborei um inquérito (em anexo) que permitirá a recolha de informação adequada. A informação será tratada em agregado, estando garantida a confidencialidade dos dados. Solicito a Vossa preciosa colaboração no preenchimento do referido inquérito, pela Sua pessoa ou por quem entenda que esteja mais habilitado para o fazer. Para responder ao inquérito poderão fazê-lo directamente no link https://docs.google.com/forms/d/1Lg15OuQ43dSs5SARVS3axEF_KIwqV-I3NkPHVNoYg84/edit ou enviar o inquérito por email ([email protected]p.pt) ou fax (225505050, A/C: Aurora Teixeira). Posso ainda, caso prefiram, ligar e recolher a informação diretamente via telefone. No sentido de cumprir os prazos estipulados para entrega da dissertação ficaria extremamente agradecido se me pudessem devolver o inquérito respondido até 08 de Abril de 2015. Estou disponível para esclarecimentos ou dúvidas que eventualmente subsistam (telemóvel 917508760). Na expetativa do Vosso melhor acolhimento, subscrevo-me endereçando os meus melhores cumprimentos. Atentamente, Pedro Oliveira Contactos: Pedro Oliveira, Telm. 917508760; email: [email protected]; fax: 225505050 (A.C.: Aurora Teixeira)