THE CLUSTER-LIFE-CYCLE ANALYSIS: A PROMISING APPROACH TO OVERCOME THE CHALLENGES POSED BY THE IMPLEMENTATION OF EU SMART SPECIALIZATION POLICY .

A REVIEW OF THE OF THE STATE OF THE ART. 

Author: Giuseppe Pronestì, B.Eng, M.Eng, PhD

 

Abstract: In a world of rapid transformations and unfettered innovations, keeping up with the fast-paced rhythm of markets’ evolution has become an imperative for regions. As a consequence competition among regions has exacerbated by inducing territories to structure progressively more effective economic development strategies. Laying behind is no longer an option for backward regions, toiling to align with economic- and market -related dynamics, which are endanger by the more likely chance to be cannibalized from market’s leader regions. This danger has been significantly considered by EU which committed to convey into one single policy construct, Smart Specialization Strategy (S3), the most relevant elements required to enhance, when not revitalize, regional economies.This article aims at highlighting how the implementation of S3 policycan benefit the EU experience with cluster and cluster policies. Specifically, by sourcing some recent and successful strand of literature, the article will disclose the potential of the Cluster-Life-Cycle (CLC) analysis as a catalyst for the implementation of S3. In so doing the work presents (i) a preliminary outlook on the importance of the S3 policy to enhance EU regional development, (ii) a short description of S3 policy and its relations with cluster and cluster policies, and (iii) ultimately casts lights on the CLC analysis as a promising approach to overcame the challenges related to S3 implementation.

 

1. Outlook on EU policy context

In a world of rapid transformations and unfettered innovations, keeping up with the fast-paced rhythm of markets’ evolution has become an imperative for economic systems. As a consequence competition among regional economies has exacerbated by inducing territories to structure progressively more effective economic development strategies. Laying behind is no longer an option for backward regions, toiling to align with economic- and market -related dynamics, which are endanger by the more likely chance to be cannibalized from market’s leader regions.

This danger has been significantly considered by EU authorities which committed to convey into some groundbreaking policy construct, the most relevant element required to enhance when not revitalize regional economies. The EU policy intervention grounds in the attempt to promote the efficient allocation of economic and financial resources,conducing to the exploitation of economic advantages, deriving from innovation(Reillon, 2016). Following this conceptual path the EU authorities launched the 2020 strategy, which aims at fostering smart, sustainable, and inclusive growth towards enabling EU regions to build a new economy, based on knowledge and innovation. One of the most important elements of the EU 2020 strategy is the Innovation Union (IU) flagship initiative.

The IU initiative targets the improvement of regional framework-conditions, and the facilitation of access to finance for R&D projects, aiming at securing the transformation of innovative ideas into actual projects, capable of outputting  products and services, ad creating jobs and growth (COM 2010, 546)[1]. The IU is a major breakthrough in the way of approaching the design of innovation policies. Indeed the IU initiative, shaped as an umbrella policy, aims at (i) encompassing all the elements that may influence the innovation process, and (ii) identifying bottlenecks and limitations pertaining to the innovation process by including six priorities. The six priorities originally envisioned within the IU initiative have been later complemented by adding two new elements, namely S3 and Cluster.

S3 is acknowledged by a communication of the EU commission (COM, 2010, 553)[2], that stressed the importance of supporting innovation processes at the regional level both to ensure a more effective allocation of public funds and stimulate private investment. Cluster is recognized by many EU policy documents as explanatory factor, capable of  facilitating the comprehension of regional economic phenomena. This latter concept is alsounderlined in the EU commission’s communication (COM, 2010, 614)[3], that states how clusters can “improve industrial competitiveness and innovation by bringing together resources and expertise, and promoting cooperation among businesses, public authorities and universities”(Reillon, 2016).

This backdrop clearly manifest the weight that the EU innovation policy assigns to S3 and cluster, as crucial and interrelated components of the current and prospective structure of regional economies. Notwithstanding the potential positive impact on regional economies, deriving from the cautious combination of S3 and cluster, they remain different matters. Consistently while many scholars noted how clusters can contribute to, and integrate the implementation of S3, certain intricated facets of the relationship between these two policy constructs remain somehow black-boxed. Only recently a promising approach to solve such intricacies has been disclosed, namely the CLC analysis.

Consistently this paper will briefly dig into the nature of cluster and S3 in the way of revealing, by sourcing the findings of some recent scholarly work, how the study of CLC can be conducive of a more effective implementation of S3 policies.

 

2. S3 and cluster, defining the policy concepts to confront the implementation challenges.

Born from the reflections of Domique Foray and the Knowledge for Growth Experts Group,S3 is the effective response to the need of re-thinking EU regional development policies towards bridging the so-called transatlantic gap. Indeed the S3 is an innovative and successful academic idea which has rapidly turn into a crucial element of the EU 2020 innovation plan(Foray, David and Hall, 2009). S3 casts light on a place-based approach to boost regional development by discovering and supporting territorial-specific potentials to specialize. In so doing S3(i) emphasizes the prioritization of policy initiatives by operating with a vertical logic and defining methods to “identify […] desirable areas for […] intervention”(Foray and Goenega, 2013),(ii) fosters the  production of smart, sustainable and inclusive growth[4], (iii) promotes scientific research, and (iv) sustains the maximization of the usage of innovations(Foray, David and Hall, 2011; Foray and Goenega, 2013; Foray, 2015). However, S3 objectives are subject to the effectiveness of the Entrepreneurial Discovery Process (EDP). The EDP (originally studied by various authors, e.g.Kirzner, 1999) is an essential element wihtin the overall S3 policy framework. Indeed, according to Dominique Foray, EDP is the engine running S3 and enabling the disclosure of regions’ hidden potential to specialize. Specifically, Dominique Foray claims that certain framework conditions for innovation at the regional level, as well as relational density and diversity of economic actors , can input EDP, while in turn EDP can drive the effective implementation of S3 by conducing to: (i) the integration of economic and entrepreneurial knowledge, (ii) the engagement of stakeholders, and (iii) the exploration of new economic domains. To sum up EDP is meant to foster the implementation of S3, by territorially recognizing entrepreneurial and economic knowledge to enlarge the regional knowledge-base, which would in turn lead towards the exploration of new specialization domains.

While the identification of specialization domains is an innovative feature of S3, the process of regional specializations is somehow tied to the“old” idea of cluster and regional clustering. Prior to understand how clustering and specializing (and consequently cluster and S3) are correlated, it is worth briefly defining the concept of cluster.

The conception of cluster has distant roots, which date back to the end of the 19th century when Alfred Marshall (Marshall, 1890)presented its early reflections on industrial districts as areas where concentration of firms settles down. Since that time this academic concept has significantly evolved as it enlarged its breadth and became a policy hit. A multitude of scholars (e.g.Hoover and Giarratani, 1984; Bianchi, Miller and Bertini, 1997; Keeble and Wilkinson, 1999; Becattini, 2002; Mccann and Sheppard, 2003; Brenner, 2006;Bevilacqua et al., 2019)produced a plethora of works dealing with the concept of cluster, its definition and its operationalization. However the actual popularization of the concept of cluster can be undoubtedly attributed to Micheal Porter. The latter scholar offered, with his study “Competitive Advantage of Nations”(Porter, 1990), a major breakthrough to the cluster concept. SpecificallyPorter (i) explained how the competitiveness of national and regional economies is affected by the occurrence of industrial clusters which, in turn depends on the availability of certain context-conditions, and (ii) provided a widely accepted definition of cluster as “a geographically proximate group of interconnected and associated institutions in a particular field, linked by commonalities and complementarities”(Porter, 1990).By drawing on the Porterian definition, geographic proximity is the core component enabling both interactions between and development of industrial clusters, which in turn support creation of robust and flourishing Nation’s economic systems. Consistently, the scholar devised a clear six-element-based model, the so-called diamond model, for explaining his own claims. The afore mentioned framework describes the elements nourishing cluster’s success(Porter, 1998, 1999, 2000b, 2000a, 2003)namely: Factor conditions, Demand conditions, Firm strategy/rivalry, Related and supporting industries, Government and Chance.

Given this short introduction, many scholars have noted that the concepts of S3 and Cluster are not equivalent, however it still possible to recognize some sort of synergy between the two policy constructs(Ketels et al., 2013). Specifically clusters are considered as a key element to be cautiously deployed in the implementation of S3.Some recent strand of literature is focusing on a promising approach to effectively implement S3 through the deployment of CLCanalysis.

 

3. The cluster-life-cycle analysis to boost S3 implementation

While the academic literature has plenty of studies disentangling the role of clusters in the design and implementation of Smart Specialization Strategies (S3), there are just few pieces of work adequately attempting to explain how the evolutionary mechanisms of the Cluster Life Cycle (CLC) can inform S3. Indeed, the linkage between CLC and S3 is a new-born and innovative academic argument, featuring a short but very promising history. This argument has been timidly prospected in some scientific articles (e.g.Ketels et al., 2013), whereas it has come into prominence just recently, less than one year ago, thanks to the seminal book "The Life Cycle of Clusters: A Policy Perspective"(Fornahl and Hassink, 2017). The latter book currently represents the first, most structured and compelling academic commitment to study the relationship between CLC and S3. It basically draws insights from literature in the field of economic geography, evolutionary economics and regional studies towards offering a review of the S3 concept under a CLC-oriented perspective. In so doing the book emphasizes the need for taking into account the CLC concept to inform S3 policy-making. The high quality as well as the importance of this manuscript’s contribution to the academic debate is unquestionable, as it firstly framed and brought to light the linkage between CLC and S3. Therefore, this book frames a groundbreaking academic argument, while it could not examine in-depth all the argument's facets. Indeed, some aspects of the linkage between CLC and S3call still for further scrutiny, particularly the question on how the CLC can guide the S3 process of discovering regions’ potentials to specialize (the so called EDP). Later on other authors added some critical thoughts to such a debate by enlarging the knowledge-base on this topic (e.g. Bevilacqua and Pronestì, 2017; Pronestì and Bevilacqua, 2019). In particular, the recently published book “Life Cycle of Clusters in Designing Smart Specialization Policies”(Pronestì, 2019c)speculates on the relationship between CLC and S3 still, whereas it also introduces several original arguments. The uniqueness of such scientific work emerges from an unprecedented conceptual model, which attempts to disentangle how the CLC analysis can guide the EDP. Moreover, the originality of this scientific work can be better understood by referring to four main elements.

  1. The book aims at exploring if, how and what potential influence can be exerted by the CLC analysis towards informing and consequently inputting S3, and EDP. In so doing, it presents an insightful conceptual model to: i) guide the understanding of the role of the CLC’s dynamics to inform S3-policy-making and support the discovery of regions’ potentials to specialize, ii) address the interpretation of the potential inputs that the CLC analysis in the S3 process, iii) contribute to the creation of CLC-based decision support systems(Pronestì, 2019b).

  2. The study roots on the idea that CLC can be described, to some extent, through using a set of macro-variables and indicators. That given, the book presents a conceptual model portraying both, clusters’ evolution and stage-specific features, according to five macro-categories of variables including ten indicators. The outputs of such indicator-based conceptual model, presenting graphically and analytically the evolutionary process of cluster’s development, are interpreted in the way of unveiling the role played by each CLC stage to inform S3-policy-making and support the discovery of regions’ potentials to specialize. The conceptual model ultimately reveals if, how, and which stage-specific cluster’s features (in terms of “Innovation and Entrepreneurship”, “Variety”, “Size”, “Spatial Significance” and “Cooperation”) could effectively inform and input the design and operationalization of the S3 and EDP (Pronestì, 2019a).

  3. The book finds that the CLC analysis can play a prominent role to foster the dynamic scrutiny of regional economic contexts and to support the design and implementation of tailor-made development policies, such as S3. However, the book moves beyond the broad finding mentioned above, towards sharply identifying the specific inputs that each stage of the CLC can provide in the way of informing the policy-making process. In other words, the book finds which phases of clusters’ evolution presents the most suitable features to inform S3-policy-making and support the discovery of regions’ potentials to specialize(Pronestì, 2019a).

Contribution in knowledge. While the book contributes to newly emerging literature on how the CLC analysis can inform smart policies attributing them with the capacity to catch the dynamism of regional economic context, it specifically targets the EDP.

The scientific works mentioned aboveclearly signal the growing consideration of scholars and practitioners with respect to the CLC analysis as a potential tool both to addressS3’s decision-making andfelicitate the comprehension of regional economic phenomena. Notwithstanding the crucial contribution provided by such scholarly works, which grounded and launched the debate on the importance of CLC analysis in S3, it is still necessary to push the research forward and to conduct further studies in order to enlarge the knowledge-base on the topic and ultimately provide practicable policy solutions.

 

4. Bybliography

Becattini, G. (2002) ‘From Marshall’s to the Italian “Industrial districts”. A brief critical reconstruction’, Complexity and Industrial Clusters: Dynamics and Models in Theory and Practice, (1841), pp. 83–106. doi: 10.1007/978-3-642-50007-7_6.

Bevilacqua, C. et al. (2019) Investigating local economic trends for shaping supportive tools to manage economic development: San Diego as a case study, Smart Innovation, Systems and Technologies. doi: 10.1007/978-3-319-92099-3_23.

Bevilacqua, C. and Pronestì, G. (2017) ‘Clusters in designing S3-oriented policies’, in 13 TH INTERNATIONAL POSTGRADUATE CONFERENCE 2017. Manchester, UK: The University of SalfordSalford, M54WT. doi: 978-1-0912337-05-7.

Bianchi, P., Miller, L. and Bertini, S. (1997) ‘The Italian SME experience and possible lessons for emerging countries’, In Executive Summary, …. Available at:

http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.194.6357.

Brenner, T. (2006) ‘Identification of local industrial clusters in Germany’, Regional Studies, 40(9), pp. 991–1004.

doi: 10.1080/00343400601047408.

Foray, D. (2015) ‘Smart Specialisation: Opportunities and Challenges for Regional Innovation Policy’, Regional Studies, 49(3), pp. 480–482. doi: 10.1080/00343404.2015.1007572.

Foray, D., David, P. a. and Hall, B. (2009) ‘Smart Specialisation – The Concept’, Knowledge Economists Policy Brief, 9(85), pp. 1–5. doi: 10.1016/j.pcad.2013.03.008.

Foray, D., David, P. A. and Hall, B. H. (2011) Smart specialization From academic idea to political instrument, the surprising career of a concept and the difficulties involved in its implementation, MTEI Working Paper. Available at: http://infoscience.epfl.ch/record/170252/files/MTEI-WP-2011-001-Foray_David_Hall.pdf (Accessed: 21 October 2015).

Foray, D. and Goenega, X. (2013) ‘The Goals of Smart Specialisation’, (01), p. 18. doi: 10.2791/20158.

Fornahl, D. and Hassink, R. (2017) The life cycle of clusters : a policy perspective.

Hoover, E. M. and Giarratani, F. (1984) Introduction to Regional Economics. 3rd edn. Edited by E. M. Hoover and F. Giarratani. New York: Alfred A. Knopf, Inc. Available at: http://d-scholarship.pitt.edu/11165/.

Keeble, D. and Wilkinson, F. (1999) ‘Collective {Learning} and {Knowledge} {Development} in the {Evolution} of {Regional} {Clusters} of {High} {Technology} {SMEs} in {Europe}’, Regional Studies Association, 33(March 2013), pp. 295–303.

Ketels, C. et al. (2013) The Role of Clusters in Smart Specialisation Strategies. doi: 10.2777/43211.

Kirzner, I. M. (1999) ‘Creativity and/or Alertness: A Reconsideration of the Schumpeterian Entrepreneur’, The Review of Austrian Economics, 11, pp. 5–17. doi: 10.1023/A:1007719905868.

Marshall, A. (1890) Principles of Economics, The Online Library of Liberty. doi: 10.1057/9781137375261.

Mccann, P. and Sheppard, S. (2003) ‘The Rise, Fall and Rise Again of Industrial Location Theory’, Regional Studies.  Taylor & Francis Group , 37(6–7), pp. 649–663. doi: 10.1080/0034340032000108741.

Porter, M. (1990) ‘Competitive Advantage of Nations’, Competitive Intelligence Review, 1(1), pp. 14–14. doi: 10.1002/cir.3880010112.

Porter, M. E. (1998) ‘Clusters and the New Economics of Competition Harvard Business Review’, Harvard Business Review, pp. 77–90. doi: 10.1042/BJ20111451.

Porter, M. E. (1999) ‘Porter on the competitive advantage of clusters’, Strategic Direction, p. 21.

Porter, M. E. (2000a) ‘Economic Development: Local Clusters in a Global Economy’, Economic Development Quarterly, 14(1), pp. 15–34. doi: 10.1177/089124240001400105.

Porter, M. E. (2000b) ‘Location, Competition, and Economic Development: Local Clusters in a Global Economy’, Economic Development Quarterly, 14(1), pp. 15–34. doi: 10.1177/089124240001400105.

Porter, M. E. (2003) ‘The economic performance of regions’, Regional Studies, 37(6–7), pp. 549–578. doi: 10.1080/0034340032000108697.

Pronestì, G. (2019a) ‘An untapped knowledge source towards implementing smart specialization: The life cycle of clusters’, in SpringerBriefs in Applied Sciences and Technology. doi: 10.1007/978-3-030-03780-2_4.

Pronestì, G. (2019b) ‘Cluster and smart specialization: Different approaches to design innovation policy’, in SpringerBriefs in Applied Sciences and Technology. doi: 10.1007/978-3-030-03780-2_2.

Pronestì, G. (2019c) Life Cycle of Clusters in Designing Smart Specialization Policies. 1st edn. Heidelberg: Springer-Verlag. Available at: https://www.springer.com/it/book/9783030037796.

Pronestì, G. and Bevilacqua, C. (2019) The life cycle of clusters: A new perspective on the implementation of S3, Smart Innovation, Systems and Technologies. doi: 10.1007/978-3-319-92099-3_26.

Reillon, V. (2016) Building the EU innovation policy mix IN-DEPTH ANALYSIS.

 

[1] Europe 2020 Flagship Initiative Innovation Union COM (2010) 546.

[2] Regional Policy contributing to smart growth in Europe 2020 COM (2010) 553.

[3] An Integrated Industrial Policy for the Globalization Era Putting Competitiveness and Sustainability at Centre Stage COM (2010) 614.

[4] The definition of Smart, Sustainable, and Inclusive growth is retrieved from: A strategy for smart, sustainable, and inclusive growth COM (2010) 2020.

Smart growth: developing an economy based on knowledge and innovation.

Sustainable growth: promoting a more resource efficient, greener, and more competitive economy.

Inclusive growth: fostering a high-employment economy delivering social and territorial cohesion.

 

 

 

 ISSN 2038-5161

Premio del Libro Europeo "Aldo Manuzio"