By Chulwoo Lee & Jihye Jeon, presented at the 18th TCI Global Conference, Daegu, 2015.

1. Characteristics and Some Cases
of Cluster Evolutionary Trajectories
Chulwoo Lee, Professor, Dept. of
Geography, Kyungpook Nat’l Univ., Korea
Jihye Jeon, PhD candidate, Dept. of
Geography, Kyungpoon Nat’l Univ., Korea
Parallel Session 1.2: Analysis of Cluster Models and Cluster Ecosystems

2. Contents
2
Ⅰ. Research Background and Purpose
Ⅱ. Adaptive Cycle Model
and Some Cases
Ⅲ. Modified Cluster Adaptive Cycle Model
1. Adaptive Cycle Model
2. Cluster Adaptive Cycle Model
1. Concepts
2. Cluster Evolutionary Trajectories
3. Significances and limits

3. 3
Ⅰ. Research Background and Purpose
 Recently, the development of ICT(Information and
Communications Technology) and the high-tech industry, and
then the globalization of economy and the shift of economic
paradigm so called knowledge based economy
⇒ Economic space including industrial districts is changing
dynamically.
 Consequently, research on industrial districts so far...
• Static studies : analysis on formation factors and existence
form(Park, 1994; Lee, 2011; Lee and Lee, 1998; 2000); development plan(Lee,
2004; Park, 2003); policy evaluation(Nam, 2004; Lee, 2005; Lee and Lee, 2007)
• Dynamic studies : discussion on evolution of industrial
districts (interests in a series of historical evolution process
such as emergence, growth, maturity and renewal of
industrial districts especially in the field of institutional
economic geography)

4. 4
 An appropriate perspective is to consider cluster as complex
system, especially complex adaptive system(Martin and Sunley, 2011).
• Complex adaptive system is a suitable view for analyzing the evolution
of cluster, so that it is characterized by ‘the feedback between various
components from micro to macro scale’ and ‘changes to external
shocks as well as self-reinforcing and self-organizing through internal
co-evolutionary mechanism’
 Among the various models which can capture complex adaptive
system, the most comprehensive model in terms of identity, stability,
exogenous forcing is ‘adaptive cycle model’(Martin and Sunley, 2011).
Ⅰ. Research Background and Purpose

5. 5
 In Korea, some studies investigated the life cycle phases of
industrial districts and the characteristics of each period by
applying the life cycle model(Jeon, 2010; Koo, 2012; Jung, 2013).
 Under recognizing the limitations of life cycle model, some studies
applied the adaptive cycle model to investigate cluster evolution,
and suggested growth factors for future sustainable
development(Huh, 2013; Nam, 2014).
Ⅰ. Research Background and Purpose
 In this context, this presentation examines the various trajectories
of industrial districts, their characteristics and some cases based
on the adaptive cycle model.

6. 6
Ⅱ. Adaptive Cycle Model
1. Adaptive Cycle Model
Concepts
 Clarifies the evolutionary process that super-system and
sub-system affect each other, changing the structure and
function of the entire system through complex feedback
processes in ecosystems with circularity(Holling, 2001)
 Posits a four-phase process(exploitation, conservation,
release, reorganization) of continual adjustment in
ecological, social and environmental systems in terms of
change of accumulation, connectedness, and resilience
• Accumulation: the potential of accumulated resources available to the
system
• Connectedness: the internal connectedness of system components
• Resilience: a measure of system vulnerability to and recovery from
shocks, disturbances and stresses

7. 7
Ⅱ. Adaptive Cycle Model
Source: Holling, 2001; Martin and Sunley, 2011
Period of experimentation
and restructuring
Accumulation-low and varied
Connectedness-low
Resilience-increases
Period of stasis and
increasing rigidity
Accumulation-slows and stabilizes
Connectedness-low
Resilience-increases
Period of growth and
seizing of opportunities
Accumulation-rapid and focused
Connectedness-increasing
Resilience-high
Period of contraction and
decline
Accumulation-disinvestment and
destruction
Connectedness-low
Resilience-increases
(Re)emergence
and growth
Stabilization,
stagnation and decline
< Adaptive cycle model of the evolution of a complex system >

8. 8
 Martin and Sunley(2011) applied adaptive cycle model to
clusters
Ⅱ. Adaptive Cycle Model
• Throughout each step, capital accumulation, connectedness, resilience
indicate the cycles.
• Three scenarios following the Release and Decline phase are shown: A,
cluster disappears; B, the cluster undergoes a phase of renewal; and C, a
new cluster emerges and replaces the old one
※Capital accumulation: the accumulation of productive, knowledge and
institutional capital; Connectedness: the extent of traded and untraded
interdependencies among cluster firms; Resilience: the capacity of firms to
respond flexibly to shocks internal or external to a cluster
< Stylized evolution of a cluster over an adaptive cycle >
Source: Martin and Sunley, 2011
2. Cluster Adaptive Cycle Model

9. 9
Ⅱ. Adaptive Cycle Model
3. Significance and limits
Significance
 The model relies on an ecosystem analogy, and also allows for
the possibility of system (cluster) renewal (recovery) as well as
replacement, or maladaptive collapse at the same time.
⇒This seems to be valuable idea to explore cluster evolution.
 The assumption that the evolution of a complex system always
occurs through a four-phase sequence is restrictive
⇒ Might be open to similar criticisms with the life cycle model
Limits
 An emphasis is not balanced between endogenous and
exogenous ‘forcing’ mechanisms to move a system though
these four phase ⇒ emphasis on endogenous mechanisms
 A rather restrictive allowance for the two-way nature of the
interaction between a cluster and its external environment

10. 10
Ⅲ. Modified Cluster Adaptive Cycle Model
 Martin and Sunley(2011) recognized numerous development
trajectories according to complex interactions between clusters and
their environment, and contingent and strategic decision-making by
cluster-based firms.
 They modified and expanded the adaptive cycle model, and then
suggested six different possible sequential trajectories.
• Cluster full adaptive cycle, Constant cluster mutation, Cluster stabilization,
Cluster reorientation, Cluster failure, Cluster disappearance
< Modified cluster adaptive cycle model >
Source: Martin and Sunley, 2011
1. Concepts

11. 11
2. Cluster Evolutionary Trajectories
Ⅲ. Modified Cluster Adaptive Cycle Model
1) Cluster full adaptive cycle (α→r→K→Ω)
 Phases and Characteristics
• Emergence, growth, maturation, decline and replacement by a
new cluster.
• The replacement cluster would draw upon resources and
capabilities inherited from the old cluster
• The cluster atrophies due to internal rigidities or exhaustion of
increasing returns effects
• But a new cluster emerges by utilizing the inherited resources
and capabilities

12. 12
Ⅲ. Modified Cluster Adaptive Cycle Model
1) Cluster full adaptive cycle (α→r→K→Ω)
 Example
• The growth of polymers after the decline of the tyre cluster,
Akron, Ohio (Carlsson, 2001)
• The growth of low-carbon technology industries, the Ruhr,
Germany
• The basis of a creative district focused on specialist retailing
after the shrinkage of the Birmingham jewellery quarter, UK (De
Propris and Lazaretti, 2008)
• An outdoor equipment and clothing industry
after the decline of textile and steel industries
(Parsons and Rose, 2005)
• The Seoul Digital Industrial Complex, Seoul,
Korea (Koo, 2012)

13. 13
Ⅲ. Modified Cluster Adaptive Cycle Model
2) Constant cluster mutation (α→r→r′→r″)
 Phases and Characteristics
• Emergence, and growth with constant structural and
technological change
• The cluster continually adapts and evolves, by the successive
development of new branches of related activity. The basic
technology would have a comprehensive characteristics
• Cluster firms are able to innovate continuously and the cluster
constantly mutates or widens in terms of industrial specialization
and technology
• There are high rates of spin-offs and spin-outs from local firms,
research institutes, or universities

14. 14
Ⅲ. Modified Cluster Adaptive Cycle Model
2) Constant cluster mutation (α→r→r′→r″)
 Example
• Open network clusters, such as Silicon Valley and Medicon
Valley (Moodysson et al., 2008)
• The Cambridge high-technology cluster, UK (Garnsey and Heffernan,
2005; Stam and Garnsey, 2009)
• The Dague Seongseo Industrial Complex, Daegu, Korea (Lee,
2007; Lee, 2008)

15. 15
Ⅲ. Modified Cluster Adaptive Cycle Model
3) Cluster stabilization (α→r→K→K′)
 Phases and Characteristics
• Emergence, growth, maturation, and stabilization
• The cluster might remain in a much reduced and restricted form
for an extended period of time
• The remaining firms in the cluster would survive by upgrading
products and/or focusing on niche or prestige market segments
• Though the cluster retains a modest degree of resilience, it
remains potentially vulnerable to (further) decline

16. 16
Ⅲ. Modified Cluster Adaptive Cycle Model
3) Cluster stabilization (α→r→K→K′)
 Example
• Lock-manufacturing cluster, the West Midlands (Bryson et al., 2008)
• Transition from the production of final goods to the production
of machinery in some Italian districts (Rabellotti et al., 2009)
• Diversification into export markets in Aberdeen oil complex
(Chapman et al., 2004)
• Machine Industrial cluster of Changwon,
Korea (Lee, 2003)

17. 17
Ⅲ. Modified Cluster Adaptive Cycle Model
4) Cluster reorientation (α→r→K→α)
 Phases and Characteristics
• Emergence, growth, onset of early cluster maturation or decline,
and reorientation
• Firms re-orientate their industrial and technological specialisms
upon reaching or nearing maturation or decline phase, and new
cluster emerges
• The cluster branches into a new form
• The more innovative leading firms may play a key role by
reacting to market saturation or a rise of major competitors
• A technological breakthrough may activate reorientation

18. 18
Ⅲ. Modified Cluster Adaptive Cycle Model
4) Cluster reorientation (α→r→K→α)
 Example
• Radical product diversification in the Montebelluna sportswear
cluster (Sammarra and Belussi, 2006)
• The Boston high-technology cluster (Bathelt, 2001)
• The financial services cluster in the City of London (Martin and
Sunley, 2011)
• The Gumi National Industrial Complex (Chung,
2011)

19. 19
Ⅲ. Modified Cluster Adaptive Cycle Model
5) Cluster failure (α→f)
 Phases and Characteristics
• Emergence and failure to take off and grow
• Any remaining firms don’t constitute a functioning cluster
• The cluster fails to achieve sufficient critical mass, externalities
or market share, the firms would create unstable innovation
• New firm formation is low and/or the firm failure rate is high,
which deters new entrants
 Example
• A digital cluster in Dublin (Bayliss, 2007)

20. 20
Ⅲ. Modified Cluster Adaptive Cycle Model
6) Cluster disappearance (α→r→K→Ω→d)
 Phases and Characteristics
• Emergence, growth, maturation, decline and elimination
• No replacement by a new cluster
• The inherited resources and competences are not sufficient or
ill-suited to form the basis of new cluster formation
 Example
• Sheffield steel (Potter and Watts, 2010), Dundee Jute (MacKay et al.,
2006), Como silk (Alberti, 2006)
• The Staffordshire pottery and ceramics district (Sacchetti and
Tomlinson, 2009)
• Coal industry in Taebaek, the abandoned
coal mine areas in Munkyung, Korea

21. 21
References
Carlsson B., 2001, Institutions, entrepreneurship, and growth: biomedicine and polymers in Sweden and
Ohio, Small Business Economics 19, 105–121.
Chung. D.C., 2011, Evolution of industrial cluster through overcoming the lock-in effect of branch plant
agglomeration.
De Propris L. and Lazaretti L., 2008, Measuring the decline of a Marshallian industrial district: the
Birmingham jewellery quarter, Regional Studies 43, 1135–1154.
Holling C.S., 2001, Understanding the complexity of economic, ecological, and social systems,
Ecosystems, 4, pp.390-405.
Huh, D.S., 2013, The evolution of the IT service industry in the U.S. national capital region: the case of
Fairfax county, Journal of the Economic Geographical Society of Korea, 16(4), pp.567-584.
Koo, Y.M., 2012, Analysis of cluster life cycle on the dynamic evolution of the Seoul digital industrial
complex in Korea, Journal of The Korean Association of Regional Geographers, 18(3), pp.283-297.
Lee. C.W., 2003, Exploring regional system of innovation in Changwon, 地理學論究, 23, pp.327-344.
Lee. K.-M., 2007, The characteristic and evaluation of the governance of the vitalization policy of
metropolitan industrial complex: the case study of Seongseo Industrial Complex in Daegu, MA.
Martin R. and Sunley P., 2011, Conceptualizing cluster evolution: beyond the life cycle model?, Regional
Studies, 45(10), pp.1299-1318.
Moodysson J., Coenen L. and Asheim B., 2008, Explaining spatial patterns of innovation: analytical and
synthetic modes of knowledge creation in the Medicon Valley life-science cluster, Environment and
Planning A 40, 1040–1056.
Nam, K.B., 2014, Path dependence and resilience process of the Seoul Digital Industrial District, Seoul,
Korea, The Geographical Journal of Korea, 48(3), pp.375-388.