Knowledge and the Climate Change Issue: An Exploratory Studyof Cluster and Extra-Cluster Effects

Jeremy Galbreath • David Charles •

Des Klass

Received: 11 February 2013 / Accepted: 16 September 2013

� Springer Science+Business Media Dordrecht 2013

Abstract Climate change, while potentially impacting

many industries, appears to have considerable significance

to the wine industry. Yet little is known about how firms

acquire knowledge and gain an understanding of climate

change and its impacts. This study, exploratory in nature

and studying firms from the wine-producing region of

Tasmania, is one of the first in the management literature to

use cluster theory to examine the climate change issue.

Firms are predicted to exchange knowledge about climate

change more readily with other firms internal to the sub-

cluster than with those external to the sub-cluster. The

hypothesis does not find support. The study also proposes

that the different characteristics of knowledge can either

increase or decrease their flows in and around clusters.

Specifically, ‘‘public’’ knowledge about climate change is

predicted to flow more freely than ‘‘private’’ knowledge

about climate change. The hypothesis does not find sup-

port. Finally, firms are expected to acquire knowledge

about climate change from sources other than cluster-

entrenched firms, and in particular peak national industry

bodies. The hypothesis finds partial support. A discussion

of the findings is presented along with future research

directions.

Keywords Australia � Climate change � Clusters �Knowledge � Knowledge exchange � Wine

Introduction

Climate change is leading to business volatility, risk, and

disruption (Hoffman and Forbes 2011). As such, it is

considered ‘‘one of the greatest economic and political

challenges facing the world economy this century’’ (Knight

2011, p. 817). NASA climate scientist Professor Jim

Hansen goes so far as to claim it is a ‘‘great moral issue’’

(cited in Carrell 2012). Therefore, firms need to acquire

knowledge about climate change, as it has the potential to

impact their strategies and operations (Winn et al. 2011).

This seems to be especially the case for wine producers

(Galbreath 2011a, 2012; Nicholas and Durham 2012).

Wine production is an industry very sensitive to climatic

changes; for example, as little as a 1 �C increase in average

temperature can dramatically affect which varieties can best

be ripened where (Keller 2010). Indicative of these tem-

perature effects, a major global study has found that in the

world’s top wine-producing regions, temperatures have risen

in the last 55 years by an average of 1.26 �C, and are

expected to rise by an average of 2.04 �C by 2050 (Jones

et al. 2005). Thus, the survival of many wine-growing

regions could be under threat due to climate change.

Given that wine producers have historically faced

shifting and changing climatic conditions from one vintage

to the next—due to the fact that they are weather-driven

enterprises—and given that wine production takes place

mainly in narrow geographic clusters (Porter and Bond

2008), there would seem to be an advantage for producers

to be freely exchanging knowledge about climate change

within specific clusters. More specifically, are wine

J. Galbreath (&) � D. Klass

Curtin Graduate School of Business, Curtin University of

Technology, 78 Murray Street, Perth, WA 6000, Australia

e-mail: jeremy.galbreath@gsb.curtin.edu.au

D. Klass

e-mail: des.klass@gsb.curtin.edu.au

D. Charles

European Policies Research Centre, School of Government and

Public Policy, University of Strathclyde, 40 George Street,

Glasgow G1 1QE, UK

e-mail: david.r.charles@strath.ac.uk

123

J Bus Ethics

DOI 10.1007/s10551-013-1901-1

producers freely engaging in knowledge exchanges about

climate change and are these exchanges influenced by their

location in a particular cluster?

Considering this question, this study seeks to explore

some fundamental aspects that have remained largely

unexplored in the cluster literature. For example, while it is

known that firms are attempting to respond to climate

change (e.g., Dawkins and Fraas 2011; Eberlein and Mat-

ten 2009; Galbreath 2011a, 2012; Le Menestrel et al.

2002), there is little research demonstrating how firms

might acquire sufficient knowledge and understanding

about the issue. Following cluster theory (Porter 1998,

2000), this study hypothesizes that given the potential

magnitude and impact of climate change on wine produc-

tion, producers in wine clusters readily exchange knowl-

edge about climate change because of geographic

proximity. However, the paper relies on a rarely studied

dimension of clusters; namely, sub-cluster effects. As sub-

clusters develop unique identities within a regional cluster,

firms are expected to exchange knowledge about climate

change more freely within the sub-cluster than with firms

in other sub-clusters in the region (Fig. 1).

A second aspect, and one that follows the work of

Tallman et al. (2004), concerns what type of knowledge

about climate change is exchanged between firms in wine

clusters. For example, in their study of 32 firms in the

aerospace cluster in Rome, Sammarra, and Biggiero (2008)

find that knowledge exchange includes technological,

market, and managerial knowledge. This finding chal-

lenges the usual emphasis on the exchanges of technical

knowledge (Munari et al. 2011). No study, however, has

explored the types of knowledge exchanged relating to

climate change. Advancing understanding of this aspect is

important because knowledge is not unidimensional nor are

firms expected to be limited in the types of knowledge they

exchange (Sammarra and Biggiero 2008).

The final aspect relates to so-called ‘‘extra-cluster’’

knowledge (Giuliani and Bell 2005). Too often the cluster

literature focuses analysis exclusively on inter-firm

knowledge exchange, but other scholars argue that firms

acquire knowledge from various sources beyond firms

within the cluster (Amin and Cohendet 2004; Malmberg

2003). In their study of 28 firms in a regional Chilean wine

cluster, Giuliani and Bell (2005) find that at least some

firms in the cluster not only engage in inter-firm cluster

knowledge exchanges, but acquire technical knowledge

from universities, suppliers, consultants, and business

associations. In the present study, a hypothesis is developed

that posits that wine firms in the regional cluster of interest

gain their most valuable knowledge about climate change

from peak national industry bodies.

The paper makes three contributions to the literature.

First, firms are expected to respond to climate change when

they are sufficiently informed and knowledgeable about the

Regional wine cluster (meso level)

Sub-cluster 1 knowledge exchange

(sub-meso level)

Sub-cluster 2 knowledge exchange

(sub-meso level)

Sub-cluster 4 knowledge exchange

(sub-meso level)

Sub-cluster 3 knowledge exchange

(sub-meso level)

internalexchange

externalexchange

internalexchange

internalexchange

externalexchange

internalexchange

externalexchange

externalexchange

The expectation is that wine sub-clusterswill develop a unique sense of identity that sees firms exchanging knowledge about climate change more readily within the sub-cluster as opposed to outside the sub-cluster

Fig. 1 Conceptualization of

knowledge exchange about

climate change in regional wine

lusters

J. Galbreath et al.

123

issue (Galbreath 2009). While research does demonstrate

that firms are making decisions to respond to climate

change, no studies have examined how they gain knowl-

edge to influence such decisions. Thus, the interest of this

study is not in the types of responses firms implement to

respond to climate change, but rather in the mechanisms or

means by which firms acquire knowledge about climate

change. Namely, this study examines inter-firm knowledge

exchanges. However, while regional clusters and underly-

ing cluster theory are taken into account, due to the nature

of the wine industry, the concept of ‘‘sub-clusters’’ is

explored. A sub-cluster is defined as a unique spatial

location within a regional wine-producing cluster. This

focuses attention on finer spatial elements of clusters,

determining if knowledge exchanges about climate change

more readily occur at the sub-cluster level (Fig. 1). This

level of analysis enables the influence of geography to be

further explored.

Second, studies on the types of knowledge exchanged in

clusters are underrepresented in the literature. This study

advances research in this area by exploring several ‘‘pub-

lic’’ and ‘‘private’’ knowledge types, determining if firms

exchange knowledge about climate change mainly through

‘‘public’’ knowledge types. Lastly, firm-level knowledge

exchange is prominent in cluster research. However,

knowledge can also be acquired from sources that are

external to firms in the cluster. This study examines several

sources of external knowledge to determine the main

source of knowledge within the cluster, thus advancing

research on extra-cluster sources of knowledge.

The paper proceeds as follows. First, background to the

research is presented, including a review of relevant

existing studies. Next, a set of research hypotheses are put

forth. This is followed by a description of the research

methods, after which the results are presented. The paper

concludes with a discussion and presentation of findings,

future research directions, and limitations.

Background

Climate Change and Wine

Wine grapes can be successfully grown in only a very

narrow geographical range. Suitable locations must have

particular climates, which are highly sensitive to changes

in climatic conditions (Keller 2010; Webb et al. 2010). For

example, as little as a 1 �C increase in average temperature

can dramatically affect where different varieties can best be

ripened and can potentially affects yield, quality and,

ultimately, economic sustainability (Jones et al. 2005;

Keller 2010). In extreme cases, entire vintages can be lost

if temperatures are too high (Fenner 2009). Hence, grape

growing and wine production are largely weather- and

climate-driven enterprises. Further, long-term changes in

climate can profoundly affect production viability, quality,

and wine styles, due to changes in winter-hardening

potential, frost occurrence, growth season lengths, and heat

accumulation (Webb et al. 2007). In addition, growing

premium qualities of specific grape varieties requires even

narrower ranges of climatic conditions, meaning that

variety-specific locations may be particularly vulnerable to

variations arising from climate change, such as more

extreme heat days, less rainfall, and increases in humidity.

In the wine industry, the evidence tends to suggest that

climate change is having an effect on the world’s main

production centers.

In a ground-breaking study of global temperature

changes, Jones et al. (2005) modeled climatic conditions in

27 of the most important wine regions in the world.

Studying cool, intermediate, warm, and hot climate

regions, the authors find that in 17 out of 27 regions

(63 %), growing season temperature averages rose between

1950 and 1999. Further, in 15 out of 27 regions (56 %),

dormant season temperature averages rose during the same

period. The majority of the rises were in US and European

wine regions, although, averaged across all regions, tem-

perature warming was 1.26 �C. Further, the researchers

calculate that temperatures are expected to rise by an

average of 2.04 �C by 2050 in the wine regions under

study. The evidence therefore suggests that climate change

poses a threat to many wine-producing regions in the

world. Further, recent evidence suggests that wine pro-

ducers are well aware of their reliance on climatic condi-

tions and eco-system services, and may act as stewards of

the natural environment, not only to ensure their own

sustainability, but to protect and care for the regions within

which they operate (Galbreath 2012). Therefore, what

mechanisms might facilitate how firms gain know-how and

information about climate change? One mechanism, and

the focus of this study, is cluster effects.

Cluster Theory

Cluster theory has become prominent in the exploration of

regional and national economies, economic growth and

development, and innovation (Martin and Sunley 2003;

Maskell 2001; Porter 1998, 2000; Rogers 1995). Growing

out of Marshall’s (1890) seminal work on industrial dis-

tricts, clusters are generally conceptualized as ‘‘a geo-

graphically proximate group of interconnected companies

and associated institutions in a particular field, linked by

commonalities and complementarities’’ (Porter 2000,

p. 16). Firms based in clusters often enjoy success rates

greater than non-clustered or isolated firms (Porter 1998).

Cluster theory suggests that success rates are mainly due to

Knowledge and the Climate Change Issue

123

interdependencies and a geographic proximity that expe-

dites the exchange of information and knowledge.

Researchers exploring cluster theory have devoted con-

siderable attention to studying the dynamics of well-known

clusters, such as technology firms in Silicon Valley and

Route 128 in the US (Saxenian 1994), wireless systems in

Scandinavia (Dahl and Pedersen 2004), textile and clothing

in Italy (Lazerson 1990), and motor sport in the UK (Henry

and Pinch 2001).

Most research on clusters pays special attention to

knowledge spillovers. Generally, a knowledge spillover is a

flow of knowledge between firms, which theory posits occurs

more frequently, and with greater affect, between firms that

are close to one another (Tallman et al. 2004). Knowledge

exchanges are critical because it is thought that these are a

key underlying mechanism that drives competitive advan-

tage, and economic growth and performance, in regional

clusters (Maskell 2001; Tallman et al. 2004). More specifi-

cally, a concentration of many firms working on a similar set

of problems often produces a widely shared understanding of

an industry and its workings, challenges, and opportunities.

A number of studies have documented the role of knowledge

exchanges in clusters, and their methodologies have inclu-

ded the study of patent data, and the use of surveys and

interviews (e.g., Dahl and Pedersen 2004; Saxenian 1994).

Other studies have sought a more in-depth analysis of

knowledge and clusters, by employing concepts such as

‘‘knowledge in the air’’. Yet, too often such studies poorly

identify the kinds of knowledge exchanged and why these

matter (Hervas-Oliver and Albors-Garrigos 2009; Huber

2012; Ibrahim et al. 2009). In their work, Tallman et al. (2004)

posit that there are essentially two types of knowledge char-

acteristics present in knowledge spillovers in clusters: com-

ponent knowledge (usually technical) and architectural

knowledge (usually organizational). Component knowledge

tends to be more explicit and ‘‘public’’, and is subject to

discovery and therefore more easily transferred. Architectural

knowledge relates more to organizational systems and tends

to be tacit and private, idiosyncratic knowledge in that it is

unique to a given firm and is more subject to mobility barriers.

From a cluster perspective, it is the mixture of the types

of knowledge that makes proximity important. While

explicit or public knowledge may be easily communicated,

such as through ICT because it can be written down, tacit

knowledge needs to be exchanged face-to-face, often

through the movement of skilled staff between firms. In

addition, informal groupings of practitioners within an

industry may exchange knowledge through communities of

practice which are based on face-to-face communication

and knowledge exchange (Brown and Duguid 2000). This

study seeks to examine both knowledge types, and to

understand the extent to which the exchange of each type

differs.

Hypotheses

Climate change impacts wine production more directly

than it does many other business sectors (Galbreath 2011a).

Understanding the impacts of climate change, therefore,

may be a key to the long-term survival and sustainability of

wine clusters. Given, climate change’s documented dis-

ruption of wine production (Webb et al. 2010), wine firms

in Australia could be expected to be exchanging knowledge

about climate change to stay informed about how climatic

conditions will impact production.

Research has documented the changing conditions in

some of Australia’s regional wine clusters due to climate

change (Galbreath 2011a; Webb et al. 2010). Of particular

importance to this study is the fact that Australia has fur-

ther delineated its regional wine clusters into distinctive

‘‘sub-clusters’’ specific to a given regional wine-producing

cluster. This has implications for knowledge exchanges

about climate change in the Australian wine industry.

Within the literature, while the geographic delineation of

clusters and the relations between different spatial scales

remains controversial (Martin and Sunley 2003), there is

generally agreement among scholars that clusters are ‘‘geo-

graphically proximate’’ (Porter 2000, p. 16)—although

‘‘proximity’’ is open to interpretation. In the context of this

study, a sub-cluster is a unique spatial location within a

regional wine-producing cluster (Fig. 1). For example, in

California, the largest regional wine-producing cluster in the

United States, there are 107 identified sub-clusters, with

Napa Valley and Sonoma Valley among the most well-

known.1 This finer spatial distinction is critical to the wine

industry as ‘‘place’’ or, as explained by the French, terroir,

describes the unique local geography of a vine’s entire nat-

ural environment: the combination of climate, topology,

geology and soil, all of which bear on the local environ-

ment’s growth and the characteristics of its grapes and wine

(Cross et al. 2011; Seguin and de Cortazar 2005).2

Of importance is the fact that a wine’s unique origin

conveys a message to buyers and consumers, and has sig-

nificant implications for branding, reputation, and price

(Cross et al. 2011). Given this place-based identity, which

appears to be increasingly linked to the sub-cluster rather

than the region (e.g. Napa Valley rather than California),

there is the possibility that inter-firm knowledge exchanges

within sub-clusters occur more naturally and frequently than

knowledge exchanges with firms in other sub-clusters within

a region. There are a few main reasons for this possibility.

1 Information on California’s sub-clusters available at http://www.

iwineinstitute.com/CALAVABYNAME.ASP.2 As a good example of sub-cluster climate effects, the Overberg,

Stellenbosch, and Paarl wine sub-clusters in South Africa experience

up to a 6 �C difference in temperature, even though there is as little as

a 40 km distance between them (Deloire et al. 2009).

J. Galbreath et al.

123

First, as wine sub-clusters increasingly compete on their

unique brand and reputation, one way this can be achieved

is through demonstration of its environmental management

credentials (including credentials related to climate

change). Building environmental credentials is predicated

on the acquisition of knowledge (Haugh and Talwar 2010).

Hence, as sub-clusters seek to differentiate themselves from

other sub-clusters (e.g., through strong environmental

management credentials), they would be expected to more

readily exchange knowledge about climate change within

their sub-cluster than with other sub-clusters. Second, there

is recognition that the exchange of knowledge is dependent

upon the location of expertise. The decision to seek new

knowledge from another is affected by awareness of that

source as a valuable reserve of relevant knowledge (Borg-

atti and Cross 2003). As individuals are likely to engage in

knowledge exchanges with others whose areas of expertise

are known to them, knowledge exchange is dependent upon

individuals’ knowing ‘‘who knows what’’ (Moreland et al.

1996). Evidence suggests that knowing ‘‘who knows what’’

is bound by geographic proximity (Uzzi 1996, 1999);

therefore, given their close proximity, the expectation is

that knowledge exchanges about climate change between

firms within a sub-cluster occur more readily than with firms

in other sub-clusters in the region. It is also expected that the

relevance of knowledge exchanged about the consequences

of climate change is higher within sub-clusters, where the

climatic and soil conditions are more homogenous than is

the case between different sub-clusters.

Clear distinctions of ‘‘place’’ appear to be emerging in the

Australian wine industry, in particular among sub-clusters

(Costley 2012; Schmitt 2013), and these distinctions could

impact the extent to which knowledge about climate change

is exchanged within a regional cluster. While the arguments

put forth do not discount the possibility of flows of knowl-

edge between sub-clusters, the expectation is that they will be

more evident within them. Hence:

Hypothesis 1 Knowledge exchange about climate change

is more widespread within wine sub-clusters than between

them.

Following Hypothesis 1, there are two aspects that

require further examination: the type of knowledge

exchanged and extra-cluster knowledge sources. Tallman

et al. (2004) argue that clusters thrive because of flows of

knowledge between firms in the cluster, which is exem-

plified in Maskell’s (2001) knowledge-based theory of the

geographical cluster. Because knowledge is argued to be

more freely shared in the cluster it is ‘‘part and parcel of the

social community—a public good for all members’’

(Tallman et al. 2004, p. 261). However, not all knowledge

has the same characteristics and therefore may be more or

less exchanged in clusters.

The resource-based view (RBV) of the firm suggests

that private knowledge, or knowledge that is idiosyncratic

to a given firm, is knowledge that is generally protected

and thus less readily transferred or exchanged (Barney

1991; Galbreath 2005). Examples include knowledge about

organizational processes, long-term strategies, and mana-

gerial routines (Teece et al. 1997). Such knowledge tends

to be tacit, is path-dependent, and specific to the firm.

Therefore, private knowledge is generally more difficult to

transfer (Tallman et al. 2004). This is because as firms seek

to build and establish competitive advantage, they might

protect private knowledge through legal property rights

(Galbreath 2005), or otherwise may be reluctant to share

within the cluster knowledge that they perceive to give

them a special or unique advantage.

Alternatively, cluster theory posits that explicit knowl-

edge, or knowledge that is generally technical in nature or

common across an industry, is freely exchanged between

firms (Munari et al. 2011). This is because explicit

knowledge is knowledge that is related to identifiable parts

of an organizational system, and tends to be more precisely

defined, documented, and communicated (Tallman et al.

2004). Examples include engineering knowledge based

upon specific scientific principles. In the case of wine,

explicit knowledge would include knowledge about

growing grapes or making wine. Explicit knowledge is

generally subject to discovery, relatively transparent, and

relatively mobile among firms who are informed and will

understand it once presented to them (Jenkins and Tallman

2010; Tallman et al. 2004).

Evidence suggests that, globally, technical and industry

knowledge (‘‘public’’ knowledge) in the wine industry

appears to be readily exchanged between firms (Giuliani

2007). In the case of the Australian wine industry, the

setting of this study, public knowledge is spread throughout

regional wine clusters (Aylward 2007a, b). National wine

peak bodies, for example, promote and educate members

about the technical and industry aspects of climate change.

On the other hand, little evidence demonstrates that private

knowledge is as freely flowing in or around wine clusters.

This might be due to the fact that any private knowledge

about climate change would be highly embedded within the

and subject to causal ambiguity.

Based on the above:

Hypothesis 2 ‘‘Public’’ knowledge about climate change

is more readily exchanged than ‘‘private’’ knowledge about

climate change in wine clusters.

Finally, according to Camagni (1991), Bell and Albu

(1999), and Giuliani and Bell (2005), in addition to inter-

firm knowledge exchanges, a cluster also has the capacity

to take advantage of so-called ‘‘extra-cluster’’ knowledge.

For example, in their study of a Chilean wine cluster,

Knowledge and the Climate Change Issue

123

Giuliani and Bell (2005) find that firms in the cluster

acquire technical knowledge from external sources,

including consultants, suppliers, and universities. While

some of these may be based within the cluster, many will

be located elsewhere, in major cities for example. In the

case of Australia, there is reason to believe the various

regional wine clusters would likely be acquiring knowl-

edge about climate change from extra-cluster sources.

More specially, Australia has a number of trade journals

that are read by the wine-producing community, including

Australian Journal of Grape and Wine Research, Australia

and New Zealand Grapegrower and Winemaker, Wine &

Viticulture Journal, and Winestate. In the past few years,

several articles on climate change have featured in these

journals (e.g., Galbreath 2011b; Hall and Jones 2010;

Webb et al. 2007). On the other hand, consultants, sup-

pliers to the industry, and universities would all be

expected to be sources of knowledge about climate change

that could be used by firms within regional wine clusters

(Giuliani and Bell 2005). However, given the structure of

Australia’s wine industry, one source would be expected to

be particularly useful to learn about climate change.

In Australia, peak bodies such as federations, regulators,

research agencies, and other intermediaries play a signifi-

cant role in the wine industry. All wine producers pay

levies to fund research and development, which includes

research and development related to climate change and the

natural environment. Industry knowledge is then dissemi-

nated by these various peak national industry bodies to

wine producers. Furthermore, EntWine, which is part of the

Winemakers’ Federation of Australia (a peak national

body), recently initiated and oversees the certification

process for the industry’s environmental management

system. EntWine offers training on environmental man-

agement and includes accountability for greenhouse gas

(GHG) emissions, while ensuring that technologies and

processes are in place to reduce GHG emissions prior to

awarding certification. Given the substantial influence of

peak national industry bodies on the wine industry in

Australia, the expectation is that they are the leading source

of extra-cluster knowledge about climate change. Hence:

Hypothesis 3 Considering extra-cluster sources of

knowledge, knowledge about climate change from peak

national industry bodies is the leading source.

Methods

Context

Australia is made up of several regional wine clusters

(Aylward 2007a, b). More specifically, most Australian

wine is produced in the regional clusters of New South

Wales, South Australia, Tasmania, Victoria, and Western

Australia. As an initial exploratory study of wine clusters,

Tasmania was chosen for this research project. Although

small by the rest of the country’s standards (producing just

over 7,000 tonnes of grapes in 2012—less than 1 % of

Australia’s overall crush), Tasmania enjoys a national and

international reputation as a producer of some of Austra-

lia’s leading premium wines, its Pinot Noir and sparkling

wines in particular winning high praise and trophies from

wine judges and critics alike. Further, after years of over-

production of wine and the effects of the global financial

crisis on the Australia wine industry, Tasmania has

emerged as one of the strongest wine regions in the country

and has clear competitive advantages and potential for

significant growth. Prices for Tasmanian grown wine

grapes are the highest in Australia and demand for its

grapes exceeds supply (100 % of wine produced is in the

super-premium and ultra-premium categories). At a time

when the Australian mainland wine industry is struggling

to cope with grape surpluses, brought about by loss of

market share in export and domestic markets, mainly for

generally lower-priced wines, the Tasmanian wine sector

remains buoyant.

Sample and Data Collection

The entire population of Tasmanian wine firms was

sourced from the Winetitles database, a directory of Aus-

tralian and New Zealand wine firms (Winetitles 2010). In

all, 97 firms were included. To collect data, a purpose-

designed questionnaire was created. The questionnaire was

discussed with a local wine trade organization in Tasmania,

who shared it with the technical committee to assess the

validity of its content. A few changes were suggested, most

of which were minor word changes. The questionnaire was

then mailed to all firms in an initial mailing, and a reminder

letter was sent 2 weeks later. Thereafter, a second reminder

letter was sent to non-respondents, along with an additional

questionnaire. Out of the 97 firms surveyed, 38 replied,

resulting in a response rate of nearly 40 %, which is out-

standing relative to most wine industry studies (Atkin et al.

2012; Sellitto 2006), and well above the typical response

rate (5–10 %) of direct mail surveys. To supplement the

main data, demographic information about each company

was collected from the Winetitles database. These demo-

graphics are presented in Table 1.

To test for non-response bias, early versus late respon-

dents were compared for the key demographic variables

used in the study. Chi square tests revealed no significant

differences between type of business (v2 = 2.46,

p = 0.48); business structure (v2 = 1.70, p = 0.43); type

of ownership (v2 = 3.16, p = 0.21); number of employees

J. Galbreath et al.

123

(v2 = 4.44, p = 0.22); and annual revenue (v2 = 2.06,

p = 0.56). Hence, non-response bias was not likely to be a

problem. Further, because the main variables of interest

were collected from a single source, a test for common

method bias was conducted. A Harman’s ex post single

factor test (Podsakoff and Organ 1986) revealed the

absence of a single general factor accounting for most of

the observed covariance in the variables. Hence, common

method bias did not appear to be a problem in this sample.

Measurement of Variables

In the context of this study, knowledge is defined as ‘‘that

which is known’’ (Grant 1996, p. 110). Knowledge can be

held by individuals or firms, and can be found in books,

journals, blueprints, manual, and the like (Grant 1996;

Inkpen and Dinur 1998; Liebeskind 1996). To measure

knowledge exchange about climate change, this study

relied on the procedure of Sammarra and Biggiero (2008).

Following their work, knowledge exchange about climate

change was conceptualized as a two-way flow, in which

knowledge can be exchanged from one firm in a sub-cluster

to another firm in that sub-cluster (or to firms in other sub-

clusters) and vice versa (Sammarra and Biggiero 2008).

However, the procedure differs from that of a typical net-

work analysis study: it entails an assessment from agent

(firm) to location (sub-cluster) rather than from agent to

agent. Hence, rather than create a roster listing all 97 firms,

a roster of the seven identified sub-clusters within Tas-

mania was listed.3

Respondents were then asked to consider the climate

change issue and the extent to which they had exchanged

any knowledge they possessed about climate change with

firms within their sub-cluster and with firms in other sub-

clusters. A four-point Likert scale was used to register

responses, which could range from ‘‘no knowledge

exchange’’ about climate change to ‘‘very high knowledge

exchange’’ about climate change with firms within their

sub-clusters and firms in other sub-clusters, but not with

any specific firm.4 These responses were mapped onto a

knowledge matrix, recording knowledge exchanges with

firms in other sub-clusters. The values of the cells in the

matrix range from cell ij = 0, indicating that a firm in sub-

cluster i exchanged no knowledge about climate change

with firms in sub-cluster j, to 3, indicating that a firm in

sub-cluster i exchanged a very high amount of knowledge

about climate change with firms in sub-cluster j.

As to the type of knowledge exchanged about climate

change, first, a categorization of knowledge types based on

a literature review was developed. Literature from inter-

national business, management, marketing, strategy, and

technology and innovation was consulted. The types of

knowledge identified were technical, industry, market,

organizational, marketing, and strategy knowledge, each of

which has been used in previous research (e.g., Boschma

and Ter Wal 2007; Kesidou et al. 2009; Sammarra and

Biggiero 2008). Next, each knowledge type was contex-

tualized for the issue of climate change (see Appendix 1).

For example, technical knowledge relates to insights on

technologies and technical enhancements for adapting to

climate change; industry knowledge relates to know-how

gained from peak national industry bodies or peers on

addressing sector requirements or government policies on

3 The seven sub-clusters include Coal River Valley, Derwent Valley,

East Coast, Huon/Channel, North West, Pipers River, and Tamar

Valley. This designation was determined by several factors, including

historical evolution of the region, categorizations by Wine Tasmania,

the industry association that oversees the affairs of Tasmanian wine

producers, and government classifications. Such sub-cluster designa-

tions are consistent with other wine-producing regions around the

world, including regions in Chile (Giuliani and Bell 2005), South

Africa (Deloire et al. 2009), and the United States (Cross et al. 2011).4 The reason for choosing a four-point scale was twofold. First, the

scale was used to get a sense of the intensity level of exchanges,

which follows the work of Giuliani and Bell (2005). Second, the

present study serves as the foundation for a larger national study;

therefore, scales were able to be assessed for validity and reliability

for future research efforts.

Table 1 Demographics

Sub-cluster

Coal River Valley 7

Derwent Valley 3

East Coast 6

Huon/Channel 4

North West 3

Pipers River 4

Tamar Valley 11

Type of business

Grape grower 11

Wine producer 2

Vertically-integrated 25

Ownership

Domestic 36

Foreign 2

Number of employees

Less than 5 29

5–9 3

10–19 3

30 or more 3

Annual revenue

Less than AUD$1M 31

AUD$1M–AUD$5M 5

AUD$6M–AUD$10M 1

More than AUD$10M 1

Knowledge and the Climate Change Issue

123

climate change; organizational knowledge relates to the

coordination and supervision of organizational resources

and processes to effectively and efficiently address climate

change impacts; and marketing knowledge relates to how

an individual firm addresses customer preferences, mar-

keting and branding, and new product development are

influenced by climate change.

Once knowledge types were contextualized, the list was

sent to four experts in the field of knowledge exchange and

clusters who reside in the United Kingdom and the United

States. After consultation, the four experts came to general

agreement upon the types of knowledge. Suggestions were

made to reword some of the examples for greater clarity

and content validity, and these were incorporated into the

final questionnaire. The respondents were asked to consider

any knowledge exchanges about climate change they had

made with other firms within Tasmania, assessing those

exchanges for each of the knowledge types (on a four-point

Likert scale as described above). Finally, after reviewing

the literature (e.g., Aylward 2007a, b; Giuliani 2004; Gi-

uliani and Bell 2005), a list of six key extra-clusters

sources of knowledge were identified; namely, peak

national industry bodies, institutions of higher education,

suppliers, consultants, trade journals, and wine firms

operating outside of the regional Tasmania wine cluster.

Respondents were asked to indicate if they had acquired

knowledge about climate change from each source, where

1 = yes and no = 0.

Results

Means, standard deviations, and correlations are presented

in Table 2. To examine Hypothesis 1, first, the proportion

of firms was calculated, by sub-cluster, reporting any level

of knowledge exchange to the total number of sub-clusters

in the sample (n = 7).5 The averaged proportion of

knowledge exchange across the sample is 0.31.6 The

intensity of knowledge exchange appears to be mixed.

More specifically, respondents were asked to rate the

intensity of knowledge exchanges about climate change

from 0 (‘‘no exchange’’) to 3 (‘‘very high exchange’’).

Taking into account knowledge exchanges across all sub-

clusters, for those not exchanging any knowledge (0), the

averaged proportion is 0.69. For very little exchange (1),

the averaged proportion is 0.15. For moderate exchange

(2), the averaged proportion is 0.11. For very high

exchange (3), the averaged proportion is 0.05. The results

suggest that, when examined across all sub-clusters, less

than half of the firms in the sample reported that they are

exchanging knowledge about climate change.

To test Hypothesis 1, that knowledge about climate

change is more widespread within sub-clusters than

between them, each responding firm was classified by its

corresponding sub-cluster, including their knowledge

exchange ratings. The proportion of exchange across each

sub-cluster was then calculated, using this value to create a

matrix for analysis in organizational risk analyzer (ORA),

Carnegie Mellon’s social network analysis software. Fig-

ure 2 demonstrates the results of the network analysis. As

can be seen, the network analysis graphically reveals

knowledge exchanges of firms in each sub-cluster (i.e., firm

to sub-cluster), and whether these exchanges are internal or

external to the sub-cluster.

To statistically compare differences, any level of

exchange by each firm within and between sub-clusters was

counted as a 1, 0 otherwise (i.e., a firm was assigned a 1 if

they had indicated any level of knowledge exchange based

on the Likert scale described above). The mean exchange

of firms within the sub-cluster (intra-exchange) was

examined relative to the mean of exchange with firms in

the other sub-clusters (inter-exchange). One-way analysis

of variance (ANOVA) tests were conducted to detect sta-

tistical differences. In all cases no statistically significant

differences were found (Coal River Valley, F = 0.741,

Table 2 Descriptives and correlations

Variable Mean SD 1 2 3 4

1. Sub-cluster

knowledge

exchange

0.64 0.77 1.00

2. Public

knowledge

exchange

0.82 0.71 0.55** 1.00

3. Private

knowledge

exchange

0.75 0.76 0.21 0.60** 1.00

4. Extra-cluster

knowledge

sources**

0.34 0.30 0.44** 0.54** 0.50** 1.00

** p = 0.01

5 To calculate proportions, a 36 9 7 matrix was created that

contained each individual firm and the seven sub-clusters. For any

given firm, if there was evidence of any level of exchange, either

within the sub-cluster or to other sub-clusters, this was counted as a 1.

Counts were then divided by the total number of firms within the sub-

cluster, which provided a proportion of exchanges. This process was

repeated for each sub-cluster to derive an average proportion of the

sub-cluster exchanges, which was then used to calculate an overall

averaged proportion for the whole sample. The process was repeated

again for each level of intensity of knowledge exchange.6 Individually, only two sub-clusters demonstrate knowledge

exchange proportions above 0.50 (Derwent Valley and East Coast).

That is, more than half of the firms in these two sub-clusters are

exchanging knowledge about climate change. As can be seen in

Appendix 2, Tasmania is a relatively isolated regional wine-produc-

ing cluster and within the region itself, sub-clusters are geographically

separated. Hence, within the Tasmanian wine region, any knowledge

exchanges among sub-clusters faces the issue of geographic distance.

J. Galbreath et al.

123

p = 0.620, Derwent Valley, F = 0.667, p = 0.678, East

Coast, F = 1.307, p = 0.280, Huon/Channel, F = 0.944,

p = 0.485, Pipers River, F = 0.667, p = 0.681, Tamar

Valley, F = 1.104, p = 0.369).7 The findings suggest that

knowledge exchange about climate change is not more

widespread internal to the sub-cluster with firms in other

sub-clusters; therefore, Hypothesis 1 does not find support.

Following the literature, Hypothesis 2 posited that

public knowledge exchange about climate change would

occur more readily than private knowledge exchanges.

Table 3 demonstrates the percentages of knowledge type

exchanges, by intensity level. Individually, and accounting

only for responses that indicate actual exchanges (i.e., a

response of 1, 2, or 3), industry knowledge (65.8 %) is the

most prominent type of knowledge exchanged on climate

change in this sample. This perhaps reflects the fact the

peak national industry bodies in Australia are investing

substantially in climate change research and in the dis-

semination of results to industry members. However, to test

differences between the means of each knowledge type, a

factor analysis of the knowledge types was conducted.

Coral River Valley = aDerwent Valley = bEast Coast = cHuon/ Channel =dNorth West = ePipers River = fTamar Valley = g

Isolates

Fig. 2 Network analysis (firm to sub-cluster exchanges). Line thickness represents intensity of knowledge exchange (very little, moderate, very

high)

7 Note: North West was not calculated as no exchanges were

indicated by these firms.

Knowledge and the Climate Change Issue

123

Factor analysis revealed two factors with Eigen values

greater than one, explaining 83 percent of the variance.

Varimax rotation demonstrated that technical, industry, and

market knowledge loaded on factor one (a = 0.88), which

was labeled ‘‘public’’ knowledge. Organizational, market-

ing, and strategy knowledge loaded on factor two

(a = 0.87), which was labeled ‘‘private’’ knowledge. The

mean for public knowledge exchange is 0.82 (SD = 0.71),

while for private knowledge exchange the mean is 0.75

(SD = 0.76). The resultant t test for the full sample indi-

cates that there is no significant difference between

exchanges of public and private knowledge (t = 0.721,

p = 0.475).

To further explore Hypothesis 2, differences in the

exchange of knowledge types were also conducted by

examining sub-clusters individually. Using t-tests to assess

the different knowledge types, there were no differences in

exchanges for Coal River Valley (t = 0.834, p = 0.436),

Derwent Valley (t = 1.000, p = 0.423), East Coast

(t = 1.168, p = 0.296), Huon/Channel (t = 1.000,

p = 0.391), Pipers River (t = 1.000, p = 0.500, and

Tamar Valley (t = 1.765, p = 0.110).8 Accounting for

both sets of tests, Hypothesis 2 does not find support.

Lastly, Hypothesis 3 seeks to determine if peak national

industry bodies are the primary source of extra-cluster

knowledge about climate change in wine clusters in Aus-

tralia. 47.2 % of the firms in the sample indicated that they

acquired knowledge about climate change from peak

national industry bodies, 19.4 % from institutions of higher

education, 8.3 % from suppliers, 16.7 % from consultants,

60.5 % from trade journals, and 8.6 % from wine firms

operating in regional clusters outside of Tasmania. To test

for differences, ‘‘yes’’ responses were coded one and ‘‘no’’

responses were coded zero. A Wilcoxon signed-rank test

was then used to compare the mean of peak national

industry bodies relative to all other sources. For institutions

of higher education the difference is significant (Z =

-2.887, p = 0.004); for suppliers the difference is signif-

icant (Z = -3.742, p = 0.000); for consultants the differ-

ence is significant (Z = -3.051, p = 0.002), and for wine

businesses outside of Tasmania the difference is also sig-

nificant (Z = -3.357, p = 0.001). However, for trade

journals, there is no statistically significant difference

(Z = -1.414, p = 0.157). Therefore, Hypothesis 3 is

partially supported.

Discussion

This is the first study, to the authors’ knowledge, that has

empirically explored clusters and the climate change

issue.9 Although exploratory—and given that wine pro-

duction is highly vulnerable to climatic changes—the

results therefore have implications that bear upon climate

change and cluster research. First, a knowledge perspective

is considered.

With respect to climate change, previous research

demonstrates that firms are responding to the issue through

both mitigative actions and adaptive actions (Galbreath

2011a, 2012). However, little is known about how firms

acquire knowledge about climate change in order to better

understand the issue. Following cluster theory, one way

firms are expected to achieve this is through knowledge

exchanges. Here, because of their geographic relatedness,

clusters develop strong collective identities (Porac and

Thomas 1995; Saxenian 1994), which give rise to a com-

munity consciousness that creates an environment for the

diffusion and sharing of knowledge about factors affecting

focal firms (Scott 1988). However, there is evidence to

suggest that within regional wine clusters in Australia (and

in other wine-producing countries as well), sub-clusters are

increasingly seeking to advance their own unique identities

within a given regional cluster, and that these identities are

increasingly critical to marketing and operational success,

as well as to their reputation (Aylward 2007b; Costley

2012; Cross et al. 2011; Schmitt 2013). Following this

shift, the present study posited that knowledge exchange

about climate change would, therefore, be more wide-

spread within sub-clusters than between them (Fig. 1).

Contrary to the hypothesis, the findings suggest that

knowledge exchange about climate change is not more

widespread within sub-clusters than between sub-clusters.

One explanation for this finding might be the way in which

wine production is affected by the climate. For example,

given wine’s dependence upon climatic conditions, the

natural environment, and eco-system services (Galbreath

Table 3 Type of knowledge exchange about climate change

Knowledge type Intensity of exchange (%)

No exchange Very little Moderate Very high

Technical 47.4 34.2 18.4 0

Industry 34.2 47.4 15.8 2.6

Market 42.1 28.9 28.9 0

Organizational 50.0 34.2 10.5 5.3

Marketing 44.7 31.6 21.1 2.6

Strategy 47.4 34.2 15.8 2.6

8 Note: North West was not calculated as no exchanges were

indicated by these firms.

9 For a related exception, see Etzion and McMahon (2012). They

study the environmental performance of industry clusters. However,

their study is not exclusive to climate change.

J. Galbreath et al.

123

2011a), in any sub-cluster, a firm’s knowledge about cli-

mate change might be equally applicable to a firm in

another sub-cluster in the same regional cluster—regard-

less of either firm’s precise location. Where this is the case,

barriers to knowledge spillovers would be lessened.

Another possible explanation for the finding might be the

easing of barriers of geographic proximity. For example,

economists interpret knowledge exchanges in clusters as

being consequent to geographic proximity (Porter 1998,

2000). Such proximity is described in terms of physical or

spatial proximity and is linked to knowledge exchanges

through its facilitation of face-to-face interaction. Simi-

larly, temporary geographic proximity has been posited as

a viable alternative to that of locations that are defined

exclusively by geography, so that, for example, meetings,

visits, or temporary colocation facilitate knowledge

exchanges (Torre 2008). However, with the advent of the

internet, social media, Skype, and other modern telecom-

munications tools, the possibility exists of technologically-

mediated knowledge exchanges. While shared communi-

ties and collective identities are still expected to be an

outworking of geographic proximity, technology reduces

physical barriers, and therefore offers a possible explana-

tion for why no differences were found between knowledge

exchanges internal to sub-clusters and those external to

sub-clusters.

Second, there is evidence to suggest that technical and

industry knowledge have become pervasive in the

knowledge exchanges of wine clusters in Australia and

around the world (Aylward 2007a, b; Giuliani and Bell

2005). However, little is known about whether firms in

clusters exchange knowledge beyond that which is tech-

nical or industry-related (Sammarra and Biggiero 2008).

This study finds that firms appear to be exchanging

knowledge about climate change by exchanging both

public and private knowledge. This might be due to per-

sonal, or relational, proximities of the sub-clusters, rather

than geographic proximities. Personal, or relational,

proximity is important to a regional clusters’ exchange of

explicit knowledge (Burt 1992; Hutchins 1991). Another

possible explanation for the lack of difference in the types

of knowledge exchanged might be the fact that private

knowledge about climate change is ‘‘similar’’ (Jenkins and

Tallman 2010), in the sense that firms in sub-clusters in

Tasmania might more willingly exchange private knowl-

edge about climate change because they face similar

conditions with respect to their need to gain as much

know-how and information on the issue, given its criti-

cality to wine production.

Hence, because of both relational and spatial proximi-

ties, ties between firms are likely to become personally

embedded (Knoben and Oerlermans 2006). As this occurs

in a regional cluster, firms develop trust between one

another, based on friendship or kinship, which encom-

passes emotional bonds and relationship-based commit-

ment (Boschma 2005). Under these conditions, while a

given firm’s private knowledge about climate change might

not become a widespread ‘‘public good’’ in the regional

cluster, it might explain why there are no differences

between the level of exchange of public and private

knowledge in the sample. That is, personal proximity and

trust-based social interactions could be one possible

explanation for why knowledge exchange about climate

change is similarly reflected in both public and private

knowledge exchanges (cf. Jenkins and Tallman 2010;

Tallman et al. 2004).

Lastly, despite the focus and emphasis of inter-firm

knowledge exchanges in clusters, some scholars have

stressed the importance of so-called extra-cluster sources

of knowledge, since the mere reliance on localized

knowledge of firms can result in entropy of the cluster

that remains locked-into an increasingly obsolete knowl-

edge base (Camagni 1991). This study finds that firms in

the sample are acquiring knowledge about climate change

from extra-cluster sources, particularly peak national

industry bodies and trade journals. The results confirm

previous studies (e.g., Giuliani and Bell 2005), suggesting

that the cluster as a knowledge system is not merely

influenced by the spatial and relational confines of firms

within the region, but instead is a complex system of

internal and external knowledge exchange and

acquisition.

Conclusion, Future Research Directions,

and Limitations

Little research has examined how firms in the wine industry

acquire knowledge so that they gain an understanding of

climate change and its impacts. This study explored spe-

cific cluster and extra-cluster effects. The findings suggest

that firms appear to be acquiring knowledge about climate

change through both types of effects. Even though less than

half of the firms in the sample are exchanging knowledge

about climate change, exchanges are nonetheless occur-

ring. But, contrary to this study’s prediction, these

exchanges are not mainly confined to sub-clusters. Firms

also appear to be exchanging knowledge about climate

change through the exchange of a variety of knowledge

types, including knowledge that is considered private and

firm-specific. Following the arguments of some scholars,

the results also suggest that firms are acquiring knowledge

about climate change from knowledge sources outside of

member embedded firms. These findings point to future

research opportunities.

Knowledge and the Climate Change Issue

123

First, future studies could use knowledge exchanges to

explore the nature of knowledge about climate change,

seeking to better understand the personal dimensions of

knowledge exchange across a regional cluster. Of partic-

ular interest might the philosophic or moral position of

actors (Markowitz and Shariff 2012) in the regional cluster

and whether or not this affects their sense of obligation to

exchange knowledge about climate change regardless of

their position spatially. Along these lines, climate change

has been acknowledged as a ‘‘great moral issue’’ (Carrell

2012; Moore and Nelson 2010). For business ethicists and

researchers with an interest in corporate social responsi-

bility and sustainability, the study of clusters could be a

fruitful avenue for future research, as there is some evi-

dence to suggest that clustered firms demonstrate higher

levels of environmental performance than non-clustered

firms (Etzion and McMahon 2012), that clusters might

possess deeply held traditions about the business–society

contract and the role of responsibility (Marquis 2003), and

that clusters might hold highly dominant values, such as a

value for environmental stewardship (Russo and Earle

2010). A better understanding of the role that clusters play

in these areas is needed.

Second, future research could further study how clus-

ters are influenced by the exchange of private, idiosyn-

cratic knowledge related to climate change and how

relational, rather than spatial, proximity might affect such

exchanges. According to Lounsbury (2007), knowledge

exchange is fundamentally shaped by cultural dynamics

that characterize specific communities, such as clusters.

Further understanding is therefore needed to determine

how such cultural dynamics could lead to an array of

knowledge exchanges between firms in clusters (Mitchell

et al. 2010), including path dependent, firm-specific

knowledge. Lastly, future research could further examine

the role of extra-cluster sources of knowledge, determin-

ing if there are circumstances in which such knowledge

sources dominate knowledge acquisition within clusters.

Such research could support those scholars who claim that

the advantages of clusters, via inter-firm knowledge

exchange, are less than received theory predicts (Huber

2012).

This study has limitations. First, the sample size is rel-

atively small. In light of this small sample, the results are

tentative at this stage, and should be interpreted with

caution. However, research on climate change in the

management literature is nascent, and scholars claim that

significantly more studies are needed to examine this

important—if not contested—topic (Ansari et al. 2011;

Goodall 2008). The present study is the first known

examination of the climate change issue in a cluster setting,

and thus serves as a foundation for future research. Second,

data on the main variables of interest were collected

through a single source. This has the potential to introduce

common method bias. Yet, a Harman’s ex post single

factor test (Podsakoff and Organ 1986) revealed the

absence of a single general factor accounting for most of

the observed covariance in the variables, indicating that

common method bias was likely minimal. Third, this study

investigated a single regional cluster, therefore generaliz-

ability is limited. However, the Tasmanian wine cluster is

increasingly seen as important to the future growth of

Australia’s wine production, for domestic consumption and

for export. Hence, it is a good setting, given the Australian

wine industry’s concern over the impacts of climate

change, both in economic and environmental terms (Rus-

sell and Battaglene 2005, 2007). Lastly, that specific

mechanisms that facilitate knowledge exchange were not

studied is a limitation. Thus, while modern communica-

tions tools such as the Internet and other electronic means

could act as facilitators, this study relied on economic

geographers’ interpretation of knowledge exchanges in

clusters as being consequent to geographic proximity

(Porter 1998, 2000). This perspective links knowledge

exchanges through its facilitation of face-to-face

interaction.

Acknowledgments This research was supported by a Linkage Grant

from Curtin Business School, Curtin University.

Appendix 1: Knowledge Type Items Used

in the Questionnaire

‘‘Public’’ Knowledge

(1) Technical knowledge Insight on technologies, tech-

nical enhancements, vineyard, and/or winery tech-

niques that relate to climate change.

(2) Industry knowledge Know-how gained from peak

industry bodies, specialist sources, or employees/

peers on addressing industry requirements or govern-

ment policies on climate change.

(3) Market knowledge Knowledge about the size of

opportunity for consumer markets sensitive to pro-

ducers’ environmental credentials, how competitors

are responding to climate change, impacts of climate

change on grape yield, quality, and price.

‘‘Private’’ Knowledge

(1) Organisational knowledge How your company has

coordinated and supervised organizational resources

and processes so that climate change impacts are

addressed efficiently and effectively.

J. Galbreath et al.

123

(2) Marketing knowledge How your company specifically

addresses customer preferences, marketing and brand-

ing, and new product development as they relate to

any climate change requirements.

(3) Strategy knowledge Insight on your company’s strat-

egy, planned competitive moves, long-term business

plans, and ability to manage change related to climate

change.

Appendix 2: Geographic Maps of Wine Production

See Figs. 3, 4 and 5.

Fig. 3 Global wine regions

(colored areas)

Fig. 4 Australian wine regions (colored areas) Fig. 5 Tasmanian wine sub-clusters

Knowledge and the Climate Change Issue

123

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