Creative Industry Stimulate Wider Economy

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1. INTRODUCTION

Nobody doubts that the creative industries

make important contributions to the artisticlife and cultural wellbeing of all countries. Theirproducts give pleasure, stimulate ideas and convey meaning. Some – though not all – of these bene-fits are reflected in commercial value. Statisticalreports document the significant contributions thecreative industries make to the UK’s economy (e.g. Andari et al. 2007; OECD 2006).

There is also extensive quantitative evidence

available on the sources and impacts of innovationin the UK (DTI 2006). While a great deal of i i h h h i d h i l d

science-based research and development activities,policymakers and academics now also recognise

the importance of creativity and design activitiesto the process of innovation (Cox 2005; DTI2005; Haskel et al. 2005; Acha 2007). Wide-spread perceptions assume that creative industries,as a focal point of creative activity, have an impor-tant role to play in innovation throughout theeconomy (Potts 2007). But robust quantitativeevidence to support this belief has been lacking.

Theory suggests a number of mechanisms by

which the creative industries may support innova-tion in the wider economy (Andari et al. 2007).Thi i i l h i

Copyright © eContent Management Pty Ltd. Innovation: management, policy & practice (2009) 11: 169–18

Creative supply-chain linkages and

innovation: Do the creativeindustries stimulate businessinnovation in the wider economy?

H

ASAN

B

AKHSHI

National Endowment for Science, Technology & the Arts, London, UK

E

RIC

M

C

V

ITTIE

Experian, London, UK

ABSTRACT

Knowledge transfer between businesses is facilitated by their supply-chain relationships. Creative businesses in sectors such as advertising, architecture and software are heavily engaged in business-to-business activity. This opens up the possibility that the creative industries, as a focal point for creativity, stimulate and support business innovation in the wider economy. This study combines

data on business-to-business relationships between creative and non-creative sectors, based oninput–output tables for the UK, and firm-level data on business innovation, taken from the Com-munity Innovation Survey, to explore whether the creative industries support innovation throughthis channel.

Keywords: creative industries, supply-chains, innovation


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whether creative businesses stimulate innovationthrough the supply-chain relationships which link them to businesses in other sectors.

We base our quantitative analysis of creativesupply-chain linkages on data extracted from

input–output tables for the UK published by theOffice for National Statistics. The resulting meas-ures are then brought together with quantitativedata on innovation performance taken from thefourth UK Community Innovation Survey (CIS4).1 Our methodological approach is to esti-mate benchmark econometric models of businessinnovation of the sort regularly used in the inno-vation studies literature, and then to test whether

variables capturing supply-chain relationships with creative businesses have additional explana-tory power.

We provide formal evidence for the first timethat businesses with stronger links to the creativeindustries appear to be more innovative, at leastin terms of their product innovations. This sug-gests that the creative industries may play a moreimportant role in the UK’s ecology of innovation

than has previously been recognised.

2. C

REATIVITY

,

SUPPLY

-

CHAIN

LINKAGES AND BUSINESS

INNOVATION

Creativity and innovation are overlapping con-cepts. In the main, creativity refers to the act of generating new ideas, approaches or actions,

while business innovation is the process of bothgenerating and applying such ideas in a commer-

cial context (Andari et al. 2007).The fundamental importance of creativity to

innovation processes has long been recognised by innovation scholars. In the chain link model of innovation, for example, innovation is a learning process in which knowledge is constantly devel-oping and being modified in an iterative series of feedback loops (Kline & Rosenberg 1986). Inno-vation takes place within a complex system of

interactions between research, the knowledgebase, invention, design, production, distributionand marketing, and existing or potential markets.Many innovations are in fact novel combinationsof old insights. Imaginative ways of thinking and

creativity are crucial for any well-functioning innovation system (Lundvall 1992).

The UK government’s Cox Review has consid-ered the importance of design in particular onbusiness innovation (Cox 2005). It points to clearevidence of the benefits of design-related solu-tions to business problems (DTI 2006), andmakes a number of policy recommendations as tohow these benefits can be promoted more effec-

tively to small- and medium-sized businesses inparticular.

All this has prompted interest in the role of thecreative economy within the innovation process.The creative industries and related terms such asthe creative sector are now commonly employedto describe a wide range of activities that involvethe commercial exploitation of creative and artis-tic inputs. The UK’s Department for Culture,

Media and Sport (DCMS) defines the creativeindustries as:

activities which have their origin in individualcreativity, skill and talent, and which have thepotential for wealth creation through the gener-ation and exploitation of intellectual property.

Thirteen different sub-sectors are defined: Advertising; Architecture; Arts & Antiques Mar-ket; Crafts; Design; Designer Fashion; Film;

Music; Performing Arts; Publishing; Software andcomputer services; Computer Games (InteractiveLeisure Software); and Radio & TV.

On this basis the UK has been shown to havethe largest creative sector in the European Unionand probably the biggest in the world whenexpressed as a proportion of national output(Andari et al. 2007). Contributing over 7% of the UK’s gross value-added and over one million

Hasan Bakhshi and Eric McVittie

1 The UK Community Innovation Survey was conducted by the Office for National Statistics on behalf of the Departmentf T d d d h d l f f h C S4 d d d S 4 f h


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employees, it is comparable in size to the finan-cial services industry.

The creative industries are widely seen as partof an avant garde of innovation-intensive, high-growth information services (Handke 2006); as a

major source of new ideas and their commerciali-sation (Barras 1990); and as an area were ‘R&Dis the main activity, while production is second-ary’ (Lash & Urry 1994).

Yet, there have been few comprehensiveattempts to study the creative industries on thebasis of economic theories of innovation. Thisneglect probably reflects the particular challenges

which the creative industries pose for the analysis

of innovation. John Howkins (2002) argues that:The conventional thinking about innovationdoesn’t capture what actually happens in thecreative industries…The problem is two-way:people who talk about innovation tend toignore what happens in the creative industries;and the creative industries tend to downplay the benefits of innovation.

In other sectors of the economy, a good deal of research has attempted to uncover what drivesinnovation performance. Innovation – at least

within areas of private activity – is often explicitly driven by the profit motive, and particularly thepossibility of earning profits by being a first orearly mover in a new market. But a range of otherfactors will influence the propensity for an indi-vidual firm to innovate, including access to new ideas from sources such as the creative industries.

Firms may, for example, obtain informationfrom other businesses simply by observing, copy-ing or adapting others’ innovations. To a largeextent, knowledge flows of this type go unrecord-ed, although measures such as patent citationsprovide a partial record of technical and scientificinnovations by one firm building on knowledgegenerated by others.

Knowledge also flows between firms as work-

ers move jobs over the course of their working lives. Research on knowledge transfer through

k bili h i l f d f i

direct investment (Fosfuri et al. 2001; Glass &Saggi 2002; Gorg & Strobl 2005). This type of knowledge transfer is likely to be particularly important for the creative industries, as creativelabour markets tend to be especially fluid with

workers having unusually high levels of mobility (Benhamou 2003).

A range of contractual relationships betweenbusinesses may also encourage the transfer of knowledge between them. Some formal relation-ships – such as joint ventures and other forms of collaboration on R&D, and technology licensing agreements – are directly aimed at supporting innovation. Caves (2000) argues that collabora-

tion on innovation is commonplace in the cre-ative industries, as the creation of new productsoften occurs in flexible networks and throughtemporary, project-based cooperation. Other for-mal links, such as buyer–seller, or supply-chainrelationships are not primarily concerned withinnovation, but may nonetheless allow either orboth parties access to knowledge which supportstheir innovation efforts.

Informal links between firms are likely vastly to outnumber formal links in practice (Powell etal. 1996). As Uzzi and Lancaster (2003) put it:

For the hundreds of formal ties among firmsthat act as information conduits, thousands of informal relationships exist among scientists,engineers, developers, managers, and otherpersonnel through which information flows.

Formal and informal links are however often

mutually supporting. Thus, Gulati (1995) andLazerson (1995) show that informal ties oftenform the basis for the development of contractualrelationships between firms. Similarly, initially formal links may develop into informal personalrelationships between individual staff members(Roy et al. 2004).

Our focus in this paper is on knowledge trans-fers involving supply-chain linkages with creative

businesses. Knowledge may be embodied in busi-ness-to-business (B2B) transactions betweenfi h h l f i di d

Creative supply-chain linkages and innovation


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and services for use in another firm’s productionprocesses (Griliches 1992; Nadiri 1993; Wolff &Nadiri 1993). In this case, one creative firm orindustry’s innovative activity may in principleaffect the technology or capacity to innovate, of

both customers of, and suppliers to, the creativeindustries.2

Roy et al. (2004) provide a detailed discussionof the ways in which supply-chain relationshipsmay contribute to innovation. Supply-chain rela-tionships give rise to a variety of interactionsbetween buyers and sellers that supportexchanges of information and the generation of new knowledge.

The knowledge exchanged may relate to: buy-ers’ specific requirements (Hallen et al. 1991);clarification of design issues and useage patternsto pre-empt problems arising in the use of inter-mediate goods and services (Clark & Fujimoto1990; Leonard-Barton 1995); and ongoing detailed technical discussions, which sometimeslead to ‘creative abrasion’ when problems arehighlighted, stimulating the search for solutions

(Leonard-Barton 1993, 1995).Interactions between buyers and sellers may range from impersonal and arm’s length (andpossibly one-off), to stronger and enduring per-sonal relationships in which informal discussionsresult in routine knowledge sharing.3 Buyers andsellers are likely to have both shared specificknowledge, providing a fruitful basis for theexchange of ideas (Burt 1987, 1992). Roy et al.(2004) propose that the more developed are

buyer–seller interactions (in terms of frequency,duration and quality), the greater the contribu-tion of supply-chain relationships to innovationsby both buyers and sellers.4

Roy et al. (2004) also point to a number of other factors which influence the link betweensupply-chain interactions and innovation. Theseinclude features of the buyer–seller relationship

which either or both parties can control – partic-

ularly commitment and trust.Gundlach et al. (1995) argue that commit-

ment is particularly important in supporting innovation within business networks. Commit-ment involves making efforts and devoting resources to maintaining the relationship (Mor-gan & Hunt 1994). Commitment by both buy-ers and sellers supports two-way communication(Anderson & Weitz 1992), and is likely to

strengthen the impact of supply-chain interac-tions on innovation.

Successful communication also depends ontrust, which usually takes time to build andtherefore tends to be a feature of enduring rela-tionships. The degree of trust between partnersdetermines the extent to which organisations are

willing and able to engage, and the depth of theirinteractions (Athaide et al. 1996; Sako 1992;

Dodgson 1993; Gambetta 1988; Gulati 1995; Joshi & Stump 1999; Morgan & Hunt 1994).High levels of trust enhance the capacity of sup-ply-chain relationships to foster innovation.

Roy et al. (2004) note that the importance of supply-chain relationships to innovation is alsolikely to depend on factors which are outside thecontrol of buyers and sellers. Supply-chain inter-actions will, for example, be more important

when the knowledge is tacit; that is, when knowl-

edge is difficult to codify and communicate, butcan be transmitted through training, ongoing personal interactions and experience (Polanyi1966). As a great deal of the knowledge generated


2 Embodied knowledge transfers through supply-chain relationships will only constitute what economists call ‘positivespillovers’ insofar as they are not reflected in the prices at which the B2B transactions take place. If instead markets arecompetitive, the firm receiving the knowledge transfer from creative businesses should be charged for the benefits they enjoy (Griliches 1992).

3 The social network literature refers to the ‘relational embeddedness’ of interactions between firms, by which is meant the

strength and quality of social attachments (Granovetter 1973; Gulati 1998; Uzzi 1996, 1997).4 Another implication of these arguments is that supply-chain relationships may stimulate broader communicationsbetween businesses. The knowledge transfers associated with these communications may involve knowledge spillovers,

if h b di d k l d f h l d


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within creative businesses is likely to be tacit innature (Crossick 2006), supply-chain interactionsmay be particularly important to knowledgetransfer involving the creative industries.

Supply-chain interactions are also more likely

to support incremental innovations whendemand for the final product – that is, at the endpoint of the supply-chain – is stable. This isbecause stable demand conditions are more con-ducive to lasting supply-chain relationships,increasing commitment and trust, building shared knowledge and allowing ongoing atten-tion to product improvements. In contrast, sup-ply-chain interactions are more likely to support

radical innovations when demand for the finalproduct is unstable, as is the case for many cre-ative businesses. Creative products often havevery short life cycles and demand conditions tendto be highly uncertain (Caves 2000; Vogel 2003;Handke 2006; Stoneman 2007).

Backward supply-chain linkages to the creativeindustries support innovation in the wider econo-my if purchases by creative businesses stimulate

innovation in their suppliers. There is a substan-tial economics literature on the importance of backward supply-chain linkages to innovationamong suppliers to some leading sectors of theeconomy, and to multinational companies – a high level of innovation in the purchaser requiressuppliers to be innovative in order to improveand develop key inputs (Rodriguez-Clare 1996;Markusen & Venables 1999). The artistic andcreative sectors may be especially demanding cus-

tomers in this regard.This brief overview of the literature shows that

the principle that supply-chain linkages areimportant for innovation is well established.However, no previous studies have focused onsupply-chain linkages to the creative industries in

particular, despite good theoretical reasons forexpecting relationships with these sectors to beimportant for innovation. That gap is what weseek to address here.

3. Q

UANTIFYING THE EXTENT OF

CREATIVE LINKAGES

This study focuses on supply-chain linkages –specifically business-to-business transactions in‘intermediate’ goods and services – as providing one potentially important means by which thecreative sector may support innovative activitieselsewhere.5

We use input–output methods to measure the

strength of supply-chain linkages from differentsectors of the UK economy to the creative indus-tries. The input–output tables tell us whichindustries buy which goods and in whatamounts, allowing us to identify and measure thepattern of supply-chain linkages between indus-tries across the economy as a whole (see Bakhshiet al. 2008 for a detailed discussion of theinput–output tables).

By analysing the UK’s input–output accountson a consistent basis for every year between 1992and 2004 for which the Supply and Use Tablesare published, we construct measures of the valueof ‘creative’ goods and services purchased by eachUK industry and the value of goods and servicessold by each UK industry to the creative indus-tries. We call the former ‘forward creative link-ages’ to the creative industries and the latter‘backward creative linkages’.

These measures are based on an industry andproduct-based definition of the creative indus-tries. We get this by mapping the ONS’s classifi-cation of input–output sectors containing creative activities to the ONS’s ‘functional’ cre-ative sectors (ONS 2006).6


5 Business-to-business transactions in investment goods may also be important in transferring knowledge between firms. Itis not, however, possible to measure the importance of these transactions using published input-output data for the UK.

6 The ONS’s Input–Output analysis of the creative industries uses a specially constructed set of nine ‘creative industry’

groupings (Fashion/Clothing; Software; Architecture; Publishing; Advertising; the Arts; Radio & TV; Distribution; andFilm), which the ONS refers to as ‘creative functions’ or ‘creative functional headings’. These are intended to approximatethe definition of creative industries provided by the DCMS. We have adjusted our data, so far as possible, to exclude‘ i ’ i i i i hi h i f i l h di ( h d d ib d i d il i NESTA )


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This approach provides a close approximationto the DCMS’s definition of the creative indus-tries, given the classification constraints in theinput–output data.

Our basic measure of the strength of forward

creative linkages with purchasers is the share of spending on creative products expressed as a per-centage of total gross output for a givenindustry.7 Similarly, the strength of backward cre-ative linkages to suppliers is measured by theshare of purchases by the creative sector in totalsales by that industry. The lack of industry detailin UK input–output data for the creative indus-tries means that we sometimes have to use sup-

ply-chain information based on widerinput–output groups, rather than their ‘creative’components alone. The accuracy of our estimates

will reflect the extent to which supply-chain pat-terns for creative products are common withother industries with which they are grouped.

The extent of supply-chain linkages to the cre-ative sector – both in terms of purchases and sales– is also likely to vary between different firms.

Our industry-based measures will miss these vari-ations, making it more difficult to pinpoint therelationships between creative businesses andfirms in other parts of the economy.

Forward creative linkages

Figure 1 shows that B2B sales are important forthe creative industries (Frontier Economics 2006;Freeman 2007; Andari et al. 2007). In particular,almost 60 percent of overall demand for creativeproducts within the UK comes from purchases by businesses as intermediate inputs. This is a highershare than for all products, and is similar to thatfor financial and business services, which include

a broad range of B2B products.This finding suggests that the creative indus-

tries are strongly integrated into the wider econo-my through their supply-chains, and that thesemay provide an important source of interactions

with other sectors. There is significant variation inthe importance of business-to-business demandacross different parts of the creative industries.Figure 1 shows that B2B demand for creative


7 Gross output is made up of the value of intermediate purchases (purchases of goods and services as inputs to otherproduction processes) plus value added in that sector. The ratio of creative product purchases to gross output therefore

h f d

FIGURE 1: IMPORTANCE OF INTERMEDIATE SALES FOR THE CREATIVE INDUSTRIES 1992–2004


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products is particularly important for Advertising, Architecture, Software and Fashion products.

Industries’ purchases of creative products areone way that the creative industries may con-tribute to innovation in other parts of the econo-

my. This can occur in at least two ways:(a) the creative industries may directly assist the

innovation processes of other sectors – suchas through software sales and advertising services; and

(b) market transactions may facilitate knowl-edge transfers between creative businessesand those businesses which purchase creativeproducts.

Industry purchases of creative productsaccounted for around 6 percent of intermediatepurchases by UK industries in total during 2004and around 3 percent when expressed as a per-centage of total industry gross output (Figure 2).Purchases of creative products are particularly important between the creative industries them-selves. Creative product purchases are equivalent

to over 8 percent of total gross output andaccount for 19 percent of intermediate purchasesby the creative industries.

Forward supply-chain linkages appear to bestronger between the creative industries and a num-ber of services sectors, with somewhat weaker linksto manufacturing and the other production sectors.

Backward creative linkages

Purchases by the creative industries of intermedi-ate goods and services produced in other indus-tries provide another potential means for thecreative industries to support business innovationin the wider economy. Creative firms may shareknowledge – either deliberately or as an unin-tended consequence of the relationships involved– with their own suppliers, or they may require

more innovative products themselves.Figure 3 plots creative industry purchases for

broad product groups as a share of total demandfor those products. Overall, creative industry inter-mediate input demands contribute around 1.6 per-cent of total product demand within the UK, but7.4 percent of demand for creative products. Notethat the above measures of creative linkages aredefined at the industry level, in that they reflect

sales and purchases between each industry and a specific set of industries characterised as ‘creative’,based on the ONS and DCMS definitions.



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4. Q

UANTIFYING THE EXTENT OF

INNOVATIVE INDUSTRIES

Our analysis of innovation is based on the inno-vation activities and performance of individualfirms, using data from the latest UK Community

Innovation Survey (CIS4). This allows us toexploit the range of information within the CIS4on other influences on innovation performancealready identified by researchers, and to controlfor these in exploring the role of linkages to thecreative industries.

Before considering this analysis, it is useful todiscuss informally the pattern of innovation inthe UK at the industry level. We therefore con-

struct a range of measures of innovation for UK industries by aggregating firm-level data from theCIS.8 We can think of the innovation measures asencompassing three distinct stages of the innova-tion process:9

1. Innovation activities: firms’ deliberateattempts to generate new knowledge andinnovations through their own research and

development, acquisition of R&D andknowledge from other firms, design activities,equipment purchases, training and marketing activities.

2. Innovation outputs: the results of firms’ inno-

vation efforts in the form of new and novelproducts and process, and of wider innovationsin organisational structure, corporate strategy,management methods, and marketing.

3. Innovation impacts: the impacts of firms’innovation activities and outputs on aspectsof business performance, including improve-ments to the range and quality of products,increases in market share or penetration of

new markets, improved flexibility of produc-tion, and reduced production costs.

Our prior is that purchases of creative prod-ucts should be more strongly related to certaintypes of business innovation activity than othersrecorded by the CIS (internal R&D, design andmarketing activities); to certain innovation out-


8 Industries are defined based on the input-output industry groups used for the creative linkage measures.9 This characterisation is based on the structural innovation model introduced by Crepon et al. 1998, and subsequently

d d b ff h l d l

F

IGURE

3: I

NDUSTRY SALES OF INTERMEDIATE INPUTS TO THE CREATIVE INDUSTRIES

, 1992–2004


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puts (product innovations, rather than processinnovations); and to certain types of innovationimpact (expanded diversity of products andimprovements in product quality, rather thanreduced cost and increased flexibility). This is

why we focus on these aspects of innovation per-formance in our analysis.

The first point to note is that the creativeindustries appear to be more innovative thanother sectors on a broad range of activity, outputand impact measures. So, over 40 percent of firmssurveyed in the creative industries report use of in-house R&D; over 20 percent report use of design inputs and almost 35 percent innovations

in marketing. Each of these percentages is higherthan their non-creative counterparts. Likewise,the creative industries have much higher rates of product innovation than their non-creative coun-terparts; the gap appears to be narrower forprocess innovation, suggesting that creative busi-nesses may play a greater role in stimulating prod-uct, as opposed to process, innovation in otherbusinesses (and that is indeed what we find in our

econometric analysis). Larger proportions of cre-ative businesses also report stronger innovationimpacts in terms of improved product quality,increased product range and expanded marketscompared with their non-creative counterparts.

A major component of our hypothesis is basedaround the idea that businesses acquire valuableinformation for innovation through their con-tacts with suppliers and customers. There is someevidence on the importance of supply-chains as

sources of information for innovation from CIS4itself (Figure 4). These data suggest that supply-chain linkages are an important source of infor-mation for innovation. This applies across allareas of economic activity, but particularly inmanufacturing where almost 60 percent of busi-nesses report that information from suppliers isimportant to their innovation.

Information from customers is even more sig-nificant, with around 62 percent of manufactur-ing firms reporting that such information isimportant to their innovation. Informationinflows also appear to be important for innova-

tion by creative businesses. More than 50 percentreport that information from suppliers is impor-tant for their innovation, and almost 60 percentsay the same about information from customers.These are higher shares than for most serviceindustries, and for the economy as a whole.

CIS4 suggests that businesses are less likely toactively cooperate with suppliers or customers oninnovation than they are to acquire information

from them. Even so, around 10 percent of firmsreport cooperating with customers on innovationactivities, and slightly more – over 11 percent –report cooperating with their suppliers. As withinformation flows, such cooperation appears tobe particularly important for creative businesses.This may have implications for innovation inother sectors with supply-chain linkages to thecreative industries.

Several published research studies using previ-ous versions of the Community Innovation Sur-vey have found that acquisition of informationand cooperation with suppliers and customershelp to explain the innovation patterns of firmsin the UK.10 We replicate such findings using CIS4 data, and then investigate also whether sup-ply-chain linkages with creative industries help toexplain innovation by businesses in other sectors.

5. E

CONOMETRIC ANALYSIS OF

CREATIVE SUPPLY-CHAIN LINKAGES

AND INNOVATION

Figure 5 divides industries in the UK (excluding the creative industries) into two halves on thebasis of their purchases of creative productsexpressed as a percentage of their gross output(forward creative linkages).11


10 Neely and Hii (1998) provide a review of the earlier literature. More recent studies using CIS3 include Janz et al. (2003),Loof and Heshmati (2000) and Griffith et al. (2006).11 The creative industries themselves are excluded since we are primarily interested in the role of creative activities in

l h h


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On all of the innovation measures, industries

with stronger links to the creative industries havea more impressive innovation performance. So,industries which purchase a greater proportion of creative products have a higher proportion of firms claiming to have engaged in in-houseR&D, design and innovative marketing. They also have higher proportions of firms reporting product and process innovations. Finally, firmsin those industries with stronger links to the cre-ative industries are more likely to report that

they have improved the quality of their products,increased their product range, expanded intonew markets or increased their share in existing markets as a result of innovation.

These patterns are consistent with a link between purchases of creative inputs and innova-tion performance – though on the basis of thisdescriptive analysis alone we cannot know how robust this relationship is.

Specification of the econometric

analysisThe fourth UK Community Innovation Survey provides a range of information on innovationbehaviour and performance on individual busi-nesses in the UK together with data on businesscharacteristics widely regarded as important deter-minants of innovation. The firm-level data coverover 16,000 firms across most economic sectors,although firms with fewer than ten employees areexcluded, as are some important service industries.

Our approach is to test for the additional sig-nificance of creative linkage variables in otherwiseconventional econometric models of businessinnovation. That is, we seek to explain differencesin the innovation performance of individualfirms based on standard ‘control’ variables andthen explore whether measures of linkages to thecreative industries add to the explanatory powerof the model.12 This approach attempts to meas-


FIGURE 4: SUPPLY-CHAINS AS SOURCES OF INFORMATION FOR INNOVATION

12 By ‘standard’ we mean variables that are typically included in econometric models of innovation in the literature. See, forl l k h d h ( )


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ure how creative linkages contribute to innova-tion, once other key influences on a firm’s inno-vation performance have been discounted. We

use binary choice probit regression techniques(further details are given in the Appendix).Our main variables are summarised in Table

1. The first set of variables relates to variousmeasures of innovation, which are divided intoinnovation activities, innovation outputs andinnovation impacts.

Our analysis focuses on the three types of innovation activity plotted in Figure 5:• In-house R&D;

• Design activities; and• Innovation-related marketing activities.

These are arguably the most likely to be influ-enced by purchases of creative intermediateinputs.13 We also concentrate on the three typesof innovation output shown in Figure 5:

• Product innovations (introduction of new orsignificantly improved goods or services);

• Novel product innovations (where the new

good or service is also new to the market); and• Process innovations (introduction of new orsignificantly improved processes).14

Finally, we consider three ways in which inno-vation can impact on a firm:• Improvements in product quality;• Increases in product range; and• Expansion into new markets or increased mar-

ket share in existing markets.

In each case, we build econometric models toaccount for why firms engaged in that type of innovation during the period covered by CIS4(2002–2004).

In our models we attempt to isolate the impactof linkages to the creative industries on each of


F

IGURE

5: I

NNOVATION PERFORMANCE FOR INDUSTRIES WITH STRONGEST AND WEAKEST FORWARD CREATIVE

LINKAGES

(

PURCHASES OF CREATIVE PRODUCTS

)

13 Other innovation activities reported in CIS4 are: external R&D; knowledge acquisition; equipment purchases, and

innovation-related training.14 CIS4 also records other types of innovation outputs: novel processes and four types of ‘wider innovation’ – corporatestrategy innovations, organisational structure innovations, marketing innovations, and the introduction of ‘advanced

’ h


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Dependent Variables - Innovation Innovation activit ies In-house R&D

Enterprise engaged in in-house R&D during 2002-4 (1 = Yes, 0 = No) CIS4 Question 13

Desig Enterprise devoted resources to design during 2002-4 (1 =

Yes, 0 = No)

CIS4 Question 13

Innovation-related marketing Enterprise engaged in marketing related to innovative products

during 2002-4 (1 = Yes, 0 = No)

CIS4 Question 13

Innovation outputs

Product innovation Introduced new or significant ly improved product (good or

service) during 2002-4 (1 = Yes, 2 = No)

CIS4 Question 5

Novel products Introduced new to market product i nnovation during 2002-4 (1 = Yes; 0 = No)

CIS4 Question 7

Process innovations Introduced new or significantly improved process during 2002-4 CIS4 Question 9

Innovation i mpacts

Improved quality

Improved product quality (1 = 'Medium' or 'High' importance)

CIS4 Question 12

Increased Increased product range (1 = 'Medium' or 'H igh' importance) CIS4 Question 12

Expanded markets Entered new markets or expanded market share (1 =

or 'High' importance)

CIS4 Question 12

Explanatory Variables - Creative Linkage Measures Purchases of creative products Purchases of creative products as % of total output for input-output

industry group (total or by product)

UK Input-Output

Accounts 2002-4

Sales to creative sectors

Purchases by creative sector as % of total demand for input-outputindustry/product group (total and by industry/product)

UK Input-Output

Accounts 2002-4

Creative employment

Employment of 'creative' occupations (DCMS definition) as % of

total employment by input-output industry group

Labour Force Survey

& UK Input-Output Accounts 2002-4

Explanatory Variables - Control Var iables

Firm size

(Log of) business turnover (total sales) in 2004

CIS

Industry

'Dummy' (0,1) variables for 2-digit SIC industry groups

CIS

Locatio Dummy' variables for UK Government Office Regions based on

CIS4 postcodes

CIS

Business type & age 'Dummy' variables recording whether the business i s part of a largerenterprise group, and whether it was established after 1 Jan 2000

CIS Questions 1 & 4

Employee qualifications % of employees with degrees in Science & Engineering subjects; %of employees with degrees in oth er subjects.

CIS Question 26

Product market area 'Dummy' variables recording whether the business' main market

areas was local, EU or global.

CIS Question 2

IP protection methods

Set of variables recording extent to which firm could employ range ofmethods to protect their intellectual property (patents, copyright, etc.)

(3 = 'High' importance, 2 = 'medium', 1 = 'low', 0 = 'none')

CIS Question 21

Barr iers to i nnovation Set of variables recording stated importance of a range of barriers to

innovation (3 = 'High' importance, 2 = 'medium', 1 = 'low', 0 = 'none')

CIS Question 19

Public 'Dummy' variables recording whether business received public

support for innovation from local, national or EU sources (1 = Yes, 0=

CIS Question 22

Innovation activities 'Dummy' variables recording engagement in range of innovationact ivities as control variable within innovation output and impactmodels (1 = Yes, 0 = No)

CIS Question 13

Cooperati o 'Du mmy ' v ariables recording whether the bus iness cooperated withsupp liers or customers as part of its innovation act ivities (1 = Yes, 0 =

No

CIS Question 18

Information sources

'Dummy' variables recording whether suppliers or customers servedas important sources of information for the business' innovat ionactivities (1 = 'Medium' or 'High')

CIS Question Q16


No

TABLE 1: INNOVATION AND CREATIVE L INKAGE MEASURES AND CONTROL VARIABLES USED IN THE

ECONOMETRIC ANALYSIS

Location

Design

Cooperation


13/22

these nine innovation measures. The measures arederived using input–output techniques which, asdescribed earlier, estimate the value of purchasesof creative products and sales to creative sectorsfor each of the 119 input–output industry

groups, as well as the share of creative purchasesand sales in total gross output and demand.

Each firm in the CIS4 dataset is allocated toan input–output industry on the basis of itsdetailed (5-digit) standard industrial classification(SIC). Firms are assumed to have the same pat-tern of supply-chain linkages as the input–outputindustry of which they are a part.15 Since we areinterested in the role of the creative industries in

supporting innovation elsewhere in the economy,creative businesses themselves are excluded fromthe econometric analysis.

The other variables listed in Table 1 are intendedto account for the broad range of other influenceson a business’s innovation performance used in theextant literature. That innovation may be related tofirm size; for example, that larger businesses aremore likely to engage in innovation activities and to

generate new innovation outputs is a commonfinding in empirical studies of innovation. We alsocontrol for various other characteristics which wemight expect to influence innovation.16 Two broadgroups of control variable merit particular mention.

First, we include a full set of industry ‘dummy’variables indicating which industry each firm

belongs to. These variables are included becauseinnovation performance is likely to vary structurally across industries, and this is unlikely to be fully cap-tured by the available data. For example, industries

will have different technological regimes that funda-

mentally influence the opportunities and incentivesfor firms to engage in innovative activities, and theirsuccess in generating innovation outputs andimpacts. Differences in innovation processes andoutcomes across industries may also lead firms toreport their innovative behaviours in different ways(NESTA 2008; Miles & Green 2008). The indus-try dummy variables within our regression analysisprovide a simple, albeit imperfect, way of control-

ling for these differences to focus on the determi-nants of innovation at the firm level – and inparticular the role of linkages to the creative indus-tries – across all sectors.17 We also include regionalvariables, defined for the UK Government Officeregions to allow for the possibility of systematic geo-graphical variations in innovation performance.18

The second important group of control variablesin our model are our proxies for potential supply-

chain knowledge transfers between businesses (not just those involving creative businesses). These areintended to measure the generic extent to whichfirms acquire information for innovation from sup-pliers and customers, and the extent of cooperationon innovation with suppliers and customers. Thesevariables are often included in econometric models


15 This assumption is necessary because we have no data on supply-chain linkages at the level of the firm to match theinnovation measures which we have at firm level in CIS4. The use of estimates of creative linkages at the industry levelraises two technical issues for the regression analysis. First, heterogeneity among firms within the same industry impliesthat supply-chain linkages to the creative sector will be imperfectly measured (in other words, subject to measurementerror) for individual firms. This limits the information content in our dataset and results in less precise estimates of theinfluence of linkages to the creative sector on innovation. It may also lead to biased estimates of these linkage effects,although without further data it is not possible to establish this. Second, the estimated ‘standard errors’ from theregression analysis (which predict the precision of the regression estimates of linkage effects) need to be adjusted to takeaccount of the use of industry-level data in a firm-level analysis – in technical language, this means that the standarderrors need to be cluster-adjusted (Moulton 1990;Wooldridge 2002, 2003).

16 Our set of control variables corresponds to that used in several recent studies of innovation using the previousCommunity Innovation Survey (CIS3), including Griffith et al. 2006. Even though we make great efforts to control forthe impact of these determinants of innovation, we cannot rule out that our linkage variables may capture some industry effects unrelated to creative linkages.

17 The industry dummies are defined at the 2-digit SIC level. This approach ensures that the industry dummies are notcollinear with the creative linkage variables, since the 2-digit SIC industries do not directly correspond to the input-

output industry groups.18 There is extensive research evidence that geographical location is an important determinant of innovation – e.g. Audretsch and Feldman (1996); Simmie (2001). We also experiment with alternative geographical variables linked to

l Al f h f h l


14/22

of innovation; they allow us to explore the impor-tance of these knowledge transfer mechanisms atthe general level. We further investigate the possi-bility that knowledge transfers are particularly strong when creative industries in particular are

involved in the supply chain.19

Results from econometric analysis

Table 2 summarises the results from our regres-sion analysis. They point to a statistically signifi-cant positive impact from creative linkages onsome but not all of our innovation measures.20

Firms in industries where purchases of creativeproducts (forward creative linkages) are impor-

tant within production are more likely to engagein design activities; more likely successfully tointroduce new and novel products; and morelikely to enjoy an expansion in their productrange as a result of their innovation activities.

Firms in industries where sales to creative busi-nesses (backward creative linkages) are importantare more likely to implement product innova-tions, and are more likely to see an increase intheir product range as a result of their innovation.

The ‘marginal effects’ in Table 2 illustrate theextent to which changes in creative linkagesincrease the probability of innovation. They imply, for example, that if the ‘average’ firm


argna e ect2

Signif.3

arg na e ect2

Signif.3

Innovation Activities

In-house R&D 0.328 . 0.802 .

Design 1.086 ** -0.322 .

Innovation-related marketing -0.143 . 0.157 .Innovation Outputs

Product innovation 2.376 *** 0.933 ***

Novel products 1.383 *** 0.009 .

Process innovations 0.856 . 0.586 .

Innovation Impacts

Improved quality 0.780 . -0.139 .

Increased range 0.866 * 1.753 *

Expanded markets 1.140 . 1.672 .

Notes:

Source: Authors based on CIS4 and UK Input-Output data

3. * = statistically significant at the 10% level or less; ** = 5% or less; *** = 1% or less. Based on robust (cluster

adjusted) standard errors.

Purchases of creative

products

Sales to creative

industries

2. The marginal effect shows the impact of a unit increase in each variable on the probability of observing each type

of innovation behaviour while holding other influences on innovation behaviour constant.

1. Control variables included in these regressions: Firm size; Industry; Location; Business type & age; Employee

qualifications; Product market area; IP protection methods; Barriers to innovation; Public support.

TABLE 2: SUMMARY OF CREATIVE LINKAGE EFFECTS ON INNOVATION MEASURES

19 We do this by including constructed variables in our econometric model which interact the input-output based creativelinkage measures with the information acquisition and cooperation values taken from CIS4.20 More econometric details of the results, including diagnostics and a more complete robustness analysis, are presented in

kh h l ( )


15/22

spends twice the amount it does on creative prod-ucts – 6 percent as opposed to 3 percent of totalgross output (this is an increase of around onestandard deviation) – the probability of that firmengaging in design innovation activities is around

three percentage points higher (22 percent com-pared with 19 percent), the probability of thefirm introducing a product innovation is sevenpercentage points higher (36 percent compared

with 29 percent), and the probability of a novelproduct innovation is higher (20 percent com-pared with 16 percent). By comparison, access toinnovation support from national government isassociated with the average firm’s probability of

introducing a product innovation being aroundeight percentage points higher. The creative link-age impacts are therefore similar in magnitude tothose of key policy variables.21

These results give some support to the generalhypothesis that supply-chain linkages to the cre-ative sector are positively related to innovationelsewhere in the economy.

They do not, unfortunately, allow us to estab-

lish the direction of this relationship. Creativelinkages may drive innovation in other sectors.However, it is equally possible that firms requirecreative inputs to support their innovation, andthat the primary determinants of innovation lieelsewhere. To investigate these issues further

would require data with a time series dimension,but these are not currently available in a formadequate to our needs.22

Nor do the results presented so far on theirown provide any evidence about the precisemechanisms by which creative linkages influenceinnovation. Our principal hypothesis has two ele-ments: first, that creative products – such as

advertising or software – are important resourceinputs into the innovation decisions of businessesin ‘non-creative’ sectors; second, that supply-chain transactions with creative businesses areassociated with knowledge transfers – possibly positive spillovers – from those creative businessesto other sectors of the economy. The results wehave reported so far do not allow us to discrimi-nate between these two possibilities.

To provide more direct evidence on the role of knowledge transfers, we examine the impact of twotypes of knowledge transmission mechanism con-sidered in CIS4, which are particularly relevant tosupply-chain transactions: specifically, knowledgeacquisition from suppliers and customers; andcooperation with suppliers and customers.

The results in Table 3 focus on innovation out-put and impact measures. Consistent with the

findings of previous published research, coopera-tion with suppliers and customers and the impor-tance of information from suppliers and customersare generally significant determinants of innova-tion performance. We go further and explore

whether these effects are stronger in firms withstronger linkages to the creative sector. We do thisby interconnecting the creative linkage measures

with the transmission mechanism variables. So, for


21 The results in Table 2 are based on regressions which do not include innovation activities in the set of explanatory variables. To test the robustness of our results, we also run the regressions including the innovation activity measures inour conditioning set. Doing so provides an indication of the effects of linkages to the creative industries given firms’levels of innovation activities (which can loosely be interpreted as their innovation ‘effort’). The results still show significant, but weaker, positive effects from purchases of creative products for both new and novel products. Thissuggests that even allowing for their existing innovative activities, firms in industries that buy more creative products aremore likely to see product innovations. The impact of sales to creative industries on new product innovation is also stillpositive and statistically significant. A similar analysis of innovation impacts , however, shows that quality is the only significant positive impact in the case of sales to the creative industries after controlling for patterns of innovationactivity. This suggests that greater purchases of creative products significantly increase the probability that a firm willimprove the quality of its products as a result of its innovation activities, even after allowing for the firm’s level of innovation ‘effort’.

22 Some panel data – covering the same firms over time – can be obtained by combining CIS3 and CIS4 data. However,these cover only a small sample of firms and a very short time period. Since measured linkages to the creative sectorchange only slowly over time, the data do not provide a sufficient basis for examining the causality between creativel k d f


16/22

example, we explore the potential role of knowl-edge transfers associated with forward creative link-ages by including interaction variables constructedby multiplying creative purchases with the extentof reported cooperation between a firm and its

suppliers on innovation, and with the extent to which a firm claims to have obtained innovation-related information from its suppliers.

In general, we find no evidence that suchknowledge transfer effects are a significant deter-minant of innovation. We do, however, obtainstatistically significant results from forward cre-ative linkages opened with information acquiredfrom suppliers for two of the innovation impact

variables: improvements in product range andproduct quality.

This is consistent with the possibility thatknowledge transfers from creative businesses tofirms purchasing creative products may supportinnovations leading to improvements in the rangeand quality of products offered.

6. C

ONCLUSIONS

This research for the first time brings togetherknowledge of the production structures relating the creative industries to the wider economy

with current understandings of the determi-

nants of business innovation. Specifically, weconstruct measures of the strength of supply-chain linkages to the creative industries andexplore their relationship to measures of inno-vation reported in the fourth UK Community Innovation Survey.

By doing so, we can investigate a range of innovation activities, outputs and impacts, con-trolling for a variety of other determinants of

innovation to test our central hypothesis – name-ly, that firms’ purchases of creative inputs andsales to the creative sector allow them to accesskey resources and knowledge which support theirinnovation activities.

Our analysis of the input–output accountssuggests that business-to-business purchasers of


T

ABLE

3: K

NOWLEDGE TRANSFER MECHANISMS

,

CREATIVE LINKAGES AND INNOVATION PERFORMANCE

Product Novel Increased Improved

Innovations products product range product quality

Marg.

2

Sigf.

3

Marg.

2

Sigf.

3

Marg.

2

Sigf.

3

Marg.

2

Sigf.

3

Explanatory variables

Cooperation with suppliers 0.107 *** 0.028 * 0.026 0.046

Cooperation with customers 0.120 *** 0.048 *** 0.075 ** 0.173

Interaction effects:

Purchases of creative products –0.296 0.068 0.469 1.045 cooperation with suppliers

Sales to creative industries 0.906 0.327 –0.163 0.779

cooperation with customersExplanatory variables

Information from suppliers 0.034 *** 0.004 ** 0.140 *** 0.168 ***

Information from customers 0.095 *** 0.036 *** 0.208 *** 0.242 ***

Interaction effects:

Purchases of creative products –0.078 0.156 0.403 * 1.038 *** info from suppliers

Sales to creative industries 0.366 0.120 0.087 –0.057 info from customers

1 Control variables in these regressions: Firm size; Industry; Location; Business type and age; Employee qualifications;Product market area; IP protection methods; Barriers to innovation; Public support; Innovation activities

2 The marginal effect shows the impact of a unit increase in each variable on the probability of observing each type of innovation behaviour while holding other influences on innovation behaviour constant.

3 * denotes statistically significant at the 10* level or less; ** 5% or less; *** 1% or less. Based on robust (clustered)


17/22

creative products, and sales to creative businesses,are particularly important between creative indus-tries themselves. Creative supply-chain linkagesare also important to many other sectors of theeconomy.

Measures of innovation from the CIS show that innovation is more likely in the creativeindustries than in many other sectors. The CISalso shows the importance of supply-chains assources of innovation, again particularly so in thecreative industries, and that such supply-chainlinkages are positively related to innovation.

We undertake an econometric analysis toexplore the relationships between creative link-

ages and innovation performance. Our resultssuggest a significant positive impact from creativelinkages for some, but not all, of the key innova-tion measures. Firms – in industries where pur-chases of creative products (forward creativelinkages) – are important in production, are morelikely to engage in design activities to: introducenew products (both new to the firm and new tomarket) and expand their product range as a

result of their innovation activities. Firms inindustries – where sales to the creative sector(backward creative linkages) are important – arealso more likely to introduce new products to thefirm and to increase their product range.

To provide more direct evidence on the poten-tial role of knowledge transfers and spilloversembodied in B2B transactions, we also examinethe impact of two types of knowledge transmis-sion mechanism contained within the CIS4

which are particularly relevant to supply-chaintransactions: knowledge acquisition from suppli-ers and customers, and cooperation with suppli-ers and customers. By interacting these measures

with our creative linkage variables we can test thehypothesis that knowledge transfers embodied insupply-chain transactions between firms in ‘non-creative’ industries and creative businesses sup-port innovation in these sectors.

There is some evidence that businesses, whichacquire information from suppliers and cus-tomers in the creative industries, are likely toenjoy greater returns in terms of improved prod-uct range and quality.

More conclusively, the results suggest thatbusinesses have enhanced innovation perform-ance – particularly product innovation – if they purchase more creative products. Our estimatessuggest that if a typical firm in the UK spendsdouble what it does on creative products –around 6 percent as opposed to 3 percent of itsgross output – the likelihood that the firmintroduces a product innovation either new to

the firm or to its market is around 25 percenthigher.23

While the policy implications of directimprovements in innovation from the use of creative inputs are perhaps less immediate thanin the case of positive spillovers – where thereare clear market failures – policymakers shouldat a minimum stress the benefits of wider andgeneric creative inputs when promoting the

contributions that design can make to businessperformance.Knowledge networks are likely to be particu-

larly important for the spread of new ideas fromcreative businesses, as a good deal of creativeknowledge is tacit. It is widely accepted that insituations where there is coordination failure –i.e. the benefits of creative knowledge sharing areenjoyed by many firms, but the fixed costs of spreading it are borne by a few – the public sector

may have a role in encouraging knowledge trans-fer networks. Our results suggest that such initia-tives must take care to recognise the importanceof knowledge sharing between creative businessesand firms in ‘non-creative’ industries too.

Our results suggest that the creative indus-tries may play a significantly bigger role in theUK’s innovation system than has been hithertorecognised.


23 Interestingly, our use of Input–Output data means that supply-chain linkages to the design sector are not included in ourd Th h l b l d b h d T ( )


18/22

A

CKNOWLEDGEMENTS

We would like to thank James Simmie for hiscontributions to an earlier version of this paperand comments by two anonymous referees.

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Received 1 May 2008 Accepted 10 December 2008


Our econometric analysis was carried out on thefirm-level CIS4 data using STATA. Ourapproach is to model the probability that a firmengaged in a particular type of innovation activi-ty (or that has produced an innovation outputor enjoyed an innovation impact) is related tothe strength of supply-chain linkages to the cre-ative industries for the industry to which the

firm belongs, and to a set of firm-level controlvariables.Specifically, we estimate various versions of the

binary response model:

pi = Pr ( y i = 1) = G (z i )

z i = + mmLim + nnC in + i

Here pi is the probability of firm i giving a ‘positive’ response ( y i = 1) for that innovation

measure. This probability is determined by the‘index’ variable z i , via the cumulative distributionfunction G (z i ). All models are estimated using the probit method, so that G (z i ) is the cumula-tive standard normal distribution.

The value of z i is assumed to be a linear func-tion of the creative linkage measures and controlvariables. Lim is the value of the creative linkagevariable relevant to firm (m =1,.., M ) refer to ‘for-

ward’ linkages, ‘backward’ linkages and ‘interac-tion’ variables depending on the specific model

i d) d h C ( 1 N) h l

for firm i of each of the N control variablesincluded in the model. , m and n are 1 + M +N parameters to be estimated.

Some versions of the model include ‘interac-tion’ variables in an attempt to identify themechanisms through which creative linkagessupport innovation. The interaction variables areobtained by simply multiplying the relevant vari-

able with the creative linkage measure. Thus, forexample, the interaction variable which attemptsto capture the role of knowledge transfers fromcreative suppliers is given by: Creative Purchases

Knowledge from Suppliers Important toInnovation. Since the knowledge transmissionmechanism variable is binary (1 = yes, 0 = no),the interaction variable is the creative purchasesmeasure for those firms who have stated thatsuppliers are an important source of information

for innovation, and zero otherwise. Similar vari-ables are constructed for other potential knowl-edge transmission mechanisms (informationfrom customers; cooperation with suppliers;cooperation with customers).

As is standard practice, we report ‘marginaleffects’ estimates, rather than the coefficient esti-mates from the probit regression (, m and n)themselves. The marginal effect shows the effect

of a change in a variable on the probability of observing a ‘positive’ response, e.g. that a firm has

d d i i Th f h

APPENDIX: ECONOMETRIC DETAILS


21/22

ative linkage variables the marginal effect isdefined by

p= p z

= g (z ) p,L = z L

where g (z ) is the standard normal distribution.Since the probit model is non-linear, themarginal effects vary depending on the values of all explanatory variables. As is conventional, wereport marginal effects calculated at mean valuesfor the regressors (i.e. for the ‘average firm’).

Econometric problems may arise due toendogeneity of explanatory variables arising fromomitted variables or because some explanatory

variables reflect firms’ choices concerning theirinnovation activities. Dealing with such problemsis difficult given the available data, since we donot have an appropriate instrument, and the purecross section nature of CIS4 precludes estimationof firm-level fixed effects.

Instead, we estimate all models including industry and region dummy variables within thecontrol variable set (C in), in an attempt to isolate

industry and region fixed effects on innovationperformance. The industry dummy variables aredefined at the 2-digit SIC level; regional dummy variables are defined based on the UK Govern-ment Office Regions.

The creative linkage measures are defined atthe industry level rather than for individual firms.Moulton (1990) shows for ordinary least squaresregressions that if aggregated (e.g. industry level)variables are included in regressions on micro(e.g. firm level) data, then the standard errors willbe underestimated, leading to mistakeninferences concerning the statistical significanceof the results. Similar problems also arise for

binary response regressions (Wooldridge 2002). Itis therefore necessary to adjust the standard errorsfor coefficient estimates within our models. Wedo this by ‘clustering’ standard errors at the levelof input–output industries (on which the linkagemeasures are based) using the relevant STATA routine. The resulting ‘robust’ standard errors areused in the significance tests reported in theresults tables.


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Introduction: Illuminating everyday realities: the significanceof video methods for social science and health research– Rowena Forsyth, Katherine E Carroll, Paul Reitano

Video: a decolonising strategy within ethnographic researchinto intercultural communication in child and family health– Julian Grant, Yoni Luxford

Authentic Representation? Using video as counter-hegemonyin participatory research with working-class women– Victoria Foster

Outsider, Insider, Alongsider: Examining reflexivity inhospital-based video research – Katherine E Carroll

Translating experience: The creation of videos of physiciansand patients in the environment of an Austrian universityhospital – Christina Lammer

Distance versus dialogue: modes of engagement of twoprofessional groups participating in a hospital-based videoethnographic study – Rowena Forsyth

Viewing the taken-for-granted from under a different aspect:a video-based method in pursuit of patient safety– Rick Iedema, Eamon Thomas Merrick, Dorrilyn Rajbhandari,Alan Gardo, Anne Stirling, Robert Herkes

Using video in the development and field-testing of alearning package for maternity staff: Supporting women fornormal childbirth – Nicky Leap, Jane Sandall, Jane Grant,Maria Helena Bastos, Pauline Armstrong

Postscript: The significance of video research methodologyfor health and social science – Alexandra Juhasz,Christian Heath, Rick Iedema

USING VIDEO IN SOCIAL SCIENCES AND HEALTH RESEARCH

A Special Issue of the International Journal of Multiple Research Approaches

Advisory Editors: Rick Iedema, University of Technology, Sydney; Christian Heath, Kings College, London; andAlexandra Juhasz, Pitzer College, Claremont CA, USA

Guest Editors: Rowena Forsyth, University of New South Wales; Katherine Carroll, University of Technology, Sydney; andPaul Reitano, University of New England, Australia

ISBN 978-1-921348-24-2 Volume 3 Issue 3 Length: ii+126 pages Format: s/c Available: November 2009

Course pack materials and evaluation copies available on request.


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Creative Industry Stimulate Wider Economy

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