scope and patterns of innovation cooperation in spanish service enterprises

8/12/2019 Scope and Patterns of Innovation Cooperation in Spanish Service Enterprises

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Research Policy 41 (2012) 602613

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Scope and patterns ofinnovation cooperation in Spanish service enterprises

Alexandre Trigo, Xavier Vence1

ICEDE Research Group and Department of Applied Economics, Facultade de Ciencias Econmicas e Empresariais, University of Santiago de Compostela, Av. Burgo das Nacins, s/n,

15782 Santiago de Compostela, Spain2

a r t i c l e i n f o

Article history:

Received 22 September 2010Received in revised form 9 June 2011

Accepted 20 October 2011Available online 21 November 2011

JEL classification:

O39L80

Keywords:

CooperationInnovationPatterns of innovationService sectorSource of information

a b s t r a c t

Examining 2148 innovating service firms from the Spanish Technological Innovation Panel 2004, thispaper utilizes Latent Class Analysis to appraise the scope of innovation cooperation in services in the

Spanish economy, in accordance with the growing weight of external information flows throughoutinnovation processes. The empirical evidence indicates that the nature ofthe service activity affects boththe partner chosen and the cooperation intensity. The results lead to the creation ofa typology ofcoop-eration composed ofthree broad profiles: service firms intensive in techno-scientificcooperation, intensivein interactions with clients and a profile with low intensity in cooperation, called lonely innovators. Theprobability that a firm belongs to the latter profile is 59%, which makes it reasonable to affirm that inno-vation cooperation is not a common practice in Spanish innovating service enterprises. Innovation outputvariables have been included in order to examine the relationship between patterns ofcooperation andinnovation performance. The findings also underline the co-existence ofdifferent cooperation patternswithin the same industry.

2011 Elsevier B.V. All rights reserved.

1. Introduction

Literature on innovation management and economics has high-lighted the increasing importance of external mechanisms ofknowledge creation such as external contacts and collaborationswith other companies or entities (Rothwell, 1992, 1994; Lundvall,1992; Gibbons et al., 1994; Oerlemans et al., 1998; Chesbrough,2003; Chesbrough et al., 2006; Vega-Jurado et al., 2009). Indeed,empirical studies have shown that enterprises rarely innovate inisolation of the economic system(Christensen and Lundvall, 2004).Lately, many authors have emphasized the weight of networksand partnerships as a way to incorporate external knowledge forinnovation, leading to the development of new products or pro-cesses in companies (Lundvall, 1985, 1988, 2007; Gerlach, 1992;Freeman, 1994; Gulati, 1998; Tether, 2002; Johnson and Lundvall,2003; Gomes-Casseres, 2003; Powell and Grodal, 2005). Among allthese types of collaboration, formalcooperationis considered a keymechanismtofortifytheinnovativecapacityofmanyfirmsthrougha synergistic atmosphere of production and value creation (Child

Corresponding author. Tel.: +34 881811653/981 56 31 00x11653.E-mail addresses: [email protected] (A. Trigo), [email protected]

(X. Vence).1 Tel.: +34 881811567/981 56 31 00x11567.2 www.usc.es/icede.

andFaulkner,1998;Tiddetal.,2001;Gomes-Casseres,2003;Tetherand Tajar, 2008; Hipp, 2010). These agreements are strategicallyimportant in open innovation processes since they enable organi-sations to accessknowledge, technology and know-how dissipatedamong other economic actors (see Chesbrough, 2003; Chesbroughetal.,2006). This evidence leads to theassumption that theconnec-tion and interaction between individuals with heterogeneous skillsand different but complementary experiences represent a collec-tive and distributed process of knowledge creation and innovation(Hayek, 1945; Andersen et al., 2000; Coombs et al., 2003).

As far as industrial analyses are concerned, the literature oninnovation economics has historically ignored the service sec-tor until recent years, so that the existing vision of innovationhas been built based on the study of R&D and innovation inmanufacturing branches. However, nowadays,economic literatureacknowledges the existence of innovative and cooperative perfor-mancein services (see, e.g.,Miles,2001; Gallouj,2002a,b;EuropeanCommission, 2004; Tether, 2002, 2005; Hipp, 2010). After twodecades, studies on innovation have proved cooperation practicesto be a cross-cutting feature in this industry; so much so that thewell-known formal representation of service innovation designedby Gallouj and Weinstein (1997) has been revised and expanded inaccordance with contemporary networked society (Vries, 2006).

In addition to this acknowledgment, many authors have recog-nized the existence of a plurality of innovation patterns within theservice industry (Soete and Miozzo, 1989; Gallouj and Weinstein,

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1997; Den Hertog and Bilderbeek, 1999; Evangelista, 2000; TetherandHipp,2000;MiozzoandSoete,2001;SundboandGallouj,2000;Hollenstein, 2003; Hipp and Grupp, 2005; De Jong and Marsili,2006; Miles, 2008; Tether and Tajar, 2008; Vence and Trigo, 2009;Trigo, 2009a,b; Hipp, 2010). In effect, recent studies have laidemphasis on the diversity of innovation patterns within certainservice sub-industry such as KIBS (Knowledge Intensive BusinessServices)orthetourismsectorforinstance(see,e.g., Hjalager,1997;Baark, 2005; Leiponen, 2005; Freel, 2006; Sundbo et al., 2007).

To illustrate such diversity, most of the studies on innovation inservices have pointed toKIBS as theleadingsub-sectorwith regardsto innovation and cooperation. This prominent performance is notonly a result of the high proportion of innovating firms, but also, byand large, a consequence of the high number of innovating firmsengaged in the majority of innovation activities and innovationcooperation. In contrast,other services such as distributive services(transport, wholesale, retail, etc.) and HORECA (hotels, restaurantsand catering) present a very low innovative and cooperative per-formance.

Therefore, due to the increasing relevance of external informa-tion flows to the firms innovative competence, in addition to thegrowing role of services in all modern economies, this paper hastwo principal purposes: to appraise the real scope of innovationcooperationinSpanishserviceenterprises,andtoexaminetherela-tionship between cooperation and innovation performance. Therange of indicators investigated includes three sets of variables:(1) formal cooperation, (2) the significance of external informationsources (3) as well as the types of innovation outputs. The firsttwo sets aim to measure the level of openness of innovation pro-cesses and the importance of external knowledge. While formalcooperation represents a specific mode of collaboration, sources ofinformation permit us to estimate the value of extramural flows ofknowledge including informal collaboration. Variables of innova-tion outputs have been included in order to study the correlationbetween the cooperative behaviour and innovation performance.

The empirical analysis is derived from the Spanish Technolog-ical Innovation Panel (PITEC) carried out in 2004. PITEC, which is

part of the Spanish CIS data, is a statistical panel whose sample iscomposed of data from the Central Business Directory (DIRCE) andthe Research Business Directory(DIRID). This paper is structured asfollows: A literature review on the scope of cooperation-orientedpatterns in services is tackled in Section 2. A descriptive analysis ofcooperationinservicesisthecoreofSection3. InSection 4, themul-tivariate technique known as Latent Class Analysis (LCA) is used inorder to create patterns of innovation cooperation in services. Themain conclusion and implication are discussed in Section 5.

2. Cooperation-oriented patterns in existing typologies of

innovation in services

The literature on innovation in services has increased signif-icantly since the late 1980s. Within this emerging field, the studyon patterns at the intra-sectoral levelhas received special attentionin recent years.Soete and Miozzo (1989) created a typology, basedon Pavitts taxonomy (Pavitt, 1984), to improve the understandingof the diversity of innovation in services. Their typology permitsthe identification of different patterns of innovation, beyond thesupplier-dominated character stressed by Pavitt in his originalcategorization (Pavitt, 1984, see also Bell and Pavitt, 1993). Theclassification created by these scholars has undoubtedly becomean imperative contribution toward an enhanced perception ofinnovation, incorporating more deeply the service sector in themainstream economic research of the discipline. This categoriza-tion of innovation in services has inspired many other studies

and has become the earliest reference in this field (see, e.g., Den

Hertog and Bilderbeek, 1999; Evangelista, 2000; Tether and Hipp,2000; Sundbo and Gallouj, 2000; Hollenstein, 2003; Hipp andGrupp, 2005; Hipp and Herstatt, 2006; De Jong and Marsili, 2006;Hortelano and Gonzlez-Moreno, 2007; Miles, 2008).

Soete and Miozzo (1989) could be considered as pioneers indescribing networking patterns of innovation in service compa-nies. The network-based innovation pattern underlined by theseauthors was not included in Pavitts popular typology (1984). AsHipp and Grupp (2005) emphasize, this gap could be due to theabsence of a distributive nature in most manufacturing activities,the source of reference for the earliest typologies. Things changewhen we are dealing with service activities. The network-basedinnovation pattern is composed of, on the one hand, scale-intensive and physical network intensive sectors (transport andwholesale trade), and on the other hand, information-intensivenetworks sectors (communication, finance and insurance services).Gallouj and Gallouj (2000, 30), however, note that the interactivenature via networks should be considered not so much one ofthe types within the taxonomy but rather a characteristic that istransversal,a trait of several if notall, types. They further note thateven those services classified as dominated by suppliers couldhold this feature. Another criticism of the contribution of Soeteand Miozzo concerns the analytical perspective of technologicaltrajectories used by them, and the absence of non-technologicalinnovations (Gallouj and Gallouj, 2000).

A similar taxonomy laid out by Evangelista (2000) based on19,000 service companies with more than twenty employees,which were included in the 19931995 Italian Innovation Survey.Factor analysis was applied to an array of aspects related to theinnovation process, including the importance of science and tech-nology based interactions and other information sources, in orderto identify innovation profiles in services. However, two criticalweaknesses in thestudycan be pointed out. First,a specific variableto measure formal innovation cooperation is missing; and, second,theindustrialclassificationistoobroad,makinguseofhighlyaggre-gate data and preventing the identification of specific innovationpatterns within each sub-sector.

Hollenstein (2003) presents a five-profile innovation typol-ogy through cluster analysis using 2731 service firms from theSwiss Innovation Survey 1999. With regard to the identification ofcooperation-oriented profiles, Hollenstein includes a large rangeof indicators, among them knowledge sources and R&D networkvariables. Unlike the aforementioned authors, this set of indicatorsembraces also non-technological aspects of innovation. It allowsfor the creation of an effective and useful innovation taxonomy forservices as far as networks and others types of external links areconcerned. In contrast to the preceding typologies, the coopera-tion aspect has been explored as a transversal feature of innovationinstead of a specific attribute found in just one or few servicebranches, as suggested by Gallouj and Gallouj (2000). Compared toEvangelistas taxonomy, Hollenstein (2003) adds a new dimension

to innovation labeled market-oriented incremental innovatorswith weak external links, which is composed of service activitiessuch as business services and wholesale trade.

Hipp andGrupp(2005) carryout an interestingempiricalanaly-sis of innovation patterns in services. The results of this remarkablestudy suggest that the patterns of innovation in services depend toa lesser degree on the sectoral classification, since each pattern canbe found in each service branch studied. This evidence strengthensthe conclusions stated by the aforementioned Sundbo and Gallouj(2000) and Hollenstein (2003). Concerning cooperation-relatedpatterns,HippandGrupp(2005) identifyaprofileofnetwork-basedservices (information networks) composed mainly of banks andinsurance companies. Technical services and R&D as well as soft-ware services are classified as knowledge intensive. The authors

also claim that most of the innovation typologies are suitable to


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604 A. Trigo, X. Vence / Research Policy41 (2012) 602613

services showing a classical innovation structure. Innovation pat-ternsdisplayedinearlierstudiesdonotembracethewholerangeofinnovative services, thus new patterns must be identified by usingalternative concepts and measuring instruments. The authors sug-gest that further theoretical and empirical studies on innovationneed to analyse manufacturing and services together, taking intoconsideration their products instead of the sector they belong to.

Following Hipp and Grupps proposal, Tether and Tajar (2008)elaborate a typology of innovation for all manufacturing and ser-vices sectors, using data from 2500 European firms included in theInnobarometer 2002. This analysis enforces the synthesis approachbuilt by several scholars combining different innovation trajecto-ries and a large set of economic activities (Gallouj and Weinstein,1997; Coombs and Miles, 2000; Hollenstein, 2003; Drejer, 2004;Hipp and Grupp, 2005; Tiri et al., 2006; Leiponen and Drejer, 2007;Castellacci, 2008; Peneder, 2010).

In order to classify enterprises in terms of their innovative fea-tures, Tether and Tajar (2008) select three specific issues fromthe survey: the firms orientation to innovation; the main sourcesof advanced technologies; and the firms perceived strengths ininnovation. Applying different statistical methods, these authorssuggest three profiles of innovation: a product-research (PR)mode; a process technologies (PT) mode; and an organisational-cooperation (OC) mode. While the first profile is composed mainlyof mediumhigh-technology manufacturing and high-technologyactivities, the second one is dominated by low and medium-lowtech manufacturing. Services, especially distribution and tradeactivities, are confined to the latter one. The PR mode tends tocooperate with universities or R&D specialists, while the OC modeis more likely to engage in cooperative practices with suppli-ers, customers and trade associations (supply-chain-cooperationrather than research-based cooperative practices). Furthermore,the innovation activities carried out by these firms are basi-callyoriented at organisational changes. However, although Tetherand Tajars taxonomy covers an extensive range of economicbranches and European-wide countries, the aggregated classifica-tion at the industrial level used to display the results, particularly

for services, may conceal internal innovation and cooperationdiversity.

All these contributions have provided substantial progresstoward a better understanding of innovation in services. However,we observe two critical constraints: First, some of these studieshave not given sufficient emphasis on the interactive dimensionthrough cooperation activities. Modern innovation practices bringto light the importance of interaction and communication amongeconomic agents, accentuating the increasing significance of exter-nal information flows in the enhancement of internal innovationcapacity, especially in some service activities. For this reason, weconsider cooperation not only an important aspect but also a keydeterminant to characterize innovation patterns at the industriallevel, and specifically in certain service branches. The second criti-

cal constraint refers to the industrial classification. The high degreeof aggregation of service activities predominant in most of thesestudies might hamper an in-depth typology for innovation. Themicro-data used in our empirical analysis, however, permits toclassifyservicesin20sub-sectors,allowingustodelimitmoreaccu-rately the realscopeof diversityof innovation cooperation patternsin services.

Thus, the analysis of innovation and the typology proposed inthispaperwillfocusessentiallyonthecooperationperformance.Asmentionedin theprevioussection,we have also deemedit relevantto include two sets of variables apart from cooperation indicators.Since the cooperation indicators refer only to the type of part-ner, we have added a set of Likert scale variables on sources ofinformation in order to measure the significance of each partner-

ship. Furthermore, innovation output variables are incorporated in

order to better understand the relationship between patterns ofcooperation and performance in innovation.

3. Empirical evidence: innovation cooperation in services

in Spain

3.1. Data and sample

The data used in theempirical analysis is derived from theTech-nological Innovation Panel (PITEC) carried out in Spain in 2004,which is a database developed by the INE (National Institute forStatistics), FECYT(Spanish Foundation for Science and Technology)and COTEC (Foundation for Technological Innovation). This panel,which is available since 2003, is an important statistical tool toanalyse the innovative performance of Spanish enterprises. Thedata provided by PITEC derives from the Central Business Direc-tory(DIRCE)andtheResearchBusinessDirectory(DIRID).Thepanelsurvey follows the Oslo Manual methodology applied in the Com-munity Innovation Survey as reference to the selection of variablesand indicators (see OECD, 2005).

The sample of the 2003 panel was composed of two differentgroups of enterprises: a sample of firms with 200 or more employ-ees and a sample of firms with intramural R&D expenditures. Thesecond edition of the panel, referring to 2004, sought to cover amajor limitation found in the first edition, incorporating firms withfewer than 200employees without innovation activities, as well asfirms with fewer than 200 employeeswhoseresearchactivities anddevelopment are outsourced.

The number of service enterprises presented in PITEC 2004 is3546,among them 2148 are innovating ones. Weconsider an inno-vatingfirmasafirmwhichhasimplementedanytypeofinnovationduring the last two years, as recommended in the Oslo Manual(OECD, 2005). Thisconcept includes, apart fromtechnological inno-vation, organisational and marketing changes. Table 1 shows the

Table 1

Distribution of servicefirms and innovating service firms, Spain.

Total firms Innovating firms %

Repair of motor vehicles,motorcycles

80 33 41%

Wholesale 391 224 57%Retail trade 216 92 43%Hotels and restaurants 207 74 36%Transport, storage and

communication130 65 50%

Supporting transport activities;travel agencies

114 57 50%

Post and courier activities 17 10 59%Telecommunications 67 55 82%Financial intermediation 217 177 82%Real estate activities 61 27 44%Renting of machineryand

equipment32 15 47%

Software consultancy andsupply

382 324 85%

Computer and related activities 124 105 85%Research and development 166 147 89%Architectural and engineering

activities260 210 81%

Technical testing and analysis 90 65 72%Other business activities 602 256 43%Motion picture and video

activities28 10 36%

Radio and television activities 36 27 75%Health andsocial work,

sanitation and similaractivities

326 175 54%

Total 3.546 2.148 61%

Source: Trigo (2009a).Data from theTechnological Innovation Panel (PITEC), 2004.


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Fig. 1. Proportion of innovating firms and proportion of innovating firms with cooperation,services, Spain (%).

distribution of service firms in PITEC 2004 database, where the dif-ferent innovation performances in services are displayed. WhileKIBS, telecommunication and financial intermediation are in theforefrontofinnovationinservices,HORECA(hotels,restaurantsandcatering) and distributive services are not leading innovators. Thisheterogeneous context reinforces the relevance of intra-industrialanalysis of innovation patterns, given the existence of differentinnovation drivers in each service branch.

3.2. Descriptive analysis of innovation cooperation in services

from PITEC

Among the different types of collaborations,3 this analysisfocusesonthespecificformofpartnershipcommonlyknownasfor-mal cooperation in joint innovation projects between the firm andotheractorssuch as customers,suppliers, competitors,universities,technology institutes, public research institutions or government.This mode of linkage must involve active cooperation on the part-ners part including purchases of knowledge and technology asdescribed by the Oslo Manual (OECD, 2005). Therefore, the notionof cooperation adopted here implies formal relationships such asstrategic marketing alliances and joint development of new tech-nologies, products or processes.

3.2.1. The scope of cooperation and innovation in services

As the first attempt to analyse the cooperative behaviour inservices, the frequencies of cooperative firms by service branchare calculated and the result is shown in Fig. 1 the horizontalaxis measures the proportion of innovating firms with cooperation

3 E.g., subcontract/supplier relations, licensing, consortia, strategic alliance, joint

venture, network (Tidd et al., 2001).

to the total of innovating firms. The number of cooperative firmsamong the sample of innovating ones is only 36%. This chart alsotacklestheinnovationperformancetheverticalaxisquantifiestheproportion of innovating firms to the total of firms. The compari-son between these two proportions brings to light the existenceof two different groups of services with different propensities tocooperate and innovate. The diagonal display of these two groupssupports the notion that the correlation between innovation andcooperation is directly linked.

Almost all highly innovative industries show a high propensityto cooperate. On the other hand, most branches with a low pro-portion of innovating firms exhibit a low propensity to cooperate.This evidence enforces the hypothesis that the higher the capacityof interaction with other economic actors, the higher the innova-tion capacity of an enterprise will be. This result leads us to classifyservice activities into two groups: one composed of those brancheswith a high proportion of innovating firms with a high propen-sity to cooperate (Group 1). This group is constituted essentiallyby knowledge-intensive business services, software and telecom-

munications. Another group is composed of activities with a lowtendency to innovate and cooperate (Group 2). The branches ofretail trade,hotels and restaurants, motion picture andvideo activ-ities are some of the branches that comprise this class. The numberof innovating firms in these service branches is relatively low, andfew of them cooperate. This evidence is also supported by OECDreports, where the low propensity to cooperate of distributive ser-vices is highlighted (OECD, 2001).

3.2.2. Patterns of partnerships

The cooperation diversity in services is also manifested in thechoice of partners. Taking into consideration the service sector asa whole, clients and universities are the most frequent partners to

services. In some branches, suppliers and consulting services are


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Table 2

Proportion of firms with cooperation,by partners, services, Spain (%).

COOP COOP1 COOP2 COOP3 COOP4 COOP5 COOP6 COOP7 COOP8

Repair of motor vehicles, motorcycles 21 15 9 3 3 12 3 0 3Wholesale 27 7 10 3 2 4 8 4 6Retail trade 13 7 2 0 1 5 3 0 1Hotels and restaurants 4 0 3 0 0 3 0 1 1Transport, storage and communication 32 5 23 0 3 2 8 0 5Supporting transport activities; travel agencies 39 11 26 9 9 2 5 4 0

Post and courier activities 10 0 10 0 0 10 0 10 0Telecommunications 51 13 27 22 5 5 33 4 9Financial intermediation 41 14 24 5 15 12 4 2 3Real estate activities 15 4 4 4 4 4 4 0 7Renting of machinery and equipment 7 0 7 0 0 7 0 0 0Software consultancy and supply 40 6 9 16 10 6 17 7 13Computer and related activities 30 7 8 10 6 4 12 5 8Research and development 72 13 26 44 23 34 59 37 49Architectural and engineering activities 52 11 14 21 12 10 27 12 22Technical testing and analysis 42 3 14 22 8 14 28 11 15Other business activities 25 6 13 7 4 7 6 3 5Motion picture and video activities 10 0 0 0 10 0 0 0 0Radio and television activities 37 19 33 4 0 7 11 0 4Health and social work, sanitation and similar activities 33 7 13 9 6 9 14 7 6

Total 36 8 14 12 8 9 15 7 11

Source: Trigo (2009a).Data from theTechnological Innovation Panel (PITEC), 2004.COOP = any kind of cooperation; COOP1 = other enterprise within the group; COOP2= clients; COOP3= suppliers; COOP4= competitors or other firms; COOP5= consultants,commercial laboratories/R&D; COOP6= universities; COOP7= government or public research institutes; COOP8= technology institutes.

clearly significant too. More specifically, firms whose productiveand innovative activities are related to technological progress tendto cooperate with suppliers, universities and technology institutesto a greater extent than those in any other enterprises (see alsoTether and Hipp, 2000; Miles, 2002; Hollenstein, 2003; Hipp andGrupp,2005;SalterandTether,2006;TetherandTajar,2008;Venceand Trigo, 2009, 2010; Trigo, 2009a). Moreover, their innovations(product innovations) tend to be breakthroughs, not only new tothe firm but also to the market. In this sense, the types of inter-actions along the innovation process would affect the scope of the

novelty of the innovations in some way. In fact, universities play aspecial role for highly innovative industries with a high propensityto engage in R&D such as KIBS and telecommunications. Cooper-ation with suppliers seems to have great weight for the KIBS andtelecommunications sectors, meanwhile suppliers seems to be lesssignificant to the others.

It is worth pointing out that the sectors with significantrelationships with science and technology-related actors are fun-damentally activities with a high propensity to engage in internalR&D and have high skilled human capital (see Vence and Trigo,2009; Trigo, 2009a). This finding highlights the importance of astrong absorptive capacity of external information and knowledgeflows,suggestedby CohenandLevinthal(1990), anditsrelationshipwiththefirmsinnovativepotential.Thisunderlinestheimportance

of highly skilled human capital as the main connection betweenthe company and these institutions of science and technology. Thenotion of the abovementioned absorption capacity induces us toassume that innovation and cooperation embrace a positive feed-back loop. In this sense, it is not only cooperation that fosters thefirms innovative capability, but also the internal innovative effortspentbyenterprisesconfersthenecessaryandfundamentalknowl-edge and capacity to interact with others. This second statement iseven more apparent in medium and high-tech sub-sectors.

Cooperation with customers is an important part of the totalweight of the partnerships, mainly to activities such as support-ing transport activities, travel agencies, financial intermediation,other business activities as well as radio and television activ-ities. In reality, these companies, plus telecommunications and

research and development firms, are the ones with the highest

tendency to cooperate with clients. However, the results suggestthat firms that cooperate with actors closer to the productivesystem such as customers, consultants, etc., tend to developnew-to-the-market innovation to a lesser extent (Kaufmann andTdtling, 2001; Tdtling et al., 2009). The type of services, whichpresent high propensity to cooperate with consultants, commer-cial labs, or private R&D institutes (labeled as coop 5 in Table 2)compared to the propensity to cooperatewith other agents, belongto a set of activities with low innovative capacity, and at the sametime with reduced inclination to cooperate.

4. Patterns of innovation cooperation in services

4.1. Methodology and data analysis

Although thestudyof theinteractiondimension in service inno-vation is not an original trend, the analysis proposed here is uniquein two aspects: the variables chosen and the method of analysisapplied.

As previously explained, the indicators selected are classifiedinto three sets. The first one refers to the partners for cooperationwhile the second one includes Likert scale variables on sources ofinformation. Whereas the former permits the identification of thenature of cooperative linkages, the latter allows for the measure-

ment of their significance. These variables are essential to achieveour objective of identifying different patterns of innovation coop-eration in services. Furthermore, a set of output-related variablesformedby the types of innovation developed by the firmwas takeninto account. These indicators are keys to describe the innova-tive performance of cooperative firms as well as to verify whetherdifferent types of innovation rely on specific sorts of cooperationarrangements. The variable selection is therefore consistent withthis articles aims of analysing the real scope of cooperation oninnovation in services in the Spanish economy, as well as that ofexamining the relationship between patterns of cooperation andinnovation performance.

Asfarasmethodologyisconcerned,weuseLatentClassAnalysis(LCA) as an alternative to traditional Cluster Analysis since the for-

mer provides more accurate results for binary-type variables. LCA


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Table 3

Variables used in theLatent Class Analysis.

Type

Indicators

innobien New or significantly improved goods Binaryinnoserv New or significantly improved services Binaryinnfabri Implementation of new methods of production Binaryinnlogis Implementation of new logistic system Binaryinnapoyo Implementation of new supporting activities Binary

inorg1 Implementation of advanced management techniques within your enterprise Binaryinorg2 Implementation of major changes t o your organisational s tructure Binaryinorg3 Changes i n the r elationship w ith o ther e nterprises o r public i nstitutions Binaryincom1 Changes in the good/service design Binaryincom2 Implementation of new sales methods or delivery Binarycoopera Cooperation: all types Binarycoop11 Cooperation: other enterprise within the group Binarycopp21 Cooperation: clients Binarycoop31 Cooperation: suppliers Binarycoop41 Cooperation: competitors Binarycoop51 Cooperation: consultants, commercial laboratories/R&D Binarycoop61 Cooperation: universities Binarycoop71 Cooperation: government or public research institutes Binarycoop81 Cooperation: technology institutes Binaryinfonew1 Significance of theinformationsource: withinthe group Likert03infonew2 Significance of the information source: suppliers Likert03infonew3 Significance of the information source: clients Likert03infonew4 Significance of the information source: competitors Likert03infonew5 Significance of the information source: consultants, commercial laboratories/R&D Likert03infonew6 Significance of the information source: universities Likert03infonew7 Significance of the information source: public research institutes Likert03infonew8 Significance of the information source: technology institutes Likert03infonew9 Significance of the information source: conferences and fairs Likert03infonew10 Significance of the information source: scientific journals and other publication Likert03infonew11 Significance of the information source: professional associations Likert03Covariant

Service branches

Source: Trigo (2009a).

Table 4

Statistic results of LatentClass Analysis using variables related to cooperation, sources of information and innovation outputs, services, Spain.

LL BIC (LL) Npar L2 df p-Value Class.Err.

4-Cluster 36202.97 74405.4 268 59625.728 1470 1.5e11450 0.05015-Cluster 35811.38 74159.3 340 58842.5487 1398 8.8e11343 0.05256-Cluster 35450.65 73975.0 412 58121.0887 1326 1.5e11248 0.06057-Cluster 35236.52 74083.9 484 57692.8251 1254 4.0e11217 0.08168-Cluster 35009.88 74167.8 556 57239.5433 1182 4.8e11181 0.0671

Source: Trigo (2009a).Data from theTechnological Innovation Panel (PITEC), 2004.

is a multivariate technique basedon conditional probabilistic anal-ysis. The objective of this statistical methodis to verifywhether theassociation between a set of observed categorical variables couldbe explained through a latent typology that is composed of differ-ent classes.4 The variables used in each analysis are summarized inTable 3.

This statistical technique has many advantages compared with

other tools. We can aver that one important advantage is the prob-ability distribution of the clusters identified. The service branchespresent different probabilities of belonging to each cluster identi-fied. One of the outputs brought about by this statistical technique,therefore, is a classification by groups based on probabilities.Anothersignificantadvantageisindeterminingthenumberofclus-ters. This is due to the existence of rigorous statistical tests thatsupport the choice of the dimension of the model (choice of thedimension regarding the best solution to data which means thenumber of clusters in the model). Another important attribute isthe possibility of using categorical variables.

4 Further technical information of Latent Class Analysis is provided in Appendix

A. See also Heinen (1996) and Hagenaars and McCutcheon (2003).

The results of Latent Class Analysis display different solutions,each one with different numbers of classes (clusters). The crite-rion for selecting the most accurate model that fit with the data setwas the Bayesian Information Criterion (BIC) due to its consistencyin comparison with other criteria, such as the Akaike InformationCriterion (AIC). Most of the empirical analysis carried out throughLatent Class Analysis has chosen such statistical criterion for model

selection. According to this criterion, the accurate model is the onewith the lowest value for BIC (see Kashyap, 1977; Schwartz, 1978).Therefore, following this principle, the precise model of coopera-tion arrangements, sources of information and innovation outputconsist of six clusters each latent class (cluster) representing adifferent pattern of responses (see Table 4).

4.2. Service typology of cooperation behaviour basedon

partnerships, sources of information and innovation outputs

Table 5 summarizes the results provided by the Latent ClassAnalysis, which can be grouped into three set of variables: inno-vation outputs, cooperation and source of information. While the

two former are expressed in percentage terms, the latter is the


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Table 5

Cooperative and innovative performances by cluster, services, Spain.

Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6

Cooperation (%)a

Cooperation: all types 1% 19% 0% 100% 100% 100%Other enterprise within the group 0% 6% 0% 21% 11% 35%Clients 0% 6% 0% 43% 17% 58%Suppliers 0% 2% 0% 54% 30% 18%Competitors 0% 1% 0% 34% 15% 20%

Consultants, commercial laboratories/R&D 0% 3% 0% 40% 12% 22%Universities 0% 0% 0% 68% 55% 14%Government or public research institutes 0% 0% 0% 39% 21% 2%Technology institutes 0% 0% 0% 58% 34% 5%

Source of information (Likert-scale 03)b

Within the group 2.43 2.00 2.41 2.72 2.41 2.44Suppliers 1.47 1.11 1.79 1.91 1.17 1.99Clients 1.50 0.64 1.79 2.31 1.59 1.43Competitors 1.06 0.30 1.51 1.79 1.05 1.27Consultants, commercial laboratories/R&D 0.51 0.18 1.39 1.88 1.02 1.08Universities 0.12 0.06 1.32 2.06 1.43 0.45Public research institutes 0.00 0.04 1.23 1.71 0.98 0.22Technology institutes 0.02 0.03 1.23 1.96 1.10 0.23Conferences and fairs 1.18 0.03 1.72 2.16 1.08 1.31Scientific journals and other publication 1.24 0.03 1.61 2.18 1.16 1.27Professional associations 0.64 0.00 1.37 1.87 0.62 0.91

Innovation outputs (%)a

Goods 46% 27% 43% 60% 53% 48%Services 48% 33% 48% 79% 52% 67%New methods of production 35% 17% 33% 60% 38% 45%New logistic system 23% 10% 15% 21% 10% 37%New supporting activities 54% 47% 48% 62% 32% 81%Advanced management techniques 61% 44% 60% 84% 39% 76%Major changes to the organisational structure 52% 37% 52% 78% 33% 66%Relationship with other enterprises or public institutions 21% 15% 28% 59% 28% 41%Changes in the good/service design 25% 7% 23% 26% 8% 36%Implementation of new sales methods or delivery 29% 10% 17% 26% 13% 39%

Source: Adapted from Trigo (2009a).Data from theTechnological Innovation Panel (PITEC), 2004.

a Eachpercentage shown can be understood as theprobability of engagement on theselected item.b This is a Likert type scale from 0 to 3, where 0= not at all relevant, 3= highly relevant.

average of answers (Likert scale 03). Each percentage showed, inthe case of the first two set of variables, can be understood as theprobability of answering yes to the given item by a firm fromcluster 1, cluster 2, cluster 3 and so forth. Although the LCA esti-mation suggested the 6-clusters solution as the most accurate onein accordance to the BIC criterion of selection, we consider thata typology of innovation cooperation in Spanish services shouldbe represented by 3 broad profiles, some of them composed ofmore than one clusters. Such aggregation has as the main decisivefactorthenatureofthepartnerships(i.e.,thosewithwhompartner-ships the cooperation are developed) because of the relevance andthe objective proposed in this article. It is also worth mentioningthat the partnership composition presents a higher level of hetero-

geneity of answers than any other set of indicators applied in thesample.The outcomes from this empirical analysis shows that the pro-

files identified are to a greater or lesser extent present in allservice sub-industry analysed. This evidence, also ensured by otherauthors in recent publications (e.g., Tiri et al., 2006; Hortelanoand Gonzlez-Moreno, 2007; Leiponen and Drejer, 2007) denotesa criticism on the original premise supported by the techno-logical paradigms and technological trajectories, where differentfirms with the same technological regime holds similar innovationpath.

4.2.1. Intensive in techno-scientific flows of information

According to the results, two of the six clusters (4 and 5)

have an analogous profile, based on techno-scientific cooperation

(similarity in the nature), but with some discrepancy in inten-sity (difference in the scope). The probability of cooperating withuniversities, technology centres or with their suppliers is higherthan any other partner. This disparity in relation to the inten-sity is also reflected in the significance of information sourcesand in the innovative intensity. It is interesting to note that thecluster with the lower cooperative and innovative performanceamong them (with a lower probability of positive responses),cluster 5, presents a higher propensity to product innovationsthan any other type (53% for innovative goods and services 52%).Hence, the service firms belonging to cluster 5 are essentiallytechno-scientific product innovators. Indeed, the probability oforganisational innovations is the lowest among the others groups

(innovations related to knowledge management systems: 39%,andrelated to thework organisation: 33%). On theotherhand,the morecooperative and innovative cluster (with a higher probability ofpositive responses), cluster 4, presents a very high probability oforganisational innovations, especially those related to knowledgemanagement systems (84%), although they have a high propen-sity also for service innovations (79%). Thus, slightly different fromthe cluster 5, service firms belonging to cluster 4 might be catego-rized as techno-scientific service/organisational innovators. Withregard to the significance of information sources, the principal dif-ference is in the magnitude of significances, not so much in nature(differences in the choice of partners to cooperate). However, weshould mention that only the most cooperative and innovativecluster (cluster 4) presents positive rates (above 1.5 Likert scale

of 03).


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Fig. 2. The triplot representation of probability distribution regarding the three-profile typology of cooperation in service innovation, services, Spain (%).

The probability that an innovating service firm from the Span-ish sample belongs to this broad profile of firms intensive intechno-scientific flows of information (composed of cluster 4 and5) is 28%. Although almost all branches have a certain probabil-ity of belonging to these two clusters, the service activities with ahigher tendency to be intensive with regards to techno-scientificflows of information are, architectural and engineering activities,telecommunications, research and development and technicaltesting and analysis. Many other taxonomies on services iden-tify this profile, for instance in Hollenstein (2003) who names itas Science-based high-tech firms with full network integration, andproduct-research mode of innovation in Tether and Tajar (2008).

4.2.2. Intensive in interactionswith clients

Another profile of interactions identified from LCA makesknown the role of the client as the main partner (cluster 6). There-fore, this cooperation behaviour is highly linked to the needs ofconsumersand usersof theservice.The probabilityof an innovatingfirm withthis profile cooperating withclients is approximately 60%(highestof theentireset ofclustersidentified). Inthis sense,it islog-ical that thecustomerreceivesthe highest ratingamongall possibleexternal sources (see Table 5). Client-led firms are very innovative,especially for process and organisational innovation (e.g., probabil-ity of 81% to be support processes innovators and 76% knowledgesystems innovators). It is also worth noting the high probabil-ity of developing service innovations (67%). Among all analysedbranches, financial intermediation is the service activities thatbest fits to this profile. According to knowledge generation, inno-

vating firms with client cooperation are intensive both in internalR&D activities and training, as well as in acquisition of machin-ery and software (Trigo, 2009a,b). The probability of an innovatingSpanish firmbelonging to this profileis only 13%. Other taxonomiesbased on innovation and networks highlight the high propensityof cooperating with customers in some service activities. Thosetypologies stress the high tendency of those firms to be also R&D-intensive. Client-led innovation (Den Hertog and Bilderbeek, 1999),information network (Soete and Miozzo, 1989; Miozzo and Soete,2001), network pattern (Sundbo and Gallouj, 2000), network basis(Hipp and Grupp, 2005) and cost-oriented process innovators withstrong external links along the value chair (Hollenstein, 2003) aresome of the labels used by other authors to describe such a pro-file. Some of the typologies found in the recent literature reveal the

proportion of firms in each profiled identified. In the Swiss case,

approximately 48% of service firms belong to a profile similar tothat one (Hollenstein, 2003).5 This proportion is very high if wetake into account the probability of just 13% found in our analysis.

4.2.3. Lonely innovators

The third identified profile is composed of three of the six LCAclusters(clusters1,2and3).Theinnovatingfirmsthatfitinthispro-file show a very low probability of cooperating. This result leads usto assume that those innovating firms are lonely innovators dueto the minor involvement with other economics actors, throughformal cooperation, along their innovation processes. However,although those three clusters display very few tendencies to coop-erate in innovation, the significance of information sources usedis quite dissimilar. Two of the three lonely innovator clusters

(cluster 1 and 3), consider relatively important the informationthat comes from customers and suppliers. These firms have lowpropensity to cooperate formally but simultaneously they placehigh value on their relationships with actors along the value chain.However, that interaction seems to be a result of business relation-ships instead of common efforts to achieve innovation. Indeed, thesignificance of information sources is higher in cluster 3 than anyother cluster, except cluster 4 and 5.

With reference to innovation outputs, all lonely innovatorspresent similar tendencies: in general, these clusters are morelikely to innovate in organisational aspects. However, there is aconsiderable difference in the propensity to innovate. Taking intoaccount the three lonely innovators clusters, cluster 2 is the leastcooperative and innovative. Its probability of innovating in prod-

uct is the lowest in the whole model (27% for product innovationand 33% for service innovation). On the other hand, clusters 1 and3 are as inclined to be product innovators as cluster 5 (intensivein techno-scientific flow of information), and even more prone interms of organisational innovation.

This is unquestionably the least advantageous of the threeprofiles identified given its weak cooperative and innovative per-formance. However, the probability that a Spanish firm belongs to

5 An important difference from the statistical method applied by Hollenstein(2003) should be stressed. While LCA is a multivariate technique based on con-ditional probabilistic analysis, the percentages of firms displayed in the ClusterAnalysis estimated by Hollenstein (2003) refer to the numbers of enterprises with

each profile.


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Table 6

Service typology of cooperation patterns based on partnerships, sources of information and innovation outputs.

Highly interactive Lowly interactive

Intensive intechno-scientific flowsof information Intensive ininteractions withclients (cluster 6)

Lonely innovators

With high cooperativeand innovativeperformance (cluster 4)

With moderatecooperative andinnovativeperformance (cluster 5)

With relatively highsignificance ofinformation sources(cluster1 and 3)

With low significanceofinformation sources(cluster 2)

Principal partner informal cooperation

Universities,technology centres andsuppliers

Universities,technology centres andsuppliers

Clients, otherenterprise within thegroup

Innovative intensitya Highly intensive Relatively intensive Highly intensive Relatively intensive Poorly intensive

Most commoninnovation

Service andorganisational

Product (goods andservices)

Process andorganisational

Organisational andproduct (goods andservices)

Organisational

Similar typologies Science-based high-techfirms with full network

integration

(Hollenstein, 2003)Product-Researchmode

ofinnovation (Tetherand Tajar, 2008)

Product-researchmode

ofinnovation (TetherandTajar, 2008)

Client-led innovation

(DenHertog andBilderbeek, 1999)Informationnetwork

(Soete and Miozzo,1989; Miozzo andSoete, 2001)Network patten

(Sundbo and Gallouj,2000)Cost-oriented process

innovators with strong

external links along the

value chair

(Hollenstein, 2003)Network basis (Hippand Grupp, 2005)

Market-oriented

incremental innovators

with weak external links

(Hollenstein, 2003)

Low profile innovatorswith hardly anyexternal link(Hollenstein, 2003)

Typical c ore sectors Research anddevelopment;Technical testing andanalysis.

Telecommunications;Architectural andengineering activities.

Financialintermediation;Supporting transportactivities; travelagencies;Radio and televisionactivities.

Wholesale;Retail trade;Post and courieractivities;Real estate activities;Software consultancyand supply;Computer and related

activities;Motion picture andvideo activities;Other businessactivities;Renting of machineryand equipment;Hotels and restaurants.

Repair of motorvehicles, motorcycles;Transport, storage andcommunication;Healthand social work,sanitation and similar.

Source: Trigo (2009a).a Innovative intensity refers to theprobability that a firm innovates (by type of innovation).

this profileis approximately 59%. This evidence is even worse whenwe take into account the employment of economic activities inSpain. Almost 65%of the employment in Spain is assigned to activi-ties with high probability of having this profile. As noted byMolero

(2006), the growth of technology-intensive sectors has been sig-nificantly lower in Spain compared to other European economies(see also Arancegui, 2002). In other words, the specialization of theSpanish economy has been concentrated mainly in areas with lowor mediumtechnological intensity. Thebranchesthat have a higherprobability of this profile are retail trade, post and courier activ-ities, repair of motor vehicles, hotels and restaurants amongothers. Indeed, most of those activities have a considerable partic-ipation in the Spanish economic structure because, among otherreasons, the existence of important weather and natural advan-tages (Gordo et al., 2006). Other authors have also highlighted theexistenceofthisprofileinservices.Forexample,inHollensteintax-onomy forSwissservices (2003), two of fivegroups have no leaningtoward establishing external linkages:market-oriented incremental

innovators with weak external links and low profile innovators with

hardly anyexternallink.IntheSwisscase,theproportionofinnovat-ing service firms with both profiles is fairly lower than the Spanishsample(approximately43%).Thiscontrastmakesevidenttheinsuf-ficiency of a cooperative mind-set in Spanish firms compared to the

international context.The Fig. 2 shows a Triplot6 representation of this typology.7 Thecloser an element is to an apex of the triangle, the greater the prob-ability,of this element having a specific profile. The Spanish servicesector presents a general trend toward the lonely innovator pro-

6 We used the software for Microsoft Excel Triplot developed by David Graham(Loughborough University) and Nicholas Midgley (Liverpool John Moores Univer-sity),and distributed free of charge. See Graham and Midgley (2000) and additionaldocumentation in http://www.lboro.ac.uk/research/phys-geog/tri-plot/index.html.

7 The probabilitydistribution has beenrecalculated taking into consideration thethree broad profiles of the typology proposed here, based on the 6-cluster LCAmodel:it hasbeenestimated theaverageof clusters 1, 2 and3 forthe profilelonelyinnovator and clusters 4 and 5 for the profile Intensive techno-scientific flows of

informationand recalculated of theweight of each profile in base100.
http://www.lboro.ac.uk/research/phys-geog/tri-plot/index.htmlhttp://www.lboro.ac.uk/research/phys-geog/tri-plot/index.htmlhttp://www.lboro.ac.uk/research/phys-geog/tri-plot/index.html


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file for the Spanish service sector. Table 6 summarizes the mainaspects andcharacteristics of thetaxonomycreated here. The threeprofiles acknowledged in the analysis of innovation cooperation inSpanish services are described regarding innovation intensity, themost common innovation developed as well as the nuances aboutthe intensity of linkages. Moreover, similar profiles found in therelated literature and typical core sectors are provided.

5. Conclusions

This article aimed to appraise the scope of innovation cooper-ation in Spanish services, in accordance with the growing weightof external knowledge exchange throughout innovation processes.We believe that cooperation is crucial for the differentiation ofinnovation patterns, especially to certain service branches. Inno-vation output variables have been included in order to examinethe relationship between cooperation behaviour and innovationperformance.

Firm-level analysis carried out in this article has permitted thein-depth scrutiny of cooperation patterns, beyond the boundary ofthe traditional standard industry classification largely applied sofar. The multivariate statistical method applied, Latent Class Anal-ysis, can be considered an original and innovative technique inidentifying patterns of innovation in services. The analysis devel-oped in this article lead to the conclusion that cooperation oninnovation is still not a very common practice in Spanish services.Furthermore, the assumption of plurality has been confirmed, asfar as the nature and the dynamics of cooperation are concerned,as demonstrated by other authors. This evidence shows the grow-ingly untenable hypothesis of a unique pattern of cooperation oninnovation in services.

The findings of the empirical analysis led to the creation of acooperation-oriented typology for service innovation composed ofthree broad profiles: intensive in techno-scientific flows of informa-tion, intensivein interactionswithclients and, finally, a profile of lowintensity in interactions, called lonely innovators. The innovating

firms intensive in techno-scientific flows of information are char-acterized by the high probability of cooperating with agents suchas technology institutes, universities and suppliers. Lonely inno-vators, on the other hand, show low probabilities of carrying outany type of innovation project with other partners. The probabil-ity that a service firm innovates in isolation is 59%. Therefore, itseems to be reasonable to affirm that innovation cooperation isscarcely performed by Spanish innovating service enterprises. Theresult is more understandable if one takes into account the com-position of the Spanish business sector and its passive profile asfar as innovation initiatives are concerned. This evidence supportstheassumptionthatformalcooperationinSpainisnotaspresentintheinnovativedynamicsof the companiescompared to othercoun-tries, as indicatedby theEuropeanCommission on many occasions.

The empirical evidence also confirms that the relationshipbetween cooperation behaviour and innovation performance isdirectly linked. In this sense, the higher the innovative level, thehigher cooperative level and vice versa. Indeed, innovation andcooperation embrace a positive feedback loop, which means that itis notonly cooperation that fosters the firms innovative capability.Empirical findings in literature have also proven that the internalinnovative effort spent by enterprises confers the necessary andfundamental knowledge and capacity to interact with others. Withregard to the innovation performance of the three-profile typol-ogy, firms intensive in techno-scientific flows of information tendto innovate mainly in product and, to a certain extent in organi-sational aspects. Client-led innovators, on the other hand, seem tobe more process innovators than any other profile. Finally, lonely

innovators are basically organisational innovators.

Theresultsleadtotheconclusionthatthenatureoftheactivitiesaffects both the nature and the intensity of the cooperation part-nership in innovation. The nature of the activities referred to herecan be expressed by the technological capacity, in other words, theintensityoftheuseoftechnologies,thetechnologicalopportunities,the growth in demand, the life cycle of the services, which describethe evolution of the subsector, as well as the degree of standard-ization or customization of the service activity. While knowledgeand technologically intensive business services demonstrate a veryactive behaviour as far as the linkages analysed are concerned,innovating distributive services (transport, wholesale, retail, etc.)and innovating HORECA (hotels, restaurants and catering) presenta very low innovative performance. However, the results under-line the co-existence of different cooperation pattern within thesame industry. Although the existence of an increasing debate onwhich one between the sectoral-determinism and strategic-choiceis the most significant factor to shape cooperation and innovationpatterns, the results lead to the conclusion that there exist clearassociation between the information flows used throughout theinnovation processes and the nature of the activity. However, it isimportanttostressthat,inthesamewaythatthedecisionthatleadsone firmto innovate is a strategic-choice, the inclination to usecer-tain information channel is also a choice in terms of managerial andinnovation strategy.

Acknowledgements

We are grateful to Prof. Stan Metcalfe, Prof. Ian Miles, Prof.Roonie Ramlogan, Dr. Davide Consoli, Dr. Shu-Li Cheng, Prof.Marcela Miozzo and Dr. Yanuar Nugroho from the ManchesterInstitute of Innovation Research (University of Manchester, UK),for motivating and helpful discussions on the original version ofthis work, and also for providing the specific computer statisticalsoftware for Latent Class Analysis. We acknowledge Prof. nxelaTroitino and other members of the ICEDE Research Group from the

University of Santiago de Compostela for interesting suggestionson previous drafts of this work. Wealso appreciate the useful com-ments made by Dr. Michele Mastroeni from INNOGEN (School ofSocial and Political Science, University of Edinburgh, UK). A pre-liminary version of this article has strongly benefited from thevaluable remarks provided by the participants in the DRUID Sum-mer Conference 2009 and the XIXth International RESER 2009Conference. The authors acknowledge the financial support fromthe European Regional Development Fund (ERDF) and from Xuntade Galicia (Competitive Reference Group 2008/041 and Project08SEC008201PR). Finally, we would also like to express thanks tothe editor and two anonymous referees for the constructive feed-back. The usual disclaimer applies.

Appendix A. Latent Class Analysis

Latent Class Analysis (LCA) is a statistical method that can beapplied to cluster, factor or regression analysis. Given the purposeof this article, we will focus on the specific module of clustering. Inthis sense, LCA is a multivariate technique used to identify clustersof related cases (latent classes or clusters) based on the analysisof the probability distribution of observed categorical variables.Its aim is to examine whether the association between categoricalvariablesobservedcanbeexplainedfromastructureorunobservedlatent variable . The LCA cluster model for categorical variablesis constructed from probabilistic modeling of the observed vari-ables {V1, V2, . . ., Vj, . . ., VJ} which is conditioned by a structure

of latent classes. Under the assumption of local independence


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