The role of knowledge-intensive business services in ...

Tourism Economics, 2014, 20 (2), 355–371 doi: 10.5367/te.2013.0276

The role of knowledge-intensive business services in Spanish local tourist production systems JOSÉ ANTONIO ÁLVAREZ-GONZÁLEZ AND Mª OLGA GONZÁLEZ-MORALES

Department Economics of Institutions, Economic Statistics and Econometrics, Faculty of Economics and Business, University of La Laguna, Camino de la Hornera s/n, Campus de Guajara, 38071 La Laguna, Tenerife, Spain. E-mail: [email protected]; [email protected]. (Corresponding author: Ma Olga González-Morales.) This paper analyses the evolution of the knowledge base in local tourist production systems (LTPS) in Spain and of knowledge-intensive business services (KIBS) and the effects on labour productivity in tourist services. Innovation depends on an enterprise’s ability to generate new knowledge and absorb external knowledge, and KIBS are external providers of knowledge. The process ends when the acquired knowledge has an effect on innovation and the impact is reflected in increased productivity. This approach assesses learning through interaction and space. It applies multidimensional scaling to detect associations between variables and LTPS. The results indicate that most changes in productivity are caused by a combination of tacit and codified knowledge, but this differs depending on the LTPS. Labour productivity also increases in the LTPS, which further increases the supply of KIBS. Keywords: local tourist production systems; KIBS; knowledge base; labour productivity in services; Spanish provinces

In the last 20 years, technological progress and innovation have played a central role in economic activity (Aghion and Howitt, 1996). The transformation of the economy and service sector poses new challenges, which extend the innovation field from industry to services (Tether, 2005). The service sector encompasses a heterogeneous set of activities with very different characteristics (Sundbo, 2002). According to Vargo and Lusch (2008) a service is the application of competences (knowledge and skills) by one entity for the benefit of another. This perspective holds that the most important facets are the abilities and qualifications of those providing services and their personalized nature (Álvarez and González-Morales, 2010; Demirkan et al, 2011). Innovation in the service sector is still an under-researched topic, but there is growing interest (Barcet, 2010; Gallouj and Savona, 2010). Studies to date have shown that innovation in the service sector is related to unincorporated

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and intangible changes, termed the non-technological innovation process (organizational changes and market) (Tether, 2005). Tourism is one of the most significant activities in the service sector. It is an industry of great importance, which, after the slump of the economic crisis (Dwyer et al, 2010, Figini and Vici, 2010; OECD, 2010; WTO, 1998), has returned to play a fundamental role in the economies of countries such as Spain, France and Italy. Tourism services are grouped into zones, predominately in tourist resorts. A tourist resort consists of a set of natural, cultural, artistic or environmental resources, considered to be an attractive product available in a certain area. Each destination offers a complex and integrated portfolio of services that provides a vacation experience designed to meet the needs of tourists (Cracolici and Nijkamp, 2008). Therefore, from the perspective of supply, a tourist destination can be analysed as a local tourist production system (LTPS). The importance of tourism, its low productivity and changes in demand have fuelled interest in innovation in the sector (Hall and Williams 2008; OECD, 2006a, 2010), and in the relationship between innovation and increased productivity (Griliches, 1990; Hall, 2011). On this basis, the objectives of this paper are (a) to identify the relevance of tacit knowledge and codified knowledge in increasing the productivity of LTPS and (b) to analyse the relevance of knowledge-intensive business services (KIBS) in the innovation processes of LTPS.

Innovation in companies For Grant (1996) and Kogut and Zander (1996), the knowledge base of companies is their basic resource, allowing them to perform their activities. Therefore, to innovate and to produce new or improved products or services, they need to modify or augment this knowledge base. Firms are located in zones and they maintain relationships in their environment. Each firm has different combinations of knowledge (Figure 1). Innovation occurs when a company expands its knowledge base (buying a more efficient machine, hiring a person with more knowledge). It can expand its internal knowledge through research, through employee training (improving their productivity by work experience or specific training derived from professional practice – learning by doing) or by incorporating external knowledge (social learning) (Cohen and Levinthal, 1990). Lane et al (2006, p 856) define the absorptive capacity of knowledge of an enterprise as the ability to use external knowledge via three mechanisms: (a) recognition and understanding of potentially valuable external knowledge for the firm; (b) assimilation of that knowledge through learning; and (c) use of assimilated knowledge to produce new or improved products or services and/or new processes. Chesbrough (2009) describes the evolution of innovation models from closed innovation models, where knowledge is generated and is used within the enterprise, to the open innovation models, where knowledge flows from outside to inside the company and vice versa. There are different types of knowledge in the innovation process. This paper uses the distinction that goes back to Polanyi (1974) between tacit knowledge

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KNOWLEDGE BASE OF ENTERPRISE

INCORPORATED KNOWLEDGE (equipment, machines)

INTANGIBLE KNOWLEDGE

CODIFIED (Patents, education)

TACIT (know-how, routines)

Figure 1. Knowledge base of the enterprise. and codified knowledge. Codified knowledge is produced mainly in public research centres, and in laboratories in large private companies, and is more easily transmitted. Tacit knowledge is closer to practical skills and is more difficult to communicate, requiring an on-going relationship. According to the predominant type of knowledge, we find different ways of learning and innovation. Jensen et al (2007) distinguish between two types of models: (a) the model of science, technology and innovation, which is based on the production and use of codified scientific and technological knowledge; and (b) the model of doing, using and interacting based on experience and learning, which is dominated by tacit knowledge. Authors such as Asheim (1999), Maskell (2001) and Porter and Stern (2001) have developed an approach that relates the types of knowledge, learning by doing and the geographical location of businesses. This relationship arises from the need to have face-to-face contact to acquire tacit knowledge and its spill over effects. The types of knowledge are not equally transferable. As Hippel (1994) noted, some knowledge is ‘sticky’ or of a local nature. This type of knowledge, mainly tacit, is incorporated in individuals, while other types of knowledge are more collective in nature (Gertler, 2003). KIBS play an important role in the economy as suppliers of intermediate inputs to other economic sectors. In the process of innovation, KIBS act as external knowledge providers (Miles et al, 1995; Muller and Zenker, 2001; OECD, 2006b). According to Hertog (2002), KIBS are part of the knowledge infrastructure, along with universities and other public research centres. KIBS play a key role in the networks and systems of innovation in the creation and marketing of new products and services, or in process changes (Kuusisto and Meyer, 2003). KIBS may also play a role as intermediaries between research centres and enterprises (Bessant and Rush, 1995).

Innovation in local tourist production systems The tourism sector is made up of many firms, most of which are small- and medium-sized enterprises (SMEs) (OECD, 2010). These enterprises offer alternative and complementary activities in a tourist destination that constitute an LTPS (Lombardi, 2003; Michael, 2007; Capone and Boix, 2008). The LTPS has a stock of companies, changing as businesses enter and leave it. Enterprises innovate through the interaction of their internal knowledge base with various external sources of knowledge: research centres, suppliers, customers and KIBS (Figure 2).

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Sources of knowledge (external knowledge)

LTPS

Universities

Market firms Entry firms (internal knowledge)

KIBS Customers

Innovation effects ¸ Productivity

Suppliers

Figure 2. Innovation in local tourist production systems. The predominance of SMEs in the tourism sector limits the sector’s ability to generate its own knowledge. These enterprises depend on their ability to absorb external knowledge and so there is a need to establish mechanisms for knowledge transfer (Argote and Ingram, 2000; Jacob et al, 2003; Shaw and Williams, 2009). In this context the role of the KIBS acquires special importance. KIBS carry out activities for SMEs (accounting, marketing) and they transfer knowledge and information to firms to help them solve problems (Miles et al, 1995; Muller and Zenker, 2001; Hertog, 2002; OECD, 2006b). In summary, D Productivity = F (innovation) = F (CT, CTS, L, EL, P, KIBS) (see Table 1 for a definition of the abbreviations). An increase in productivity depends on innovation. In turn, innovation in tourist services depends on increases in tacit knowledge (CT, CTS, L), codified knowledge (EL, P) and innovation facilitators (KIBS) (Álvarez and González-Morales, 2006). As a result, the following three hypotheses are proposed. H1: In LTPS innovation, tacit knowledge is more relevant than codified knowledge. Therefore, Δ tacit knowledge implies Δ productivity in the LTPS. H2: In the LTPS, within the concept of codified knowledge, knowledge incorporated in people (education) is more relevant than non-incorporated knowledge (patents). Therefore, Δ education implies a greater effect on Δ productivity. H3: KIBS are more relevant in the LTPS, when tacit knowledge is greater than codified knowledge. Therefore, Δ KIBS produces a greater effect on Δ productivity in those LTPS with greater tacit knowledge.

Data and methodology This research uses the LTPS of a previous study (Álvarez and González-Morales, 2003), in which the province is used as the unit of analysis (Appendix Table A1). (In the nomenclature of territorial units for Eurostat statistics, provinces, NUTS 3, are small regions for specific diagnoses.) Although the Spanish provinces have disparate activities, the calculation of a coefficient of specialization can be used to detect the prevalence of particular activities. The use of the province also provides us with more precise knowledge of local production characteristics, although in some cases more than one production system may overlap in the same province, making it possible to identify different types of production. The importance of economic specialization appears to be reinforced

Number of enterprises in KIBS (KIBS): ratio between number of these firms and total DIRCE (INE), 1999–2009 enterprises. We use the industries included in the classification of KIBS, based on the OECD classification (depends on the ratio of RandD as a percentage of GDP). See Tables A2 and A3.

Labour productivity: quotient between gross value added and employment (jobs). Labour productivity in the service sector: quotient between the gross added value in services and employment (jobs) in the service sector.

Facilitators

Effects of innovationa

Note: aD Productivity = F (innovation) = F (CT, CTS, L, EL, P, KIBS).

Educational level of labour force (EL): weighted sum of active population at each educational level, expressed on a percentage scale (ranging from 0 to 100). The value 0 corresponds to the illiterate population, or primary school studies only, and the value 100 to the population with university education. This index is an application of IEPO in Spanish (educational index of the employed population), used by Martínez et al (1993) but applied to the active population. Patents applied for by province (P): ratio between number of patent applications and the economically active population, multiplied by 1,000.

Codified knowledge

Regional Accounts of Spain (INE), 1997–2007 Regional Accounts of Spain (INE), 1997–2007

Statistics of Industrial Property OEPM, 2000–2009

EPA (INE), 2000–2007

EPA (INE), 2000–2007

FORCEM, EPA (INE), 1999–2009

FORCEM, EPA (INE), 1999–2009

Continuous training (CT): ratio between total number of workers participating in training and total number employed. Continuous training in the service sector (CTS): ratio between number of workers participating in training in service sector and number employed in service sector. Learning by doing (L): duration of contract (number of temporary employees divided by total number of employees).

Tacit knowledge

Source

Measurement

Variable

Table 1. Variables used by provinces.

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by a study of the prevailing knowledge base, which affects the capacity for future development. Álvarez and González-Morales (2003) estimate the specialization coefficients of activities in all Spanish provinces, applying a cluster analysis. The result is eight production systems (see Appendix Table A1). In particular, the production system of tourist services (trade and tourism) consists of eight provinces. In a later study, Álvarez and González-Morales (2004) provide a further analysis of the activities of this group. They note the existence of two distinct subgroups of provinces: (a) Cadiz, Seville, Ceuta and Melilla specialize in trade; and (b) the Balearic Islands, Malaga, Las Palmas de Gran Canaria and Santa Cruz de Tenerife specialize in tourism. This is why the present study focuses on the latter group of Spanish provinces. Specifically, they have a predominance of large hotels, with more than 100 workers per hotel dedicated to the hotel industry, which places them at the head of the Spanish provinces in tourist terms. However, there may be differences in the pattern of tourism development among the LTPS. Table 1 specifies the variables used, the measurement of these variables and the source of information. A dynamic analysis was performed for the testing of the hypothesis. The evolution of the same variables is studied during the period of analysis (Table 1) through the development of index numbers. Variables have a positive sign when their values increase over time, or at least are above the LTPS mean average – except for learning by doing measured by temporality, which is interpreted positively when its value decreases or remains relatively stable. Correlations are calculated between the variables in Table 1 (tacit knowledge, codified knowledge and facilitators) and labour productivity in the service sector. A statistical technique of multivariate analysis (ALSCAL) is also applied to observe possible associations between study variables and the position of the LTPS.

Results of correlations and scatter plots Table 2 shows the evolution of variables using index numbers. These indices are weighted with respect to the Spanish mean average (which has the value of 100) to allow comparison between this and the results of the LTPS individually and the mean of the LTPS as a whole. The results of the variance and standard deviation with respect to the Spanish mean and the LTPS mean are also presented. In general, it can be seen that the LTPS mean has risen with regard to KIBS enterprises compared to the Spanish mean. The other variables also evolve above the Spanish average, except for training services, total productivity and productivity in services. The most striking results are observed in the evolution of patents with respect to the Spanish mean, because of the large increase in the Malaga region following the creation of a technology park attracting innovative companies. The results of the variance and standard deviation analysis reinforce this assertion, as a high LTPS mean result is obtained in the case of patents, while the less dispersed variables are temporality, productivity in services and total productivity.

102.7 119.4 101.6 104.4 100.0 107.0 61.9120 52.0419 7.8684 7.2140

Balearic Islands Malaga Las Palmas de Gran Canaria Santa Cruz de Tenerife Spanish mean LTPS mean Spanish variance LTPS variance Spanish standard deviation LTPS standard deviation

Source: See Table 1.

KIBS

Provinces

99.7 94.4 111.8 102.2 100.0 102.0 40.6020 39.7819 6.3720 6.3073

Education

109.3 94.7 112.1 114 100.0 107.5 68.9470 57.6219 8.3034 7.5909

Total training

104.8 83.6 97.4 102.8 100.0 97.2 70.1520 68.5275 8.3757 8.2781

Training in services 98.8 94.8 105.2 102.2 100.0 100.3 15.0400 15.0275 3.8781 3.8765

Temporality

65.7 305.4 43.3 88.1 100.0 125.6 11,155.2250 11,023.8969 105.6183 104.9947

Patents

Table 2. Evolution of variables by province, LTPS mean and Spanish mean (Spanish mean, index numbers = 100).

92.9 99.2 95.3 101.7 100.0 97.3 13.0670 11.5819 3.6148 3.4032

95.8 99.4 95.2 99.3 100.0 97.4 5.0780 3.7519 2.2534 1.9370

Total Productivity productivity in services

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Table 3. Correlations between labour productivity in the service sector and other variables. Correlations

Labour productivity in the service sector

KIBS Patents Education Temporality Total training Service training

0.6909 0.6948 0.6909 –0.5248 –0.3109 –0.4546

If we calculate the correlations between labour productivity in services with other variables, the results show that the correlations are generally low (Table 3). The results are significant for codified knowledge (patents and education) and KIBS. They are significant for tacit knowledge only in the case of temporality (learning by doing); training is not significant. The significant variables in labour productivity in the service sector can be seen in the scatter plots as follows: first, the relationship with codified knowledge (education and patents); second, with tacit knowledge (temporality); and third, with facilitators (KIBS).

115.0

Education index

110.0

105.0

100.0

95.0

90.0 90.0

95.0

100.0

105.0

110.0

115.0

Productivity in services index

Figure 3. Relationship between the evolution of education and productivity.


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320.0 300.0 280.0 260.0 240.0

Patents index

220.0 200.0 180.0 160.0 140.0 120.0 100.0 80.0 60.0

320.0

300.0

280.0

260.0

240.0

220.0

200.0

180.0

160.0

140.0

120.0

100.0

80.0

60.0

40.0

40.0


Figure 4. Relationship between the evolution of patents and productivity.

Codified knowledge and labour productivity Two indicators of codified knowledge have been considered: an education index, IEPO in Spanish, and an indicator of R&D patents. The evolution of the educational level of the active population is very uneven among LTPS. The case of Las Palmas de Gran Canaria, which has a higher increase than the national mean (111.8), and the case of Malaga, which has a variation less than the national mean (94.4), are especially important. Figure 3 shows that the disparity in the evolution in the educational levels of the active population in LTPS (see LTPS mean) is not reflected in higher labour productivity in the service sector. The other indicator of codified knowledge is patents, which represent the generation of technological knowledge within LTPS. The relationship between patents and the development of labour productivity in the service sector highlights two issues (Figure 4). On the one hand, regarding the applications for patents, Malaga is predominant (305.4) compared to the other provinces, which are below the Spanish mean. On the other hand, the LTPS mean indicates that the evolution of the index during the period is not reflected in higher labour productivity in the service sector. In summary, the evolution of indicators of codified knowledge does not reflect a significant impact on the evolution of labour productivity in LTPS,

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Temporary index

105.0

100.0

95.0

90.0 90.0

95.0

100.0

105.0

110.0


Figure 5. Relationship between the evolution of temporality and productivity. all of which are in a similar situation, regardless of the evolution of educational levels and patents.

Tacit knowledge and labour productivity As has been noted above, training is not significant with respect to the two indicators of tacit knowledge. Thus, only temporality as a proxy for learning by doing and its relationship with productivity in the specific case of services are considered here. Malaga is the province with the lowest temporality evolution (94.8) and Las Palmas de Gran Canaria has the strongest growth in the temporality (105.2). In this case, the increase in productivity in Malaga is significantly higher than in Las Palmas de Gran Canaria although the difference compared to Santa Cruz de Tenerife is much lower (Figure 5).

Facilitators and labour productivity Finally, Figure 6 shows the unusual situation of Malaga (119.4), which, although it has the greatest growth in the supply of KIBS and no significant increases in productivity, finds itself above the LTPS mean. Santa Cruz de Tenerife (104.4) is in second place and is also above the LTPS mean in the index evolution of productivity in services. Therefore, the increase of KIBS does seem to have a positive effect on the increase of labour productivity in the service sector.


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120.0

KIBS index

115.0

110.0

105.0

100.0

95.0 95.0

100.0

105.0

110.0

115.0

120.0


Figure 6. Relationship between the evolution of KIBS and productivity.

Results ALSCAL For the final step, a multivariate statistical analysis is used to study the association between all variables and the relative situation of LTPS with respect to these variables. Multidimensional scaling, ALSCAL, can be used to classify objects by considering two or more of their characteristics, and reproduces them in a perceptual map. This analysis reduces the dimensions of the variables used and allows grouping and the construction of a coordinate graph. SPSS 19.0 was used. The data fit is evaluated by the coefficient ‘s-stress’, which assumes values between 0 and 1 (0 is a perfect fit and values above 0.2 are associated with maladjustment). The squared correlation (RSQ) is an indicator of goodness of fit and is interpreted as a proportion of common variance of disparities explained by the dimensions (it is better the closer it is to 1) (Vivanco, 1999). The values ‘s-stress’ = 0.09013 and RSQ = 0.95394 are considered satisfactory in this analysis. Figure 7 shows the grouping of the variables in two dimensions and the relative location of the LTPS. The closer the variables are the less the distance between them, indicating that there has been less progress during the study period. Different patterns of behaviour in the evolution of labour productivity and its determinants can also be seen in Figure 7.

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2.0

Productivity in services Total productivity

1.0 Education

Dimension 2

Temporality

0.0

Total training

Patents

KIBS

Training in services

–1.0

–2.0 –2.0

–1.0

0.0

1.0

2.0

Dimension 1

Figure 7. Multidimensional scaling plot, Euclidean distance model. Malaga is associated with higher productivity (total and services), and with the highest growth in patents and KIBS. Malaga is also the province that displays a smaller increase in its learning by doing (temporality). Santa Cruz de Tenerife also has significant growth in labour productivity, although in this case, growth is associated with increased training, educational level and temporality. The other two LTPS show a different behaviour. In the case of the Balearic Islands, the increase in labour productivity has been less significant, despite the significant increase in training. Similarly, Las Palmas de Gran Canaria experiences low growth in productivity, with a greater evolution in temporality, despite the increase in the level of education and training of its workforce.

Conclusion The revolution in information technology, communication and transportation has had a significant impact on the tourism sector and the competitiveness of traditional destinations, requiring them to introduce new strategies. This article has examined the provinces, NUTS 3, that specialize in tourism, LTPS, although they may also have other important economic activities. Labour


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productivity in the service sector in each province is used as an indicator that best fits the LTPS. Certain conclusions from this analysis deserve further comment. The LTPS do not exhibit the same behaviour. This is possibly because the type of tourism specialization is not the same in all LTPS. As a result, the combination of factors that contribute to the growth of labour productivity in the service sector is not the same in all LTPS. First, the increases in productivity in the LTPS do not have a significant relationship with individual indicators (tacit knowledge, codified knowledge, facilitators of innovation). However, the confluence of various factors is what determines the increase in productivity. This limits the testing of the established hypotheses that consider the factors on an individual basis. Second, there is an evolution of KIBS above the Spanish mean in all the provinces, but they do not follow the same growth pattern. Malaga has the largest increase in KIBS, patents and reduction of temporality. Santa Cruz de Tenerife is the province with the second largest increase in KIBS, a relative growth of patents and a very positive development in education and training. Both provinces experienced the greatest increases in productivity (total and services). In the other two LTPS, the Balearic Islands and Las Palmas de Gran Canaria, labour productivity has grown less. In both cases, the increase in KIBS was lower but, in the case of Las Palmas de Gran Canaria, it was accompanied by a significant increase in codified knowledge (education), and deterioration in learning by doing (greater temporality). In the case of the Balearic Islands, this is accompanied by a positive trend in the tacit knowledge indicators. In summary, and with reference to the stated hypotheses, the following can be concluded. First, tacit knowledge by itself does not seem to be relevant to increases in productivity, but it is relevant when associated with other factors. In the two LTPS with the greatest evolution in productivity (Malaga and Tenerife), the factors are different but are a combination of tacit and codified knowledge. In the case of Malaga, the factors are patents and low temporality (learning by doing) and in the case of Santa Cruz de Tenerife, education and training. Therefore, H1 and H2 are partially confirmed. Second, in the LTPS where the supply of KIBS has increased most, total productivity and service has also increased most (see the cases of Malaga and Tenerife), confirming H3. References Aghion, P., and Howitt, P. (1996), ‘Research and development in the growth process’, Journal of Economic Growth, Vol 1, pp 49–73. Álvarez, J.A., and González-Morales, O. (2003), ‘La base de conocimientos de los sistemas provinciales de producción españoles: una introducción’, in Díez-García, M.D., ed, Formación y Calidad en las Empresas de la Europa del Sur, Club Universitario, Alicante. Álvarez, J.A., and González-Morales, O. (2004), ‘Base de conocimientos y capacidad innovadora de los sistemas locales de producción turística españoles’, in Grao, J., ed., Economía de la Educación. Actas de las XIII Jornadas de la AEDE, AEDE and Universidad del País Vasco, Bilbao. Álvarez, J.A., and González-Morales, O. (2006), ‘L’apprentissage, l’innovation et la compétitivité dans les clusters touristiques: une étude comparée entre l’Espagne et l’Italie’, Revue d’Economie Régionale & Urbaine, Vol 4, pp 551–574. Álvarez, J.A., and González-Morales, O. (2010), ‘Innovative capacity in tourism destinations: an application to Spanish Destinations’, in Díaz-Pérez, F.M., ed, Competitive Strategies and Policies for Tourism Destinations: Quality, Innovation and Promotion, Nova Publishers, New York, pp 99–123.

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TOURISM ECONOMICS

Appendix Table A1. Local systems of production. Provincial production system

Provinces

Activity and Main activity percentage of total national activity

Predominant size of establishments and percentage of activity

Extractive production system

Almería, Asturias, Badajoz, Burgos, Cuenca, Granada, Guadalajara, Huelva, León, Lleida, Lugo, Murcia, Ourense, Palencia, Pontevedra, Segovia, Soria, Teruel, Zamora Albacete, Alicante, Cáceres, Ciudad Real, A Coruña, Jaén, La Rioja, Salamanca, Toledo Barcelona, Castellón

1E 51.8%

Metal ore mining, non-metallic mineral and nonenergetic minerals

Large 71.4%

1I 24.1%

Manufacture of food products and beverages, clothing industry and leather goods Chemical industry, construction machinery industry, paper industry Manufacture of furniture, manufacture of electrical machinery and material

Medium 29.3%

Production systems, textile and food

Production systems of the chemical and metal industries Production Córdoba, Valencia, systems of Zaragoza furniture and electrical equipment manufacturing System of pro- Ávila, Cantabria, duction and Girona, Huesca, distribution Tarragona, Valladolid of electricity Production system of tourist services

2 20.8%

3 15.7%

4 8.6%

Baleares, Cádiz, 5 Málaga, Las Palmas, 13.8% Santa Cruz de Tenerife, Sevilla, Ceuta y Melilla

Production Madrid system of knowledge Metallurgical Álava, Guipúzcoa, production Navarra, Vizcaya system and social capital formation

7. 6 18.2% 9, 3, 2 10.8%

Production and distribution of electricity and gas

Large 27.6%

Medium 20%

Small 8.7%

Trade and tourism

Small and medium in activities related to trade (17%) and large in hospitality activities (49.9%) Computer Services, Large R&D, Financial Activity 7 (32.2%) Services Activity 6 (34.8%) Associated activities, Small in associated recycling, metal activities (8.5%) and large in metal (18.8%) and manufacturing equipment (17.2%)

Note: Definitions of activities (CNAE-93): 1 – Extractive industry, textile and food (1E, extractive; 1I, textile and food); 2 – Metal, wood and cork industry; 3 – Manufacturing equipment industry; 4 – Construction, energy production and distribution; 5 – Trade and tourism; 6 – Transport, communications and final services; 7 – Public administration and technical services; 8 – Education and health activities; 9 – Other social activities.


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Table A2. Classification of KIBS activities. 72 Computer and related activities

Computer equipment consultancy Software consultancy and supply of software Data processingDatabases related activities Maintenance and repair of office machinery, accounting software and computer hardware Other computer related activities

73 R&D

Natural and technical science R&D Social science and humanities R&D

74 Other business services

Legal activities, accounting, bookkeeping, auditing, tax consultancy, market research and surveys of public opinion, consultation and advice on management and business management, portfolio management Architectural services and engineering and other activities related to technical advice Testing and analysis Publicity Personnel selection and placement Investigation and security services Industrial cleaning activities Miscellaneous business activities

Source: INE.

Table A3. Evolution of the KIBS establishments (base index numbers, base year 1999). LTPS

72 Computer activities

73 R&D

74 Other business activities

Total 72+73+74

218.3 364.4 235.1 240.5 223.6

143.3 173.8 220.6 214.1 150.4

163.0 186.2 158.6 163.1 157.4

165.0 191.8 163.1 167.7 160.6

Balearic Islands Malaga Las Palmas de Gran Canaria Santa Cruz de Tenerife Spanish mean Source: DIRCE (INE, 1999 and 2009).