Innovative business effort in a mediterranean region, same characteristics and/or same spatial distribution?

García-Alcober, María P.; Mateos-Ansótegui, Ana Isabel; Pastor-Gosálbez, María Teresa Article Innovative business effort in a mediterranean region, same characteristics and/or same spatial distribution? Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: García-Alcober, María P.; Mateos-Ansótegui, Ana Isabel; Pastor-Gosálbez, María Teresa (2023) : Innovative business effort in a mediterranean region, same characteristics and/or same spatial distribution?, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 11, Iss. 11, pp. 1-16, https://doi.org/10.3390/economies11110274 This Version is available at: https://hdl.handle.net/10419/328899 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Citation: García-Alcober, María P., Ana Isabel Mateos-Ansótegui, and María Teresa Pastor-Gosálbez. 2023. Innovative Business Effort in a Mediterranean Region, Same Characteristics and/or Same Spatial Distribution? Economies 11: 274. https://doi.org/10.3390/economies 11110274 Academic Editor: Richard J. Cebula Received: 29 September 2023 Revised: 19 October 2023 Accepted: 24 October 2023 Published: 3 November 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). economies Article Innovative Business Effort in a Mediterranean Region, Same Characteristics and/or Same Spatial Distribution? María P. García-Alcober 1,2,*, Ana Isabel Mateos-Ansótegui 3and María Teresa Pastor-Gosálbez 2,3 1 Department of Economics and Business, Universidad Cardenal Herrera-CEU, CEU Universities, C. Luis Vives, 1, 46115 Alfara del Patriarca, Spain 2ESI International Chair@CEU-UCH, Universidad Cardenal Herrera-CEU, CEU Universities, 46115 Alfara del Patriarca, Spain; [email protected] 3Department of Economics and Business, Universidad Cardenal Herrera-CEU, CEU Universities, C. Carmelitas, 3, 03203 Elche, Spain; [email protected] *Correspondence: maria.gar[email protected] Abstract: Business innovation is fundamental for sustained economic growth at the regional level. Knowing the common characteristics of innovative companies and their location is essential to carry out appropriate economic policies. To this end, we have carried out a double analysis: one grouping of companies according to characteristics and another by geolocation. This study focused on one of Spain’s 17 autonomous communities, the Comunitat Valenciana, a region characterised by significant industrial diversity. Our results show, among other things, that size is not a differentiating factor when it comes to innovation, and that there is a positive relationship between physical clustering and productivity. Keywords: regional economy; innovative companies; industrial activity 1. Introduction In a globalised and constantly changing world, business innovation has become an unignorable necessity for companies to survive. Innovative activity increases business productivity and, consequently, local economic growth. This activity is characterised by a series of features such as novelty, creativity, and the transferability of results to society as a whole, which makes it a field of study of special interest. For all of these reasons, it is essential to know the characteristics of the companies that carry out innovation in order to help the different agents involved in promoting local economic development in making decisions. There is no consensus in the literature on certain relationships between firm characteristics and innovation. In particular, the relationship between firm size and innovation has been much debated. Although the literature tended to link firm size with innovation in a positive way, for example Audretsch and Acs (1991); Mowery et al. (1996); or Camisón- Zornoza et al. (2004), we now ask ourselves: do smaller firms really innovate less? In this sense, as Baumann and Kritikos (2016) has pointed out, although smaller firms have more problems in obtaining financing and being able to innovate, it is also true that they need innovation to survive. In a globalised and continuously evolving competitive environment, small firms have to adapt quickly to constant changes in order to avoid closure, as they do not have the financial cushion or support of a stronger business group. Along the same lines, the study carried out by Calvo (2000) concludes that innovative companies in the Spanish manufacturing sector are small and belong to small and medium-sized technology sectors. This discrepancy of opinions, as indicated in a 1996 OECD report (Symeonidis 1996), has led to a debate regarding policy decisions over whether to concentrate on promoting market concentration and firm size or on giving subsidies or grants to smaller firms to increase innovation. Economies 2023,11, 274. https://doi.org/10.3390/economies11110274 https://www.mdpi.com/journal/economies Economies 2023,11, 274 2 of 16 Another variable linked to innovation is the existence of agglomeration economies, which can arise from the geographical concentration of firms (Jacobs 1969). In this case, we might also ask whether innovative firms are geographically clustered. Here, Carlino and Kerr (2015) offer an extensive literature review on the relationship between innovation and business agglomeration. The innovative behaviour of companies is not homogeneous across the territory. In this study, we will focus on the analysis of innovative companies located in Spain’s Valencia Region. The Regional Innovation Indicator 2021 published by the European Union presents a ranking of the different regions. The Valencia Region is classified as a region of moderate innovation, slightly above the Spanish average and below the European average. This, together with its sectoral diversity, as described by Galleto and Boix Doménech (2006), justifies the choice of this territory for our analysis. There is extensive existing literature on this region. Studies by Pérez et al. (2006), Hervás-Oliver et al. (2021), and García-Alcober et al. (2021) provide overall analyses. At a sectoral level, there are studies on the footwear sector, such as those by Ruiz-Ortega et al. (2016) and Marco-Lajara et al. (2021); the ceramics sector has been analysed by, among others, Molina-Morales et al. (2017), Hervás-Oliver et al. (2018), and Albors-Garrigos and Hervás-Oliver (2019); the textile sector has been studied by Pla-Barber and Puig (2009) and Molina-Morales and Expósito-Langa (2013), not to mention other sectors and/or authors. Firms are usually classified according to the productive sector to which they belong when considering their level of technological innovation. However, following the approach of Gkotsis et al. (2018) regarding EU firms, in this study we will analyse firms that innovate independently of their activity, as we are more interested in their innovative behaviour than in their affiliation to a certain productive sector. In short, this paper aims to classify innovative companies in the Valencia Region according to certain economic and financial characteristics and, subsequently, it will analyse their geographical location in order to determine whether or not there are economies of agglomeration. Knowing what companies that innovate have in common is fundamental when it comes to implementing appropriate policies for the promotion and support of such companies, which, as mentioned previously, are essential for sustained local economic growth in the medium and long term. As a methodology, we will use the k-means clustering algorithm, as described by Likas et al. (2003), which will allow us to group the companies according to different economicfinancial and size characteristics. At the same time, we will use a geographical location methodology, SaTScan, to analyse the level of territorial concentration of the studied firms. Geographic Information Systems software, using geolocation coordinates, will allow us to determine the existence of statistically significant business groupings. Our results highlight, for example, that size and age do not limit the possibilities for innovation, that there is evidence that physical clustering enhances productivity, and that smaller, newly created or highly indebted firms tend to be geographically dispersed. The paper is structured as follows: in Section 2, following this introduction, we analyse the data and methodologies used, both k-means and SaTScan. In the subsequent section, we present the results obtained by both methodologies. Then, in the fourth section, we compare and discuss the results within the context of the current literature and, present conclusions. 2. Materials and Methods 2.1. Materials In general, studies of business clusters focus on manufacturing companies or on companies in a particular sector. In this study, we analyse innovative companies in the Valencia Region as a whole. The concept of innovation can be understood in different ways. Some authors, such as Molina-Morales and Expósito-Langa (2013), take into account the innovation effort variable and define it as R&D expenditure over total turnover. Other authors consider innovation to be the result of this expenditure and measure innovation as patents or new products obtained. In this sense, Belso-Martínez et al. (2020) link innovation Economies 2023,11, 274 3 of 16 to the production of new products or production processes. However, as Bell (2005) points out, innovation can also be considered as the development and implementation of new ideas to solve problems. For this reason, Galleto and Boix Doménech (2014) speak of two different innovation models: STI (Science, Technology, and Innovation) and DUI (Doing, Using, and Interacting). Given the difficulty of measuring innovation and the diverse criteria used in the existing literature, in this paper we will consider innovative companies as those that make an innovative effort, in line with Molina-Morales and Expósito-Langa (2013). In this sense, we are going to consider the companies that have any expenditure in R&D in their accounts. Moreover, we will add the companies that receive any public subsidy, because there are companies that have R&D expenditures that cannot be accounted for as such because they do not satisfy all the requirements of the Spanish General Accounting Plan. For instance, to be added to the Plan, they have to be identifiable, measurable, or susceptible to economic valuation, among other limitations. We use the SABI 1 database to conduct the study. The year being analysed is 2019, as the following financial years, 2020 and 2021, would not be representative for a study of this type due to the impact of the COVID-19 pandemic. For the Valencia Region (Comunitat Valenciana), in 2019, we found a total of 1429 companies that can be considered innovative, distributed across the different branches of activity indicated by the National Statistics Institute (INE). Data Description We now undertake a purely descriptive analysis of the companies under review, with the aim of finding out which productive sector they belong to, their size, whether or not they receive research subsidies, and their degree of openness to the outside world. Table 1 shows their distribution by sector: Table 1. Distribution of innovative companies by productive sector in Comunitat Valenciana. Sectors % Innovative Companies over Total Innovative Companies in CV % Innovative Companies over Total Companies in CV by Sector Agriculture, livestock, forestry, fisheries and extractive industries 0.98% 0.64% Manufacturing industry 16.79% 4.80% Chemicals, pharmaceuticals and metallurgy 29.39% 8.68% Furniture manufacture, energy supply, water and waste treatment 7.63% 2.92% Construction and ground transport 21.27% 0.73% Maritime transport, air transport and postal services, accommodation services 2.17% 0.38% Telecommunications, financial services, insurance 8.82% 0.68% Research and development, veterinary, rental and employment-related activities 10.01% 2.11% Security and research, education and health activities 2.59% 0.58% Creative, artistic and performing arts activities, libraries 0.35% 0.16% Source: Own elaboration with CNAE data. It can be seen that “Chemicals, pharmaceuticals and metallurgy” is the productive sector with the highest proportion of innovative companies at 29.39%. Moreover, it is the sector with the highest proportion of innovative firms, 8.68% of the firms in this sector. At the other extreme, the “Creative” sector represents the lowest percentage of the sample. Table 2shows that two thirds of the innovating companies are small (small and microbusinesses), and only 5.11% of those innovating are large companies. It can be pointed out that near 50% of the Chemicals, pharmaceuticals and metallurgy innovation companies have a medium or large size; in sum, they are the biggest ones. Table 3shows the distribution of the firms according to whether they receive R&D subsidies, showing that 1 in 10 firms innovate without resorting to subsidies. By sector, we can observe that “Maritime transport, air transport and postal services, accommoda- Economies 2023,11, 274 4 of 16 tion services”, “Agriculture, livestock, forestry, fisheries and extractive industries” and “Construction and ground transport” are the sectors with the lowest subsidy levels. Table 2. Distribution of innovative companies by size and sector. Sector % Large % Medium % Small % Micro Agriculture, livestock, forestry, fisheries and extractive industries 0.0% 14.29% 64.3% 21.43% Manufacturing industry 4.2% 31.25% 55.0% 9.58% Chemicals, pharmaceuticals and metallurgy 5.7% 41.67% 42.1% 10.48% Furniture manufacture, energy supply, water and waste treatment 11.9% 25.69% 43.1% 19.27% Construction and ground transport 3.6% 24.34% 52.6% 19.41% Maritime transport, air transport and postal services, accommodation services 6.5% 22.58% 45.2% 25.81% Telecommunications, financial services, insurance 4.8% 11.11% 38.9% 45.24% Research and development, veterinary, rental and employment-related activities 1.4% 16.78% 38.5% 43.36% Security and research, education and health activities 13.5% 24.32% 32.4% 29.73% Agriculture, livestock, forestry, fisheries and extractive industries 0.0% 0.00% 60.0% 40.00% Overall total 5.11% 28.55% 46.05% 20.29% Source: Own elaboration with CNAE data. Table 3. Distribution of innovative companies according to whether they receive R&D subsidies or not by sector. Sector % No R&D Subsidies % Receive R&D Subsidies Agriculture, livestock, forestry, fisheries and extractive industries 21.4% 78.6% Manufacturing industry 6.7% 93.3% Chemicals, pharmaceuticals and metallurgy 8.6% 91.4% Furniture manufacture, energy supply, water and waste treatment 4.6% 95.4% Construction and ground transport 21.1% 78.9% Maritime transport, air transport and postal services, accommodation services 38.7% 61.3% Telecommunications, financial services, insurance 5.6% 94.4% Research and development, veterinary, rental and employment-related activities 1.4% 98.6% Security and research, education and health activities 5.4% 94.6% Agriculture, livestock, forestry, fisheries and extractive industries 0.0% 100.0% Overall total 10.29% 78.6% Source: Own elaboration with CNAE data. In the context of foreign trade, Table 4shows that 37% of the companies do not carry out any type of foreign trade, and the “Chemicals, pharmaceuticals and metallurgy” sector is the one with the highest level of foreign trade. Table 4. International trading activity by sector. Sector % Export Activity % Import Activity % Export/Import % No Foreign Trade Agriculture, livestock, forestry, fisheries and extractive industries 21.4% 7.1% 28.6% 42.9% Manufacturing industry 15.8% 7.9% 55.8% 20.4% Chemicals, pharmaceuticals and metallurgy 21.9% 6.0% 56.2% 16.0% Furniture manufacture, energy supply, water and waste treatment 9.2% 8.3% 35.8% 46.8% Construction and ground transport 11.5% 9.5% 43.8% 35.2% Maritime transport, air transport and postal services, accommodation services 16.1% 3.2% 16.1% 64.5% Telecommunications, financial services, insurance 13.5% 4.8% 7.9% 73.8% Research and development, veterinary, rental and employment-related activities 14.0% 6.3% 11.2% 68.5% Security and research, education and health activities 5.4% 0.0% 0.0% 94.6% Agriculture, livestock, forestry, fisheries and extractive industries 20.0% 0.0% 20.0% 60.0% Overall total 15.61% 6.93% 40.45% 37.02% Source: Own elaboration with CNAE data. Economies 2023,11, 274 5 of 16 2.2. Methods Based on this information, the aim of this work is to identify those innovative companies in the Valencia Region that, because they share a series of characteristics, could be seen as belonging to a particular category of company. This would enable these groups to be categorised and so facilitate decision-making in the provision of incentives for innovationrelated activities. The methodology that we use to group the companies that carry out R&D activities in the Valencian Region is the k-means algorithm. This is one of the most widely used unsupervised machine learning algorithms and can be implemented using different software (KNIME, MATLAB Spectral, Python, R, etc.). This type of analysis has already been used in the field of industry, e.g., Gkotsis et al. (2018) and Rastogui et al. (2020). The k-means 2 cluster analysis technique 3 is a multivariate technique that groups the cases of a data set (variables) according to the similarities between them. This algorithm uses quantitative variables to calculate the Euclidean distance and detect patterns of behaviour. This analysis allows us to detect the optimal number of groups and their composition solely on the basis of similarities across the data (assuming no specific distribution for the quantitative variables). We use the library developed in RStudio to implement this algorithm4. The following variables are used to group the companies reviewed: - Years of activity: Years since the company was founded until 2019; - Number of employees: Total number of employees according to SABI data in 2019; - Intangible fixed assets: Net volume of “Intangible assets”, i.e., as shown on balance sheets, net of accumulated depreciation. Sum of Research and Development, Patents, Administrative Concessions, etc.; - Tangible fixed assets: Net volume of “Property, plant and equipment”, i.e., as shown in the balance sheets, net of accumulated depreciation. Sum of Land, Buildings, Technical Installations, Vehicles, etc.; - Value added: The result of correcting the profit or loss for the year by adding certain items that were subtracted as expenses for the year. Therefore, VA is calculated by adding to the profit and loss for the year the amount of corporate income tax, staff costs, depreciation and amortisation payments, and financial expenses for the year; - EBITDA: Earnings Before Interest, Taxes, Depreciation, and Amortisation; - Bank debt: Debts contracted with financial institutions, which generate both long- and short-term financial outgoings; - SE: Shareholder Equity (or Core Funding): Comprises the sum of Share Capital, Reserves, and Profit and Loss for the financial year; - Cash-Flow: sum of Profit for the year (Profit after tax) and the depreciation and amortisation expenses for the year. On the other hand, another of the aspects that we are interested in analysing is whether innovative companies are physically grouped together and how to locate these groupings in the studied territory. To carry out this analysis, we use a geographical location methodology, in this case the SaTScan software tool 5 (https://www.satscan.org/) 6 (accessed on 14 June 2021). This is a geographic information system that uses geolocation coordinates to identify statistically significant business clusters. This software allows us to detect the existence of spatial agglomerations that are statistically significant by applying the Kulldorff (1997) scan. In our study, we analyse the existence of circular zones containing a minimum of 15% of technology firms—calculated over the total business population—in order to take into account economies of proximity and scale. This methodology has previously been used by López and Páez (2017) for Canadian high-tech companies and García-Alcober et al. (2021) for technology companies in the Valencia Region. So far, studies on clustering have focused only on spatial location. However, with this paper we go a step further to identify whether there is also the clustering of firms with similar characteristics in nearby locations. Both clusters, physical location and according to Economies 2023,11, 274 6 of 16 characteristics, are fundamental when it comes to understanding and being able to boost the innovative effort of companies in this Spanish region. 3. Results First, we proceeded to detect whether there are any shared characteristics in the innovative firms. Using a k-means technique, we identified whether the innovative firms have common characteristics in terms of age, size (measured by the number of employees), corporate indebtedness (calculated as the ratio of bank debt to equity, bank debt to cashflow, and cost of debt to EBITDA) in order to capture the portion of profits that goes to cover the financial burden, and cost of debt to EBITDA in order to capture the share of profits that goes to cover the financial burden of debt, productivity (measured by the value added per employee and EBITDA per employee), installed capacity—property, plant, and equipment (proxied by the volume of tangible fixed assets per employee), and R&D expenditure (proxied by the volume of intangible fixed assets per employee). Each of the types of company grouped together by these characteristics is termed a cluster. In Table 5, we can see that there are 11 clusters. However, Clusters F, G, H, I, J, and K consist of only one or two companies, which have such particular characteristics that they cannot be aligned with other companies. Therefore, we consider only Clusters A, B, C, D, and E, which have more than 25 companies each, to be relevant. According to these results, we can distinguish five different clusters of innovative companies, whose characteristics are described below. Table 5. K-means results based on SABI data for innovative companies in 2019. Cluster ID No. Companies Years Active No. Employees Intangible Fixed Assets per Employee Property, Plant, and Equipment per Employee Value Added per Employee EBITDA per Employee Bank Debt/SE Financial Expenses/ EBITDA Bank Debt/ Cash-Flow A 761 −0.65 −0.24 0.00 −0.19 −0.14 −0.23 −0.01 −0.04 −0.11 B 79 −0.11 −0.09 0.35 0.96 1.21 2.78 −0.10 −0.05 −0.21 C 522 0.93 0.07 −0.13 0.07 −0.03 −0.06 −0.05 −0.04 −0.07 D 28 −0.14 −0.06 0.04 −0.15 −0.23 −0.41 0.53 0.14 4.95 E 30 0.63 5.23 −0.12 0.19 0.00 0.00 −0.08 −0.13 −0.15 F 2 −0.01 0.27 −0.18 0.32 0.21 −0.26 −0.12 0.56 0.39 G 2 −0.81 −0.50 21.09 1.14 1.04 2.47 −0.17 0.11 −0.21 H 1 −0.78 −0.21 −0.03 0.17 33.58 −4.23 −0.20 −0.13 −0.28 I 1 −0.56 −0.50 −0.18 28.12 −0.05 0.48 −0.13 0.39 0.02 J 2 −0.42 −0.42 0.21 0.16 −0.20 −0.55 −0.02 23.43 0.10 K 1 0.48 0.63 −0.18 0.20 −0.19 −0.53 33.25 4.85 2.78 Source: Own elaboration with k-means results. Cluster A: Comprising 761 companies, it is the largest. It is characterised by younger and smaller firms, both in terms of number of employees and net investment in property, plant, and equipment, with both variables being below average. This indicates that start-ups are usually small; Cluster B: Comprising 79 companies, it is characterised by high productivity, based on both Value Added per employee and EBITDA per employee; Cluster C: The main characteristic of companies of this type, composed of 522 companies, is their age; that is, they are the most experienced companies in the sample; Cluster D: This category groups together companies that are characterised by a higher level of indebtedness, both in terms of bank debt itself and the cost of this debt and its proportion in relation to the company’s equity; Economies 2023,11, 274 7 of 16 Cluster E: Characterised by the fact that they are larger companies in terms of the number of employees. We then relate the different types of clusters to the variables that define the composition of the sample: size, openness to the outside world, and being in receipt or not of R&D subsidies. These relationships are analysed in the tables below. As shown in Table 6, more than half of the firms in Cluster A are small or microbusinesses (80%), which is consistent with one of the main characteristics of this cluster, being the smallest firms in the sample. Table 6. Characterisation of clusters by firm size. K-MEANS CLUSTER Size A B C D E Large 1.7% 6.3% 4.6% 3.6% 100.0% Medium 17.9% 24.1% 46.6% 28.6% 0.0% Micro-business 31.3% 27.8% 3.6% 21.4% 0.0% Small 49.1% 41.8% 45.2% 46.4% 0.0% Overall total 100.0% 100.0% 100.0% 100.0% 100.0% Source: Own elaboration. Cluster B, which comprises the most productive companies, although small companies and micro-enterprises again predominate, accounting for almost 70% of the total number of companies in this cluster, is larger in size than Cluster A. In Cluster C, which includes the oldest enterprises, the number of micro-enterprises is not very significant (3.6%), with small and medium-sized enterprises predominating. In Cluster D, whose main characteristic is a high level of debt, the presence of large companies is very low (3.6%), with most being small businesses (46.4%). Finally, Cluster E consists solely of large firms, which is consistent with its main characteristic. The results shown in Table 7allow us to deduce that companies are not receiving R&D subsidies based on their age, since more than 90% of the youngest (Cluster A) as well as more than 90% of the oldest (Cluster C) receive subsidies. However, there is a relationship between debt and receiving or not receiving subsidies. Half of the companies characterised by a high level of debt do not receive subsidies (Cluster D). What is not clear is whether this lack of subsidies is a cause or an effect of their financial problems. Table 7. Cluster relationship according to receipt of R&D support. K-MEANS CLUSTER R&D Subsidy A B C D E 0 8.5% 13.9% 9.8% 50.0% 16.7% 1 91.5% 86.1% 90.2% 50.0% 83.3% Overall total 100% 100% 100% 100% 100% Source: Own elaboration. Table 8indicates whether there is any relationship between the different clusters and foreign trade. As can be seen, half of the Cluster A companies (the youngest and smallest) have no foreign activity, as is also the case with Cluster D companies, which are the most indebted. Cluster B companies, which, as we have seen, are the most productive, also tend to have more foreign activity (two thirds of these companies). Companies in Clusters C (the oldest) and E (the largest) engage in more international trade. In short, company size and experience also imply greater openness to foreign trade. Economies 2023,11, 274 8 of 16 Table 8. Relationship between clusters and foreign trade activity. K-MEANS CLUSTER Exp/Imp A B C D E No foreign trade activity 50.9% 31.6% 18.0% 50.0% 13.3% Does export 11.6% 16.5% 20.5% 21.4% 23.3% Does import 7.5% 8.9% 5.4% 10.7% 13.3% Exp/Imp 30.1% 43.0% 56.1% 17.9% 50.0% Overall total 100.0% 100.0% 100.0% 100.0% 100.0% Source: Own elaboration. Once the innovative companies in the Valencia Region have been analysed according to the criteria obtained via k-means, we proceed to study their link to the territory using a georeferencing technique, SaTScan. The SaTScan analysis indicates that there are six significant business clusters, as set out in Table 9. Table 9. Statistically significant business groupings7. 1 2 3 4 5 6 Radius 26.71 km 15.88 km 11.34 km 12.22 km 6.36 km 8.04 km Population 8697 11,310 5418 1746 3025 4100 Number of cases 220 232 113 46 55 63 Expected cases 66.14 86.02 41.21 13.28 23.01 31.18 Observed/expected 3.33 2.7 2.74 3.46 2.39 2.02 Relative risk 3.75 3.03 2.89 3.55 2.45 2.07 Percent cases in area 2.5 2.1 2.1 2.6 1.8 1.5 Log likelihood ratio 121,013,856 93.456707 44,585,636 25,126,552 16.482704 12.981459 p-value <0.00000000000000001 <0.00000000000000001 1.1 ×10−16 7.4 ×10−90.000026 0.0007 Source: Own elaboration with k-means results. Once these data have been collected, we located these clusters on a map of the Valencia Region. As shown, there are two clusters (numbers 1 and 5) in the province of Alicante (the localities of El Comtat and Baix Vinalopó), one cluster (number 4) in the province of Castellón (the locality of L’Alcora), and three clusters in the province of Valencia (numbers 2, 3, and 6) (the localities of Buñol, La Ribera and Sagunto) (Figure 1). The productive activities of each SaTScan grouping can be seen in Table 10. The physical groupings of innovative companies broadly coincide with the location of the clusters and/or industrial districts found in the previous literature on the Valencia Region. Thus, three geographically concentrated traditional regional industries stand out in the Valencia Region: the textile sector in the Alcoi/Ontinyent area (inland Alicante), footwear in the Vinalopóarea (a little further south than the previous area), and ceramics in the province of Castellón. Our Cluster 1 is located in the interior of the province of Alicante, an area where the traditional textile industry was predominant, as indicated by Miret- Pastor et al. (2011), Pla-Barber and Puig (2009), and Molina-Morales and Expósito-Langa (2013). Our analysis in Table 10 shows that one of the most frequent activities found in this grouping continues to be the textile sector. We also find plastics and rubber, machinery, and wholesale trade, which can be considered complementary to the textile sector. In the interior of the province of Valencia we find Cluster 2. This cluster contains the greatest diversity of economic activity, with the food, chemical, plastics, rubber, machinery, and wholesale trade industries predominating. As Membrado-Tena et al. (2019) point out, the food industry in the Valencia Region has shown great resilience over the past decade thanks to its innovation and international outlook. Economies 2023,11, 274 15 of 16 hypothesis, we define a predetermined type. 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