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Creative capacity for sustainable development: A comparative analysis of European and Turkish rural regions Research Memorandum 2011-20

Aliye Ahu Akgün Tüzin Baycan Peter Nijkamp

Creative capacity for sustainable development: A comparative analysis of European and Turkish rural regions1 Aliye Ahu Akgün Istanbul Technical University  

Tüzin Baycan Istanbul Technical University

Peter Nijkamp VU University Amsterdam

Abstract

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Creative capacity in the field of regional sciences means the capability of any region to generate knowledge and thus to achieve innovation and the diffusion of the output of the innovative activity while obtaining the viability and sustainability of this process. Although creative capacity studies mainly focus on urban regions, the late rural studies and empirical evidences showed that rural region has a great potential capacity in terms of its five components viz. knowledge; innovation; entrepreneurship; creativity; and networks. But, these opportunities have shown by a rural specific approach rather than an urban approach. On this basis, by taking into consideration these discussions in the literature, we assume that the rural creative capacity can be evaluated by the recent changes in rural regions that show the capability of rural regions to exploit its knowledge as an output. On this purpose, in this study, we aim to evaluate which component is relatively important to identify the level of rural creative capacity. Therefore, the study focuses on 60 villages from Europe and 17 villages from Turkey by deploying the data obtained from the in-depth questionnaires. This study is a first attempt at settlement level with an optimistic approach to measure the opportunities lying at the heart of rural regions. The results of the study showed that creativity in terms of traditions is the most important component in both cases while European villages have more opportunities and do not have the latent rural problems while Turkish villages are still suffering from the well-known rural problems that their capacity exists but it is very limited.

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 This study is  based on the PhD thesis of  Aliye Ahu Gülümser  supervised  by Tüzin  Baycan‐Levent  and Peter  Nijkamp.  

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1

Creative Capacity in the Field of Regional Science

Creative capacity refers to the capability of individuals, groups and organisations – especially businesses – to generate new knowledge and to apply it in both commercially viable and sustainable ways. Psychologists have long worked in this field (e.g., Wallas, 1926; Guilford, 1950). More recently, regional science with a focus on geographical settings which are able to attract relatively more so-called creative actors has also focused on the topic especially in urban regions (Florida, 2002; Scott, 2006). Regional creative capacity is the starting point of a region’s sustained competitive advantage and its success route, therefore explains the economic growth and the development (Gülümser et al., 2010). According to the results of our previous study on measuring the creative capacity, the five components of creative capacity - mentioned in the study of Gülümser and her colleagues (2010) - , viz. knowledge; innovation; entrepreneurship; creativity; and networks are seen as the real engine of economic and sustainable growth particularly over the long term period (Florida, 2002, 2005; Forte et al., 2005). The urban studies which have been focused on innovation and other components of creative capacity, in other words the human advance has a long history. Therefore, the typical cases of such studies are urban regions. In contrast, rural regions have become subject to creative capacity studies in the last few years. These late applications showed that rural creative capacity calls for a rural specific approach as urban specific creativity theories and methods misrepresent/underestimate the creative capacity of rural regions (McGranahan and Wojan, 2007). The recent changes and increasing attractiveness of rural regions have led researchers to bring up the concept – rural creative capacity – in the field of regional sciences. Especially in Europe, this attractiveness is not obtained by technology-related advances nor by the innovation nor even by the availability of infrastructures like it is in the urban regions but rather by the quality of life and locality characteristics in other words by the intervening opportunities lying at the heart of rural regions (Gülümser et al., 2009). This article develops and evaluates a rural-specific approach to the measurement of creative capacity in selected European and Turkish rural settlements. It also highlights the dimensions of creativity contributing to sustainable rural development while discussing the creative capacity in the European rural context which differs from other continents’, i.e. America and

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Oceania, rural context. Relevant data and information were obtained from in-depth questionnaire surveys conducted in 60 European and 17 Turkish rural regions. Section 2 explores the measurement of creative capacity while, and Section 3 applies the chosen technique to selected rural regions -. Section 4 compares, contrasts and explains the different creative capacities of European and Turkish rural areas. We conclude by charting a future research agenda.

2

Measuring Creative Capacity of Rural Regions

In an early research (Gülümser et al., 2010), an extended literature overview is accomplished, and the components of rural creative capacity have been extensively discussed. On the basis of the findings of this research, to better explain how we will measure the creative capacity, in this section, we will roughly discuss urban specific measurements and how we put them into operation for rural regions in our sample. The creative capacity concept has five components, viz. knowledge; innovation; entrepreneurship; creativity; and networks. Knowledge as the component of creative capacity is the input of the innovation process (see for further information ‘Knowledge Production Function’ in Grilliches, 1990 and Audretsch, 2003). Each region has and produces its own knowledge (Maskell et al., 1998) and can convert it into a success. For rural regions, local/tacit knowledge in the form of skills and traditions (e.g. organic productions) is also accepted as the key development resource (Ray, 1998; Ploeg et al., 2000; Marsden, 2003; Ploeg and Renting, 2004; Tovey, 2008). Knowledge as an input in the success route of obtaining sustained competitive advantage in knowledge-based economy is usually measured by values, culture and traditions in the region (Gülümser et al., 2010). Although it is difficult to measure and interpret the knowledge of a region, the changes occurring in the daily routines, production, traditions and the increasing importance of local knowledge which is forming the basis of today’s creativity in rural areas may be useful to measure capacity of rural regions. The second component – innovation – has its paradoxical structure for urban regions and also for rural regions. Innovation or the output as the (created) knowledge is measured by research and development (R&D) structures of both private and public initiatives ; its rate of returns, expenditures and employment; patents; technological parameters; productivity; diversity of industries; characteristics of region with a special focus on innovative activities and sectors Page | 3    

(Grilliches, 1990; Rogers, 1998; Audretsch, 2003). The dynamism of innovation process is not easy to measure and the transition of economies due to changing trends in the world brought up questions about the disadvantages of using some special parameters. One of such disadvantages is the dependency of the process on technology and R&D that does not give too much chance to the traditional knowledge. In other words, traditional knowledge is evaluated as less important than scientific knowledge in most of the innovation studies. But this is a biased view in knowledge-based world, that traditional knowledge is a scarce and desired good providing useful inside especially for R&D (Fonte, 2008). Therefore, while evaluating the rural creative capacity, the lack of high innovative activities make difficult to measure the innovation but the increasing use of technology in the local processes and also in the daily life can be seen as an innovative activity that the rural regions are not used to do so. In other words, although there exist rural regions where high-tech exists, the majority of rural areas still prefers not to allow the technology use even in daily life or is still suffering from the lack of technology use. The third component is entrepreneurship is one of the key elements of competitiveness and development. Basically, entrepreneurship does not refer only to the entrepreneur as an individual but as a change agent in an environment and it is very much related to the notions of social capital, human capital and networks (Noteboom, 1999; Cooke, 2002; Elfring and Hulsink, 2003; Westlund and Bolton, 2003). The rural economy is very much related to entrepreneurship/self-employment (van Leeuwen and Nijkamp, 2006), but rural local employment suffers to be allocated inside the region and also from the lack of education possibilities. Indeed, the traditional innovative processes do not call for high-education but rather a high-experience. To keep alive the traditional innovative processes, it is important to sustain local knowledge and cultural diversity. Therefore, traditional innovative processes can capture more local employment rather than bringing labour force from outside to create new job opportunities for rural areas lacking employment resources. Thus, the contribution of such processes to human capital will be not only to increase the experience-based employment but also to increase the number of human capital by creating new job opportunities which can lead sustainable development in rural regions. Therefore, this component can be measured as the widening of job opportunities rather than increasing the skilled employment. The main component of creative capacity is no doubt ‘creativity’. The notion, having more than 60 definitions elucidated with respect to arts has a long history especially in the field of Page | 4    

psychology (Sternberg, 1998). In contrast, creativity in regions or cities − recently popularized form of creativity − is popularized by Florida (2002). Being recently popularized, not much contribution is put in the measurement of creativity but rather Florida’s measurement has been criticised. Regional creativity is measured by different indexes i.e., bohemian index; gay index; diversity index with a special focus on a group of people socalled ‘creative class’ who work in so-called ‘creative industries’. Factors attracting creative class are not the job opportunity but cultural supply; tolerance; openness to new ideas, new people and new lifestyles and also stimulus or inspiration for new experience. Due to the recent development of the notion of creativity, it remains obscure and open to discussion that many criticism on scientific and empirical side of the study (i.e. Glaeser, 2005; Qian and Stough, 2008) or mentioned that the field must be revisited (Scott, 2006; Peck, 2007). Thus, empirically and statistically biased and misinterpreted including the paradoxical sometimes insufficient side of the measurement is accepted also in the academic arena. However, it is also evident that creativity provides a boom in the economy and increase competitive advantage of the region (Scott, 2006). Although the concept is new, especially for rural regions which are a composite of close social defensive localism, their openness and being able to involve technology in their traditional processes can be seen as the creativity in rural regions. The last component of regional creative capacity is ‘network’. Network, which realizes the transfer of product – particularly knowledge −, became the key parameter of obtaining success in knowledge-based economy. Through network(s), innovation can create a sustained competitive advantage thus a region can be visible. Therefore, from a European perspective, networks depends on the distance of the region which can be evaluated in terms of the physical distance, which refers to the distance of rural regions to the nearest urban centres, innovation clusters and their geographical accessibility, the economic distance, which means the place of rural regions and their products in the global markets, and the social distance, which points out the networks and relations of rural people through which they become capable of transferring knowledge and accepting the novelty (Gülümser et al., 2010). Physical distance of rural regions as peripheral and lagging regions which are the interest of rural studies and its effect on the returns of R&D – in a broader sense on innovation and technology – are discussed widely in the literature. Different views, i.e. Schumpeterian view and neoclassical view brought up a conflict of interests (see Rodriguez-Pose, 2001). For instance, neoclassical theories was side to invest in R&D especially in peripheral and lagging Page | 5    

rural regions which can create economic convergence while Schumpeterian view – also von Thünen Model and core-periphery theories – highlighted the complexity of the connection of R&D and growth with a link to the spill-over effects from one region to the neighbouring regions. Alston et al. (2000) supported the importance of R&D investments and their increasing rate of returns in rural regions stressing on the paradoxical side of these effects. Rural areas are dynamic by being involved in different networks and offering a diffusion of knowledge, i.e. relocalisation of food, culture etc. being more innovative depends on location (Lundvall, 1992; Cooke et al., 1994). Employment growth of rural regions is positively associated with innovative activity in nearby metropolitan areas only if the metropolitan area is a highly active centre of innovation and entrepreneurship (Barkley et al., 2006). In contrast, late studies (i.e. McGranahan and Wojan, 2007; Gülümser et al., 2009) showed that the increase of creative class does not depend on the physical distance or R&D, but depends on the attractiveness capacity of rural regions and the motivation of entrepreneurs. Thus, there is no single sign of the association between physical distance and rural creative capacity but the reduction of the physical distance which can be obtained by the increase of the accessibility and different mode of transportation and also by the decrease of travel time and cost can be measured by the increase of individuals reaching the rural regions with their own abilities. The economic distance of rural regions is much related to the ability to use and to adapt communication technologies to the entrepreneurship in order to succeed in knowledge based economy (Keeble, 1993; North and Smallbone, 2000; Malecki, 2003; Cannarella and Piccioni, 2005). Although rural firms and entrepreneurs learn how to use the new ICT tool, they are not able to use it in their business due to the shortage of the dispersion of communication infrastructure in rural regions (Grimes, 2000). This has slightly changed in our era but this change is not valid all over the world. In other words, rural regions are not yet efficient beneficiaries of the ICT era, therefore they are still using the old generation of ICT, i.e. computer but not involved to the new generation technologies, and e-commerce widespread in the world. This situation creates an unfair competitive arena in the global market for the rural economy. According to the Keeble’s (1993) findings, rural and small town SMEs development is closely related to the growth in the wide economy of new specialised niche markets in which small firms can supply efficiently. Thus, while evaluating rural creative capacity, the widening of the market, the selling of local rural products to the outside can be a better measurement.

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The third type of distance - social distance - is a measure of the closeness between players in a strategic interaction and has recently been acknowledged to have a profound influence on individual decisions (see Akerlof, 1997). There are evidences that social distance matters more than physical distance and even more than economic distance in terms of cooperating and transferring knowledge while creating knowledge externalities (Autant-Bernard et al., 2007). Social distance is also fundamental for diminishing economic distance (See Granovetter, 1985 the phenomenon of embeddedness). Most of the European rural regions possess a very defensive localism in terms of accepting the new (Winter, 2003). Therefore, in rural regions, the acceptance of newcomers as well as new economic activities that can create and increase opportunities in terms of increasing human capital, innovation, adaptation and economic diversity, is one of the most important determinants of rural creative capacity with the continuity of the existing. Thus, instead of measuring the extension and density of social networks, to measure the openness and tolerance of rural inhabitants, in other words the ability to change the social networks and relations of the region. Here, we propose to measure the components of rural creative capacity by the changes occurring in rural regions to better estimate the capability of rural regions to be adapted into the new era. The next section evaluates the critical components of rural creative capacity with a special focus on 60 villages from Europe and 17 villages in Turkey.

3

The Critical Components of Rural Creative Capacity

In this study, we aimed to evaluate the critical components of rural creative capacity while focusing on the examples of rural settlements from Europe. In this section, we will analyse the creative capacity of selected rural regions in Europe, i.e. Belgium, France and Italy, and Turkey to identify the critical components of creative capacity.

3.1 Prefatory remarks: the survey Today, the changes occurring in rural areas have attracted much attention. Especially the demographic changes and their consequences have become the interest of both theoretical and empirical studies. On this basis, in this study, we investigated rural settlements by means of an in-depth questionnaire survey. We conducted our survey first in Europe between July and August 2008 in 60 member settlements of The Association of the Most Beautiful Villages in Belgium (Wallonie Region), France and Italy as the associations exist only in these countries in Europe. The reason to choose such an Association is to capture the Page | 7    

creativity of already successful rural settlements in terms of creativity. We administered a similar survey in Turkey between March and May 2009 in 17 rural settlements using a multistage sampling frame (see for further information Gülümser, 2009). The multi-stage sampling frame covers the variables for classifications related to the ones of the Association, i.e. rural characteristics, counterurbanisation and economic diversity. Therefore, Turkish rural regions included in our sample are relatively successful rural settlements in terms of attracting population and creating economic diversity. The questionnaire has four main parts: (1) general information; (2) the settlement’s environmental characteristics, viz. natural amenities, climate characteristics etc.; (3) nonlocal relations and connections; and (4) changes occurring in the last 10 years in the region. Parts 1 and 2 were designed to reveal the similarities and the differences of the characteristics of the villages, while Part 3 was designed to measure the attractiveness of villages, and Part 4 was designed to evaluate the changes occurring in the villages. When preparing the questionnaire, the aim was to highlight the perception of relevant experts, i.e. the administrative responsible of the villages, with a special focus on the changes occurring in rural regions. The Turkish questionnaire was almost the same as the one used for the European cases, except the second part, which is related to the environmental characteristics of the villages. The reason not to include this part related to the characteristics of the villages was that we visited all the villages thus we were able to answer these questions during the field surveys. The questionnaire of villages for Europe and Turkey are intrinsically the same however due to the operational definitions they differ in terms of partition and in terms of some additional information needed to be collected, i.e. the data on the association for the European case. The questionnaires were translated into French, Italian and Turkish in order to avoid any language problems. The survey in Europe was conducted via emails and out of 254 reached member villages only 60 responsible of the member villages have returned the questionnaire. In addition, in 17 Turkish villages, the questionnaire was conducted face-to-face by visiting all the villages. Due to the lack of internet use in Turkish villages, face-to-face interviews were a must to conduct this research. Although it can be thought that face-to-face questionnaire is different than the email questionnaire, during the data collection in Turkey no further explanation is provided to the interviewees to obtain a relevant comparative research. In the next sub-

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section, we explained how we retrieved our data from the questionnaires and the methodology applied to determine the critical components of rural creative capacity.

3.2 Data and methodology Capacity of a region means what exist and what can exist or be absorbed by a region. The creative capacity of a region is one of the basic identifier to reach success in sustainable development. Regional creative capacity and its components are mainly measured by returns and output of the innovative processes of which effectiveness and efficiencies depend on creative capacity (see for further details Gülümser et al., 2010). In other words, creative capacity of a region is usually measured by the visible strengths of regions rather than their intervening opportunities. But creative capacity of rural regions depends on the capability of region to be changed. Due to the multidimensionality of creative capacity, in this section, we aimed to generate an overall score for creative capacity, thus to define the relatively most important component of rural creative capacity. To reach our aim, Principal Component Analysis (PCA) is deployed in this section for the evaluation of data obtained from 60 European and 17 Turkish rural settlements, separately. PCA is used to transform the set of originally mutually correlated variables into a new set of independent variables (Hair et al., 1998). It is a non-stochastic approach and it only deals with the common variance of the original variables. It first derives the first factor or the first principal component, which is supposed to account for the greatest part of the common variance. The second factor is supposed to account for the next greatest part of the common variance, and so on. A minimum part of the common variance is set, and factors below this critical level must be eliminated. The relative lengths of the lines that express the different variable combinations are called critical values. The list of variables used in the analysis is given in Table 1. It is widely accepted in the literature that the creative capacity of a rural region is very much related to the changes occurring in them. Therefore, in order to calculate an overall score of creative capacity for each village, we used changes occurred in terms of the five components of the creative capacity. The data is retrieved from the questionnaire applied in the villages, thus the variables are ranging by the 5 level Lickert scale depending on the level of change as the data is collected so. The data obtained by questionnaire reflects the opinion of the responsible of the settlements. The reason to use collected data and changes is the lack of secondary data for Page | 9    

rural regions at settlement level. Thus, using stock rates or kind of data is not possible to obtain at a small scale of rural settlement.

Table 1 Variables used for creative capacity score in Europe and Turkey Component

Variable

Knowledge

Skill

Innovation

Technology

Entrepreneurship

Human capital

Creativity

Creative activity

Network

Economic distance Social distance Physical distance

Definition The increase in the use of traditional ways and skills in production The increase in the use of technology (i.e. daily use of computer and internet) The increase in the number of job opportunities The increase in the use of technology and skills in job The increase in the product sell to other cities Changes in social relations The increase in the private transportation

Range 1=very low; 2= low; 3=average; 4=high; 5=very high

Therefore, we accept that the increase in the use of traditional ways and skills in the production processes is related to the knowledge component which is related to the increasing importance and the continuity of tacit knowledge. In addition, the use of technology shows that the widely spreaded technology in today’s world is also accepted in rural regions and it leads to innovation and technology. For the component of entrepreneurship, we used the human capital not in the sense of skills but rather as the creation of job opportunities to be able to include the opportunities to create a human capital accumulation in the village. The lack of job opportunities and usually high unemployment rate including high elderly population rate in most European and Turkish rural regions showed that human capital widening is the visible contribution to the development in rural regions rather than increasing human capital skills which can follow this widening. The creativity component in the literature is usually equivalent to R&D activities or patents. But, we also mentioned that in the countryside most people do not register their innovative products, and also the R&D activities exclude the local knowledge in their process which may not contribute to the rural creative capacity. Thus, here, we measured creativity component as the increase of the combination of technology and local talent in economic activities. The last component ‘network’ is a complex component itself that we distinguish it with three types of distance, viz. economic, social and physical and included in our analysis each variable as a separate variable. Among these variables, as the European villages in our sample were accessible villages and the Turkish ones were remote, the ability to provide private transportation is taken into account as the physical distance variable. The next sub-section provides the results Page | 10    

of the PCA analysis applied for both European and Turkish cases, thus Section 4 compares the results about European and Turkish rural creative capacity.

3.3 Empirical evidence from European and Turkish rural regions Creative capacity is a multi-dimensional concept and includes both old and new resources for regional development. On this basis, it is difficult to calculate one single score which can explain it. Therefore, we applied the PCA which is a multi-criteria analytical tool that helps to reduce the number of variables in order to create a new component. According to the results of the analysis on European rural settlements, we obtained one single score that enabled us to explain 75 percent of the total variance. The factor scores of 60 European settlements range between -2.40 to 1.70 (see Appendix). Negative scores showed that the rural region is not changed in a positive way in terms of creative capacity that it could not benefit very well from its intervening opportunities. In addition, positive scores mean that rural region, compare to the other regions in our sample, is used more its intervening opportunities in order to increase its creative capacity. This shows that each settlement is unique in terms of their creative capacity and their capacity depends on the opportunities they have. In addition, the results of the PCA on Turkish rural settlements suggested that we can explain 84 percent of the total variance but this time with three components. Obtaining more than one component shows that Turkish settlements differ very much from each other by different components of creative capacity that generating one single score may lead us to lose information about the capacity of Turkish rural regions. Therefore, we calculated an overall score by a simple adding of the scores of three calculated components as the result of the PCA. As a result, we obtain one single creative capacity score for each Turkish rural settlement in our sample which changes between -2.58 and 4.43 (Appendix). While applying the PCA, our aim was not to generate a score to rank the settlements but it was more to show how different can be the capacity of each settlement and to determine the critical component(s) of the rural creative capacity. Therefore, instead of component scores, here, we use the communality of each variable to explain the rural creative capacity and its components. In other words, we use loadings of each variable while defining the complex structure of creative capacity. The extraction communality is the estimate of the variance in each variable accounted for by the components. The high level of communality indicates that the extracted components represent the variables well. Page | 11    

The communalities of each component to explain the creative capacity of rural settlements are mentioned in Table 2. The results suggest that the components of creative capacity in Europe and Turkey differ slightly from each other. However the critically important and less important components are the same. In other words, creativity is the most important component to determine rural creative capacity while physical network are the less important component.

Table 2 Communality of components in the creative capacity score Component of Creative Capacity

Variable

Creativity Innovation Network Knowledge Entrepreneurship Network Network

Creative Activity Technology Social Distance Skill Human Capital Economic Distance Physical Distance

Communality Europe Turkey 0.94 0.90 0.94 0.82 0.89 0.86 0.88 0.84 0.83 0.87 0.78 0.85 0.76 0.70

The opportunities in rural regions are very diverse Therefore, the local knowledge and the converting it into innovative activities are important in terms of capacity analysis but on the other hand, the results show also that rural regions in our sample whether they are located close to urban regions or not can have a high rural creative capacity. From an opportunistic perspective, it can be also stated that the creative capacity, in other words, the ability to attract so-called ‘creative class’ is not very associated with physical networks as creative class, most of the time, comes into rural regions because they are remote and undiscovered. Therefore, the undiscovered regions which are remote and located far beyond the relatively more developed centres may have more creative capacity and more beneficiary than the accessible ones. In the next section, we will discuss the results of our analyses while comparing the rural creative capacity of Turkish and European rural regions in detail.

4

A Comparative Evaluation: Turkish and European Rural Regions

In the previous section, we discussed the results of the PCA about the European and Turkish rural settlements separately. Therefore, the evaluation of different cases from Europe and

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Turkey – developed and developing countries – showed that the opportunities and capacity in rural regions differentiate from each other. The results on European settlements in our sample show that in order to explain rural creative capacity, the main variables are the creativity and innovation linked to the local characteristics of rural regions (Table 2). The following important variables are the social networks inside the village and its tradition. Moreover, neither economic distance nor physical distance is as important as the other components. The results of the analysis show also that the rural creative capacity in European villages in our sample depends on technology in daily life and its junction with the tacit knowledge. Furthermore, rural aspect remains as rural with the capacity of the area reflecting its rural uniqueness, i.e. social characteristics and traditions which are also relatively important components of rural creative capacity. The results of the analysis on Turkish settlements show that creativity in other words, to be able to meet technology and the local knowledge is the most important identifier of the rural creative capacity in Turkish villages. This is followed by the entrepreneurship component that most of the Turkish villages face still the difficulty to expose their economic opportunities and convert them into strengths. In addition, social networks, economic networks and local knowledge are relatively important identifiers of rural creative capacity of Turkish villages. Physical distance having the lowest communality suggests that neither the remoteness of a rural region nor to shorten the physical distance have a great effect on increasing or identifying the creative capacity. We cannot state that European and Turkish rural regions are absolutely different from each other. Although each region is unique, the general problems like the appearance of novelty and the reaction of rural regions to the innovative activities and also the importance of locality characteristics have a similar basis in both European and Turkish rural regions.

5

Concluding Remarks

The capacity of a region to be developed is measured by the capability to attract economic change agents that is related to the newly popularized concept, i.e. creative capacity. This is not a single dimensional concept but rather a multi-dimensional concept covering the five components, viz. knowledge, technology, entrepreneurship, creativity and networks. The focus of early regional creative capacity studies mainly focusing on urban regions, the underestimation of rural creative capacity by the application of urban specific methodologies Page | 13    

and the increasing attractiveness of rural regions in recent years have led us to come up with a rural specific evaluation about rural creative capacity. On this basis, in this study, after offering how to operationalize urban specific evaluations to measure rural creative capacity, we aimed to identify the critical factors of rural creative capacity with a special focus on 60 European and 17 Turkish rural regions by means of Principal Component Analysis (PCA). According to our recent literature review (Gülümser et al., 2010) and the late evaluation of rural creative capacity, in this study, we discussed the urban specific measurements and thus we come up with the argument that to measure the rural creative capacity by using the changes occurring in rural areas can give a better estimation. Therefore, we retrieved our data from the 60 questionnaires conducted in Europe and 17 questionnaires conducted in Turkey that we applied PCA in both cases separately. The results of the PCA analysis showed that the creativity is the most important components while the physical networks do not have an efficient impact on the rural creative capacity. While this was the overall conclusion of our evaluation, the results stated also that rural regions from Europe and Turkey are not very different although, their level of capacity especially in terms of innovation and economic networks as well as social networks differs from each other. For instance, Turkish rural regions are still suffering from the lack of technology thus it is relatively less important while determining the capacity of Turkish villages. On the contrary in Europe, innovation with creativity is the most important identifier of rural creative capacity. Due to the uniqueness of rural areas as well as their huge number in Turkey (more than 34000 villages) and in Europe, our sample remains relatively small. Therefore, this prevents us to prescribe strategies for the development of rural areas. However this is the case, our future research challenge is to apply this methodology to urban Europe and Turkey to see the comparative outcome of both rural and urban regions.

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Appendix 1. The dataset and Creative Capacity scores Village Lagrasse San Donato Val di Comino Gordes Morano Calabro Saint Lizier Fources Novara di Sicilia La Flotte-en-Re Gourdon Bova Bienno Volpedo Neive Zavattarello Fagagna Castel di Tora Geraci Siculo Civita di Bagnoregio Gradara Cusano Mutri Mombaldone Borgio Verezzi Castel del Monte Furore Saint Quirin Orvinio Tourtour Giglio Castello Stilo Sauveterre de Rouergue Oramala Sainte Agnes Vernazza Cutigliano La Roche-Guyon La Bastide Clairence Asolo Moresco Montsoreau Pettorano sul Gizio L'Aqu Bettona Le Bec Hellouin Saint Benoit du Sault Ars en Re Crupet Chardeneux Pietracamela Campo Ligure Navelli Mirmande Belves Montefioralle Canale Chiusa Roussillon Brisighella Massa Martana Ricetto di Candelo Buonconvento Offida Akçeşme Alballar Alhan Başköy Beldibi Çakallar Değirmenl Dereköy Emiraşikl Halil beş Kapakli Karaçakal Karahasan Mahdumlar Ovacik Ovapinari Susuz şah

Country France Italy France Italy France France Italy France France Italy Italy Italy Italy Italy Italy Italy Italy Italy Italy Italy Italy Italy Italy Italy France Italy France Italy Italy France Italy France Italy Italy France France Italy Italy France Italy Italy France France France Belgium Belgium Italy Italy Italy France France Italy Italy Italy France Italy Italy Italy Italy Italy Turkey Turkey Turkey Turkey Turkey Turkey Turkey Turkey Turkey Turkey Turkey Turkey Turkey Turkey Turkey Turkey Turkey

Creative Capacity 0.50 0.53 -2.40 0.00 -0.86 -0.16 0.06 0.76 0.59 0.31 0.42 0.42 0.62 -1.36 0.11 1.21 1.00 -0.99 0.32 0.46 -0.29 -2.40 0.96 0.91 -1.96 0.23 -0.17 0.04 -0.55 1.70 -2.40 1.70 0.71 -0.52 -1.58 0.54 -0.26 0.80 0.29 0.47 -2.40 0.18 0.19 0.39 0.07 0.14 0.65 -2.40 -0.19 0.79 0.98 0.04 -0.76 0.30 0.86 0.40 -0.49 0.99 0.28 0.21 -0.83 4.43 -1.26 0.45 0.21 0.56 0.24 -2.27 -2.58 2.22 -0.63 0.4 -2.15 -1.18 0.4 0.88 1.1

Tradition

Technology

3 4 0 5 2 2 4 4 3 4 4 4 4 2 4 5 5 2 4 4 4 0 4 5 0 4 3 5 3 5 0 5 4 2 1 4 4 5 4 5 0 4 3 4 3 4 4 0 3 4 3 3 2 5 3 3 4 4 4 3 5 5 1 3 1 3 2 2 4 5 4 4 2 4 1 5 5

4 4 0 2 2 3 3 4 4 4 4 4 3 1 4 5 4 3 3 4 3 0 4 4 0 4 3 4 2 5 0 5 4 2 1 4 4 4 3 4 0 3 3 3 3 2 4 0 3 4 5 3 2 3 4 4 3 4 3 3 2 5 4 1 1 5 3 1 5 4 1 4 1 4 5 2 2

Human Capital 4 4 0 3 2 2 2 5 3 3 4 4 4 1 3 5 4 1 3 4 2 0 5 5 4 2 3 3 1 5 0 5 4 2 1 2 2 4 4 2 0 3 3 4 4 4 3 0 3 5 5 1 2 3 4 4 2 5 3 4 1 1 1 1 1 1 2 2 1 1 3 2 1 2 1 4 1

Creative Activity 3 4 0 4 2 3 2 4 3 3 3 3 4 1 3 5 4 1 4 4 2 0 4 4 0 3 2 2 2 5 0 5 4 3 1 4 2 4 3 3 0 3 3 3 3 2 4 0 3 4 4 3 2 3 4 3 2 4 3 4 1 1 3 1 1 3 2 3 4 1 4 4 4 2 5 3 5

Economic Distance 4 3 0 0 0 3 2 4 5 3 3 4 4 1 2 0 4 1 3 2 3 0 4 5 0 2 1 3 3 5 0 5 4 3 1 4 2 4 3 4 0 2 3 4 2 2 4 0 3 2 4 4 2 3 4 4 2 4 3 4 4 5 2 1 1 1 4 3 2 4 3 4 1 1 5 4 2

Social Distance 3 2 0 3 2 3 4 3 3 3 3 3 3 1 3 5 4 1 3 3 2 0 3 4 0 4 3 1 2 5 0 5 5 2 1 3 2 4 3 5 0 3 4 3 3 4 3 0 1 4 3 3 2 3 4 3 2 4 3 3 1 1 0 0 0 1 0 0 1 1 0 1 0 0 1 0 1

Physical Distance 4 4 0 3 3 3 4 3 5 3 3 2 4 2 2 5 4 3 3 3 2 0 5 1 0 3 4 3 3 5 0 5 1 2 1 4 2 2 3 1 0 4 3 3 3 4 4 0 3 4 5 4 2 3 5 3 1 4 4 1 4 3 1 1 1 3 5 4 4 5 5 2 5 4 4 5 5

Page | 18    

2007-1

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2007-2

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2007-13

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Measuring the performance of Italian regions on social and economic dimensions, 20 p.

2007-14

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2007-15

Maria Teresa Borzacchiello Peter Nijkamp Eric Koomen

Accessibility and urban development: A grid-based comparative statistical analysis of Dutch cities, 22 p.

2007-16

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A framework for developing semantic differentials in IS research: Assessing the meaning of electronic marketplace quality (EMQ), 64 p.

2007-17

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Changing trends in rural self-employment in Europe, 34 p.

2007-18

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De ruimtelijke verdeling van economische activiteit: Agglomeratieen locatiepatronen in Nederland, 35 p.

2007-19

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2008-1

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2008-2

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2008-3

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2008-4

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Leadership in online knowledge networks: Challenges and coping strategies in a network of practice, 36 p.

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Regional development and monetary policy. A review of the role of monetary unions, capital mobility and locational effects, 27 p.

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2008-9

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2008-11

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2008-12

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2008-14

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A generalized goals-achievement model in data envelopment analysis: An application to efficiency improvement in local government finance in Japan, 24 p.

2008-15

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Peter Nijkamp Mediha Sahin

study on Amsterdam, 33 p.

2008-16

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2008-17

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2008-18

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2008-19

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2008-20

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2008-21

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2008-22

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2008-23

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2008-24

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2009-1

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2009-2

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2009-5

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2009-6

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2009-11

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2009-14

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2009-48

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2009-49

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2009-50

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2009-51

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2009-52

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2009-53

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2009-55

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2009-56

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2009-57

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2009-58

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2010-1

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2010-2

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2010-3

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2010-4

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2010-5

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2010-6

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2010-7

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2010-8

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2010-9

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2010-10

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2010-11

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2010-12

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2011-2

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2011-3

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2011-4

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2011-6

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2011-7

Amitrajeet A. Batabyal Peter Nijkamp

A Schumpeterian model of entrepreneurship, innovation, and regional economic growth, 30 p.

2011-8

Aliye Ahu Akgün Tüzin Baycan Levent Peter Nijkamp

The engine of sustainable rural development: Embeddedness of entrepreneurs in rural Turkey, 17 p.

2011-9

Aliye Ahu Akgün Eveline van Leeuwen Peter Nijkamp

A systemic perspective on multi-stakeholder sustainable development strategies, 26 p.

2011-10

Tibert Verhagen Jaap van Nes Frans Feldberg Willemijn van Dolen

Virtual customer service agents: Using social presence and personalization to shape online service encounters, 48 p.

2011-11

Henk J. Scholten Maarten van der Vlist

De inrichting van crisisbeheersing, de relatie tussen besluitvorming en informatievoorziening. Casus: Warroom project Netcentrisch werken bij Rijkswaterstaat, 23 p.

2011-12

Tüzin Baycan Peter Nijkamp

A socio-economic impact analysis of cultural diversity, 22 p.

2011-13

Aliye Ahu Akgün Tüzin Baycan Peter Nijkamp

Repositioning rural areas as promising future hot spots, 22 p.

2011-14

Selmar Meents Tibert Verhagen Paul Vlaar

How sellers can stimulate purchasing in electronic marketplaces: Using information as a risk reduction signal, 29 p.p

2011-15

Aliye Ahu Gülümser Tüzin Baycan-Levent Peter Nijkamp

Measuring regional creative capacity: A literature review for rural-specific approaches, 22 p.

2011-16

Frank Bruinsma Karima Kourtit Peter Nijkamp

Tourism, culture and e-services: Evaluation of e-services packages, 30 p.

2011-17

Peter Nijkamp Frank Bruinsma Karima Kourtit Eveline van Leeuwen

Supply of and demand for e-services in the cultural sector: Combining top-down and bottom-up perspectives, 16 p.

2011-18

Eveline van Leeuwen Peter Nijkamp Piet Rietveld

Climate change: From global concern to regional challenge, 17 p.

2011-19

Eveline van Leeuwen Peter Nijkamp

Operational advances in tourism research, 25 p.

2011-20

Aliye Ahu Akgün Tüzin Baycan Peter Nijkamp

Creative capacity for sustainable development: A comparative analysis of European and Turkish rural regions, 18 p.