1. INTRODUCCIÓN
1.5 VARIACIÓN DEL MOR Y MOE DE LA MADERA
From a systemic perspective, innovation is not an isolated activity, but part of a complex “socio-economic” system, in which a group of private firms, public research institutes, and several other facilitators of innovation interact within an institutional framework (Beije, 1998, cited in Schrempf, Kaplan, & Schroeder, 2013). Therefore, innovation is determined not only by the internal factors discussed above, but also by external factors such as boundaries of the system, and institutions. The existing literature identifies geographic proximity and cultural policy as two major determinants of innovation in the museum sector. Geographical proximity
Innovation literature emphasizes the importance of geographic factors in the system of innovation by addressing the potential relationships between regions, clusters, and innovation demonstrated in the empirical observation. On the one
hand, innovators are geographically concentrated (Breschi & Malerba 1997); on the other hand, innovative activities are embedded in regional and local systems, based on clusters (Porter 1990; Porter 1998), such as biotechnology and ICTs in “Silicon Valley” in California, or new media in Hollywood, Los Angeles and “Silicon Alley” in New York (Cooke & Memedovic 2003). A similar phenomenon is also visible in the museums sector. A good example is the so-called Golden Triangle of Art of the Prado Museum, the Reina Sofı́a Museum, and the Thyssen- Bornemisza Museum gathered around the Paseo de Prado in the historical center of Madrid. Another example is Berlin’s Museum Island embracing five world- renowned museums on the banks of the River Spree in the heart of Berlin.
Based on such observations, scholars tend to attribute local innovation and economic development to geographical proximity. Malmberg and Maskell (1997) pointed out that product innovation, new forms of organization or new skills are involved in interactive processes within industrial systems embedded in a broader and space-based cultural and institutional context, whilst shared spatial embeddedness such as proximity, affinity and trust, in turn, contribute profoundly to the success of innovation. They further argued that the modern development of transportation and telecommunication could not replace the persistent, regular and direct face-to-face contact on which information and knowledge exchange are based. Therefore, the more tacit knowledge is involved, the more important is geographical proximity between actors who partake in the interaction. Generally, the shorter the spatial distance between participants, the less costly and smoother is the interactive collaboration, and the more probable that innovation succeeds.
Perhaps proximity matters in arts and cultural organization because cultural innovation and production are greatly reliant on symbolic knowledge (Asheim & Hansen 2009) that is embodied in the arts and humanities knowledge and skills, which are deeply tacit and must be accumulated and transferred gradually between individuals (Bakhshi et al. 2008). In the case of museums, both arts and humanities research as well as the development of new cultural products, and the adoption of external technologies require frequent interaction with suppliers (e.g. high-tech companies and universities) and users (e.g. visitors and community), which can grow in intensity if these suppliers and users are
close to the museum geographically. This explains to some extent why many arts and cultural organizations, such as Italian art restoration firms, display positive correlations between the number of innovation and the distance to their suppliers/distributors as well as the extent of the collaboration with universities and research centers (Verbano et al. 2008).
On the basis of the above, we may propose that geographical proximity is positively correlated with the extent of innovation in museums; the closer a museum is to relevant researcher centers or technological suppliers, the more it engages in innovative activities.
Cultural policy
The impact of institutions on innovation can be explained with two arguments. From the micro perspective, institutional scholars view innovative activities, like R&D, as an institutionalized category of organizational activity that has meaning and value in many sectors of society (Meyer and Rowan 1977); therefore, decision-making with regard to innovation within the firm is determined by institutional factors (Hatimi 2003). On the basis of Scott's (2001) institutional framework, empirical studies were conducted to test the relationships between institutional forces and innovation. The results demonstrated that regulative, normative and cognitive institutions contributed to the choice of innovation and performance of various items from different individuals, organizations and sectors (Shane 1993; Shane et al. 1995; Berrone et al. 2007; Alexander 2012; Lee & Pan 2014).
From the macro perspective, Schumpeterian scholars place the emphasis on the institutional network in the production and innovation systems, in particular the National System of Innovation (AÓlvarez & Marı́n 2010). This network of institutions embrace the whole complex of factors ranging from industrial policy and science policy to basic education, industry structures, taxation systems and wage incentives, which shape a series of interactions within the system such as the inter-firm cooperation in research clubs, the integration of research, design and production in cooperative relations between the divisions within a firm, or the firms within a keiretsu (Dore 1988). Through a series of comparative studies on industrialized counties, these scholars arrived to the
conclusion that it was favorable institutions that benefited industrial competition and economic performance through innovation at national, regional, or sectorial levels (Freeman 1987; Lundvall 1992; Nelson 1993; Breschi & Malerba 1997; Cooke et al. 1998)
Cultural economics literature concerns itself with institutional forces of museum innovation by concentrating on the impact of different modes of cultural policy on technological and organizational innovations in museums across countries. In detail, cultural policies related to museums can be classified into the continental European model and the Anglo-Saxon model in terms of the nature and extent of governmental intervention in the cultural management, and in terms of the role that the State plays in terms of funding (Vicente et al. 2012). Museums under the Anglo-Saxon model (e.g. British museums) enjoy a high degree of managerial and financial autonomy, as well as multiple sources of funding, whilst museums under the continental European model (e.g. French museums) are controlled to a large extent by the government at various levels and rely mostly on public funding.
An empirical study shows that there is a significant difference in the degree of museum innovations in European countries, among which the British museums exhibit the highest level of innovation in both, the technological and organizational domains whilst the French museums show the lowest (Vicente et al. 2012). Lusiani and Zan (2010) gave particular emphasis to the high degree of autonomy in managerial decision-making and budgeting of surplus funding as a necessary condition for the success of organizational innovation in the cultural sector. These findings may suggest that a favorable cultural policy for innovation could be benefit from reduced governmental intervention and increased organizational jurisdiction in both management and finance.