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knowledge internalization and absorptive processes. As concerns knowledge search and collection processes, one
can assess the geographical as well as the social dimensions of a cluster’s’ knowledge management activities. First,
knowledge search should aim at both internal and external knowledge sources. Pavitt (1984) demonstrates the
importance of internal technological knowledge, as opposed to external technologies by citing that “59 percent of
3013 significant innovations in the United Kingdom from 1945 to 1979 were based on knowledge from within
innovating firms” (cited in Garud and Nayyar 2004: 138). The literature has shown that the external environment
(e.g. customers, suppliers, universities etc) within which a firm or cluster exist plays an important role in its
innovation capability. Secondly, one can further investigate the social dimension of knowledge search, i.e. whether
such activities occur by informal (day to day activities, friendships etc) or formal (formal networking, competitor
analysis, alliances, collaborative projects, mergers and acquisitions, environmental scanning techniques etc)
mechanisms. For example, Ingram and Robert’s (2000) study on friendship in the context of the Sydney hotel
industry shows how cohesive networks of competing managers have a positive effect on hotel performance, these
informal friendships among competitors benefit organisations through collaboration, the mitigation of competition,
increased information exchange (Uzzi 1996) and encourage a level of conformity to group norms and central
tendencies. Informal friendship networks appear most effective when cohesive in nature (Ingram and Roberts 2000)
as opposed to the higher performing non-redundant networks as put forward in Granovetter’s (1995) ‘strength of
weak ties’ theory.
Because of the above, the position of a cluster within the economy as well as its links with the external
environment can crucially affect the amount and quality of information it can collect. Indeed, Tsai (2001) found that
better performance can be achieved if organizations occupy central network positions, as the latter provides them
access to knowledge created by other firms (Kalling, 2003). In investigating stocks and flows of organizational
knowledge within the biotechnology industry, Decarolis and Deeds (1999) concluded that a firm’s geographical
location can reliably predict its performance, as geographical location can either hinder or foster knowledge
diffusion and the ability of the firm to capture knowledge (Kalling, 2003). In other words, depending on the
relational links and centrality of a tourism cluster within a destination, firms have different levels of accessibility to
knowledge regarding its quantity, quality, timeliness and exclusivity. The more knowledge a cluster has access to,
the more NPD capabilities the cluster can further develop. Overall, the cluster’s centrality and relations (internal and
external, formal and informal) determines’ its identification and assimilation capacity (i.e. its potential absorptive
capacity that it enhances its ability to process further knowledge and create new products.
Furthermore, the success of knowledge networks and clusters are likely to be due, in part, to the enhanced
absorptive capacity attributed to collaborating entities with similar knowledge bases (Cohen and Levinthal 1990),
the perceived ease of mobility ascribed to knowledge flowing within cohesive and non-redundant networks (Maskell
2001), as well as a reduction in the ability to imitate knowledge resources due to the idiosyncratic nature of network
creation and development (Andersson et al 2002). In other words, the cohesion of the cluster and its inter-cluster
social links and culture play an important role for its innovativeness and competitive capacity. The more cohesive
the cluster is, the more actors are connected between them, the more trust and social norms will develop, and the
more efficiency in coordinating and controlling the collective actions developed by the group. Thus, the more
embedded in the local environment the actors in the cluster are, the lower the transaction costs of the collective
activities of transforming the available knowledge and exploiting it in the form of NPD. In other words, cohesion of
clusters increases their transformation and exploitation capacity, i.e. the realized absorptive capacity (Zahra and
George, 2002).
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