ABSTRACT
In par ticu lar, tacit know ledge consists either in the cap abil it ies of an indi vidual actor to think and to act alone, or in his or her relational cap abil it ies in interacting with different actors, thus facilitating tight integration with these actors. These latter cap abil it ies have a col lect ive nature. In par ticu lar, the cap abil it ies of an indi vidual or of an organ iza tion may refer to that indi vidual’s or organ iza tion’s capabil ity to select and in ter pret in forma tion, cognitive frame and sys tem of value, attitude to risk taking and entre pren eurship, and creative and learning cap abil it ies. On the contrary, relational cap abil it ies consist in the “auto matic” co ordination between some actors, who react to external stimulus fol low ing specific “routines”, in the capabil ity to learn together through a pro cess of interactive learning and in the leadership and governance cap abil it ies neces sary for the organ iza tion of joint actions. In fact, tacit know ledge is related to the fact that actors may be capable of co ordinating their action with that of other actors when they react to external stimuli in an automatic way according to specific “routines” which have been interiorized, have often not been expli citly codified and are only based on ex peri ence. Moreover, through interactive learning pro cesses and building new con nections, actors learn how to learn together with other actors and jointly modify the rules of the learning pro cess and common schemes of in ter pretation of exter nal in forma tion. Tacit know ledge is also repres ented by the implicit esteem and power that indi vidual firms or entre pren eurs enjoy in the local business com mun ity, as the organ iza tional and managerial capabil ity to govern or steer the actions of other actors goes far beyond codified knowledge. The lim ita tion of the linear model does not consist only in overlooking various im port ant types of know ledge different from sci ent ific codified know ledge, such as engineering based tacit know ledge, but espe cially in the fact that it focuses on the pro cess of trans fer of know ledge rather than that of the genera tion of know ledge. R&D activity should not be con sidered as a black box trans forming inputs into outputs; neither is it the only pro cess for creating new know ledge. On the contrary, the generation of know ledge is ana lysed by cogni tive theories (Lundvall & Johnson, 1994; Nonaka & Konno, 1998; Loasby, 2002, 2003; Egidi & Rizzello, 2003; Metcalfe & Ramlogan, 2005), which explain know ledge and in nova tion as the result of an interacting learning pro cess occur ring in a network made by various actors. On the basis of these prin ciples, the model of “territorial know ledge manage ment” (TKM) identifies a logical and tem poral sequence of six phases and factors in the pro cess of interactive learning and in nova tion (Cappellin, 2003a, 2007; Cappellin & Wink 2009): external stimulus, accessibility, receptivity, identity, creativity and governance, as indicated in Figure 8.2. While these con cepts have indi vidually been extensively de scribed in the eco nomic liter at ure, they have not previously been linked together in a coherent model based on the cognitive science liter at ure. In fact, the external “stimulus” induced by the oppor tun ities of demand or the pressure of com peti tion, or change in technolo gies (Kline & Rosenberg, 1986; Fagerberg, 2005), determines a ten sion which leads to the search for a solution to the prob lems of the firm. This search pro cess
is facilitated by a lower geographical and/or organ iza tional distance or by a higher “accessibility” to potential com plement ary partners (Karlsson, 1997; Howells, 2002; Cappellin, 2004; Boschma, 2005; Simmie, 2005; Torre & Rallet, 2005). It also requires that these latter have a low cognitive distance or an appro priate “receptivity” or absorption capa city (Cohen & Levinthal, 1990; Antonelli, 2005). Then, the cre ation and strengthening of a common “identity” made up of common values, a sense of common belonging, trust relationships, social or rela tional capital and high institutional proximity (Capello, 1999; Crevoisier & Camagni, 2000; Nooteboom, 2000; Capello & Faggian, 2005) is the prere quis ite for coopera tion among firms and their search for joint solutions. These new solu tions are the result of “creativity” (Florida, 1995; Cappellin, 2003b; Wink, 2007), or of the capabil ity of the various local actors to combine different and com plement ary pieces of know ledge in an ori ginal manner and to interact between them in the framework of a col lect ive learning pro cess (Morgan, 1997; Maillat & Kebir, 1999; Kaiser & Prange 2004; Geenhuizen & Nijkamp, 2006). Finally, these new ideas can be translated into eco nomic in nova tions only when appropriate organ iza tions and institutions or “governance” (Powell, 1990; Cooke & Morgan, 1998) promote the com mit ment of appropriate real resources and fin an cial funds, and enhance the integration of the new ideas with com plement ary production capabilities. Moreover, in nova tion leads to a pro cess of learning and the de velopment of the cap abil it ies of the indi vidual actors, thus improving the various factors indi cated above. Finally, in nova tion of a firm is going to change the external selec tion envir on ment for other firms, and it may represent the stimulus to in nova tion for them. This indicates that in nova tion and learning are dynamic and cumula tive pro cesses occurring in a regional or national in nova tion sys tem and also across regions and countries. This sequence of relationship appears most clear in the case of medium technology sectors, such as auto mo bile production or ma chine tool production. In these sectors the production pro cess is or gan ized in a complex supply chain, and the “stimulus” to in nova tion derives both from clients and from special ized
suppliers that are located (“accessibility”) in the same geographical area or belong to the same sector or network. The pro cess of co makership leads to a form of interactive learning, as the cognitive distance (“receptivity”) between the firms is very low and the cre ation of new solutions (“creativity”) emerges through various forms of coopera tion, which are facilitated by industry asso ci ations and other inter medi ate institutions (“identity”) and are or gan ized in the framework of pro jects steered by various institutions and ad hoc organ iza tions through contractual ar range ments (“governance”) linking the various firms. However, a very sim ilar pro cess of cognition and inter action between various actors can be identified even in high technology productions and in R&D pro grammes, and the systemic-cognitive model of in nova tion can also be applied to these sectors. The sys temic-cognitive model of in nova tion differs from the more traditional Regional Innovation System approach (Cooke & Morgan, 1998; Cooke et al., 2003) in its expli cit ref er ence to the cognitive liter at ure. It allows estab lishment of a logical relationship, as indicated in Figure 8.2, between the various factors that determine the cre ation of know ledge and are coalesced in an unpre dict able manner in the Regional Innovation System approach, which rather focuses on the trans fers of given technologies. In par ticu lar, the sys temic-cognitive model of in nova tion, by focusing on the dynamic and cumulative pro cess of inter action between the various factors of the cognitive and learning pro cesses, overcomes the prevailing static nature, based on the actual spatial and social structure of the territory, of the inter actions and of the flows of tech no logy between the various actors in the Regional Innovation System approach and in some respects also in the Innovative Milieux approach (Capello, 1999; Crevoisier & Camagni, 2000). Moreover, the sys temic-cognitive model may be applied to explain not only the geographical clusters but also the sectoral clusters, which overcome the regional boundaries. Therefore, a rad ical shift of per spect ive is needed from the concept of tech nologies, R&D expenditure and rational pro cess of optimization of indi vidual firms to a new per spect ive focused on the pro cesses of know ledge cre ation and learning within networks, and on the de velopment of the in ternal cap abil it ies of firms and actors. In par ticu lar, the sys temic/cognitive model underlines the im port ance of three concepts: (1) connectivity, (2) creativity and (3) speed of change (Cappellin, 2003b; Cappellin & Wink, 2009).
