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perspective that is strictly related with knowledge sharing dynamics. In this framework,
social dynamics plays a crucial role in the process of diversii cation of the i rms in an
evolutionary perspective. According to our empirical i ndings, social paths seem more
inertial when compared to knowledge development trajectories.
In an inter-organizational structure without formal hierarchy - such as a local network
among independent agents - reputation and trust may represent a crucial issue in order
to explain i rm evolution. We have found that good reputation can over time polarize
knowledge l ows towards a i rm and inl uence its evolution pattern, thus improving its
strategic positioning inside the network structure (in terms of centrality). It is an agreed
opinion that reputation is a strategic asset (Dentchev and Heene, 2003) and that it can
be easier for i rms with a positive reputation to build and maintain networks based on
reciprocal trust (Noorderhaven et al., 2002).
These connections give a sustainable competitive advantage to the actors in the local
network because it is unique and impossible to imitate and because they shape the inter-
nal accumulation of (intangible) resources (Leana and Van Buren, 1999). According to
our results, it is important to adopt a network structure perspective in order to develop
local policies. The aim is to create a 'learning local environment' that supports evolu-
tion and diversii cation of (inter-)organizational routines based also on the appraisal of
social heterogeneity (Asheim and Cooke, 1999; Boschma and Lambooy, 1999). These
propositions lead to a rethink of local development policies by enhancing both i rm- and
network- specii c resources, by considering both business and social variety. A recom-
mendation arising from our research is that managers, entrepreneurs and local institu-
tion administrators should improve their ability to know, to read and to interpret social
networks and their trajectories, which means recognizing i rm dif erences too.
Notes
*
This chapter is the outcome of a strict collaboration between the three authors. However, A. Zucchella
wrote sections 1 and 2, S. Denicolai wrote sections 3 and 4, and G. Cioccarelli wrote section 5.
1.
For instance, see Chapters 7, 11 and 15.
2.
Founded in 1969, 'Mori' is a growing market and public opinion research agency. See http://www.mori.
com/reputation/.
3.
Because of the redemption rates, the study of centrality degrees refers to the related sub-networks within
the tourism cluster as a whole.
4.
The questionnaire reported a set of key dei nitions about the critical notions used, in order to guarantee a
shared comprehension among all respondents.
5.
The data processing was developed with 'SAS Enterprise Miner'. The p -value refers to the correlation index.
6.
According to the research model in Figure 13.1, the aim of the datamining process was to check only
the bidirectional correlation between trust - network-specii c resources - and reputation heterogeneity -
i rm-specii c resources - instead of proposing a regression model for reputation estimation, which implies
the identii cation of dependent and independent variables.
References
Almeida, P. and B. Kogut (1999), 'Localisation of knowledge and the mobility of engineers in regional net-
works', Management Science , 45 (7), 905-17.
Aringhieri, R., E. Damiani, S. De Capitani Di Vimercati, S. Paraboschi and P. Samarati (2006), 'Fuzzy tech-
niques for trust and reputation management in anonymous peer-to-peer systems', Journal of the American
Society for Information Science and Technology , 57 (4), 528-37.
Asheim, B.T. and P. Cooke (1999), 'Local learning and interactive innovation networks in a global economy',
in Edward J. Malecki and Päivi Oinas (eds), Making Connections: Technological Learning and Regional
Economic Change , Aldershot: Ashgate.
Barber, B. (1983), The Logic and Limits of Trust , New Brunswick, NJ: Rutgers University Press.
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