Corporate impression formation in online communities - determinants and consequences of online community corporate impressions

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The purpose of this study is to gain in-depth knowledge of how the members of online communities form impressions of organisations that use online communities in their communication activities. Online impression formation has its peculiarities and in order to succeed companies need to better understand this phenomenon. In order to appreciate and evaluate an interaction, those involved in it must know their own identity. Hence, individuals as well as companies engage in identity production by trying to project a favourable impression. The process of identity production can take place in both the offline and the online world. This study focuses on the online world, more specifically on online communities, by investigating how online community members form impressions of companies that produce their identities in online communities. Technology has changed customer behaviours dramatically. People have embraced the Internet to meet and interact with one another. This behaviour is in line with the postmodern assumption that there is a movement towards re-socialisation. Online communication platforms connect people globally and give them the possibility to interact and form online social networks. These platforms are interactive, and thus change the traditional way of communication. Companies therefore have to embrace those interactive ways of communication. In the online world consumers are quick to react to communication weaknesses. Inappropriate corporate communication activities can affect the image they have formed of the company in question

Management of Indian Ocean tuna

Depuis 1983, la pêche thonière à la senne a connu un développement spectaculaire dans l'Océan Indien. Presqu'exclusivement centrée sur les Seychelles, cette pêcherie est pour ce pays une excellente opportunité; encore faut-il qu'une bonne gestion assure la pérennité de l'exploitation de ces ressources. Le développement de la pêche à la senne est encore trop récent pour obtenir de bonnes évaluations des stocks à partir des méthodes classiques. Néanmoins, à l'aide des connaissances déjà acquises dans l'Océan Indien et l'Océan Atlantique, une première approche de l'importance de ces ressources est réalisée. Des deux stocks concernés, le listao considéré comme une espèce opportuniste, peut accepter une exploitation intensive moins contrôlée que celle de l'albacore. Il semble que le ou les stocks d'albacore de l'Océan Indien puissent supporter un accroissement des prises, mais compte tenu des nombreuses incertitudes, il paraît raisonnable de maintenir l'effort de pêche des senneurs à des niveaux proches du niveau actuel. (Résumé d'auteur

A Markov state modelling approach to characterizing the punctuated equilibrium dynamics of stochastic evolutionary games

Stochastic evolutionary games often share a dynamic property called punctuated equilibrium; this means that their sample paths exhibit long periods of stasis near one population state which are infrequently interrupted by switching events after which the sample paths stay close to a different population state, again for a long period of time. This has been described in the literature as a favorable property of stochastic evolutionary games. The methods used so far in stochastic evolutionary game theory, however, do not fully characterize these dynamics. We present an approach that aims at exposing the punctuated equilibrium dynamics by constructing Markov models on a reduced state space which approximate well this dynamic behavior. Besides having good approximation properties, the approach allows a simulation-based algorithm, which is appealing in the case of complex games

Constructing Markov State Models of Reduced Complexity from Agent-Based Simulation Data

Agent-based models usually are very complex so that models of re- duced complexity are needed, not only to see the wood for the trees but also to allow the application of advanced analytic methods. We show how to construct so-called Markov state models that approximate the origi- nal Markov process by a Markov chain on a small finite state space and represent well the longest time scales of the original model. More specif- ically, a Markov state model is defined as a Markov chain whose state space consists of sets of population states near which the sample paths of the original Markov process reside for a long time and whose transition rates between these macrostates are given by the aggregate statistics of jumps between those sets of population states. An advantage of this ap- proach in the context of complex models with large state spaces is that the macrostates as well as transition probabilities can be estimated on the basis of simulated short-term trajectory data