This is the accepted version of the paper.This version of the publication may differ from the final published version. the relevance of the creative industries, a high growth sector, has been pointed out as long as its strong and positive effects on jobs and economic growth. In the current context of rapid globalization and technological development, the innovation system is getting even more complex because it implies a shift in research focus from the supply to the demand side environment (consumption-driven economy). Permanent repository linkThe authors focus on theoretical approaches coming from management and media studies able to explain the current paradigm shift in innovation and knowledge production and use: the Triple Helix model (and its developments) and Systems Theory. As an interesting case study, the Creative Enterprise Australia (CEA) is analyzed according the theoretical approaches shown. The paper tries to shed new light on the evolving role of knowledge pointing out the overlapping relationships between all the actors involved and the interpenetration of systems, and the prominent appointment of the media as an interpretative framework of the convergence of the depicted theories.

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Goal programming (GP) is an important class of multi-criteria decision models widely used to analyze and solve applied problems involving conflicting objectives. Originally introduced in the 1950s by Charnes et al. (Manag Sci 2:138-151, 1955) the popularity and applications of GP has increased immensely due to the mathematical simplicity and modeling elegance. Over the recent decades algorithmic developments and computational improvements have greatly contributed to the diverse applications and several variants of GP models. In this paper we present a state of the art literature review on GP applications in three selected (prominent and popular) areas, namely engineering, management and social sciences. Forthcoming in Annals of Operations ResearchAbstract Goal programming (GP) is an important class of multi-criteria decision models widely used to analyze and solve applied problems involving conflicting objectives. Originally introduced in the 1950s by Charnes et al. (1955) the popularity and applications of GP has increased immensely due to the mathematical simplicity and modeling elegance. Over the recent decades algorithmic developments and computational improvements have greatly contributed to the diverse applications and several variants of GP models. In this paper we present a state of the art literature review on GP applications in three selected (prominent and popular) areas, namely engineering, management and social sciences.

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Venture capital has proven to be an essential resource for economic growth, especially in some technological clusters. The focus is on the way the venture capitalist makes the investment decision and the portfolio selection. The aim of this paper is to formulate the venture capital investment problem through the Goal Programming model where the Financial Decision-Maker's preferences will be explicitly incorporated through the concept of satisfaction functions. The proposed model will be illustrated by using data from an Italian venture capital fund.

We add stochastic technological progress, modelled as a geometric Brownian motion with drift, to an augmented Uzawa–Lucas growth model. Under a particular combination of parameters we derive a closed form solution to the model and analytical expressions which show that uncertainty reduces the optimal levels of consumption and increases the proportion of human capital devoted to producing new human capital