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The paper explores the process of early growth of entrepreneurial science-based firms. Drawing on case studies of British and Dutch biopharmaceutical R&D firms, we conceptualize the speed of early growth of science-based firms as the time it takes for the assembly (or combined development) of three types of critical resources - a functionally-diverse management team, early fundraising and development of technology. The development of these resources is an unfolding and interrelated process, the causal direction of which is highly ambiguous. We show the variety of paths used by science-based firms to access and develop these critical resources. The picture that emerges is that the various combinations of what we call "assisted" and "unassisted" paths combine to influence the speed of firm growth. We show how a wide range of manifestations of technology development act as signaling devices to attract funding and management, affecting the speed of firm development. We also show how the variety of paths and the speed of development are influenced by the national institutional setting.
Author supplied: In a production environment where different products are being made in parallel, the path planning for every product can be different. The model proposed in this paper is based on a production environment where the production machines are placed in a grid. A software entity, called product agent, is responsible for the manufacturing of a single product. The product agent will plan a path along the production machines needed for that specific product. In this paper, an optimization is proposed that will reduce the amount of transport between the production machines. The effect of two factors that influence the possibilities for reductions is shown in a simulation, using the proposed optimization scheme. These two factors are the redundancy of production steps in the grid and the
Tinto’s integration theory has highly influenced research on student success in Europe and America. However, due to the complexity of the theory and the enormous amount of variables, the theory is not suitable for use in regular evaluations in higher education.By including only the best-proven predictive variables, I reduced the amount of variables from Tinto’s theory, avoiding the capitalization of chance and establishing a more easy to use model for teachers and management. The latent variable ‘satisfaction’ was built by using a fraction of the original manifest variables. It was tested, using principal component analysis, in a previous study to prove a good fit of the model. In this paper I focus on the role of background variables (gender, ethnicity, previous education and living situation), to measure their possible influence. A multi-group comparison (X2 difference test) in SPSS AMOS is conducted and path analysis is done to uncover differences on individual paths between the variables.This paper is part of my PhD research, wherein I investigate the possible influence of the use of social media by first year students in higher education.