Foreign direct investment through acquisitions and implications for technological upgrading: case evidence from Tanzania

Using a case study approach, this paper examines the role of privatisation on the industrial landscape of Tanzania. We examine the impact of foreign direct investment (FDI) through acquisitions on technology transfer within the acquired firm as well as the development of linkages to other firms based in the host country. Our results suggest that technological upgrading has occurred following FDI, the intensity of which reflects the type of firm-specific assets of the parent multinational enterprise (MNE), as well as the pre-acquisition state of these industrial activities. Improved backward linkages are also evident with local economic agents, but their type and extent – reflecting Tanzania's comparative advantage in the primary sector – confirm that capabilities both within the acquired firms and also in the industrial base of the host country greatly influence the magnitude and intensity of technological upgrading. 'Narrower' technology gaps between the MNE affiliate and the domestic sector are more likely to result in backward linkages and determine the type of technological content of inputs sourced locally rather than within the MNE.

Collaborative eLearning Assistant Network: caring agents are conscious agents

Would a research assistant - who can search for ideas related to those you are working on, network with others (but only share the things you have chosen to share), doesn’t need coffee and who might even, one day, appear to be conscious - help you get your work done? Would it help your students learn? There is a body of work showing that digital learning assistants can be a benefit to learners. It has been suggested that adaptive, caring, agents are more beneficial. Would a conscious agent be more caring, more adaptive, and better able to deal with changes in its learning partner’s life? Allow the system to try to dynamically model the user, so that it can make predictions about what is needed next, and how effective a particular intervention will be. Now, given that the system is essentially doing the same things as the user, why don’t we design the system so that it can try to model itself in the same way? This should mimic a primitive self-awareness. People develop their personalities, their identities, through interacting with others. It takes years for a human to develop a full sense of self. Nobody should expect a prototypical conscious computer system to be able to develop any faster than that. How can we provide a computer system with enough social contact to enable it to learn about itself and others? We can make it part of a network. Not just chatting with other computers about computer ‘stuff’, but involved in real human activity. Exposed to ‘raw meaning’ – the developing folksonomies coming out of the learning activities of humans, whether they are traditional students or lifelong learners (a term which should encompass everyone). Humans have complex psyches, comprised of multiple strands of identity which reflect as different roles in the communities of which they are part – so why not design our system the same way? With multiple internal modes of operation, each capable of being reflected onto the outside world in the form of roles – as a mentor, a research assistant, maybe even as a friend. But in order to be able to work with a human for long enough to be able to have a chance of developing the sort of rich behaviours we associate with people, the system needs to be able to function in a practical and helpful role. Unfortunately, it is unlikely to get a free ride from many people (other than its developer!) – so it needs to be able to perform a useful role, and do so securely, respecting the privacy of its partner. Can we create a system which learns to be more human whilst helping people learn?