Browsing by Author "Mafunda, Dugushilu"

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    Development of an integrated innovation linkage model for assessing innovation system using regression- matrix techniques: the case of SIDO-SMEs` Network in Tanzania
    (Unversity of Dar es Salaam, 2008) Mafunda, Dugushilu
    Literature on innovation system shows that innovation activities are costly and risky.literature further shows cost and risks of innovation activities are high between product design and prototype development. Without taking account of peculiarities of SMEs innovation activities, SMEs are bound to stagnate, reverse or fail to grow. Attempts have been done to mitigate such systems. However, assessment of such system in in developing countries has been limited by adoption of various western models which most of them are dependent on input-output value data.The innovation linkage attributes could be one of the multifaceted efforts to assess innovation linkages as they are costly and time effective and risk free. However, various methods of assessing innovation systems using innovation linkages are done without alienating innovation activities from innovation linkage attributes. They are also unable to tell precisely how the effects of innovation activities influence the innovation system perfo1•1nance. The study identifies and accounts for the type and nature of system linkage attributes and their contribution to technology changes in the system using An Integrated Innovation Linkage Model. The Model combines the interaction of innovation system components whose linkage attributes to the system are interpreted using regression-matrix techniques. Using a sample of 104 SMEs from SIDO Network, the study establishes that SIDO Network is characterized by innovation linkages which are attributed by education, knowledge and skills and training. The study further establishes that innovation linkage attributes contribute to improving the SIDO innovation system performance, though with weak predictive technology factor (Tf) of 1.28,1.42 and 1.58(in a scale of between 0.9) for education, knowledge and skills and training respectively. The measure of technology charge in form of the technology factor (Tf) which is predictive can be used as a tool for assessing technological innovation systems.