PhD Thesis
Research and development (R&D) organisations in Tanzania use old systematic design
models that focus on the mere agro-technologies prototypes development, instead of
innovation of agro-technology for diffusion. A serious gap exists in the incorporation of the
agro-technologies diffusion factors in design models in R&D organisations in Tanzania.
The twin valley of technology death describes the technology development failures based
on business vision disregard. Technology prototypes or services are developed, though are
not linked to business setup, and that they don‟t get ripe to earn money through commercial
sales. This study identified that there is no customised model for agro-technology diffusion
in research and development organisations in Tanzania. Structured questionnaires,
interview with R&D organisation staffs and stakeholders and observation of activities in
these R&D organisations were used to collect data from sources identified. Literatures on
engineering design, technology development for diffusion and various models for
innovation were studied. The factors that were linked with agro-technology were identified
and their related variables and hence the model was developed, that proved to be useful in
guiding technology developers in ensuring the good final diffusion of technologies to
above 95% significant level. Regression analysis and system dynamic model development
and analysis were used to organise identified factors into agro-technology innovation
diffusion model. The model was calibrated and validated using data collected from various
R&D organisations in Tanzania between the year 2011and 2013. Factors that were included
in the model are: relevance of needs identification, need identification, interpretation of
variable into design specification, agro-technology validation process, agro-technology
information generation and proper agro-technology packaging and agro-technology
development stages importance. These factors were found to affect agro-technology
diffusion at a rate between 10 and 65%. It was noted that the development of technology
for diffusion is more than the prototype development. By using the model with its useriii
interface provides guidance to agro-technology developers that the control of innovation
diffusion is above 95% confidence interval. However further work to improve the model
especially on time adjustment and other socioeconomic factors like human resource
requirement, fixed capital and R&D organisation rationalisation in Tanzania that has to be
done
Dar es Salaam Institute of Technology (DIT)