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dc.contributor.authorKuuya, Patrick Masette
dc.date.accessioned2016-03-01T14:17:40Z
dc.date.available2016-03-01T14:17:40Z
dc.date.issued2015-10
dc.identifier.urihttp://ir-library.ku.ac.ke/handle/123456789/14311
dc.descriptionA thesis submitted to the school of humanities and social sciences in partial fulfillment of the requirements of the Degree of Doctor of Philosophy in Economics of Kenyatta University.en_US
dc.description.abstractThe government of Kenya, assisted by donor agencies, has since the 1980s, sought to enhance workers‘ productivity in the informal sector. To achieve this objective, the strategy adopted was to replace the use of archaic indigenous and inappropriate imported technologies with locally designed and produced adapted technology. Although since its inception in 1979,KIRDI has been adapting imported technology for use by entrepreneurs in the informal sector, the rate of adoption has been low. The main objective of this study was to investigate factors that influence adoption of adapted technology in Kenya‘s informal sector. The study adopted a logistic model, which, with the help of descriptive statistics, was used to analyse data collected from 320 respondents from six districts, two each from the Nairobi, Kisumu and Nyeri counties. The study‘s descriptive statistics showed that perceived usefulness of the technology, perceived ease of use of the technology, perceived reliability of the technology to the adopter, and the perceived suitability of the technology to the Jua Kali environment factors were important influences on the decision to adopt adapted technology. The results also showed that the overwhelming majority of adopters of adapted technology got information about the adapted technology through the interpersonal contact communication channel. The regression on the 13 predictor variables was done to get marginal effects. The regression results showed that six out of seven technology-specific variables in the logit model were important in explaining the decision to adopt adapted technology. These were: cost of machine; machine‘s maintenance cost; number of workers needed to operate the machine; wage bill of the workers operating the machine; cost of energy; and perceived conferment of higher social status (in the local community) to the owner. The regression results also showed that three out of five human factor variables, individually, made a significant difference in the decision to adopt adapted technology. These were: age of adopter; level of education; and marital status of the adopter. These regression results implied that government institutions and donor agencies involved in designing and producing adapted technology should ensure that the adapted products were cheaper and easier-to-use than their non-adapted imported substitutes; required low servicing and maintenance cost; and should not be labour intensive, but be perceived to be attractive to the adopters who wished to be self employed. The results also showed that high wages of workers were a disincentive to adoption. This implied that the government should avoid extending the mandatory minimum wage requirement to entrepreneurs in the informal sector. In addition, the results implied that the government and donor agencies should target the youth and women groups when promoting adoption of adapted technology; and that special emphasis on standardized quality products during skills training in vocational and other tertiary institutions should be prioritized. Finally, for information access, the policy implication was that government should resurrect the programme of building infrastructural facilities, such as construction of industrial sheds to be rented by jua kali artisans, as per government policy enunciated in Sessional Paper No. 2 of 1992. These sheds could double as information dissemination centres for potential adopters.en_US
dc.language.isoenen_US
dc.publisherKenyatta Universityen_US
dc.titleAdoption of adapted technology by micro and small enterprises in the informal sector in Kenyaen_US
dc.typeThesisen_US


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