A framework for adoption of e-agricultural information services by enterpreneural youths in Kiambu, a case of Kikuyu sub-county
Youth unemployment is a major challenge to many developing Nations like Kenya, yet the youths have little interest in agriculture, and there is little or no effort to entice them towards agriculture using technological innovations. The need to reverse the youth’s mind set and harness their passions and energy by re-directing it towards agricultural production is what necessitated this study. The main objective of the study was to formulate a framework for adoption of e-Agricultural Information Services (eAIS) by the youths out of school, who are aged 15 to 34 years. The aim was to re-direct their passions towards agriculture with enhanced knowledge and efficiency. The study adopted descriptive research design and Extended Technology Acceptance Model by Rongers (2003). Primary data was collected by a pre-tested semi structured questionnaires on 111 youths using simple random sampling method across active farming youth groups within Kikuyu Sub-county. Secondary data was gathered from reliable sources such as government agricultural reports, journals and e-resources. Content analysis was used to analyse respondent’s views on the Framework for adoption of eAIS. Descriptive statistics such as means, modes, frequencies and standard deviations were generated using SPSS and Smart PLS 2.0 M3. Graphical presentations such as charts, graphs, frequency tables and others were used as appropriate. Completely coded dataset in SPSS was transferred to Smart PLS SEM, a Partial List Square Structural Equation Modeling tool. The tool was used to model the framework, to estimate path coefficients or weights, to investigate the interactions between dependent and independent variables and possible associations established. Statistical significance testing of the relationships between variables and/or the hypotheses revealed that, for 0<p<0.05, the following null hypotheses were rejected. H02: JR does not influence PU, H03: Image does not influence PU, H05a: Experience does not influence PU, H06a: PEOU does not influence PU, H05b: Experience does not moderate SAN, H06b: PEOU does not influence ITU, H010: TOS does not influence ITU, H012: ITU does not influence UB and H013: UB does not influence Adoption of eAIS. The following null hypotheses were accepted for p>0.05: H01: OQ does not influence PU, H04: SAN does not influence PU, H07: Voluntariness does not moderate SAN, H08: PU does not influence ITU, H09: SAN does not influence ITU and H011: Connectivity does not influence ITU. The study concluded that the framework was composed of experience moderated SAN, Experience, Image, JR and TOS and all the TAM constructs. The study further concluded that from the eAIS adoption conceptual framework’s Output quality, moderating factor Voluntariness, Connectivity, the relationship between SAN and PU and the relationship between SAN and ITU were eliminated. The study also concluded that there were challenges that faced the respondents and the study itself. The results of analysis were used to formulate a framework for adoption of eAIS. The framework was for guiding the youths of Kikuyu Sub-county into adopting e- Agricultural Information Services (eAIS). The findings can further be useful to the Sub-county and County Agricultural Officers for disseminating agricultural information, for strategic planning and development by the Counties, National Government, policy makers and NGOs. The research recommends further studies to validate the inclusion of TOS into the framework and the elimination of some constructs and relationships of the extended TAM from the frame work with respect to the formulated eAIS model.