Esta investigación tiene como propósito identificar cuáles son los factores más determinantes para un posible comprador de un auto híbrido en comparación a un auto convencional en Lima Metropolitana, es por esto que se han utilizado herramientas cualitativas y cuantitativas para obtener un resultado; esto permitirá a las marcas de autos saber que factor es más importante para el consumidor y potenciarlo a través de estrategias de marketing directo e indirecto. En el análisis cualitativo desarrollado a través de un focus group se encontraron diferentes afirmaciones y negaciones que son respaldadas a través del análisis cuantitativo con el modelo estadístico logístico binario; en este caso se demostró que todas las variables son importantes para un posible comprador de un auto híbrido; pero unas tienen mayor importancia que otras y esto determinaría que se deben llevar a cabo estrategias que estén relacionadas al precio y diseño del auto; ya que son las variables que obtuvieron mayor importancia en el análisis. / This investigation has as purpose identify which are the factors more important for a possible buyer of a hybrid car in comparison of a conventional car in Lima Metropolitana, that is why qualitative and quantitative tools have been used to get a result; this will allow car brands to know which factor is more important for the consumer and to empower it through direct and indirect marketing strategies. In the qualitative analysis developed through a focus group, different affirmations and negations were found that are supported through quantitative analysis with the statistical binary logistic model; in this case it was shown that all the variables are important for a possible buyer of a hybrid car; but some are more important than others and this would determine that price and design should be the ones that have to have the focus of the strategies because they are the variables that obtained more importance in the statistical analysis. / Tesis

This exploratory study assessed attitudes and perceptions of smallholder farmers towards agricultural technologies in Kakamega County, Kenya. Through a mixed-methods sequential design, the study evaluated the key variables predicting farmer adoption of agricultural innovations. While social sciences provide a clear human-driven pattern explaining the process of choices and behaviors regarding technology use, there is still little clarity on the influences of adoption decisions among smallholder farmers in rural Kenya. Using the diffusion of innovations theory, the study explored the attitudes and perceptions of smallholder farmers toward technology adoption in seven sub-counties of Kakamega County (Lurambi, Ikolomani, Shinyalu, Mumias East (Shianda), Malava Butere, and Khwisero). The study design utilized a quantitative survey of 245 smallholder heads of households, followed by focus group discussions to further probe attitudes, values and practices that could influence technology adoption. The survey questionnaire tested two hypotheses: (H1) socio-demographic characteristics are related to agricultural technology adoption; and, (H2) farmer access to extension services was related to agricultural technology adoption. A binary logistic regression model was used to quantitatively estimate socio-demographic variables presumed to influence the adoption of agricultural innovations. Subsequently, four informal focus group discussions of 28 discussants was conducted across representative sub-counties (Lurambi, Shianda, Malava and Ikolomani), to elicit an in-depth understanding of farmers’ perspectives on technology adoption. The focus group participants included farmers recruited from among survey participants. The qualitative research instrument sought to answer three questions, (RQ1) what are farmer attitudes and perceptions towards agricultural technologies; (RQ2) what socio-cultural values influence farmers’ choice of agricultural technologies; and, (RQ3) what sources do farmers use for obtaining information on agricultural technology? Quantitative results included a principal component analysis (PCA) in which 14 attitudes questions were reduced to five conceptual clusters. These clusters included: challenges in accessing modern agricultural technologies (explained 19.09% of the total variance); effectiveness of agricultural technologies (11.88%); enjoyment of agricultural technologies (10.02%); social influence in use of technology (9.47%); and experience with agricultural technologies (8.13%). A logistic regression model indicated that independently age (.07), education (.10), and off-farm income (.08) were significantly associated with adoption of technology at the 90% confidence level when controlling for all other variables in the model. However, agricultural extension (.42) was not a significant predictor of agricultural technology adoption in this model. Qualitative results provided rich insights which enhanced findings from the survey data. Key insights in the thematic analysis included: farmers’ ambivalence about agricultural technologies; lack of trust in agricultural agents; low levels of agricultural technology knowledge; extension services as the main source of information dissemination to farmers; predominance of gender in determining agricultural technology adoption; and gender inequity in agricultural decision-making. In conclusion, the study results suggested that a mixed-methods approach was valuable in probing the nuances of farmers’ perceptions of agricultural extension and technology adoption among smallholder farmers. The results supported the following recommendations: the agricultural extension efforts could be more effectively structured in order to support the dissemination of agricultural information; the issue of gender should be adequately addressed by engaging male and female in collaborative agricultural efforts to help break the barrier of gender inequity; and future research would benefit from disaggregating public and private extension services as a more robust method for determining their individual effects in the promotion of agricultural innovations among smallholder farmers.

Climate change implication and rural livelihood capitals remain the major inextricable dimensions of sustainability in this twenty first century globally. As a result, the impact and outcome of climate change on rural livelihood capitals, including economic development cannot be overemphasized in Ngaka Modiri Molema District Municipality of the North West Province of South Africa, where the study took place. It is one of the largest maize production regions in South Africa, where a preponderance of the people in the province obtain their livelihood from agriculture which contributes enormously to the promotion of household’s food security. The study, therefore, investigated the adaptation strategies, awareness of climate change, factors that influenced climate change adaptation in North West Province of South Africa, with the aim of ascertaining the effects of climate change on livelihood capitals among small and emerging maize farmers. Stratified random sampling technique was used to select three hundred and forty-six (346) farmers who were interviewed from the study area, while a pre-tested questionnaire was administered to the maize farmers, aiming at matters related to climate change impact on livelihood and adaptation. Data were analyzed using descriptive statistics while inferential statistical tools employed were Principal Component Analysis, Two-Stage Least Square regression model, Binary Logistic regression model, and Tobit regression model. The results of the study showed that climate change was linked to rural livelihood capitals as climate change awareness, low profit and co-operative finance were statistically significant (p<0.05). The study also established that majority of the rural farmers in the study area were aware of climate change, in which farm size, education, ownership of the farm, information received on climate change, source of climate change information, climate change information through extension services, channel of information received on climate change and support received on climate change were statistically significant (p<0.05). Factors such as farm size, household gender, type of farms, who owns the farm, land acquisition, source of climate change information, support received on climate change, and adaptation barrier were statistically significant (p<0.05) and influenced climate change adaptation in the study area. Conclusively, climate change is entwined with rural livelihood, and the variables that are significant to the study were identified. It was therefore recommended that government intervention, access to information, extension service and support, farmers’ networking, adoption of drought and heat stress tolerant seeds, indigenous knowledge should be improved, practiced and promoted among the rural farmers and the stakeholders involved in the study area. / Agriculture, Animal Health and Human Ecology / D. Phil. (Agriculture)