Masters Theses and Dissertationshttp://repository.mut.ac.ke:8080/xmlui/handle/123456789/28742024-03-28T14:44:58Z2024-03-28T14:44:58ZIntegration of E-Supplier Management and Organizational Performance of Parastatals in Nakuru CountyWanjiku, Nyokabi Ruthhttp://repository.mut.ac.ke:8080/xmlui/handle/123456789/64012024-02-17T11:25:11Z2022-10-01T00:00:00ZIntegration of E-Supplier Management and Organizational Performance of Parastatals in Nakuru County
Wanjiku, Nyokabi Ruth
This study sought to investigate the integration of e-supplier management and its influence on organizational performance. The study aimed to specifically; analyse e-information sharing and its influence on organizational performance, investigate e-tendering and its influence on organizational performance, assess e-payments influence on organizational performance and examine e-point of sale influence on organizational performance. Since suppliers are an important asset for organisational performance, it is imperative that they are well managed to establish mutual benefits. Therefore, the goal was to investigate whether parastatals had integrated e-supplier management in their already existing ICT capabilities to influence organizational performance. The study was anchored on two theories; the innovation diffusion theory to indicate the level of ICT integration and the Technology Acceptance Model (TAM), to show how compatibility and user belief are important for integration and performance. The study used null hypotheses to test the influence of each objective on organizational performance. The study adopted a descriptive research design. The study was undertaken in five (5) selected parastatals or state-owned organizations in Nakuru municipality with a total population of 236 employees in selected departments. By use of purposive sampling, a sample size of 91 respondents was used. The study used qualitative and quantitative research methods. The study applied closed and open-ended questionnaires with a five-point Likert scale, to collect data from the respondents. The data collected was coded using Statistical Package for Social Sciences (SPSS), analysed using descriptive and inferential statistics and presented using tables. The study also adopted multiple regression and ANOVA tests to test the influence, hypotheses and relationship between e-supplier management and organizational performance respectively. E-supplier management integration was moderate (M=3.42, SD=.97). The findings inferred that e-supplier management was statistically significant in predicting the performance of parastatals at p<.05 and that 21.9% of organizational performance was influenced by e-supplier management variables. It also showed that e-information sharing influences organizational performance at 9.4%, e-tendering at 3.3%, e-payments at 2.6% and epoint of sale at 22.1%. The study also showed e-information sharing, e-tendering, epayments and e-point of sale had a weak positive correlation with organizational performance at .325, .214, .197 and .481 respectively. The study recommends parastatals full integration of e-supplier management capabilities and to work with suppliers to allow accrual of benefits that will improve overall organizational performance. The study recommends further research be done on other components of e-supplier management such as eSRM, contracts management and e-supplier selection.
Master of Business Administration (Procurement and Supply Chain Management Option), 2022
2022-10-01T00:00:00ZA Support Vector Machine and Artificial Neural Network Model for Enhanced Image Classification of Maize Leaf DiseasesOchango, Vincent Mbanduhttp://repository.mut.ac.ke:8080/xmlui/handle/123456789/64002024-02-17T11:25:03Z2022-10-01T00:00:00ZA Support Vector Machine and Artificial Neural Network Model for Enhanced Image Classification of Maize Leaf Diseases
Ochango, Vincent Mbandu
Image classification accuracy is the total number of images predicted correctly out of the total images in the test dataset in the field of computer vision. Classifying the images accurately is still a challenge due to single image classification models being biased and having high variance. The research created a combination of two models (Artificial Neural Network + Support Vector Machine), the maize leaf disease image features that were extracted were passed to the developed model which classified the diseases with high accuracy compared to the single models. Dimensionality reduction was also considered to reduce the computational complexity and this was achieved by using the Histogram of Oriented Gradient feature descriptor which extracted only relevant features and through away information that was not necessary. The relevant features were considered as the key point since an image was differentiated from each other using the key points. The developed model input was the features extracted which were in a form of a vector space known as an array of numbers and each number represented a particular feature. The developed image classification model consists of two modules; the feature extraction module and the image classification module. The feature extraction module was integrated to work together with the classification module and the features extracted by the feature extraction module were normalized to make them scale-invariant and less susceptible to light which is one of the factors that usually affects image classification accuracy. The classification module was also adjusted by combining two classifiers; Artificial Neural Network and Support Vector Machine and the main reason were for the Support Vector Machine to replace the softmax layer used for classification in the Artificial Neural Network since the Support Vector Machine have the hyperplane component which is a line that accurately separates data belonging to different classes and this made SVM to classify maize leaf disease images accurately. The Support Vector Machine also has the capability of minimizing the generalization error on unseen data which resulted in better prediction results. The common rust, leaf spot, and northern leaf blight and healthy images were used during the feature extraction process, training, and validation of the model. The feature extraction methods were compared on how they perform with image classification models to find out which feature descriptor performs best. The experimental results indicated that the Histogram of Oriented Gradients performs well with the image classifiers compared to KAZE and Oriented FAST and Rotated BRIEF and the Histogram of Oriented Gradients method reduces computational complexity during the image feature generation process. The model which was a combination of three methods, Histogram of Oriented Gradient, Artificial Neural Network, and Support Vector Machine emerged the best in terms of image classification. The experimental outcome based on performance metrics indicated that the developed model had a 0.95 accuracy score. The experimental result shows that the Histogram of the Oriented Gradient together with Artificial Neural Network and Support Vector Machine classifier is the best combination model for maize leaf disease identification since it produced the highest accuracy score compared to the other image classification models. The researcher finally recommends the model to be used today and in the future when it comes to classifying maize leaf disease images.
Master of Science in Information Technology, 2022
2022-10-01T00:00:00ZEffects of Stress Control Techniques by Middle Level Employees on Strategic Performance Management of Murang’a County GovernmentKinuthia, Sandra Waithirahttp://repository.mut.ac.ke:8080/xmlui/handle/123456789/63992024-02-17T11:23:15Z2022-11-01T00:00:00ZEffects of Stress Control Techniques by Middle Level Employees on Strategic Performance Management of Murang’a County Government
Kinuthia, Sandra Waithira
Globally, employees experience stress at the workplace. It has become a real exertion in most organizations especially in the developing countries where the significance amount of stress at the workplace has had influence on employees’ strategic performance. Organizations need employees to get the job done because employee’s strategic performance is important to the success of the organization. Stress control techniques are designed to help employees reduce stress at the workplace. The impact of stress on the employee performance has a direct impact on the implementation of organizational strategies. Despite having stress control techniques in the organizations, research studies show that stress at the workplace has not been controlled effectively, thus affecting employee strategic performance management. Increased stress levels have led to reduced work performance and subsequently the strategic goals and objectives of the organization are not met. Thus, the study sought to address the issues that cause stress at the workplace and their influencing factors and therefore focused on job design, training, employee welfare programs and incentives as the key stress control techniques. The general objective is to establish the effect of stress control techniques by middle level employees on strategic performance management of Murang’a County Government. The research study adopted descriptive survey which is the method of research which concerns itself with the present phenomena in terms of conditions, practices, beliefs, processes, relationships, or trends. The study was confined to Murang’a county government in Murang’a County. This is where the strategic decisions and the central planning of Murang’a County are operated.The middle-level employees of Murang’a County Government were the target population, which they comprised of 4,254 respondents. Stratified random sampling was used as the most appropriate sampling technique, since the population is not a homogeneous group. Systematic sampling was used in each group to come up with the respondents which involves selecting the samples at regular intervals from the sampling frame. A sample size of 366 respondents were selected and administered with questionnaire as a data collection instrument by the researcher. Out of those served with questionnaires, 346 filled and returned the questionnaires. The response rate therefore was 94.5%. The study findings inferential statistics correlation and regression analysis depicted that there is a significant positive effect on job design (rho=0.6530, p-value <0.05), training (rho =0.608, P value <0.05), employee welfare programs (rho = 0.514, p value <0.05), incentives (rho = 0.521, p value <0.05) and middle-level employees’ strategic performance. Therefore, county government should endeavour to address and adopt stress control techniques focusing on job design, training, employee welfare programs and incentives to improve employee strategic performance for the organisation to achieve their strategic objectives.
Master of Business Administration (Strategic Management Option), 2022
2022-11-01T00:00:00ZSpatial Modelling of Malaria Prevalence in KenyaMorris, Mwenda J.http://repository.mut.ac.ke:8080/xmlui/handle/123456789/63932024-02-17T11:21:05Z2022-10-01T00:00:00ZSpatial Modelling of Malaria Prevalence in Kenya
Morris, Mwenda J.
Malaria is a leading cause of deaths in Kenya. A vector-borne disease caused by parasite of genus plasmodium; the disease is introduced into the human circulatory system from bites caused by infected female anopheles’ mosquitoes. A lot of effort and resources has been put in the fight against malaria, with large amount of national budget being used in the fight against malaria in developing countries which has led to underdevelopment, impoverished livelihoods and low human development index. Malaria burden affects the world’s poorest countries. About 90% of the malaria burden is reported in sub-Saharan Africa. Malaria cases are significantly high in countries of south-East Asia, Western Pacific region, Mediterranean and the Americas. As of 2017, five countries India, Uganda, DR Congo and Mozambique accounted for half of malaria cases reported around the world. In Kenya, the disease has led to impoverished livelihoods with the poorest communities of the country being the most affected. The disease has led to high mortality cases in children under five years and pregnant women. Loss of man hours and work days among adults in the country, leading to low productivity. Studies have shown that there has been a general lack of knowledge on how select demographic and social economic conditions risk factors affect the prevalence of malaria in Kenya. The method of the study involved performing the spatial models for malaria prevalence in Kenya while relaxing the assumptions of stationarity. The assumptions of linearity allowed some covariates like age to have a non-linear effect on prevalence of malaria. Using random walk model of 2nd order and the assumption of stationarity, it allowed covariates to vary spatially. Conditional autoregressive model was used. Data from malaria indicator survey of 2015 (KMIS2015) was used for the study. Both the social-economic and demographic variables were used as predictor variables. These included education level, wealth index, age, access to mosquito nets and place of residence. From the study, demographic and social-economic factors were found to have significant impact on Prevalence of malaria in Kenya. Most cases of malaria were reported in lake, western and coastal regions. The most prone areas were Kisumu, Homabay, Kakamega and Mombasa. There were less cases in central Kenya counties like Nyeri, Tharaka-Nithi with a significant number reported in arid and semi-arid regions of Northern-Kenya counties of Garissa, Mandera, Baringo. Rural population was more susceptible to malaria compared to those in urban areas. The odds of getting (verse not getting malaria) in urban places of residence increases by 0.84, which is estimated to .096, CIs 95% (0.70, 1.01), and a p-value .069. Malaria prevalence varied significantly from one region to another. The study established that Spatial autocorrelation exists among regions mostly due to weather patterns, geography, cultural practices and socio-economic factors.
Master of Science in Statistics, 2022
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