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Digital Marketing Strategy and Brand Loyalty among Customers of Airtel Network, Kenya
(European Journal of Business and Strategic Management, 2025-03-13) Aliata, Victor L.
Purpose: Digital marketing encompasses all marketing efforts that use an electronic device or internet. As digital platforms are increasingly incorporated onto marketing plans and everyday life and as people use digital devices instead of visiting physical shops, digital marketing is becoming more prevalent and efficient. According to Communications Authority of Kenya (CAK), Airtel networks Kenya ltd has had its market share increase marginally from 21.4% in 2018 to 27.2 in 2020.It is uncertain if the increase can be attributed to its digital marketing strategy thus the paper explores the effect of digital marketing strategy on brand loyalty among customers of Airtel network Kenya. Methodology: Primary data was collected from a target population of 100 third year Bachelor of Business Administration(BBA) students from Tom Mboya University, Kenya. Study sample of 80was extracted for the study using Yamane (1967).Purposive sampling technique was used to collect data. Those targeted are the ones assumed to be subscribers of airtel network ltd, Kenya. Secondary data was accessed through Airtel’s network limited annual reports. The study utilized correlation research design. Findings: Study findings revealed that digital marketing accounts for 41.0% significant variance in customer loyalty, (R2=.410, F(1, 78)=55.934, p<.05). It is also clear that digital marketing has a positive significant effect on customer loyalty (β=.646, p<.05). This implies that for every improvement in digital marketing, customers become more loyal. Therefore, digital marketing has a positive and significant influence on Airtel subscribers’ customer loyalty. Unique Contribution to Theory, Practice and Policy: The study recommends an enhancement of the digital marketing strategies in Airtel Kenya. Emphasis should be to push most of the services into the digital platform. This will improve service delivery and further positive implication on brand loyalty of Airtel. This will also lead to positive word of mouth advertisement which will aid in introducing new customers on board.in order to encourage the subscribers carry out snow balling reference to others and so increase customer subscription.
A Mathematical Model for Effective Fungicide Use in Rice Blast Re-Infection
(Journal of Mathematical Analysis and Modeling, 2025-06-15) OCHWACH, JIMRISE O.; OBITA, BONFACE O.; OKONGO, MARK O.
Rice blast, caused by the pathogenic fungus Magnapor the oryzae, poses a significant threat to rice pro-duction, especially in regions like Kenya where rice is both a staple food and a key economic commodity. This study introduces a comprehensive mathematical model designed to investigate the dynamics of rice blastre-infection following fungicide application on infected crops. The model is formulated as a system of ordi-nary differential equations (ODEs) that categorizes the rice population into five compartments: Susceptible, Protected, Infected, Recovered, and Secondary Infected. The positivity and boundedness of solutions were established, ensuring that the model is both mathematically and biologically well-posed. Using the stability theory of differential equations, the model was analyzed, and the basic reproduction numberR0was derivedvia the Next Generation Matrix method. The existence of both the Disease-Free Equilibrium (DFE) and the Endemic Equilibrium Point (EEP) was demonstrated. Stability analysis revealed that the DFE is locally and globally stable whenR0<1, and unstable whenR0>1. Conversely, the EEP is locally asymptotically stablewhenR0>1. Sensitivity analysis identified the fungicide application rate (π) as the most influential pa-ra meter in reducing rice blast re-infection. Numerical simulations were conducted to support the analytical findings, demonstrating that effective fungicide use can substantially decrease disease prevalence, there by enhancing rice yield and promoting sustainable agricultural practices. This study contributes meaningfully to the field of plant disease modeling and provides a robust framework for future research into the epidemiology and management of crop diseases.
EFFECT OF CASH POSITION ON FINANCIAL PERFORMANCE A SURVEY OF SELECTED SMALL RETAIL ENTERPRISES IN NAKURU CITY
(African Journal of Emerging Issues, 2025-02) Njoroge, Charles; Ombati, Robert; Wachira, Kevin
Purpose of the Study:The study was to determine the effect of cash position on the financial performance of selected small retail enterprises in Nakuru City, Kenya.Statement of the Problem:Small retail enterprises in Nakuru City often operate for years without significant growth, mainly selling similar, non-standard products. Many experience financial losses or minimal profitability, with low sales, return on equity, return on assets, and net profit margins.Methodology:The study utilized stratified random sampling to select 344 small retail enterprises from various sectors. Data was collected through structured questionnaires and analyzed using descriptive and inferential statistics. Descriptive statistics included frequencies, percentages, means, and standard deviations, while multiple linear regression was applied for hypothesis testing at a 5% significance level.Results of the Study:The study found that cash position was a significant predictor of financial performance among small retail enterprises in Nakuru City. Cash position was correlated to the financial performance of selected small retail enterprises in Nakuru City, Kenya.Conclusion and Policy Recommendation: The study concluded that cash position significantly influenced financial performance among small retail enterprises in Nakuru City. It recommended that these enterprises strategically manage liquidity, forecast receipts and payments, and assess the opportunity cost of cash and time value of money for better financial outcomes.
Performance Evaluation of Intrusion Detection Systems on the TON_IoT Datasets Using a Feature Selection Method
(https://dl.acm.org/, 2024-12-06) Maseno, Elijah M.; Wang, Zenghui; Sun, Yanxia
As Internet of Things (IoT) technology develops so quickly, security issues with IoT devices have come to light. IoT is an array of intelligent devices connected via a network to provide various services. The amount of data generated by these devices has an impact on how well the current intrusion detection systems (IDS) function. The generated dataset consists of irrelevant features which reduces the performance of IDS, making IoT ecosystem vulnerable to cyberattacks. The researchers have suggested the feature reduction technique as a potential solution to the current problem. The proposed method seeks to reduce the feature count by removing the redundant feature subset. Several machine learning methods have been successfully implemented in this discipline. This study proposed the application of hybrid feature reduction technique. The research combined Convolutional neural network (CNN) and Long short-term memory (LSTM); CNN extracted local features and decreased dimensionality, while LSTM identified long-term relationships in the data. SVM and Random Forest classifiers models were used to classify the chosen feature subset. This study employed the TON_IoT Datasets, an up-to-date dataset, to evaluate the model. During data preprocessing, the study applied SMOTETomek data pre-processing technique to address class imbalance in the dataset. With the decreased feature subset, the classification models fared reasonably well; RF had a 98% accuracy rate while SVM had a 91% accuracy rate, showcasing the suggested methodology’s potential for creating efficient IDS.
Hydromagnetic Casson Nanofluid Flow Past a Wedge in a Porous Medium in the Presence of Induced Magnetic Field
(American Journal of Applied Mathematics, 2025-01-20) Nyaga Danson; Ochwach Jimrise; Kirimi Jacob; Okongo Mark
Casson fluid is a shear thinning liquid assumed to have an infinite viscosity at zero rate of shear and a zero viscosity at an infinite rate of shear. Casson fluids at times flow past wedge-shaped objects, as is the case in crude oil extraction and geothermal systems. Nanoparticles tend to improve thermal conductivity of base fluids. The impact of Magnetohydromagnetics (MHD) and induced magnetic field on heat and mass transfer flow finds application in engineering and industries. It plays an important role in design of transpiration cooling and aerodynamics extrusion of plastic sheets. Several authors have studied effects of induced magnetic field on different types of fluid flow. However, little has been studied on the impact of induced magnetic field on Casson Nanofluid flow past a wedge. In this study, the equations governing the nanofluid flow were reduced to a system of highly nonlinear ordinary differential equations by using boundary layer theory. The resulting boundary value problem is then numerically solved in MATLAB by using the Boundary Value Problem 4th-order collocation (BVP4C). The local skin friction, mass transfer rate, and heat transfer rate are displayed in a table while graphs illustrate the influences of pertinent physical entities on the temperature, nanofluid velocity, concentration of nanoparticles and magnetic induction. The study’s findings will improve the body of knowledge on Casson Nanofluid flow past a wedge, which is important for plasma, fossil fuels, blood flow in the circulatory system, glass fibre manufacture, petroleum production, and magma dynamics. Due to their low thermal conductivity, electrically conducting Casson fluids have rather poor heat transfer; however, thermal conductivity is improved when nanoparticles are introduced and induced magnetic fields are considered significant. As a result, manufacturers can create solutions using the findings of this study. The issues surrounding induced magnetic fields are significant in several industrial applications, including geothermal systems, liquid metals, fibre or granular insulation, electrolytes, and ionised gases.