Master of Science in Computer Information Systems

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AI Based Framework for Government Oversight of Personal Data Consent Compliance A Case Study Of Nairobi County
(KeMU, 2024-09) VUNDI,MUSYOKA, GEOFFREY
In the rapidly evolving digital landscape, protecting individual privacy and ensuring compliance with personal data regulations have become critical priorities. This study addresses the growing challenge of insufficient government oversight in monitoring real time compliance with personal data consent, with a focus on Nairobi County as a case study. It introduces an AI-based framework designed to automate the detection of privacy breaches, verify adherence to consent agreements, and strengthen regulatory enforcement processes. Grounded in regulatory compliance theory, the research aims to enhance the capacity of oversight bodies by utilizing AI technology to analyze vast datasets, improving the speed, accuracy, and efficiency of compliance monitoring. A mixed-methods research design was adopted, integrating both qualitative and quantitative approaches. Key stakeholders from prominent organizations such as Safaricom PLC, the Kenya Revenue Authority, Equity Bank Kenya, the Ministry of Information, Communications, and Technology (ICT), and the United Nations Office at Nairobi (UNON) were engaged. Data was collected through semi-structured questionnaires and in-depth interviews with government regulators and private sector representatives responsible for managing personal data. A purposive sampling method was employed, selecting 195 respondents to ensure a comprehensive and representative dataset. Data analysis involved thematic analysis for qualitative data and statistical techniques for quantitative data. Findings indicate that the AI-based framework significantly improves the detection and prevention of data privacy violations, optimizes compliance processes, and reduces reliance on manual oversight. Enhanced governance structures and heightened user awareness emerged as crucial factors in promoting better compliance. However, challenges such as regulatory adaptation and limited resources were identified. The study concludes that AI holds transformative potential for government oversight by increasing transparency, accountability, and operational efficiency. It recommends that regulatory bodies, particularly the Ministry of ICT, adopt AI-driven solutions and foster public-private partnerships to ensure effective, comprehensive data governance. This approach is vital for addressing emerging privacy challenges in a data-driven world
Assessment of the Effectiveness of Non-Technical Approach to Cyber Security Management for Kenya Ministry of Lands and Physical Planning Nlims System.
(KeMU, 2024-10) NDUNG’U, GABRIEL
Cyber security threats and vulnerabilities are influenced by human behavior, making them complex systems. The conventional reductionist approach to managing technical aspects of cyber security is limited, as it cannot predict the non-technical aspects of cyber security. The complexity approach, however, offers a robust and effective way to predict social aspects of cyber security. This study aims to assess the effectiveness of the Ministry of Land's approach to non-technical cyber threats and vulnerabilities. Current studies focus predominantly on the technical aspect cyber security and less on social aspect of cyber security at the detriment of the social side so does the setup and implementation of information security system. In aggregate system that focus on the technical side are weak on the social aspect and predisposed to social-engineering attack, Data will be collected through face-to-face interviews with Ministry of land staff linked to the NLIMS system in Nairobi. The findings indicate that at least 70% of staff lack knowledge of social engineering attacks, their conduct, and skills to prevent or stop them. Lower-rank staff access information they are not authorized to access through the workstation resource sharing policy. The study reveals that KMLPP's non-technical approach to cyber security is ineffective in protecting sensitive information and preventing staff from accessing sensitive data through dumb star diving. It also highlights the vulnerability of lower-rank staff to shoulder surfing and workstation privacy exploits. The study proposes a socio-technical cybernetic enterprise model, which focuses on staff relationships and restricts information access without appropriate privileges, ensuring a more secure environment for NLIMS.
Behavioral Detection and Prevention of Cheating during Online Examination using Deep Learning Approach
(KeMU, 2023) Kiura, Gabriel Muchangi
With the expansion of new technologies over the last years learning has grown and universities are utilizing it in offering online exams. Cheating during has also gone up regardless of the technologies or means which universities are using. The study addressed the issues that are experienced during online evaluation of student taking exams, in universities. Currently many student engage in exam malpractice through copying during online exam. To be able to determine the behavioral metric data was downloaded from the free data repository. The data was processed, validated, trained and evaluated. Quantative research methods was used in this research. By analyzing distinct behavioral patterns and strategies employed by cheating students, the research provides valuable insights into the motivations and factors that drive such behavior. The study also identifies significant visual features present in images that indicate instances of cheating, which enhance the performance of deep learning models. Various deep learning models, including Dense Net, Mobile Net, ResNets, and Convolutional Neural Networks (CNN), are developed and evaluated for detecting and classifying cheating behavior during online examinations. The evaluation results show that the Mobile Net model achieved the highest test accuracy of 93.4%, outperforming the other models. It demonstrated strong predictive ability, accurate classification, and efficient computation time. Additionally, the identification of significant visual features and the development of deep learning models tailored for cheating detection contribute to the field of automated cheating detection, providing a foundation for future research. However, certain limitations should be acknowledged. The performance of the deep learning models may be influenced by the quality and diversity of the training dataset, and further investigation is needed to determine their effectiveness in detecting evolving cheating strategies. Based on the evaluation findings and identified limitations, several recommendations are proposed. Firstly, improving the quality and diversity of the training dataset through data collection was recommended to enhance the performance of deep learning models. Continuous model training is essential to adapt to emerging cheating strategies, requiring regular incorporation of new instances of cheating behaviors into the training dataset. Further exploration and refinement of significant visual features can enhance model accuracy through feature engineering techniques. Ensemble methods, such as model averaging or stacking, should be considered to improve overall model performance. Collaboration among researchers, educators, and policymakers from different educational contexts can facilitate cross-context evaluation and provide insights into the generalizability of the models. The findings of the research can be used by policy maker when making decision patterning online exams to ensure there is credibility of the online exams. The findings also forms the bases of academia future research to improve on this research. Ethical considerations, including privacy concerns and fairness in the detection process, should be addressed transparently. Lastly, educational institutions should prioritize creating awareness and fostering a culture of academic integrity through comprehensive guidelines and student education
A Bio-Data Privacy Protection Framework for the Telecommunications Industry.
(KeMU, 2018-10) Katuu, Christopher Mwendwa
Telecommunications are since long subject to privacy discussions, not so much for questions about the ringing phone but for the sensibility of the information that can be extracted from telephone services. It is against this backdrop that this study sought to examine privacy issues concerning bio-data in the telecommunications sector in Kenya and subsequently develop a framework to address the challenges. The objectives were to examine threats and evaluate measures in place for ensuring privacy of bio data in telecommunication firms; to develop a framework on bio-data privacy and adopt one that can be used by the telecommunications sector to address the challenges of bio-data privacy and to validate the developed bio data privacy protection framework. A descriptive survey targeting information security professionals and users of information systems was carried out. All Kenyan telecommunications firms were targeted from where the departments directly involved in data security were purposively selected. A total of thirty one (31) IT security professionals participated in this survey. A structured questionnaire was used for collecting primary data for the study. From the study, quantitative as well as qualitative data was gathered. The collected data was analysed thematically using the Statistical Package for Social Sciences (SPSS) version 21 to generate frequency tables and finally cumulative percentages which were thereafter tabulated and also presented in graphs and pie charts using Microsoft excel. The findings indicated that bio data privacy threats were rife in the telecommunication sector. The major bio-data privacy threats were computer viruses followed by data leakage. The other reported threats were denial of service attacks, software bugs, and spamming. The frequency of occurrence of the threats was reported to be rare. From the findings, training was the leading strategy for protecting bio data since majority of the respondents had received training in information security. A small numbers had not received any structured training on information security. In addressing the constraints and challenges faced by telecommunications firms, the findings assisted the researcher to develop a framework to be adopted by stake holders to ensure that there is privacy of customer‘s bio data. The proposed framework was reported by many respondents as being clear and systematic in the protection of bio data privacy. In conclusion, the study observes that bio data privacy threats are real in the ever growing and lucrative Kenyan telecommunications industry. It is recommended that telecommunication firms must identify and understand the privacy and security regulations that apply to the bio data they store, process, and transmit. They need also to implement a comprehensive bio data privacy protection framework like the one developed by the researcher.
Deep Learning Approach for Detection and Prediction of Pest Infections On Plants in Greenhouses
(KeMU, 2025-09) Sambu, Bridgite Mueni
Pest infestations remain a serious threat to greenhouse agriculture ultimately resulting in reduced yields, increased cost of production, and food safety concerns. Traditional pest monitoring strategies are predominantly manual in nature, which can be laborious, time-consuming, and exhibit human error. These downfalls can affect timely action on issues and a positive relationship management with pest monitoring can mean excessive pesticide use with financial and/or environmental impacts. This paper proposed an AI-enabled hybrid deep learning model to automate pest detection and estimate the probability of a pest outbreak. The hybrid model combined Convolutional Neural Networks (CNNs) for spatial (image-based) pest identification with Long Short-Term Memory (LSTM) networks to estimate probabilities of outbreaks based on sequences of environmental ((local) environmental data temperature, humidity, as well as, recorded counts of pests) and pest counts to help improve timing of interventions and proactive pest management solutions. The study made use of both primary and secondary datasets. The primary dataset was collected from three greenhouses in Limuru, Naivasha, and Thika, Kenya as well as high-resolution crop images (48 mega-pixels) representing various pest infections. Local environmental data for temperature, humidity, and pest counts were collected from the greenhouses to use as sequential variables in the LSTM component of the workflow and assist with estimating the accuracy of predicted outbreaks. Secondary datasets of pre-annotated pest images and historical climate records, including PlantVillage and IP102, provided bulked training data that improved models’ robustness and generalizability to unseen datasets. The researcher ensured stratified sampling to capture representation of all greenhouse types, farm sizes, and agro-climatic conditions. As part of preprocessing, all datasets underwent the following steps: image augmentation, noise removal and feature normalization. All model training, including hyperparameter tuning, occurred in a GPU-enabled Google Colab environment, and early stopping was used to avoid overfitting. The hybrid CNN-LSTM model produced a classification accuracy of 94.7%, precision of 93.9%, recall of 93.8%, and F1-score of 93.2%. The time-series predictive component of the LSTM model produced strong predictive performance with a mean absolute error (MAE) of 0.14, and R² value of 0.89. This showed that the environmental and measured sequence data of pests improve outbreak prediction. It was shown that the hybrid model accurately identifies pest infections, as well as predicting their outbreaks, which will contribute to providing early and timely interventions in the pest control system. Both high-resolution image data and measurements of local environments support a scalable and resilient choice for greenhouse pest management - the use of less pesticides, leading to sustainability in agriculture. In conclusion, this study has demonstrated the effectiveness of a hybrid deep learning framework for integrated pest management with operational and financial implications.
Deep Learning Network Intrusion Detection With the Conv1d-Lstm Model: Integrating Cnn and Lstm for Superior Performance
(KeMU, 2024-09) Lukogo, Cikambasi Ciza
The escalating frequency and complexity of cyber-attacks present significant threats to corporate networks, resulting in financial losses, reputational harm, and possible data breaches. Traditional Intrusion Detection Systems (IDS), which rely on predefined signatures and rules, have proven inadequate in addressing these advanced threats due to high rates of both false positives and negatives. This inadequacy necessitates the development of more advanced intrusion detection methods. This thesis introduces a novel AI-based intrusion detection model leveraging deep learning techniques to enhance corporate network security. The proposed model utilizes convolutional neural networks (CNN) and recurrent neural networks (RNN) to analyze network traffic data from the comprehensive CSE-CIC-IDS-2018 dataset, which encompasses a wide array of attack types and provides a realistic representation of modern network traffic. By capturing complex patterns and temporal dependencies in the data, these deep learning algorithms are particularly effective in detecting sophisticated intrusion attempts A key contribution of this research is the development of a hybrid detection approach that fuses convolutional neural network and recurrent neural network algorithm. This hybrid model enhances detection accuracy and reduces false alarms. The model's performance is rigorously evaluated using metrics such as precision, recall, and F1 score, demonstrating superior detection capabilities with a 99.97% in precision, 99.95% in recall, 99.97% in accuracy, and 99.96% in F1-score outperforming the other models. To address challenges related to data quality, the study incorporates extensive data preprocessing steps, including feature selection, encoding, and scaling. The high computational demands of training deep learning models are mitigated using cloud-based resources. Furthermore, visualization strategies are used to enhance the model's interpretability, offering a glimpse into its decision-making process. The findings of this research have significant implications for network security administrators, researchers, educators, and policymakers. Network security administrators can apply these insights to enhance their defensive strategies against cyber threats. Researchers and educators can leverage the advanced methodologies presented in this study, while policymakers can utilize the findings to inform the development of more effective network security policies and standards. This research advances the field of cybersecurity by proposing and evaluating a novel AI-based intrusion detection model. It underscores the critical importance of integrating advanced AI methodologies into IDS frameworks to protect corporate networks from evolving cyber threats. By improving the accuracy and reliability of intrusion detection systems, this study contributes to the overall security of digital operations in organizations worldwide, highlighting the transformative potential of AI in contemporary cybersecurity.
Determinants of Digital Credit Applications Adoption and Continuous Use in Kenya Case Study of Nairobi and its Environs.
(KeMU, 2021-10) Palanga Nyongesa Josephat,
The major goal of this research was to investigate the determinants of digital credit applications adoption and continuous use in Kenya. Digital credit is the borrowing and paying of loans using a digital platform. Since the world’s first digital credit solution was initially launched in Kenya over eight years ago, the market has expanded very rapidly opening up the economy and financial sector in a manner never experienced before. These loan disbursements happlicationen via mobile networks with clients using their smart phones and other gadgets to easily request and receive credit facilities through suggested applications. Nowadays, most commercial banks and other able lenders offer digital credit via privately developed applications, which have the ability to quickly and efficiently disburse very expensive short-term loans. This descriptive-analytical study was conducted to check the motivation behind the ever-growing use of these financial technologies. This was done on a targeted population of 600 smart phone users drawn from Nairobi County, its environs and 2 rural towns through purposeful random sampling. Data was collected using reliable and valid questionnaires and the data sets were analyzed using IBM’s SPSS version 23 (Statistical package for social sciences). Qualitative data analysis was done using content analysis on the emerging independent variables (technological, economic and social factors) and the dependent variable. Then inferential statistics, which included regression and correlation analysis, were carried out to derive the determinants of digital credit applications adoption and continuous use. In total, 420 smart phone users responded to the survey, which represented about 70% of the targeted population. There was a significantly positive correlation between technology and the dependent variable (β3=0.580, p=.000). Also, the economic factor had a very significant correlation with the adoption and continuous use of digital credit (β1=0.362, p=.000). In conclusion, the findings indicated that the social factor had little significance in influencing the adoption and continuous use of digital credit applications. However, the technological and economic factors positively determined the adoption and continuous use of digital credit applications in Kenya. The study recommends that more research be conducted on the underlying variables to unearth any deeper motivating factors to this continuous unregulated and expensive borrowing.
Development of A Machine Learning-Based Model Using a Decision Tree for Detecting Fake News: Analyzing Techniques for Accurate Content Verification
(KeMU, 2025-09) Tomba, Kinkosi Esther
With the growing spread of information through social media and online news, identifying fake news has become increasingly important. To explore this issue, the Pew Research Center conducted surveys in the U.S. to examine how adults access news on social platforms, focusing on the behaviors and demographics of users who rely on these channels. This research sought to address a major gap in traditional fake news detection approaches, which were largely manual and lacked the sophistication of advanced machine learning and AI methods. Such conventional techniques struggle to handle the complexity and contextual manipulation of information, where accurate facts can be framed misleadingly. To overcome these shortcomings, the study developed a machine learning–based model to detect fake news by analyzing article content and recognizing patterns of misinformation. It utilized advanced natural language processing (NLP) techniques and supervised learning algorithms. Decision Trees (achieving 99.67% accuracy), Logistic Regression (99.13%), and Random Forest (99.15%). Processes such as tokenization and TF-IDF were applied to train the model on the ISO Fake News dataset, which combined real news from Reuters.com with fake news from unreliable sources flagged by PolitiFact and Wikipedia. Model performance was evaluated using accuracy, precision, recall, and F1-score, all reaching 99.67%, demonstrating exceptional detection capability. This work contributes to the field of machine learning by enhancing NLP methods and improving the effectiveness of fake news detection models. Future research is encouraged to expand datasets, incorporate multiple languages, employ deep learning like RNNs, CNNs, and Transformers (e.g., BERT and RoBERTa) for richer contextual understanding, and establish benchmarks based on real-world case studies.
Digital Storytelling for increasing Information Security Awareness among Kenyan Smartphone Users: A Case Study of Nairobi and Eldoret
(KeMU, 2020-11) Kosgey, Lynet Jesang
The rapid increase of smartphone proliferation in Kenya has resulted in users being dependent on their phones for everyday activities. This increased interaction with smartphones has posed a significant threat to users’ privacy and security. The exposure to threats demonstrates a need to understand users’ security awareness and behavior when using their mobile phones. Therefore, this study sought to establish the level of security risk awareness and user behaviour among smartphone users in Kenya, in order to design a customized security awareness model with the aim of reducing information security risks. The specific objectives include: to investigate information security-related behavior among Kenyan smartphone users, and to design and evaluate the effectiveness of a digital storytelling model in increasing information security awareness among smartphone users. The investigation was informed by the Protection motivation theory and unified theory of acceptance and use of technology. The study adopted a quantitative approach and used experimental research design in two phases of the study. The study population of the study included smartphone users in the cities of Nairobi and Eldoret. Simple random sampling was adopted to select a sample of 393 smartphone users in the first phase, and 277 smartphone users in the second phase. The main research instrument for the study was a questionnaire. The quantitative data was analyzed using descriptive statistical methods. The results showed that most Kenyan Smartphone users are aware of security threats facing them and are greatly concerned about related security risks. However, users still make poor behavioral choices related to the use of smartphones, making them vulnerable to cybersecurity attacks. One of the poorly adhered to security practices among participants was the use of public Wi-Fi. Consequently, this research uses a digital storytelling information security model to design a media that seeks to increase awareness on the use of public Wi-Fi among Kenyan smartphone users. The prototype was tested among 277 Kenyan smartphone users. The results indicate that digital storytelling was highly effective among an average of 65% of the participants in increasing the understanding of risks and secure behavior related to the use of public Wi-Fi. Further, the outcome indicates that an average of 70% of the participants had a high likelihood of adapting behavior that increases their security when using public Wi-Fi. Thus, the study concluded that digital storytelling is an effective method for increasing information security awareness among smartphone users. Based on these findings, the study recommended that a digital storytelling model approach can be adopted by smartphone manufactures, telecommunication companies, and information security organizations to promote behavior that will safeguard the privacy and security of end-users who utilize communication devices. In addition, the study recommended that information service providers and security agencies should include user needs and feedback when designing security awareness media; smartphone users should always practice secure behavior; and mobile applications should be regulated, and apps be ranked based on user security. In addition, software developers should develop more applications where by default the user data is protected.
Enhancing Diabetes Classification Using A Weighted Ensemble Of Tabnet, Xgboost And Random Forest
(KeMU, 2025-09) Obunge, Duncan Ogindo
The increasing prevalence of Diabetes mellitus (DM), a leading global health challenge, is significantly impacting the healthcare systems. Accurate and interpretable classification models are crucial in advancing early diagnosis and effective intervention. While traditional machine learning techniques like Extreme Gradient boosting (XGBoost) and RandomForest based models have demonstrated robust classification performance on tabular medical datasets, however, they have continued to face challenge of model interpretability. Deep learning models, like TabNet, cater the two-pronged benefits of feature selection learning and interpretability via attention mechanisms. This study developed a weighted ensemble model that combines TabNet, XGBoost and RandomForest based models to address the trade-off between interpretability and strong performance. The study utilized the Pima Indian Diabetes dataset as secondary data and expert clinical validation. The dataset, contained 768 tuples with 8 features, related to diabetes risk factors. The ensemble assigns optimized weights to the classifications of the three models, drawing on their complementary strengths. The results indicated that the weighted ensemble model outperformed the individual models; while preserving interpretability. The implementation achieved a balanced accuracy of 0.8630 ± 0.0146 (median 0.8350), precision of 0.8163 ± 0.0442 (median 0.8018), recall of 0.9376 ± 0.0341 (median 0.8900), F1 score of 0.8401 ± 0.0110 (median 0.8436), and ROC-AUC score of 0.9026 ± 0.0172 (median 0.9044), while the traditional machine learning models based on XGBoost attained (0.8103 ± 0.0270 (0.8150) balanced accuracy, 0.7888 ± 0.0287 (0.7890) precision) and RandomForest achieved (0.8060 ± 0.0250 (0.8100) balanced accuracy, 0.7451 ± 0.0312 (0.7768) precision) algorithms. Feature importance analysis revealed the top most significant predictors of diabetes based on normalized scores as; glucose level (≈1), followed by age (≈0.458), insulin (≈0.434) and body mass index(BMI) (≈0.13) hence providing valuable clinical insights. This research contributes a novel computational framework that leverages a weighted ensemble learning techniques while preserving model explainability; a critical advancement for healthcare-aligned machine learning systems. This methodological contribution extends beyond diabetes classification to potentially benefit various clinical decision support systems operating on limited-feature medical datasets.
An Examination of Threats and Countermeasures Relating to Healthcare Cyber Risks. The Case of Kenyatta National Hospital (KNH)
(2025-09) Stephen, Ario Okongo
The increasing reliance on digital technologies in Kenya’s healthcare sector has heightened the need for robust cybersecurity measures to protect sensitive patient data and ensure operational continuity. This study examined cybersecurity threats and countermeasures at Kenyatta National Hospital (KNH), the country’s largest public referral hospital, to develop a contextually relevant framework for enhancing data protection and institutional resilience. Specifically, the research investigated perceived cyber risks, the influence of the Kenya Cybercrime Act, and ethical data protection practices on the effectiveness of the hospital’s cybersecurity framework. Guided by the Socio-Technical Systems (STS) Theory, the study adopted a descriptive cross-sectional research design, utilizing a quantitative approach. A stratified random sample of 370 KNH staff, including ICT personnel, clinicians, and health records and admins, were surveyed using structured questionnaires, achieving a 98.6% response rate (365 valid responses). Data analysis involved descriptive statistics, correlation, and multiple regression techniques, ensuring robust insights into the relationships among key variables. Findings on cybersecurity threats revealed high perceived risks, particularly from external attacks (M = 4.18, SD = 1.09, Var = 1.19), device vulnerabilities (M = 3.99), and insider threats (M = 3.92). Correlation analysis showed that threats were strongly associated with the Cybercrime Act (r = 0.602, p < 0.01) and moderately with ethical guidelines (r = 0.485, p < 0.01), but insignificantly with the cybersecurity framework itself (r = 0.055, p = 0.297). Regression confirmed a significant negative coefficient for threats (B = -0.182, p = 0.007), indicating that heightened threats weaken the framework’s effectiveness. Analysis of the Kenya Cybercrime Act demonstrated moderate correlations with ethical guidelines (r = 0.539, p < 0.01) and a weaker positive correlation with the cybersecurity framework (r = 0.191, p < 0.01). In regression, the Act had a positive but marginally insignificant coefficient (B = 0.136, p = 0.054; Beta = 0.129), suggesting that while legal provisions support cybersecurity, their influence is not yet robust. For ethical guidelines, results showed a moderate correlation with the cybersecurity framework (r = 0.294, p < 0.01). Regression identified ethical guidelines as the most influential predictor (B = 0.303, p < 0.001; Beta = 0.309), confirming their pivotal role in strengthening KNH’s cybersecurity posture. The overall regression model was statistically significant (F(3,361) = 14.267, p < 0.001) with R = 0.326, R² = 0.106, and Adjusted R² = 0.099, indicating that the three predictors jointly explained about 10.6% of the variance in the hospital’s cybersecurity framework. The study concludes that ethical data protection guidelines are the strongest determinant of a resilient cybersecurity framework, while rising threats undermine readiness and the Cybercrime Act contributes moderately. It recommends strengthening ethical enforcement, embedding role-based staff training, enhancing legal compliance, and allocating resources to mitigate threats. Future research should simulate incident scenarios and assess patient data literacy across hospitals. Keywords: Cyber Threats, Cybersecurity, Data Security Framework, Ethical Data Protection, Healthcare, Kenyatta National Hospital, Kenya Cybercrime Act, Socio-Technical Systems Theory
Factors affecting the Post-Implementation Evaluation of Information Systems Used in Small and Medium Sized Enterprises (SMES) in Meru County Kenya
(KeMU, 2022-10) Francis, Salome Kagendi
The purpose of carrying out this research was to propose a Framework on The Factors Affecting the Post-Implementation Evaluation of Information Systems Used in Small and Medium Sized Enterprises of Meru County. The study was guided by four main objectives which were to; determine the implementation status of the information systems implemented in the small and medium sized enterprises of Meru County, identify the evaluation parameters of IS, establish the challenges of IS at the post-implementation level and to propose a Framework for the SMEs in Meru County. An information system brings about automation, information and transformation. The organizations adopt information systems that are flexible and organize continuous upgrading of MIS staff. The reviewing of the information systems on post-implementation level is an important step that ensures the IS works appropriately. The benefits that are realized from information systems are realized until four or five years after the adoption of the systems. The criteria for evaluating information systems are cited as user satisfaction, functionality, adaptability, the popularity of suppliers and maintenance. Some SMEs in developing countries are discontented with their IS investments and are inadequate in meeting their needs. This result from the information system installed is inefficient, ineffective and obsolete. More so, the SMEs do not have qualified personnel to develop in-house soft wares; hence opting for the off-shelf soft wares. The research that has been done in other counties shows that the human threatening factors in the post-implementation of information systems are the main reasons for the malfunctioning of the information technologies. These human factors include the users who may lack adequate training to handle the systems; and lack of sufficient communication from the executives on the systems. The study has used the Information Systems Continuous Theory, Technology Fit Model and the TOE Framework. The research used descriptive survey research design which was employed to enable the researcher to obtain the required information. The population of the study was 800 SMEs in Meru County. The sample size was 240 which was 30% of the total population of SMEs using the information systems. The research data was collected using questionnaires which had both open ended and closed questions. The data collected was analyzed using SPSS (version 20). The data was edited and coded in SPSS for analysis. In addition to descriptive analysis; factor analysis and analysis of variance (ANOVA) were also made to gain more insight about the factors that affect post-implementation. The study concluded that implementation of information systems led to efficiency in report generation, enhanced data sharing between departments and improved access to information in the SMEs operating in Meru County. Subsequently, there was swiftness in access to information, improved customer service and enhanced the level of output. In addition, the criteria for evaluating an information system include user friendliness, functionality, user performance and acceptance. Hence an information system implemented in SMEs should be user friendly, perform the functions for which it was meant to and enhance user performance. Also; the high costs involved in upgrading were the major obstacles of information systems at the post implementation level. Some of the recommendations put forth were that the users should be engaged in continuous training and upgrading in order to handle the information systems effectively
Framework for Adoption of Cloud Computing by Small and Medium-Sized Enterprises in Meru County.
(KeMU, 2021-03) Odero, Eunice Achieng
Cloud Computing is a technology that has emerged in the market and it is widely used across the world for data processing of information and storage capability. It is a growing trend and is seen as a game-changer to SMEs business growth and development as far as technology deployment and usage is concerned. Organizations everywhere are trying to leverage Cloud Computing to achieve business missions and goals. Many believe it's just a marketing strategy while others see it as a benefit on how information technology is delivered. In the case of Kenya, many SMEs are still struggling to survive in an on-going global business cycle for growth and development. Cloud Computing on the other hand offers many opportunities that can help such SMEs improve their business and use technology more efficiently and effectively to reduce the cost of equipment and services. The main objective of the study was to develop a framework for the adoption of Cloud Computing by SMEs, covering: identification of usage, challenges and decision making support in the process of Cloud Computing adoption. The usage of Cloud Computing is still low among SMEs in Meru County mainly because of lack of expertise to give them training. Moreover, Cloud Computing adoption is impeded by the competing interest to deal with challenges such as poor network connectivity, need to ensure ease of use, ensure manageable cost implications and competition. The study developed a framework for adoption of Cloud Computing to be used by SMEs in Meru County. The theoretical underpinning of the framework is based on the theories of reasoned action, technology acceptance model and interplay of technology, organization and environment among others. A descriptive research design was adopted in conducting this study and stratified sampling was done covering the various players in the adoption process who included ICT managers, business owners and consultants. The data analysis was both qualitative and quantitative, the data analysis tools used included a laptop and SPSS software. A survey was carried on SMEs within Meru County. Primary data was analyzed to show the relationship between independent and dependent variables. This study ensured that all ethical considerations such as confidentiality, privacy and integrity were observed. The results in this study shows that decision making, application used and the type of model used are the major factors to be considered for adoption. A framework was proposed that SMEs can adopt and reap the benefits of a systematic adoption of Cloud Computing. This framework is from the results of this study and it has been demonstrated that SMEs and other stakeholders will achieve cost cutting, reduced timeframes in embracing technology and enhanced fit in the technology ecosystem that the SMEs operate in. The study focused on storage of data.
A Framework for Enterprise Resource Planning Implementation Based on Key Success Factors in Kenya: A Case of Kenya Insurance Sector Organizations.
(KeMU, 2018-09) Njoroge, John Lewis
This thesis aimed to presents a framework for Enterprise Resource Planning system implementation among the Kenyan Insurance Industry, it reviewed the Critical Success Factors and analysis the relationships between success factors and indicators. The Critical Success Factors used in the research were chosen from literature review. In this study a conceptual framework was developed focusing on the Critical Success Factors are classified into four categories namely: Technology, Organization, Environment and cultures. To address the study objectives, a survey questionnaire was considered to be the most appropriate research method. Descriptive statistic method was applied to the analyzed data collected by interviewing the employees within the following organization levels: staff at management level, IT staff and users involved in the development and use of the Enterprise Resource Planning system. From the findings, lack of composition of the correct project team and failure support of the top management would highly influence the success of Enterprise Resource Planning systems implementation in insurance companies in Kenya. The study also established that the most effective means of capping these challenges would be to ensure the project was led by the right team, users involved in the Enterprise Resource Planning systems implementation should have a cross functional understanding of the organization, and the top executive lead by the Chief Executive Officer to support the project and should be seen supporting the project fully. The study recommends aligning the Cultural attributes such as dedicating project resources which include employees fully into project, the organizational attributes also need to be consider such as whether to change organizational processes to fit the Enterprise Resource Planning or whether to customize, in additional to this, consideration of Environment pressures and IT structures need to be considered for the success of the project.
Framework for Integration of Social Media for Collaborative Learning in Institutions of Higher Learning in Kenya
(KeMU, 2020-11) Joyce, Kamau Waithera
The increasing advancement in technology is creating new opportunities that can be incorporated in learning especially in institutions of higher learning. Majority of stakeholders in the institutions of higher learning, preferably the lecturers and students possess smartphones and computers as well as active social media accounts. The increased usage of social media coupled with the necessity to interact with people all over other would is posing a challenge to integrate social media into learning, that will enrich the formal learning happening in institutions of higher learning. This integration is of great importance especially during challenging and difficult times as witnessed during the Covid-19 period that saw learning come to a standstill for more than six months. Therefore, this study sought to suggest and come up with a framework for social media integration in collaborative learning, that allows learners to interact with others and tutors all over the world in sharing learning content. The study was guided by several theories that support adoption of information systems and technology among users, notably, Technology Acceptance Model (TAM), TOE Framework Model and UTAUT Model. The study employed descriptive survey research design. The target population was all the 11 institutions of higher learning in Meru County. The total number of respondents was 150 including student representatives and lecturers. Stratified sampling was used to select 108 respondents to participate in the study. Structured questionnaires were used to collect the data for analysis. Descriptive and inferential statistics were employed in data analysis. The findings of the study indicated that the institutions of higher learning allow access to social media sites using the institution’s resources but there is no specific policy on social media usage. The study also established that institutions of higher learning have reliable Internet but limited computer resources based on 80% of the respondents. Social media exposure was found to be high based on social media accounts possessed by respondents and the frequency of usage. The regression model adopted in this study indicated that the independent variables explain 93.8% of the variations in collaborative learning. The study concluded that there is positive and significant relationship between institutional governance, technological infrastructure, and social media usage/exposure on collaborative learning. Thus, a framework for social media integration would be a success considering the relationship of the independent variables. The study recommends that, administration should have a general and favourable policy regarding technology usage as well as encourage students to share learning content via social media. Further research can be undertaken to identify challenges facing information system adoption in universities
A Framework for Optimizing Pharmacy Inventory Management System Performance Using Cloud Computing and Machine Learning a Case Study of Nairobi County
(KeMU, 2024-09) Chebet, Kelvin
The purpose of this study was to address how pharmacy inventory management systems can be improved using cloud computing and machine learning. The main aim was to enhance efficacy, accuracy and efficiency in inventory management practices within the pharmaceutical sector. The problem identified was about the inefficiencies and challenges present in conventional stock control methods such as manual tracking mechanisms and outdated ones. Because of these inefficiencies, issues such as stock-outs, excesses, and lack of real-time information critical for decision-making processes arise. To overcome this challenge, a quantitative research design was used where data was collected through questionnaires and interviews from a diverse group of pharmacy personnel. The sample included public and private pharmacies in Nairobi County through stratified random sampling. The methodology involves the use of questionnaires for quantitative data collection on ongoing inventory management practices as well as technological readiness. This study expects that by utilizing cloud computing and machine learning algorithms there will be an inclusive framework created for optimizing pharmacy inventory management systems. The results indicated a need for the implementation of the proposed machine learning and cloud computing framework as the respondent indicated a high dissatisfaction it their current inventory management systems which were indicated to have major challenges that contributed to financial losses, customer dissatisfaction among other. Additionally, this research provides practical recommendations for implementing cloud computing platforms or machine learning solutions which could transform the traditional approach to inventory management thereby enhancing patient care outcomes.
ICT Implementation Framework for Managing Explicit Knowledge in Secondary Schools in Meru Central Sub-County
(KeMU, 2022-10) Arimi, Harriet Karwitha
Kenya’s vision 2030 acknowledges the centrality of the role of knowledge management systems in boosting international competitiveness, wealth creation and improving the general social welfare. However, ICT tools in knowledge creation, storage and sharing is not well articulated in Kenyan secondary schools. The purpose of this study was to examine ICT implementation framework for managing explicit knowledge in secondary schools in Meru Central Sub-county. The specific objectives were to assess the influence of ICT tools in knowledge creation, knowledge storage, knowledge sharing, and to develop an ICT framework for implementing explicit knowledge management in secondary schools. This study was anchored and guided by three theories which were: knowledge-based view theory, the Unified Theory of Acceptance and Use of Technology and the organization learning theory. Mixed methodology was used to collect data from 16 secondary schools. The schools were sampled using simple random sampling method to sample 30% of the 51 private and public secondary schools to obtain 16 sampled secondary schools. The study further used systematic proportionate sampling method to get equal proportions on the sampled secondary schools stratum. That is, boys, girls or mixed and day secondary schools to ensure good representation of schools. The 16 principals, 143 teachers, 16 school board chairpersons and 16 head prefects participated in the study. The teachers were sampled using proportional simple random sampling method.Interview, questionnaires and document analysis were used to collect data. Pre testing was done in Nkuene girls’ secondary school, Nkubu high school and Ntharene day secondary school in Imenti South Sub- County in Meru. Descriptive statistics such as percentages, frequencies and mean were used to analyze quantitive data while text analysis method was used to analyze qualitative data. The study also used system prototyping whereby an explicit knowledge management system was tested in a simple random sampled school in Meru County and put into use at Giaki secondary school for one month and the results were recorded. The study found out that e-learning was not yet fully operational in secondary schools in Meru central sub-county. There were still major set-backs like lack of proper technology and personal initiatives on technology usage by various users such as students and teachers. Therefore, the ministry of education should liaise with the ICT ministry to fasten the broadband connectivity project to secondary schools in Meru County. This will enable them have reliable internet. The principals and teachers should campaign on students’ usage of social media to expand on knowledge sharing. The ministry of education should come up with specific guidelines to develop and customize frameworks in schools. Access to the materials provided in the developed ICT based knowledge management system prototype in this study would create more knowledge, share and store new knowledge for future users. This system further provided a multivalent platform for different departments to access and manage explicit knowledge. If this system is duplicated in various secondary schools, it would be beneficial for users to have a reliable system free from third parties’ interference when managing explicit knowledge.
Information Management Systems Strategies to Support The Integration of Kenya’S E-Government Services A Case Study of The Kenya Revenue Authority
(KeMU, 2024-09) Tejwok, Simon
The purpose of this research thesis was to examine how information management system methods can be applied to successfully integrate electronic government service modules for effective service delivery and revenue collection. Despite reporting higher collections, the Kenya Revenue Authority (KRA) failed to meet its income objectives for the 2022–2023 fiscal year by Kes107 billion. The domestic excise tax was one of the bands that fell short of the goal, performing below average at 91.4 percent to achieve Kes 68.124 billion. However, the taxman claimed that earnings from the betting excise tax came to Sh6.65 billion, exceeding the objective of Kes 5.72 billion. This result was ascribed by KRA to the betting companies' smooth integration into the KRA tax system. This integration has greatly increased revenue collection and expedited the sector's tax payment procedure. The agency connected its systems with the betting companies' systems around the end of 2022. This connectivity has made it possible to collect taxes in real-time and has increased visibility into the revenue these businesses generate. This descriptive-analytical study used a descriptive research design to investigate the information systems management strategies for the integration of e-government services with the Kenya Revenue Authority as a case study. This was done on a targeted population of 256 management staff extracting a sample size of 72 respondents using structured questionnaires. Interviews were conducted on normal tax payers, corporate taxpayers from 2 select companies, 2 KRA commissioners related to the ICT integration strategy and 4 senior ICT staff attached to the ICT authority. The questionnaires received responses from 66 staff members in total, or almost 91% of the intended audience. IBM's SPSS version 26 was used to examine the data sets. The emergent independent variables (information systems, digital infrastructure and stakeholder involvement) and the dependent variable underwent qualitative data analysis. After that, inferential statistics were used to determine how information management system strategies would influence KRA system integration with the e-government platform. This included regression and correlation analysis. Information systems and stakeholder participation had a positive connection with the dependent variable. Digital infrastructure strategy did not have a significant correlation with integration of e-government services as most respondents did not agree with the compliance of standards and reliability of the current infrastructure at KRA (data center issues M=2.59, SD=1.268; Network infrastructure issues M=2.91, SD=0.944). In conclusion, the findings indicated that information systems strategy had a strong significance on the integration with e-government services despite the weak significance of the digital infrastructure variable. A proper information management system to check the flow of revenue data is necessary. This research focused on examining the utilization of information management systems integration strategies by the KRA.
Integrating Ai Tools for Real-Time Anomaly Detection in Cloud Vpns: A Case of Owncloud.
(KeMU, 2025-09) Boyani, Momanyi Zipporah
The increasing reliance on cloud-based Virtual Private Networks (VPNs) has significantly improved the security and scalability of digital infrastructures. However, the increasing complexity of these systems introduced new challenges in ensuring their security, particularly in detecting anomalies such as unauthorized access, abnormal traffic, and data breaches in real time. This research addressed the problem of inadequate anomaly detection in the dynamic cloud VPN environments by investigating the integration of Artificial Intelligence (AI) tools to enhance real-time threat detection, using OwnCloud as a case study. The research aimed to identify effective AI models for anomaly detection, develop a real-time AI-based prototype, and evaluate its performance in detecting anomalies within cloud VPN traffic. Both supervised and unsupervised machine learning techniques were explored, including Isolation Forest and Long Short-Term Memory (LSTM) models. Simulated VPN traffic data was generated using Mininet, and Apache Kafka was employed to stream this data in real time to a Spark-based AI detection engine. Anomaly detection outputs were logged and visualized using the Kibana dashboard, while alerts were configured to trigger based on spikes and deviations from normal traffic patterns. The prototype demonstrated the feasibility of AI-based tools in identifying unknown and evolving threats more effectively than traditional signature-based systems. Unlike conventional methods that rely on historical data and static thresholds, the AI-driven system adapted to emerging threat patterns and significantly reduced false positives. The comparative analysis of AI models confirmed that the hybrid (LSTM + Isolation Forest) model was the most effective AI-based approach for anomaly detection in simulated cloud VPN traffic. It not only delivered superior performance metrics but also demonstrated adaptability in real-world scenarios where labeled anomalies are scarce, and encrypted traffic restricts payload inspection. The model recorded the highest Precision of 0.94, Recall of 0.91, F1-Score of 0.92, and Accuracy of 0.93. The developed AI-based prototype system effectively achieved real-time anomaly detection in OwnCloud VPN traffic. Its hybrid architecture, based on LSTM and IF, delivered accurate, timely, and interpretable results, hence validating its potential for integration into real-world cloud security systems. The ROC curve for the real-time anomaly detection prototype revealed exceptional performance, with an AUC score of 0.98 confirming its effectiveness in distinguishing between normal and anomalous traffic. The findings highlighted the potential of AI to improve the responsiveness and accuracy of intrusion detection mechanisms in cloud-based environments. In conclusion, the research successfully demonstrated that AI tools can enhance real-time anomaly detection in the cloud VPNs, offering improved threat response and reduced false alarms. This research will add to the existing knowledge of AI integration to improve the security of cloud VPNs by exploring a case study in real life. It is recommended that future implementations expand on this approach by integrating more advanced deep learning models, refining real-time alert systems, and applying the solution to diverse cloud platforms to further validate scalability and robustness.
M-Shopping Application’S Construct: Modelling Proximity and Route Map With Regard to Consumers’ M-Shopping Behaviour in Kenyas Nairobi Metropolitan Region
(KeMU, 2024-09) NDOLO, AGBESTERS MWOVE
Mobile shopping only relates to specific elements of the purchase process, mostly in business-to-consumer and consumer-to-consumer scenarios. M-shopping is one of the most popular online pastimes in the world, with e-commerce sales reaching 4.28 trillion US dollars globally in 2020 and revenues predicted to reach 5.4 trillion US dollars in 2022. Mobile commerce (M-commerce) diverges from traditional electronic commerce (E-commerce) due to disparities in its user interface and the correlated factors of risk, interactivity, ubiquity, localization services, and patterns of usage. Social media is influencing consumer purchase decisions more and more, overshadowing conventional opinions on goods and services. This study sought to assess proximity and route map with regard to consumers’ M-shopping behavior in Kenyas Nairobi Metropolitan Region. The objectives of the study were; to investigate place convenient modelled in the current m-shopping applications, to examine the influence of proximity and route map in the current m-shopping applications and to model proximity and route map in an m-shopping application. A cross-sectional research design was adopted and a sample size of 106 respondents determined using a simple random sampling technique. The primary data was collected through structured survey questionnaires, after which, STATA software was used for its analysis. The study established a statistically significant relationship between the convenient in m-shopping applications and consumer’s m-shopping behaviour (ꭕ2=6.370a, p=.041<0.05). A statistically significant relationship between the proximity in the current m-shopping applications and consumer’s m-shopping behaviour was also noted (ꭕ2=13.234a, p=.001<0.05). Further, it established a positive statistically significant relationship between the route map in the current m-shopping applications and consumer’s m-shopping behaviour (ꭕ2=72.192a, p=.000<0.05). Analyzing the influence of proximity distance on consumer’s behavior revealed that, consumers tend to favor businesses located closer to their current location when making buying decisions through m-shopping applications. Further, provision of vendor-consumer real-time navigation assistance would enhance consumer’s behavior. Despite the high deployment of mobile shopping applications, less attention is given to the proximity distance and route map aspects in the mobile shopping landscape. Consequently, this negatively impacts on the consumer’s m-shopping behavior. Besides, this study serves as an intervention target for improving the consumer’s behavior. It’s therefore recommendable that the developers should adopt a holistic and consumer-vendor centered approach to mobile applications. In addition, implementation studies should be employed to test this model and identify other factors that may be useful to effectively improve on the m-shopper’s behavior. Future research opportunities lie in exploring advanced technologies such as augmented reality (AR) integration with route maps in M-Shopping applications or analyzing data-driven approaches for personalized recommendations based on both proximity distance and historical buying patterns. By continuously evolving strategies informed by consumer behavior analysis within M-Shopping applications, businesses can stay ahead in meeting the dynamic needs of tech-savvy consumers in markets like Nairobi Metropolitan where digital innovation continues to drive shopping trends forward.