SU+ Digital Repository

SU+ is an online repository for the preservation and promotion of assorted digital content at Strathmore University

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[ISSN 2519-5883]
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Communities in DSpace

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Now showing 1 - 5 of 7

Recent Submissions

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Predicting the success of early-stage African startups using machine learning
(Strathmore University, 2025) Ndung'u, M. W.
Africa's share of global venture funding is estimated to be around 1%; meaning that only a very small portion of worldwide venture capital investment goes towards African startups. This presents a challenge for entrepreneurs, investors, and policymakers seeking to foster innovation and economic growth. This study aims to bridge this gap by leveraging machine learning models to predict the success of African startups based on key factors: business operating status, number of funding rounds, and business age. Unlike prior research, which has predominantly focused on Western markets and defined success through acquisitions or IPOs, this study specifically examines African startups, addressing the continent’s unique entrepreneurial landscape. The research utilizes CrunchBase data spanning from 2000 to 2024, encompassing 28,851 startups, applying three machine learning models—Logistic Regression, Support Vector Machines, and Random Forest—to evaluate startup success. The dataset was split into training and validation sets, ensuring robust model performance assessment. Results indicate an exceptionally high accuracy of 99-100%, with strong sensitivity but lower specificity, highlighting potential dataset imbalance. Despite this, the machine learning models outperform traditional probability-based approaches by capturing non-linear relationships and complex interactions between startup success factors. This provides a more nuanced and data-driven approach to early-stage business evaluation compared to simplistic probabilistic models. The findings offer practical implications for investors by enabling more informed decision making, for entrepreneurs by identifying key success drivers, and for policymakers by informing strategies that enhance startup ecosystems in Africa. Future work should focus on balancing the dataset, incorporating additional predictive features, and expanding testing to ensure greater generalizability. This study contributes to the growing body of research on startup success prediction, offering a tailored approach for the African market and providing valuable tools for practitioners in the entrepreneurial and investment space.
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Enhancing data protection in Kenya: evaluating the ambiguities in data retention policies and their impact on privacy and security
(Strathmore University, 2025) Wachira, G. R. N.
The President signed into law the Kenya Data Protection Act, 2019 on 8th November 2019. The Data Protection Act is an answer that aimed at ensuring Kenyans were empowered with enforceable privacy rights over their personal information, while providing clear guidelines for private and public institutions to handle their users’ data with care, due to the increased call for protection of both personal and private information, which may be readily and easily accessible in this digital era.
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Kenya’s Data Protection Act and its jurisdictional reach: governing cross-border data transfers
(Strathmore University, 2025) Mureithi, R. I. N.
This dissertation examines the jurisdictional reach of Kenya’s DPA in regulating cross border transfers, focusing on its extraterritorial enforcement and compliance. The study evaluates whether the DPA adequately addresses the risks and challenges posed by international data transfers, emphasizing its role in safeguarding the right to privacy. The dissertation also takes a step at critically assessing the Data Protection Act's extraterritorial jurisdiction. Furthermore, the study outlines Kenya’s current legal framework. Evidence of gaps in enforcement and compliance is presented, alongside risks associated with inadequate regulation of cross-border data transfers. The findings underscore the need for policy and legal reforms to enhance the DPA's efficacy. This research offering insights into enhancing the DPA’s jurisdictional reach and fostering data privacy in a globalized digital environment.
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Integrating Kenya’s visa policy shift with its counterterrorism measures; enhancing border security framework on the Kenya - Somalia border
(Strathmore University, 2025) Gitau, L. W.
With the introduction of a new digital platform for tourist authentication, there seems to be a disregard to the country’s possible exposure to insecurity, given Kenya’s history of terrorist attacks. The study seeks to infer the impact of Kenya’s transition to an open visa policy on national security, with a specific focus on its insufficient integration with counterterrorism measures. The analysis focuses on synchronising the existing counterterrorism policies with the open visa policy in order to enhance the functionality of the visa as a line of defence against terrorism. This study addresses gaps within current national frameworks in effectively mitigating foreign threats so as to amplify security measures and counter potential threats while facilitating the free movement of people under Kenya’s new open visa status
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The Feasibility of the implementation of the Twin-Peaks model of financial regulation in Kenya
(Strathmore University, 2025) Mutuma, M. K.
This study investigated the inefficiencies of Kenya’s fragmented sectoral financial regulatory system and its vulnerability to systemic collapse, hypothesizing that adopting the Twin-Peaks model, which features two specialized regulators, could enhance stability and consumer protection. The sectoral model, where regulators oversee specific institutions regardless of industry, suffers from overlap and confusion, undermining its effectiveness amid potential economic crises. The research explored whether the Twin-Peaks model could address these flaws by examining its application elsewhere. Key research questions included: how does Kenya’s sectoral model impact financial stability; what benefits has the Twin-Peaks model delivered in other jurisdictions with a similar financial sector history; and is transitioning to this model feasible for Kenya? Data was collected by applying the doctrinal legal research method in the performance of a comparative analysis; reviewing the Twin-Peaks model’s implementation in a similar jurisdiction, alongside Kenya’s financial performance metrics and regulatory reports. Findings revealed that the sectoral model’s inefficiencies heighten risks of instability, while the Twin-Peaks model, with its streamlined dual-regulator structure, fosters transparency, competition, and resilience, as evidenced in the selected jurisdiction. The study recommends Kenya adopt the Twin-Peaks model to eliminate regulatory overlap, strengthen oversight, and safeguard against economic shocks. A phased transition, supported by stakeholder collaboration and capacity building, is advised to ensure successful implementation.