Research Articles

Knowledge Generation and Strategy Implementation in Public Sector Organizations in Kenya: A Case of the Kenya Bureau of Standards.

Fri, 06 Feb 2026 00:00:00 GMT

Knowledge Generation and Strategy Implementation in Public Sector Organizations in Kenya: A Case of the Kenya Bureau of Standards. Kang’iri, N. J.,; Muchangi, D.,; Odunga, P. This study examined the influence of knowledge generation on strategy implementation in public sector organizations in Kenya, with a focus on the Kenya Bureau of Standards. Government institutions generate vast amounts of knowledge; however, they lack structured frameworks for knowledge generation leading to inefficiencies in strategy implementation. The study was guided by Resource-based view theory. The study adopted mixed-methods research design, incorporating descriptive, case study, and correlational approaches to allow for both broad and in-depth exploration of the study variables. The research was conducted at KEBS offices countrywide, with a target population of 1,080 employees. A pilot study was done at Nakuru with 36 participants, while the final sample size was 292 employees. Stratified random sampling was used to ensure a diverse representation of employees across various departments. Primary data was collected using structured questionnaires and in-depth interviews, while secondary data was obtained from policy documents, institutional reports, and relevant literature. Quantitative data analysis involved descriptive and inferential statistics using SPSS. Descriptive statistics, including measures of central tendency, frequency distributions, and percentages, were used to summarize data. Inferential analysis, including correlation and multiple regressions were conducted to assess the relationships between knowledge management practices and strategy implementation. Qualitative data was analyzed thematically to provide deeper insights into the impact of knowledge management on strategic processes. Overall, the study showed that effective knowledge generation positively influences strategy implementation in the public sector. The results revealed that knowledge generation is a moderate, significant and positive influencer of strategy implementation. The study recommended that the organization’s management review its knowledge generation policy to update and align it to the strategic goals of the organization.

Automorphism of Zero-Divisor Graphs of Nilradicals of Semilocal Ring

Fri, 20 Feb 2026 00:00:00 GMT

Automorphism of Zero-Divisor Graphs of Nilradicals of Semilocal Ring Kiplagat, P; Lao, H; Kayiita, Z. A zero-divisor graph of a commutative ring R denoted as Γ(R), is a graph whose vertices are the zero divisors of the ring. Any two distinct vertices of the graph are incident if and only if their product is zero. Zero-divisor graphs provide a powerful interface between commutative algebra and graph theory by encoding algebraic annihilation relations into combinatorial structures. While the graph-theoretic properties of Γ(R) have been extensively studied, comparatively little is known about their automorphism groups, particularly for graphs arising from nilradicals of semilocal rings. In this paper, we investigate the automorphism groups of Γ(R) associated with the nonzero nilradical of the semilocal ring Zpnqn, where p ̸= q are primes and n ≥ 2. We show that the valuation structure induced by the prime-power decomposition yields a canonical partition of the vertex set into invariant layers. This stratification rigidly constrains graph automorphisms and forces the automorphism group to decompose as a direct product of symmetric groups indexed by valuation levels. Explicit formulas for these automorphism groups are obtained, thereby extending and unifying earlier results for local rings.

Caregiver Sociodemographic Factors Associated with Adherence to Zinc Treatment of Childhood Diarrhea in Kirinyaga County, Kenya.

Sun, 28 Dec 2025 00:00:00 GMT

Caregiver Sociodemographic Factors Associated with Adherence to Zinc Treatment of Childhood Diarrhea in Kirinyaga County, Kenya. Mwangi, L. W.,; Munyekenye, G; Nderu, D. Background: Zinc supplement is critical for managing childhood diarrhoea. However, adherence to zinc treatment remains low in low-resource settings. This study determined the association between zinc utilization for treatment of diarrhoea among children below five years old and caregivers’ sociodemographic factors in Kirinyaga County, central Kenya. Methods: A cross-sectional study was conducted from February to March 2025, involving 223 caregivers and 25 healthcare workers across four hospitals. Results: Only 20% (45/223) of children in this study received zinc treatment for the recommended period of 10–14 days. Low adherence to zinc treatment was associated with caregiver level of education and employment status (p<0.05). Lack of IMNCI training (96%; 24/25) among health workers was observed, despite most of them having access to the IMNCI guidelines. Conclusions: Findings of this study underscore the need to sensitize caregivers on the benefits of zinc supplements in management of childhood diarrhea. These efforts should be complemented with periodic health worker training on guidelines that support effective management of childhood diarrhea in Kirinyaga County, particularly the IMNCI protocol.

A Systematic Literature Review on Phishing Email Detection Using Natural Language Processing Techniques

Fri, 24 Jun 2022 00:00:00 GMT

A Systematic Literature Review on Phishing Email Detection Using Natural Language Processing Techniques Muriithi, N. M.,; Mwangi, E; Malanga, K. Every year, phishing results in losses of billions of dollars and is a major threat to the Internet economy. Phishing attacks are now most often carried out by email. To better comprehend the existing research trend of phishing email detection, several review studies have been performed. However, it is important to assess this issue from different perspectives. None of the surveys have ever comprehensively studied the use of Natural Language Processing (NLP) techniques for detection of phishing except one that shed light on the use of NLP techniques for classification and training purposes, while exploring a few alternatives. To bridge the gap, this study aims to systematically review and synthesise research on the use of NLP for detecting phishing emails. Based on specific predefined criteria, a total of 100 research articles published between 2006 and 2022 were identified and analysed. We study the key research areas in phishing email detection using NLP, machine learning algorithms used in phishing detection email, text features in phishing emails, datasets and resources that have been used in phishing emails, and the evaluation criteria. The findings include that the main research area in phishing detection studies is feature extraction and selection, followed by methods for classifying and optimizing the detection of phishing emails. Amongst the range of classification algorithms, support vector machines (SVMs) are heavily utilised for detecting phishing emails. The most frequently used NLP techniques are found to be TF-IDF and word embeddings. Furthermore, the most commonly used datasets for benchmarking phishing email detection methods is the Nazario phishing corpus. Also, Python is the most commonly used one for phishing email detection. It is expected that the findings of this paper can be helpful for the scientific community, especially in the field of NLP application in cybersecurity problems. This survey also is unique in the sense that it relates works to their openly available tools and resources. The analysis of the presented works revealed that not much work had been performed on Arabic language phishing emails using NLP techniques. Therefore, many open issues are associated with Arabic phishing email detection