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Access status: Open Access ,
RELATIONSHIP BETWEEN GUEST EXPERIENTIAL VALUE AND BEHAVIORAL INTENTIONS OF BUDGET HOTELS IN MACHAKOS COUNTY, KENYA: THE MODERATING ROLE OF PRICE
(Murang’a University of Technology, 2026-08) Mwalimu, Zipporah Mueni
The growing demand for affordable yet high-quality hospitality experiences has driven the rise of budget hotels in Kenya. However, despite their increasing presence, limited empirical evidence exists on how guest experiential value particularly service quality, guest perception, and satisfaction influences behavioral intentions in this segment. Previous studies in Kenya have predominantly focused on luxury or mid-tier hotels, leaving a contextual and empirical gap regarding how experiential value shapes repeat patronage and positive word-of-mouth in budget hotels. This study, therefore, sought to evaluate the relationship between guest experiential value and behavioral intentions of budget hotels in Machakos County, Kenya, a moderating role of price.The study was guided by the Expectancy Disconfirmation Theory, Theory of Reasoned Action, and Rational Choice Theory. A mixed-method approach employing a convergent parallel research design was adopted. The target population comprised 45 registered budget hotels in Machakos County. Using purposive sampling, 10 hotels were selected, from which 10 managers (one per hotel) and 344 guests were drawn, making a total of 354 respondents. Data were collected using structured questionnairesfor guests and semi-structured interview guides for managers. Quantitative data were analyzed using descriptive and inferential statistics, including ordinal logistic regression and Spearman’s rho correlation, while qualitative data were analyzed thematically. Findings revealed that service quality (β = 4.294, p = 0.000), guest perception (χ² = 299.959, df = 1, p = 0.000), and guest satisfaction (β = 2.427, p = 0.000) significantly influenced behavioral intentions. Furthermore, price significantly moderated this relationship (Measure = 0.620; p = 0.015). Thematic analysis of qualitative data highlighted guests’ appreciation for affordability and cleanliness but pointed to the need for improved staff responsiveness and amenities. The study concludes that enhancing service quality, managing guest perceptions, and maintaining fair pricing are critical to strengthening guest loyalty and positive behavioral intentions. It recommends that hotel managers adopt value-based pricing strategies, invest in service quality training, and leverage guest feedback systems for continuous improvement. The study contributes to knowledge by extending the application of behavioral and economic theories in explaining consumer behavior within Kenya’s budget hotel sector and provides an empirical basis for policy and managerial decisions promoting sustainable hospitality growth.
Access status: Open Access ,
A HYBRID MODEL FOR IMPROVING THE EFFICIENCY OF FOG LAYER IN THE INTERNET OF THINGS
(Murang’a University of Technology, 2026-08) Kanuku, Watson Musyoki
Fog computing relies on many interconnected, decentralized devices working together to process fast moving and diverse data. The rapid growth and complexity of this data, in terms of volume, speed, variety, and variability, often slow down data transfer and processing. This delay affects real-time IoT applications like smart healthcare and autonomous vehicles. Despite the importance of responsive data handling, fog layer efficiency has not received enough attention. With more edge devices coming online, there's an urgent need for faster data processing and response. This study developed a hybrid machine learning model combining DBSCAN for clustering, and KNN for classification, to improve fog computing efficiency. A mixed methods approach grounded in pragmatic research philosophy was used. First, a systematic literature review identified key variables influencing fog efficiency. These were ranked using a Random Forest-Recursive Feature Elimination (RF-RFE) algorithm. Input from IoT experts selected purposively was sought through a survey. Snowball sampling was used to identify 30 respondents to validate the ranking. Key variables included Packet Loss Rate, Queue Time, Latency, Channel Utilization, and Throughput. Using a Design Science Research framework, the hybrid DBSCAN+KNN model was developed and tested using the YAFS simulator. The hybrid model outperformed the standard KNN algorithm, achieving 95.33% accuracy, compared to KNN’s 93.67%. It also reached a recall of 99%, surpassing both classic KNN 95.2% and ensemble KNN 89.68% from previous studies. These results show that DBSCAN effectively pre-processes data by grouping similar values and reducing noise before classification by KNN, leading to faster and more accurate processing. This hybrid approach offers a promising way to boost fog efficiency in time-sensitive IoT environments, unlocking the full benefits of smart devices.
Access status: Open Access ,
SUPPLY CHAIN INNOVATION AGILITY, FIRM SIZE AND DEMAND FORECASTING PERFORMANCE OF OIL MARKETING COMPANIES IN KENYA
(Murang’a University of Technology, 2026-07) Ruth, Wanjiku Nyokabi
Demand forecasting performance has remained a challenging subject in supply chain management due to the difficulty involved in accurately planning, tracking, and monitoring demand in unpredictable markets. Innovation plays a critical role in improving forecasting accuracy and enabling real- time demand tracking. The purpose of this research was to determine the significance of actively incorporating agile supply chain innovations in oil marketing companies to enhance the accuracy and timeliness of demand forecasting. Specifically, the study sought to analyse the influence of business process innovation on the demand forecasting performance of oil marketing companies in Kenya; examine the influence of technological innovation on the demand forecasting performance of these companies; assess the influence of network structure innovation on demand forecasting performance of oil marketing companies in Kenya; establish the impact of customer- centric innovation on demand forecasting performance of oil marketing companies in Kenya; and evaluate the moderating influence of firm size moderates the relationship between supply chain innovation agility and demand forecasting performance. The research was guided by four theories: Dynamic Capabilities Theory, Systems Theory, Resource- Based Theory, and Network Theory. A descriptive design with a mixed- methods approach was adopted. The target population comprised 145 registered oil marketing companies in Kenya. A purposive sampling technique was used to select 81 companies. The study utilised both open- ended questionnaires and secondary data sources. Quantitative data were analysed using SPSS version 27, while qualitative data were analysed manually. The adjusted R2indicated that 81.1% of the variables accounted for demand forecasting performance, with BPI and TI showing positive but not significant effects, and CCI and NSI demonstrating positive and significant effects. Introducing firm size as a moderator increased the adjusted R2to 94.5%. However, after moderation, there was a partial significant effect, and the influence on demand forecasting performance decreased; BPI (β = -0.065, p = 0.018), NSI (β = -0. 078, p = 0.273), CCI (β =- 0.177, p = 0.005) had negative effects, while TI (β = 0.047, p = 0.513) had a positive effect. The study concluded that, individually, the SCIA components were positive and significant; however, when regressed together, they tend to share resources, thereby reducing explanatory power and significance. The study also concluded that firm size moderation was partial and context-specific. The study thus recommends decentralising decision-making, empowering regional units, streamlining internal processes to increase flexibility and responsiveness, and investing in integrated digital platforms, real-time data-sharing systems, and collaborative forecasting practices to improve visibility across the supply chain. It also proposes leveraging advanced customer analytics, market intelligence systems, decentralised customer relationship management, and IoT and digital twin technologies that precisely adjust demand, monitor petroleum consumption rates, and visualise the supply chain to identify trends that may influence future demand.
Access status: Open Access ,
A DEEP LEARNING MODEL FOR DECISION SUPPORT OF STAKEHOLDERS IN CROP DISEASES AND PEST MANAGEMENT
(Murang’a University of Technology, 2026-08) Ochango, Vincent Mbandu
Crop diseases and pest infestations pose a serious threat to food security and the livelihoods of farmers across the world, posing substantial difficulties to the agricultural industry. Stakeholders, such as farmers, agronomists, and agricultural extension officers, need decision support systems that are both accurate and fast to handle these difficulties. A deep learning model tailored to decision support in crop disease and pest management was the intended outcome of this study. The goals were to create a hybrid model for correct crop diseases and pest classification using a deep neural network, create an easy-to-use software platform, validate the model's generalizability in different agricultural contexts, and identify and analyze important factors affecting decision support. The first objective of the research included a thorough examination of the main features of deep learning models that influence decision support for crop diseases and pest management. The collection of highresolution, labelled datasets of crop images from the Kaggle website was done with an emphasis on data quality. These datasets represented diverse forms of crop diseases and pest infestations. In order to increase the classification accuracy, a hybrid model was built that combined a Convolution Neural Network (CNN) with Support Vector Machines (SVM). This model leveraged the capabilities of both techniques. Stakeholders were able to upload images of affected crops and get prompt diagnoses and recommendations when the model was integrated into an intuitive software platform. Extensive testing was conducted across several datasets, including tomatoes, maize, and pests, to assess the model's robustness and generalizability. On the maize data set, the hybrid CNN-SVM model achieved 97.13% accuracy, on the tomato dataset, it achieved 97.04% accuracy, while on the pest dataset, it achieved 97.02% accuracy. Analyses comparing DenseNet, AlexNet, EfficientNet, and VGG showed that their accuracy rates were lower; DenseNet achieved 96.99% for maize, 92.60% for tomatoes, and 96.94% for pest classification. Stakeholders were able to improve their capacity for proactive pest and disease management in crops through the use ofthe user-friendly software platform's proactive decision support. Data quality, efficient feature extraction, model design, and environmental conditions consideration were some of the important factors emphasized by the study as impacting decision support in crop diseases and pest management. In order to get reliable predictions, the results highlighted the need to use high-quality training data and complex model architectures. The study proved that deep learning models greatly enhancedagricultural decision-making by addressing problems related to crop diseases and pest management. When it came to decision support for stakeholders dealing with pest control and crop diseases, the developed hybrid model and its incorporation into an intuitive software platform provided substantial improvements. The hybrid model,according to the research, improved agricultural sustainability and production by providing correct insights in a timely manner. To achieve broad acceptance and influence in the agricultural sector, future work should attempt to enhance the software platform, validate the model's performance across several agricultural settings, and increase the datasets. The findings and methods presented here transformed crop diseases and pest management by providing useful information for decision support systems in the agricultural sector.
Access status: Open Access ,
ANALYSIS OF TERRESTRIAL RADIONUCLIDES ( 232TH, 238U, 40K) AND SELECTED HEAVY METALS IN SEDIMENT, WATER, FISH AND FOOD CROPS FROM THE LAKE TURKANA REGION
(Murang’a University of Technology, 2026-02) Atambo, Vincent Otwori
Human activities such as mining and natural processes like earthquakes can release radionuclides and heavy metals into the environment, which are then transported by water into reservoirs, sometimes exceeding safe levels. Elevated concentrations of these elements threaten aquatic ecosystems, reduce water quality, and can bioaccumulate in food chains, posing risks to human health. The Lake Turkana Basin, an important source of water, and livelihoods for surrounding communities, is particularly vulnerable, making baseline data critical for environmental monitoring and public health protection. Previous studies on RiftValley lake systems have indicated that geological formations often contribute to elevated background radiation and mineral leaching into the water column. The objective of this research was to establish a radiological and elemental baseline for the Lake Turkana Basin by quantifying the activity concentrations of 232Th, 238U, and 40K, and assessing heavy metal toxicity in both geological and biological matrices to evaluate the associated public health risks. The objective was realized by studying sediment and water samples collected from the eight centers found along the Weatern shores of Lake Turkana namely Kerio, Eliye, Kalokol, Long’ech, Kataboi, Nachukui, Nareng’ewoi, and Nariokotome. Sediment and water samples were also collected from River Turkwel at Lodwar town and Kang’atosa. Sorghum and cassava were obtained from ten farms at Kang’atosa, and fish were sourced from Kalokol and Long’ech fish markets. The samples for radiometric analysis were dried to remove moisture, homogenized to create a fine, uniform powder, and sealed in standardized counting containers to establish secular equilibrium before measurement. Samples were analyzed for radionuclide concentrations of 232Th, 238U and 40K using a High Purity Germanium (HpGe) detector while Microwave Plasma–Atomic Emission Spectroscopy (MP-EAS) was used to analyze concentrations of the metals lead, copper, arsenic, cadmium, chromium, nickel, zinc and barium in water and fish samples. Before heavy metal analysis, water samples were filtered, immediately acidified with concentrated nitric acid to prevent metal precipitation, fish sampleswere dried, homogenized, and then underwent wet acid digestion (usually using nitric acid to dissolve the organic matrix and fully release the heavy metals into a liquid solution suitable for MP-AES nebulization. The activity concentrations of 232Th, 238U and 40K were used to determine the radiation hazard indicators in sediment samples such as Absorbed Dose Rate (ADR), Annual Effective Dose (AED), Excess Lifetime Cancer Risk (ELCR), External Hazard Index (Hex), Internal Hazard Index (Hin) and the Gamma Index. Also, the activity concentrations of the three radionuclides was used to determine AED in cassava and sorghum samples. Descriptive statistics was used to statistical variables such as the mean, standard error, standard deviation, variance, skewness, kurtosis and range in the data. Analysis of Variance (ANOVA) between groups for different variables in sediment samples was done. Heavy metal concentrations were used to determine water quality indices as well as health risk parameters in fish samples. Pearson correlation was used to determine the correlation between activity concentrations of 232Th, 238U and 40K in fish samples and concentrations of As, Ni, Ba, Cu, Zn, Pb, Cd and Cr in fish samples. Results found sediments from Kalokol to have the highest activity concentration levels of 232Th (75.5 Bq/kg) and 238U (64.5 Bq/kg), while sediments from Nareng’ewoi had the highest activity concentration level of 40K (811.7 Bq/kg). Overall average activity concentrations in sediments from all regions were 38.7 ± 7.53 Bq/kg for 232Th, 30.1 ± 5.80 Bq/kg for 238U, and 664.4 ± 65.43 Bq/kg for 40K. Although average concentration levels of 232Th and 238U were below world averages, 40K exceeded them. All radium equivalent activity values for sediments were safely below the 370 Bq/kg limit. In agricultural products, activity concentration of 40K was notably high in sorghum (699.34 Bq/kg) and cassava (804.42 Bq/kg). Fish species also showed varying radionuclide concentration levels, with Nile Perch having the highest 238U (49.56 ± 2.81 Bq/kg). Radiological hazard indicators in sediments were generally within permissible safety limits. Although the ingestion dose from cassava (0.152 mSv/yr) remained within safe limits, the average dose from sorghum (1.110 mSv/yr) exceeded the ICRP recommended limit. Surface water analysis revealed Arsenic as the most prominent heavy metal (0.756 ± 0.033 mg/L average). The Metal Contamination Index (0.0002–0.0026) was low, but the Heavy Metal Evaluation Index (46.207–122.431) indicated significant pollution, mainly from arsenic (As) and zinc (Zn). Cadmium (Cd) was undetected in fish, while Nile perch showed the highest concentrations of As (0.732 ± 0.045 mg/kg) and Zn (0.769 ± 0.057 mg/kg), as well as the greatest bioaccumulation. Health risk assessment from fish consumption revealed negligible non-carcinogenic risks, with hazard index values below 1. Correlation analysis showed strong positive relationships between ²³⁸U and As (r = 0.900, p = 0.001) and between ²³⁸U and Zn (r = 0.792, p = 0.011). This research establishes a vital geochemical baseline for the Lake Turkana Basin, revealing a complex interplay between natural geological background radiation and anthropogenic heavy metal enrichment. While the radiometric profile characterized by elevated 40K but generally safe levels of 232Th and 238U reflects the unique volcanic lithology of the East African Rift, the significant Arsenic and Zinc signatures in surface waters point toward localized pollution that necessitates active environmental oversight. Crucially, the discovery that sorghum consumption in certain areas may lead to an ingestion dose exceeding ICRP limits (1.110 mSv/yr) underscores a specific public health vulnerability that simple water testing would miss. Ultimately, while the basin remains largely resilient to non-carcinogenic risks at present, these findings serve as a critical early-warning system, advocating for integrated water-soil-food monitoring to ensure that the region’s mining and agricultural sectors do not compromise the long-term biological integrity of the ecosystem.