Strathmore University

As the impact of social media grows, understanding the mechanisms through which social media affects employee behaviour increases. Employing social capital theory, we investigate the mechanisms through which social media usage affects organisational citizenship behaviour (OCB) of faculty in Kenyan private universities. OCB is an important aspect of universities’ performance, given the high level of autonomy in universities. We develop a theoretical model that posits direct links to OCB of three social media usages (social, cognitive, and hedonic) which affect OCB. We also posit indirect links (using autonomy as a mediator) that affect faculty’s intrinsic motivation for OCB. Using descriptive cross-sectional survey, a mediated model was tested on 388 faculty. Results revealed: 1) social media usage significantly impacts OCB, with social and cognitive having a positive relationship, and hedonic having a negative relationship with OCB; 2) social media usage tends to increase autonomy. Findings of this study contribute towards job performance improvement.

Human behaviour was tipped as the mainstay in the control of further SARS-CoV-2 (COVID-19) spread, especially after the lifting of restrictions by many countries. Countries in which restrictions were lifted soon after the first wave had subsequent waves of COVID-19 infections. In this study, we develop a deterministic model for COVID-19 that includes dynamic non-pharmaceutical interventions known as social dynamics with the goal of simulating the effects of dynamic social processes. The model steady states are determined and their stabilities analysed. The model has a disease-free equilibrium point that is locally asymptotically stable if R0<1. The model exhibits a backward bifurcation, implying that reducing the reproduction number below one is not sufficient for the elimination of the disease. To ascertain the range of parameters that affect social dynamics, numerical simulations are conducted. The only wave in South Africa in which interventions were purely based on human behavior was the first wave. The model is thus fitted to COVID-19 data on the first wave in South Africa, and the findings given in this research have implications for the trajectory of the pandemic in the presence of evolving societal processes. The model presented has the potential to impact how social processes can be modelled in other infectious disease models.

This article examines the challenges encountered by university law clinics in their legal aid projects that are designed to assuage national access to justice deficits. Towards the resolution of these challenges, it proposes a sustainability paradigm that bears significant implications for lawyers, legal educators and other clinic collaborators. The article begins by locating the pragmatic, moral and legal underpinnings of the social justice mission of university law clinics. The subsequent analysis of legal aid provided by universities is supported by data on clinic activity collected through interviews of clinic directors and focus group discussions with student leaders in university law clinics in Kenya. The findings reveal the multiple challenges that clinics confront as being in leadership, lack of representational capacity, inadequate resourcing, under-explored potential, poor institutional synergies and sub-optimal placement in university structures. The article then establishes a link between these setbacks and the sustainability of the clinics, arguing that reframing the problem in Kenya as one of sustainability yields potential avenues for addressing the challenges in ways that are progressive and impactful.

This study proposes a novel hybrid Firefly Algorithm, Genetic Algorithm, and Ant Colony Optimization Algorithm (FAGAACO) for spectrum allocation in TV White Space (TVWS) networks. The Genetic Algorithm (GA) was used in the design to provide cross-over chromosomes to both the Firefly Algorithm (FA) and the Ant Colony Optimization Algorithm (ACO), thereby improving the exploration abilities of FA and ACO and preventing FA and ACO from becoming trapped in local optimum. The proposed algorithm was implemented using MATLAB R2018a. Simulation results show that in comparison with a hybrid of the Firefly Algorithm and Genetic Algorithm (FAGA), the proposed algorithm achieved 13.03% higher throughput, 1.3% improved objective function value and 5.03% higher runtime due to the good accuracy of the proposed algorithm. Based on these improvements, the proposed algorithm is therefore an efficient spectrum allocation technique in TVWS networks.

We model the interaction between interest rates and equity markets using wavelet analysis. This approach allows us to study the lead–lag relationships in an intuitive way considering variation across frequencies and over time. Analysis is done progressively on varying scales where the lower scales encompass high-frequency components of the data over a shorter time scale; whereas, higher scales encompass low-frequency components over a longer time scale. We use daily data obtained from Kenya for the period October 2003–2019. The Nairobi Securities Exchange 20 share index returns are used as a proxy for equity returns; whereas, the interbank rates represent interest rates. Three key findings emerge: (1) There is at least 2-month delay in the correlation between interest rates and equity market returns, (2) The correlation is lower in the lower time scales of 4–8 days and higher in higher time scales of 512–1024 days, and (3) Equity returns lead interest rates in Kenya. Unlike common practice of assuming static relationships in asset markets, our findings reiterate the need for modelling dynamic relationships considering delays, time variation and scaling over time horizons.

Prior research on social networks and consumer technology usage has used diverse theoretical frameworks to study the extent to which social networks, in their various forms, are related to consumer technology usage. However, the adoption and utilization of these theoretical frameworks has led to fragmentation of findings, and a lack of consistency in the conceptualization and operationalization of key social network constructs. There is, therefore, a need for a comprehensive systematic review of studies on the interrelations between social networks and consumer technology usage, with a view to identifying the common areas of focus, major weaknesses, emerging trends, and directions for future research. Using the Population, Intervention, Comparison, Outcomes (PICO) framework, this paper relies on five research questions to examine the various frameworks that have been used to study social networks in relation to consumer technology usage, as well as their shortcomings in terms of consistency in conceptual frameworks, the research contexts commonly studied, areas of study focus as well as emerging trends. The paper concludes by proposing future areas of research, which include: the development of a theoretical framework to guide the study of the relationship between social networks and consumer technology usage; the moderating roles of consumer demographic characteristics; the mediating role of consumer behavioral characteristics; and the influence of technology-enabled social networks in conditioning consumer attitude towards technology and consumer technology usage in different contexts.

AI has been revolutionary in improving different professional fields. In the legal sector, AI is utilized, in a number of jurisdictions, for different purposes both at the bar and bench level. The study investigates the efficacy of an AI algorithm in completing missing data in digitized documents, i.e., how AI can be utilized to achieve data completeness of precedents in the judiciary through text classification in order to achieve an optimal foundational basis for the creation of data sets that will facilitate the utilization of AI for different purposes. The Employment and Labor Relations court is used as a case study. The study analyzed the efficacy of 5 text classifier models: passive aggressive, linear regression, decision tree, random forest, and support vector machine (SVM) model. The results obtained from the study show that text classification can be automated successfully using machine learning techniques to generate case metadata. The accuracy of the text classifier methods utilized in the study range between 82% and 98%. Despite the data limitations faced in this study, the results obtained help increase confidence that advanced NLP techniques have matured enough to be applicable to legal text in the Kenyan Judiciary. Findings from the study suggest that the success rates of the text classifier techniques are not merely dependent on text content, but the context of this content is also a determining factor - the nature of the cases and the structure of the legal system play an important role in the performance of text classifier models.

With the rapid increase of diabetes mellitus cases in the world, management and control of the disease has become a complex and highly dynamic process. This challenge requires a multifaceted approach to manage and control the complications associated with the hyperglycaemia or hypoglycaemia conditions. This paper presents a mathematical model for determining the influence of combined intervention strategies in the management and control for the plasma glucose of the type II diabetes. System dynamics (SD) techniques were used in modelling the sub-compartments of biological systems of an Identifiable Patient (IP). The system dynamic model developed gave an illustration on how typical glucose-insulin dynamics occur at different intervention strategies involving varying amounts of carbohydrates taken, intensity of physical exercises, stress levels and the amount of exogenous insulin administered. The model was conceptualized within a semi-closed loop system representing the patient ecosystem by extending the Bergman Minimal Model. Stochastic differential equations (SDE) were used to capture the non-linear, continuous time varying interactions of the measurements associated with plasma glucose-insulin dynamics. The estimated results from the model showed combined intervention strategies of reduced amounts of carbohydrates intake, reduced stress levels and varying moderately high-to-low exercise intensity at a constant unit of exogenous insulin produced good plasma glucose levels control.

Background The contribution of artefenomel to the clinical and parasiticidal activity of ferroquine and artefenomel in combination in uncomplicated Plasmodium falciparum malaria was investigated. Methods This Phase 2a, randomized, open-label, parallel-group study was conducted from 11th September 2018 to 6th November 2019 across seven centres in Benin, Burkina Faso, Gabon, Kenya, and Uganda. Patients aged ≥ 14–69 years with microscopically confirmed infection (≥ 3000 to ≤ 50,000 parasites/µL blood) were randomized 1:1:1:1 to 400 mg ferroquine, or 400 mg ferroquine plus artefenomel 300, 600, or 1000 mg, administered as a single oral dose. The primary efficacy analysis was a logistic regression evaluating the contribution of artefenomel exposure to Day 28 PCR-adjusted adequate clinical and parasitological response (ACPR). Safety was also evaluated. Results The randomized population included 140 patients. For the primary analysis in the pharmacokinetic/pharmacodynamic efficacy population (N = 121), the contribution of artefenomel AUC0–∞ to Day 28 PCR-adjusted ACPR was not demonstrated when accounting for ferroquine AUC0–d28, baseline parasitaemia, and other model covariates: odds ratio 1.1 (95% CI 0.98, 1.2; P = 0.245). In the per-protocol population, Day 28 PCR-adjusted ACPR was 80.8% (21/26; 95% CI 60.6, 93.4) with ferroquine alone and 90.3% (28/31; 95% CI 74.2, 98.0), 90.9% (30/33; 95% CI 75.7, 98.1) and 87.1% (27/31; 95% CI 70.2, 96.4) with 300, 600, and 1000 mg artefenomel, respectively. Median time to parasite clearance (Kaplan–Meier) was 56.1 h with ferroquine, more rapid with artefenomel, but similar for all doses (30.0 h). There were no deaths. Adverse events (AEs) of any cause occurred in 51.4% (18/35) of patients with ferroquine 400 mg alone, and 58.3% (21/36), 66.7% (24/36), and 72.7% (24/33) with 300, 600, and 1000 mg artefenomel, respectively. All AEs were of mild-to-moderate severity, and consistent with the known profiles of the compounds. Vomiting was the most reported AE. There were no cases of QTcF prolongation ≥ 500 ms or > 60 ms from baseline. Conclusion The contribution of artefenomel exposure to the clinical and parasitological activity of ferroquine/artefenomel could not be demonstrated in this study. Parasite clearance was faster with ferroquine/artefenomel versus ferroquine alone. All treatments were well tolerated. Trial registration: ClinicalTrials.gov, NCT03660839 (7 September, 2018).

HIV infection remains a global public health problem. Infections arising from commercial sex workers and injection drug users continue to fuel further spread of HIV, hence threatening Kenya’s Vision 2030 of achieving zero new HIV infections. In an attempt to study the transmission trend between these two risk groups, a deterministic model for the spread of HIV is formulated. The model is designed in such a way that it allows for free transition between the risk groups. The basic reproduction number is derived. The existence of a transcritical bifurcation and a possible saddle-node bifurcation is shown. The epidemiological consequence of a backward bifurcation is that the classical requirement of having the reproduction number less than unity, while necessary, is no longer sufficient for disease elimination from the population. It is further shown that in the absence of drug user saturation, the model does not exhibit this phenomenon. Numerical simulations show that an increase in the PrEP uptake leads to a decline in the number of HIV patients under ART. Thus, a combination of PrEP uptake and ART would reduce the spread of the disease appreciably. These findings can guide the policy makers on development of effective strategies aimed at limiting and eventual elimination of the spread of HIV.

This paper examines bank concentration, competition, and financial stability nexus across five emerging countries (Kenya, Tanzania, Uganda, Rwanda and Burundi) within the East African Community (EAC). The methodological approach applied provides a critical and original contribution to the existing literature by testing the various theories explaining the relationships between bank concentration, competition, and stability. A two-step system Generalised Methods of Moments (GMM) is employed on a sample of 149 banks with 1,805 annual observations over the period 2001–2018. The findings reveal that high concentration and low competition lead to more financial stability and less probability of bank default risk. In addition, a non-linear relationship between competition and stability is not observed, revealing that greater competition undermines bank stability and makes banks more vulnerable to default risk. The findings thus lend to support the concentration-stability hypothesis that greater market power leads to more bank stability even after controlling for bank-specific, industry, and macroeconomic variables. The findings provide a significant policy contribution on the trade-off between bank concentration and competition, and the evaluation of financial stability.

This paper provides a systematic review of literature on corporate risk disclosure (CRD): meaning, measures of quality of CRD and directions for future research. This was achieved by obtaining journals from the Association of Business Schools (ABS) 2021 journal ranking guide. The next step involved a detailed search on journal databases to identify how the word “quality” and the term “corporate risk disclosure” have been used. The search produced 59 accounting and non-accounting articles published between 2004 and 2021. The findings show that there is an increase in the number of studies on quality of CRD during the study period. The study also found that there are two perspectives commonly used to conceptualise quality of CRD, namely pre-modern and modern perspectives. In addition, there is no uniform basis to study and measure quality of CRD. The paper encourages researchers to precisely state their perspective of risk before engaging in quality of CRD research for their output to be meaningful. The study generates important insights for regulators and policymakers when measuring quality of CRD. © 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.

The coronavirus pandemic (COVID-19) has triggered a public health and economic crisis in high and low resource settings since the beginning of 2020. With the first case being discovered on 12 th March 2020, Kenya has responded by using health and non-health strategies to mitigate the direct and indirect impact of the disease on its population. However, this has had positive and negative implications for the country’s overall health system. This paper aimed to understand the pandemic’s impact and develop lessons for future response by identifying the key challenges and opportunities Kenya faced during the pandemic. We conducted a qualitative study with 15 key informants, purposefully sampled for in-depth interviews from September 2020 to February 2021. We conducted direct content analysis of the transcripts to understand the stakeholder’s views and perceptions of how COVID-19 has affected the Kenyan healthcare system. Most of the respondents noted that Kenya’s initial response was relatively good, especially in controlling the pandemic with the resources it had at the time. This included relaying information to citizens, creating technical working groups and fostering multisectoral collaboration. However, concerns were raised regarding service disruption and impact on reproductive health, HIV, TB, and non-communicable diseases services; poor coordination between the national and county governments; shortage of personal protective equipment and testing kits; and strain of human resources for health. Effective pandemic preparedness for future response calls for improved investments across the health system building blocks, including; human resources for health, financing, infrastructure, information, leadership, service delivery and medical products and technologies. These strategies will help build resilient health systems and improve self-reliance, especially for countries transitioning from donor aid such as Kenya in the event of a pandemic.

Purpose of Review Voluntary male medical circumcision (VMMC) has been a cornerstone of HIV prevention in Eastern and Southern Africa (ESA) and is credited in part for declines in HIV incidence seen in recent years. However, these HIV incidence declines change VMMC cost-effectiveness and how it varies across populations. Recent Findings Mathematical models project continued cost-effectiveness of VMMC in much of ESA despite HIV incidence declines. A key data gap is how demand generation cost differs across age groups and over time as VMMC coverage increases. Additionally, VMMC models usually neglect non-HIV effects of VMMC, such as prevention of other sexually transmitted infections and medical adverse events. While small compared to HIV effects in the short term, these could become important as HIV incidence declines. Summary Evidence to date supports prioritizing VMMC in ESA despite falling HIV incidence. Updated modeling methodologies will become necessary if HIV incidence reaches low levels.

An occasional recreational vacation is a necessity for many people. It provides a perfect opportunity for the body and the mind to get much-needed rest after weeks, months, or years of daunting tasks and a break from routine. It also gives people morale as they perform their usual tasks afterward. Unfortunately, many people are unable to afford a vacation not only internationally but also locally due to the high costs involved. This makes many people prefer spending their holidays with extended families, by, for instance, traveling to their rural homes as opposed to taking a vacation. To boost the tourism sector in our country that is being promoted by initiatives such as ‘Tembea Kenya’, we should encourage domestic tourism. Another challenge is the experience in hotels that some people do not like that would entirely cause them to opt to spend their holidays differently, for example, the lack of privacy in the shared accommodation facility, the limited space in hotels, the numerous restrictions, the level of cleanliness in the shared facility especially during the COVID-19 crisis, etcetera. The aim of this project was to solve the problem by coming up with a technological means of enabling people to make reservations for vacation homes with each other such that they mutually benefit from the program thus eliminating the fee for renting out the house. This solution was implemented using a web-based application and applied the K-Nearest Neighbors machine learning algorithm that was used to classify homes based on the features available.

Ring-infected erythrocytes are the predominant asexual stage in the peripheral circulation but are rarely investigated in the context of acquired immunity against Plasmodium falciparum malaria. Here we compare antibody-dependent phagocytosis of ring-infected parasite cultures in samples from a controlled human malaria infection (CHMI) study (NCT02739763). Protected volunteers did not develop clinical symptoms, maintained parasitaemia below a predefined threshold of 500 parasites/μl and were not treated until the end of the study. Antibody-dependent phagocytosis of both ring-infected and uninfected erythrocytes from parasite cultures was strongly correlated with protection. A surface proteomic analysis revealed the presence of merozoite proteins including erythrocyte binding antigen-175 and −140 on ring-infected and uninfected erythrocytes, providing an additional antibody-mediated protective mechanism for their activity beyond invasion-inhibition. Competition phagocytosis assays support the hypothesis that merozoite antigens are the key mediators of this functional activity. Targeting ring-stage parasites may contribute to the control of parasitaemia and prevention of clinical malaria.

Background Efficiency refers the use of resources in ways that optimise desired outcomes. Health system efficiency is a priority concern for policy makers globally as countries aim to achieve universal health coverage, and face the additional challenge of an aging population. Efficiency analysis in the health sector has typically focused on the efficiency of healthcare facilities (hospitals, primary healthcare facilities), with few studies focusing on system level (national or sub-national) efficiency. We carried out a thematic review of literature that assessed the efficiency of health systems at the national and sub-national level.Methods We conducted a systematic search of PubMed and Google scholar between 2000 and 2021 and a manual search of relevant papers selected from their reference lists. A total of 131 papers were included. We analysed and synthesised evidence from the selected papers using a thematic approach (selecting, sorting, coding and charting collected data according to identified key issues and themes).FindingsThere were more publications from high- and upper middle-income countries (53%) than from low-income and lower middle-income countries. There were also more publications focusing on national level (60%) compared to sub-national health systems’ efficiency. Only 6% of studies used either qualitative methods or mixed methods while 94% used quantitative approaches. Data envelopment analysis, a non-parametric method, was the most common methodological approach used, followed by stochastic frontier analysis, a parametric method. A range of regression methods were used to identify the determinants of health system efficiency. While studies used a range of inputs, these generally considered the building blocks of health systems, health risk factors, and social determinants of health. Outputs used in efficiency analysis could be classified as either intermediate health service outputs (e.g., number of health facility visits), single health outcomes (e.g., infant mortality rate) or composite indices of either intermediate outputs of health outcomes (e.g., Health Adjusted Life Expectancy). Factors that were found to affect health system efficiency include demographic and socio-economic characteristics of the population, macro-economic characteristics of the national and sub-national regions, population health and wellbeing, the governance and political characteristics of these regions, and health system characteristics.Conclusion This review highlights the limited evidence on health system efficiency, especially in low- and middle-income countries. It also reveals the dearth of efficiency studies that use mixed methods approaches by incorporating qualitative inquiry. The review offers insights on the drivers of the efficiency of national and sub-national health systems, and highlights potential targets for reforms to improve health system efficiency.

Forex markets are full of uncertainties largely influenced by the forces of demand and supply. The rates usually adjust depending on the prevailing status of the economy, politics and the monetary policies. The forex market consists of multiple dealers and online forex trading platforms. Predicting forex market prices is a complicated process and subjective in nature for forex dealers, economists and businesspersons. The potential to make losses due to poor speculative guesses is quite high for multinational corporations located in more than one economy. The aim of this study is to develop a model for forex market price prediction. We develop the model using the Data-Driven modelling technique. We source the Central Bank of Kenya's (CBK) historical dataset to achieve this. The dataset is divided into training and testing data by a splitting of 80-20. We construct the model by combining time series regression with resilient back-propagation neural networks on the select currencies. The unique behavior of each of the currency necessitated separate implementation of the model output. This realized increased accuracy and lower error levels hence efficiency and optimality. Successful predictions are conducted up-to eight months forward. We realize accuracy levels ranging 88-98% and SSE of 0.496-2.667. The wider the historical data range, the higher the accuracy and consequently, the longer the prediction horizon.

Background In most low- and middle-income countries, health facility regulation is fragmented, ineffective and under-resourced. The Kenyan Government piloted an innovative regulatory regime involving Joint Health Inspections (JHI) which synthesized requirements across multiple regulatory agencies; increased inspection frequency; digitized inspection tools; and introduced public display of regulatory results. The pilot significantly improved regulatory compliance. We calculated the costs of the development and implementation of the JHI pilot and modelled the costs of national scale-up in Kenya. Methods We calculated the economic costs of three phases: JHI checklist development, start-up activities, and first year of implementation, from the providers’ perspective in three pilot counties. Data collection involved extraction from expenditure records and key informant interviews. The annualized costs of JHI were calculated by adding annualized development and start-up costs to annual implementation costs. National level scale-up costs were also modelled and compared to those of current standard inspections. Results The total economic cost of the JHI pilot was USD 1,125,600 (2017 USD), with the development phase accounting for 19%, start-up 43% and the first year of implementation 38%. The annualized economic cost was USD 519,287, equivalent to USD 206 per health facility visit and USD 311 per inspection completed. Scale up to the national level, while replacing international advisors with local staff, was estimated to cost approximately USD 4,823,728, equivalent to USD 103 per health facility visit and USD 155 per inspection completed. This compares to an estimated USD 86,997 per year (USD 113 per inspection completed) spent on a limited number of inspections prior to JHI. Conclusion Information on costs is essential to consider affordability and value for money of regulatory interventions. This is the first study we are aware of costing health facility inspections in sub-Saharan Africa. It has informed debates on appropriate inspection design and potential efficiency gains. It will also serve as an important benchmark for future studies, and a key input into cost-effectiveness analyses.

Kenya's Ministry of Health established the Health Benefits Package Advisory Panel (HBPAP) in 2018 to develop a benefits package for universal health coverage. This study evaluated HBPAP's process for developing the benefits package against the normative procedural (acceptable way of doing things) and outcome (acceptable consequences) conditions of an ideal healthcare priority-setting process as outlined in the study's conceptual framework. We conducted a qualitative case study using in-depth interviews with national level respondents (n = 20) and document reviews. Data were analysed using a thematic approach. HBPAP's process partially fulfilled the procedural and outcome conditions of the study's evaluative framework. Concerning the procedural conditions, transparency and publicity were partially met, and were limited by the lack of publication of HBPAP's report. While HBPAP used explicit and evidence-based priority-setting criteria, challenges included the lack of primary data and local cost-effectiveness threshold, weak health information systems, short timelines, and political interference. While a wide range of stakeholders were engaged, this was limited by short timelines and inadequate financial resources. Empowerment of non-HBPAP members was limited by their inadequate technical knowledge and experience in priority-setting. Lastly, appeals and revisions were limited by short timelines and lack of implementation of the proposed benefits package. Concerning the outcome conditions, stakeholder understanding was limited by the technical nature of the process and short timelines while stakeholder acceptance and satisfaction were limited by lack of transparency. HBPAP's benefits package was not implemented due to stakeholder interests and opposition. Priority-setting processes for benefits package development in Kenya could be improved by publicizing the outcome of the process, allocating adequate time and financial resources, strengthening health information systems, generating local evidence, and enhancing stakeholder awareness and engagement to increase their empowerment, understanding and acceptance of the process. Managing politics and stakeholder interests is key in enhancing the success of priority-setting processes.