Faculty of Computing ,Library and Information Science (FCLIS).

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Open Access
The Role of Artificial Intelligence (AI) in Transforming Small and Medium Enterprises (SMEs):
(Applied Data Science and Analysis, 2025) Mugisha, Justin; Francis Adyanga, Akena; Ocan, Johnson
AI is of great interest to researchers and practitioners as a means of achieving the necessary progress in the business industry. However, the role of AI in transforming SMEs is not well documented. The study assessed the role of AI in transforming SMEs globally. The study investigated the current state of AI in SMEs, challenges, and opportunities. This study reviewed a total of 1,021 published articles, mainly from 1992-2024. The review was performed using scientifically cited and indexed databases, namely Dimensions, Web Science, Elsevier Scopus, and Google Scholar. The study demonstrates how AI enables SMEs to improve competitiveness, streamline operations, and conform to sustainability objectives by tackling particular issues such as scarce resources, operational inefficiencies, and cyber threats. The study closes knowledge gaps in how SMEs, particularly those with limited resources, might benefit from affordable AI tools and platforms. Also, it was found that building workforce capacity through collaborations and customized training programs can help close the skills gap, while improving cybersecurity and implementing efficient data management frameworks can help with privacy issues. However, despite the growing frame of literature on AI packages, studies specializing in AI embracing on the organizational level stay restrained. The study findings emphasized regional integration within the EAC through technology transfer and the development of SME capability. The current study aligns with Uganda’s NDPIII (2020/21–2024/25), under the innovation and technology application pillar, accelerating industrial growth.
Open Access
Deep Learning Techniques in DICOM Files Classification: A Systematic Review
(BON VIEW PUBLISHING PTE.LTD., 2025) Mabirizi, Vicent; Kawuma, Simon; Natumanya, Deborah; Wasswa, William
The digital imaging and communications in medicine (DICOM) format is a widely adopted standard for storing medical imaging data, integrating both image and metadata critical for clinical diagnostics. However, its complexity poses challenges for deep learning applications, particularly in extracting and processing this dual-layered data. This review analyzes 23 peer-reviewed studies published between 2014 and 2024, sourced from PubMed, Google Scholar, PLOS, Science Direct, and IEEE databases. Guided by Arksey and O’Malley’s scoping methodology, the review reveals that existing deep learning techniques typically rely on converting DICOM images into simpler formats like JPEG, TIF, or PNG for classification, a process that often results in metadata loss and reduced classification accuracy. Frameworks such as MONAI, NVIDIA Clare, SimpleITK, and OpenCV facilitate direct DICOM processing but face limitations, including overfitting, challenges with data heterogeneity, and inefficiencies in handling large datasets. This review emphasizes the urgent need for developing a robust convolutional neural network architecture capable of directly processing DICOM data to preserve metadata integrity and enhance predictive performance, paving way for more reliable and scalable medical imaging solutions.
Open Access
Green gram yield prediction using linear regression
(African Journal of Rural Development, 2025) Tumusiime, Robert; Mabirizi, Vicent; Mirembe, D.P; Arinanye, T.R
Predicting crop yields before harvest is key in enabling farmers make critical decisions as far as postharvest management is concerned. Besides, yield prediction plays a critical role in agriculture enterprise selection hence promoting food and nutrition security in a community. It is worth noting that various factors including ecological zones characteristics and farm management practices can vary significantly from season to season and farm to farmer, hence affecting crop yields. Given the importance of crop yield prediction in agriculture enterprise development and investments, a number of approaches have been adopted by farmers and breeders alike. These approaches range from controlled ideal condition analysis by breeders to the use of advanced plant physiological feature analysis using satellite image processing techniques. While a number of popular crops like rice and maize have a number of models proposed, limited yield prediction studies have been done on neglected crops like green gram. Therefore, this paper discusses the proposed green gram crop yield prediction model based on a stepwise linear regression technique using ecological zone characteristics, farm management practices and historic crop yield as the key variables. The study used a dataset of 107 records (gardens) and 9 features obtained from National Semi-Arid Research Institute (NaSARRI), Serere, Uganda. The predictor variables used were; soil type, soil PH, soil fertility, rainfall distribution, weeding practice, pest and disease management, fertilizer application, plant spacing, and cropping system. The model was evaluated for precision and evaluation result revealed that, with a mean absolute percentage error (MAPE) of 16.8%, the proposed model had a precision of 96.4% was deemed accurate in predicting green gram yield.
Open Access
Electronic Marketing and Service Quality of on-Line Merchandisers: A Case Study of Jumia Uganda.
(Kabale University, 2024) Ssemaluulu, Paul Mukasa; Isingoma, Fenehansi; Mugavu, George
The study examined electronic marketing and service quality of online merchandisers, a case study of Jumia Uganda. The specific objective was to find out the relationship between cart abandonment rate and service quality in Jumia Uganda. The research employed a cross-sectional survey design to investigate the association between electronic marketing efforts and service quality. A mixed-methods research approach, incorporating both quantitative and qualitative methodologies, was deemed highly relevant for this study. In addition, from the 255 target respondents, a definite sample size of 101 was determined using William G. Cochran's (1997) formula. The study revealed significant correlations between various factors in the context of Jumia Uganda's electronic marketing. The study revealed that a moderate positive correlation (r = 0.422**) was identified between cart abandonment rate and service quality, emphasizing the critical role of service quality in customer retention and reducing cart abandonment. It was recommended that: Jumia should launch remarketing campaigns to re-engage customers who abandoned their carts. Personalized messages and incentives should be used to encourage them to return and finalize their transactions.
Open Access
Diagnosis and Classification of Tuberculosis Chest X-ray Images of Children Less Than 15 years at Mbarara Regional Referral Hospital Using Deep Learning.
(Kabale University, 2024) Kawuma, Simon; Kumbakumba, Elias; Mabirizi, Vicent; Nanjebe, Deborah; Mworozi, Kenneth; Mukama, Adolf Oyesigye; Kyasimire, Lydia
Tuberculosis (TB) is an underestimated cause of death in children, with only 45% of cases correctly diagnosed and reported. It is estimated that 1.12 million TB cases occurred among newborns, children, and adolescents aged less or equal 14 years. In Uganda, TB prevalence is 8.5% in children and 16.7% in adolescents. Treatment and diagnosing TB is challenging and its high mortality rate is due to many lacks in the diagnosis of this illness especially among children. As a strategy to curb TB mortality rate in children, there exists a need to improve and expedite the screening for TB among children. Chest X-ray (CXR) is commonly used in TB burdened countries like Uganda to diagnose TB patients but interpretation of the patient’s radiograph needs skilled radiologists who are few. To this end, this research aims to close the TB mortality gap in children by applying AI, primarily deep learning techniques, to detect TB in children. The study created five models, one from scratch and four pre-trained Transfer Learning (TL) and were trained and verified using digital CXR radiograph images of children who visit the TB clinic at Mbarara Regional Referral Hospital. The model classifies clinical images of patients into normal or Tuberculosis. TL models; VGG16, VGG19, Inception V3, and ResNet50 outperformed scratch model with validation accuracy of 79.91%, 69.21%, 53.0%, 51.09% and 50.01% respectively. We hope that once the deep learning models are implemented and adopted by the radiologist, it will reduce the time spent by radiologist while analysing CXR images.
Open Access
Electronic Marketing and Service Quality of On-Line Merchandisers: A Case Study of Jumia Uganda.
(Kabale University, 2024) Ssemaluulu, Paul Mukasa; Isingoma, Fenehansi; Mugavu, George
The study examined electronic marketing and service quality of on-line merchandisers: a case study of Jumia Uganda. The specific objective was to find out the relationship between cart abandonment rate and service quality in Jumia Uganda. The research employed a cross- sectional survey design to investigate the association between electronic marketing efforts and service quality. A mixed-methods research approach, incorporating both quantitative and qualitative methodologies, was deemed highly relevant for this study. In addition, from the 255 target respondents, a definite sample size of 101 was determined using William G. Cochran (1997) formula. The study revealed significant correlations between various factors in the context of Jumia Uganda's electronic marketing. The study revealed that a moderate positive correlation (r = 0.422**) was identified between cart abandonment rate and service quality, emphasizing the critical role of service quality in customer retention and reducing cart abandonment. It was recommended that: Jumia should launch remarketing campaigns to re-engage customers who abandoned their carts. Personalized messages and incentives should be used to encourage them to return and finalize their transactions.
Open Access
Assessing the Effectiveness of Tools Used for Lecturer and Course Evaluation in Institutions of Higher Learning: A Case Study from Ugandan Universities.
(Kabale University, 2024) Mabirizi, Vicent; Karungi, Monica; Murangira, Jones; Muhoza, B. Gloria; Mutebi, Michael; Mbago, Ronald; Kohabohebwa, John Ivan; Birungi, Ruth
Blended learning, a pedagogical method integrating face-to-face and online instructions methodologies, has been identified as a strategic educational approach since its inception in the late 1990s. Moreover, its adoption especially in developing countries such as Uganda was widely recognized during the COVID-19 pandemic’s acceleration of digital learning adoption. However, this adoption has paused many challenges in evaluating learning content, teaching methodologies, and their impact on student progress. This study therefore, explores the critical role of quality assurance in higher education, focusing on the assessment of lecturer performance and course content. Apparently, paper-based mode of evaluation is the commonly used method in Ugandan universities, posing issues of privacy, delayed analytics, and ever-increasing operational costs. To address these challenges, this research proposes the development of an automated assessment system, informed by a benchmarking study across four universities. By adopting insights from existing evaluation practices, the proposed system aims to enhance the efficiency, accuracy, and students’ privacy during lecturer and course assessment. The implementation of this automated system at Kabale University promises to streamline evaluation process, ultimately enhancing teaching quality and academic outcomes.
Open Access
Application Of ArtifiCial Intelligence in E-Governance: A Comparative Study of Supervised Machine Learning and Ensemble Learning Algorithms on Crime Prediction.
(Kabale University, 2024) Niyonzima, Ivan; Muhaise, Hussein; Akankwasa, Aureri
In the developing world, the daily activities of humans’ social, political and economic life make it vital and easy to encounter the phenomenon of crime. Crime is an unnecessary evil in society and for any economic, social and political activities to run smoothly, criminal offenses must be completely eliminated from society. Advancement in information and communications technology enables law enforcement agencies to collect a huge amount of crime data, and the data collected by these organizaions have been doubling every two years. It has been found out that only 17% of the collected crime data is used in their operations today and several studies have noted that Law Enforcement Agencies are data rich but information poor. Machine learning, a subfield of artificial intelligence, has been used by government agencies in developed countries in different operations like face recognition, computer forensics, image and video analysis to identify criminals and crime predictions. It is therefore time for developing countries to leverage such technologies in order to reduce crimes. Therefore, this study proposes the application of supervised machine learning techniques in the prediction of crimes basing on the past crime data. During this study, we used open-source crime data from the UCI Machine learning repository to train and validate our algorithms. The performance of supervised machine learning and ensemble learning algorithms was done using crime data. The supervised machine learning algorithms used include K-Nearest Neighbor (KNN), decision tree classifier (CART), Naïve Bayes (NB) and Support vector machine (SVM). The ensemble learning algorithms used include AdaBoost (AD), Gradient Boosting Classifier (GBM), Random Forest (RF) and Extra Trees (ET). We used an accuracy metric to measure the performance of the algorithms. Python 3 was used in all the experiments using windows 10 laptop with 8GB RAM and 2.0GHZ processor. The performance of the supervised machine learning algorithms using the original datasets includes 60.33%, 56.24%, 57.01% and 59.06% for KNN, CART, NB, and SVM respectively. The performance of ensemble learning algorithms using the original datasets includes 58.58%, 59.81%, 55.23% and 55.74% for AD, GBM, RF and ET respectively. Experimental results revealed that KNN generally performed better when compared to the rest of the algorithms. we then developed a crime prediction model based on KNN and its prediction accuracy was 66% on our test dataset. The use of Artificial Intelligence has the potential to ameliorate several existing structural inefficiencies in the discharge of governmental functions. Machine learning, a subfield of artificial intelligence, has been used by government agencies in developed countries in crime analysis and predictions. It is therefore time for developing countries to leverage such technologies in order to reduce crimes.
Open Access
Masked and unmasked Face Recognition Model Using Deep Learning Techniques. A case of Black Race.
(Kabale University, 2023) Mabiriz,I, Vicent; Ampaire, Ray Brooks; Muhoza, B. Gloria
Currently, many institutions of higher learning in Uganda are faced with major security threats ranging from burglary to cyber threats. Consequently, the institutions have recruited and deployed several trained personnel to offer the desired security. As human beings, these personnel can make errors either by commission or omission. To overcome the limitation of trained security personnel, many face recognition models that detect masked and unmasked faces automatically to allow access to sensitive premises have been developed. However, the state-of-the-art models are not generalizable across populations and probably will not work in the Ugandan context because they have not been implemented with capabilities to eliminate racial discrimination in face recognition. This study therefore developed a deep learning model for masked and unmasked face recognition based on local context. The model was trained and tested on 1000 images taken from students of Kabale University using a Nikon d850 camera. Machine learning techniques such as Principal Component Analysis, Geometric Feature-Based Methods, and double threshold techniques were used in the development phase while results were classified using CNN pre-trained models. From the results obtained, VGG19 achieved a higher accuracy of 91.2% followed by Inception V 3 at 90.3% and VGG16 at 89.69% whereas the developed model achieved 90.32%.
Open Access
An Empirical Study of Bugs in Eclipse Stable Internal Interfaces.
(Kabale University, 2024) Simon, Kawuma; Nabaasa, Evarist; Bamutura, David Sabiiti; Mabirizi, Vicent
The Eclipse framework is a popular and widely used framework that has been evolving for over a decade. The framework provides both stable interfaces (APIs) and unstable interfaces (non-APIs). Despite being discouraged by Eclipse, application developers often use non-APIs which cause their systems to fail when ported to new framework releases. Previous studies showed that applications using relatively old non-APIs are more likely to be compatible with new releases compared to the ones that used newly introduced non-APIs. Furthermore, from our previous study about the stability of Eclipse internal interfaces, we discovered that there exist 327K stable non-API methods as the Eclipse framework evolves. In the same study, we recommended that 327K stable non-API methods can be used by Eclipse interface providers as possible candidates for promotion to stable interfaces. However, since non-APIs are unsupported and considered to be immature i.e., can contain bugs, to this end, there exists a need to first investigate the stable non-APIs for possible bugs before they can be promoted to APIs. In this study, we empirically investigated the stable non-API for possible bugs using the Sonarqube software quality tool. We discovered that over 79.8% of classes containing old stable non-API methods have zero bugs. Results from this study can be used by both interface providers and users as a starting point to analyze which interfaces are well tested and also estimate how much work could be involved when performing bug fixing for a given eclipse release.
Open Access
Comparison of Deep Learning Techniques in Detection of Sickle Cell Disease.
(Kabale University, 2023) Mabirizi, Vicent; Kawuma, Simon; Kyarisiima, Addah; Bamutura, David; Atwiine, Barnabas; Nanjebe, Deborah; Oyesigye, Adolf Mukama
Recently, the transfer learning technique has proved to be powerful in enhancing the development of deep learning methods for sickle cell disease (SCD) detection as a complement to the clinical method where a hemoglobin electrophoresis machine is used. This is evidenced by some models and algorithms with ≥90% prediction accuracy. From the literature, most of the proposed methods are trained and tested on pre-trained deep learning models like VGG16, VGG19, ResNet, Inception_V3, and ReNet. However, training and testing of these methods are limited to one model and separate datasets which may lead to biased results due to implementation in a variation of these models which affects the results produced. To this end, there exists a need to evaluate the SCD models using the same dataset. Thus, in this research study, we carried out a comparative investigation and evaluated predominate pre-trained models used to detect SCD using the same dataset to ascertain which one has the best accuracy. We used a secondary dataset obtained from an online dataset. In our study, we have discovered that Inception V3 yielded the highest accuracy of 97.3% followed by VGG19 at 97.0%, VGG16 at 91%, ResNet50 at 82% and ReNet at 67%, and the CNN-scratch model achieved 81% accuracy. Results from our study will aid researchers and industry practitioners in making decisions on the best deep-learning model to use while detecting SCD.
Open Access
An algorithm to Detect Overlapping Red Blood Cells for Sickle Cell Disease Diagnosis.
(Kabale University, 2024) Mabirizi, Vicent; Kawuma, Simon; Safari, Yonasi
In Africa, Uganda is among the countries with a high number of babies (20,000 babies)born with sickle cell, contributing between 6.8% of the children born with sickle cell every year worldwide and approximately 4.5% of the children born with hemoglobinopathies worldwide. It is estimated that by 2050, sickle cell cases will increase by 30% if no intervention is put in place. To facilitate early detection of sickle cell anemia, medical experts employ machine learning algorithms to detect sickle cell abnormality. Previous research revealed that algorithms for recognizing the shape of a sickle cell from blood smear by fractional dimension, cannot detect sickle cells if applied to blood samples containing overlapping red blood cells. In this research, the authors developed an algorithm to detect overlapping red blood cells for sickle cell disease diagnosis. The algorithm uses canny edge and double threshold machine learning techniques and takes overlapping red blood cell images as inputs to detect if these cells are sickle cell anemic. These images have a scale magnification of (200×, 400×, 650×) pixel taken using a microscope. The algorithm was tested on a total of 1000 digital images and the overall accuracy, sensitivity, and specificity were 98.18%, 98.29%, and 97.98% respectively.
Open Access
A Mobile Based Technology to Improve Male Involvement in Antenatal Care.
(Kabale University, 2024) Muhoza, B. Gloria; Ssemaluulu, Paul Mukasa; Mabirizi, Vicent
The World Health Organization Technical Working Group on maternal health unit recommended a minimum level of care to be four visits throughout the pregnancy for pregnant mothers [1]. The first visit which is expected to screen and treat anaemia, and syphilis, screen for risk factors and medical conditions that can be best dealt with in early pregnancy and initiate prophylaxis if required (e.g., for anaemia and malaria) is recommended to be made before the end of the fourth month of pregnancy. The second, third and fourth visits are scheduled at 24–28, 32 and 36 weeks, respectively. Male involvement in Antenatal health care has been described as a process of social and behavioural change that is needed for men to play more responsible roles in maternal health care to ensure women's and children’s wellbeing. A study by Okoth [1] reported that, in Uganda, male involvement in antenatal care stands at only 6% and this has been attributed to social, economic and cultural related factors. The situation worsens with the lack of an effective coordinated platform for males sharing their experience in taking part in ANC and this has affected the process of antenatal care service delivery. Objective. To assess the role of mobile technology in improving male involvement in antenatal care by developing a mobile-based technology which sends SMS reminders to male partners encouraging them to escort their pregnant wives for antenatal care services. Research questions. What are the challenges towards the limited antenatal care-seeking behaviours among pregnant mothers? What are the causes of limited male involvement in antenatal care? What roles do ICTs play in enhancing Antenatal Care seeking behaviours among pregnant mothers and in increasing the male involvement in Antenatal Care? Method. We purposively selected pregnant mothers whose phones, had been receiving antenatal care services from Kabale General Hospital and reported staying with their male partners. The recruited participants were interviewed together with their male partners. STATA 13 software was used to define participants’ demographics while qualitative data were analysed using content analysis to come up with classes describing participants’ perceptions. Results. Participants reported that reminding them of their next antenatal visit via SMS reminder plays a significant role towards their antenatal care-seeking behaviour. Conclusion. Mobile health could be a potential approach to improving male involvement in antenatal care through sending timely SMS reminders to both the expectant mother and her male partner reminding them of their next antenatal visit.
Open Access
Assessing Medical Students’ Learning Style Preferences at Kabale University Medical School, Uganda.
(Kabale University, 2024) Muhaise, Hussein; Businge, Phelix Mbabazi; Ssemaluulu, Paul; Kyomugisha, Patricia
This article is based on an empirical study conducted to assess and establish the preferred learning styles of medical students in the Kabale University Medical School. The study was prompted by a paradigm shift in teaching-learning strategies from the conventional knowledge-based medical curriculum to competency-based medical education (CMBE). In line with the learners’ diversity and inclusion, CBME liberalizes the learning environment by providing a variety of learning methods. Hence, this study aimed to ascertain the preferences of medical students’ learning styles concerning competency-based learning approaches. Procedurally, the study employed online survey methods, and the respondents included 160 medical (MBChB) students, all from Kabale University School of Medicine. The data collected were captured on SPSS version 26 and subjected to t-test analysis. Besides, Visual, Aural, ReadWrite, and Kinaesthetic (VARK) learning inventory was used to determine the student’s learning preferences, while a t-test was used to establish the relationships between the demographic profiles and the learning styles. Notably, the Aural learning style produced the highest mean score of 7.21 ± 3.61, followed by Kinaesthetic (6.43 ± 3.22), ReadWrite (6.12 ± 2.23) and Visual (4.04 ± 2.42). Relatively, t-test results showed significant (p < 0.05) differences in learning styles between preclinical and clinical students. However, the t-test results for gender factors for all the learning dimensions were insignificant (p > 0.05). Pre-clinical students prefer visual and read-write learning styles, while clinical students prefer kinaesthetic and visual learning styles. Based on the findings, this study believes that identifying the learners’ preferred learning styles will help educators choose the most effective teaching methods.
Open Access
The Taxonomy Mobile Learning Applications in Higher Institutions of Learning in Ugandan Universities: A Case of Kabale University, Uganda.
(Kabale University, 2024) Muhaise, Hussein; Businge,Phelix Mbabazi; Ssemaluulu, Paul; Muhoza, Gloria
Since the use of mobile devices outpaces that of laptops and desktop computers today, the usability of these mobile devices is an important consideration. When mobile learning (a new kind of electronic learning) takes shape, bringing an important feature of mobility, the trend expands deeper into teaching and learning. Usability describes the quality characteristics of software product usage; hence, usability testing is a crucial concern in developing companies for the success of product deployment and use. The vast majority of existing usability evaluation approaches were created for desktop software development. As a result, currently, existing models do not specifically address mobile learning, presenting a gap that we aimed to remedy. The study developed a model that estimates usability as a function of aggregated usability influencing factors. To provide a more comprehensive model, the proposed model includes essential features from other accessible models and incorporates the majority of those that assist mobile learning. A mobile learning prototype application was designed, tested, and installed to evaluate the efficiency of the developed model, coupled with a task list for objective research. Using a sophisticated statistical technique, the feedback from the experiment and survey was then utilized to assess and validate the prototype application in terms of high, average, or low usability. The findings act as guides for eLearning-developing businesses to create more relevant mobile learning applications with high levels of usability.
Open Access
Evaluation of Learning Management Systems for Success Factors.
(Kabale University, 2024) Muhaise, Hussein; Adeyemi, A. L.; Muteb,I.J.
Evaluating an information system for success is key. Evaluating the success factors of a Learning Management System (LMS) is essential in the perspective of information systems success in a developing country context. eLearning is vital to the educational system considering its benefits and impacts, particularly in accessing learning from remote areas, suitable for different learners’ categories, and minimal resource utilization in terms of cost and time. Through the literature reviewed on eLearning and Information Systems, the desire to determine the variables that measure the success factors for information systems continues. Existing Information System (IS) success models do not sufficiently evaluate eLearning in developing country, Uganda inclusive as the current IS success models are generic in nature. This study aimed to describe a model of information system success tailored to the eLearning system, Kampala International University as a case study, a Uganda’s context. To address the above objective, a field study was conducted, using a questionnaire to determine factors for information systems success in Uganda, a sample size of 370 respondents were used from a population size of 5500 using the Sloven formula. The respondents comprise of 340 students, 27 lecturers and 4 administrators. The identified success factors are skills & training,infrastructure and management support. Data were cleaned and analyzed using Statistical Package for Social Scientists version 20.0 (SPSS). This study adopted Delone and Mclean’s Information System Success model (2016) and extended it using factors obtained from the field study survey. Analysis was performed to evaluate the model. Results of the study showed that all the independent variables management support, infrastructure, skills and training are positively related to the dependent variable of intention to and use of information system. There exists a strong relationship between the multiple independent variables and the dependent variable. All factors identified has a positive impact in explaining the variation in intention to use and use of the system with r coefficients of 0.343, 0.406 and 0.406 respectively. The results of the study presented a model of success factors for the Learning Management System, eLearning specifically. For future research, this study recommends conducting qualitative studies to delve deeper into the nuanced perceptions and experiences of learners and teachers as well as looking into policies that can promote eLearning especially in developing countries.
Open Access
An examination of the correlation between website traffi c and the quality of services provided by Jumia Uganda.
(Kabale University, 2024) Ssemaluulu, Paul Mukasa; Isingoma, Fenehansi; Mugavu, George
An attempt to divorce E-marketing from current business operations is a futile one worldwide presently. The two are like Siamese twins since a strong digital marketing platform benefits from current business operations very well. Website development became fundamental following the discovery of the Internet in early 1983 and the World Wide Web in 1989. It is on such background that Jumia Uganda, an online business platform was conceived. The research employed a cross-sectional survey design to investigate the association between electronic marketing efforts and service quality. A mixed-methods research approach, incorporating both quantitative and qualitative methodologies, was deemed highly relevant for this study. In addition, from the 255 target respondents, a definite sample size of 101 was determined using William G. Cochran's (1997) formula. However, just like any usual business, web-based businesses like the ones of Jumia Uganda are not short of incapacitation which among others is low website traffic which impacts the sales of such businesses directly. The paper provides a detailed literature review concentrating on the nexus between website traffic and quality services. Based on empirical evidence, we conclude that the moderate positive correlation coefficient of (r=0.390**) suggests a correlation between website traffic and service quality. As website traffic increases, there is a tendency for service quality to improve. The significance level for both correlations was 0.000, indicating that the correlation coefficients were highly significant at the 0.01 level (2-tailed). This positive correlation implies a connection between website traffic and service quality. From the empirical conclusion, we are in a position to come up with a key recommendation stating that to enhance its online presence, Jumia should consider diversifying its website content with engaging formats such as blog posts, videos, and infographics to appeal to a broader audience. Consistent updates and the regular refreshment of content are crucial for maintaining visitor interest.
Open Access
Cart abandonment rate and service quality of on-line merchandisers: a case study of Jumia Uganda.
(Kabale University, 2024) Ssemaluulu, Paul Mukasa; Isingoma, Fenehansi; Mugavu, George
The study examined electronic marketing and service quality of online merchandisers, a case study of Jumia Uganda. The specific objective was to determine the relationship between cart abandonment rate and service quality in Jumia Uganda. The research employed a cross-sectional survey design to investigate the association between electronic marketing efforts and service quality. A mixed-methods research approach, incorporating both quantitative and qualitative methodologies, was deemed highly relevant for this study. In addition, from the 255 target respondents, a definite sample size of 101 was determined using William G. Cochran's (1997) formula. The study revealed significant correlations between various factors in the context of Jumia Uganda's electronic marketing. The study revealed that a moderate positive correlation (r = 0.422**) was identified between cart abandonment rate and service quality, emphasizing the critical role of service quality in customer retention and reducing cart abandonment. It was recommended that: Jumia should launch remarketing campaigns to re-engage customers who abandoned their carts. Personalized messages and incentives should be used to encourage them to return and finalize their transactions.
Open Access
Enhance Research and Innovation in ICT products, Applications, and Services
(Kabale University, 2024-02-09) Businge, Phelix Mbabazi
The need for most secondary schools in Uganda, especially in Kigezi region, to embrace Information and Communication Technology (ICT) has been facing a number of challenges which had not been clearly documented. In order to ensure the efficient integration of ICT in the teaching and learning process in these schools, there is need to understand these challenges and know how best they can be handled. The purpose of this study was to establish whether ICT was being integrated in the teaching and learning process among selected secondary schools in the Kigezi region by assessing the usage of ICTs in the teaching and learning process; establish the ICT infrastructures currently available in the selected schools and how they are being used; and, ascertaining the challenges secondary school teachers face in integrating ICT in the teaching and learning process. The study was guided by MICTIVO model (2009) of ICT integration which captures most of the factors for the Integration of ICT in education by looking at infrastructure and policy, perceptions, competences and integration at micro-level, and not only at ICT integration in curriculum development. The study adopted a cross-sectional descriptive survey design to collect data at one point in time from all the selected secondary schools. The quantitative approach was the main approach while the qualitative was the complementary approach. The target population included students (1943) and teachers (24), from Kigezi Region Districts of Kabale, Rukiga and Rukungiri. The six (6) schools were selected among the top schools from which Kabale University has been admitting the highest number of students for different courses in different academic years. The schools represented schools from both urban and rural settings. From the six schools, a sample of 332 students and 24 teachers were required to participate in the study. Data was collected by means of a questionnaire. Data analysis was conducted using descriptive and inferential statistics with the aid of Statistical Package for Social Sciences (SPSS). The findings from the study indicated that ICT was not being used in the teaching and learning process with average response mode of 1[never used ICT]. Furthermore, it was revealed that most of ICT Infrastructures -- computers, printers, Internet connection among others -- were not available [Average response mode of 1], In terms of ICT skills and competences, it was found out that respondents lacked skills in Organizing computer files in folders and sub-folders; Producing a text and using a word processing Programme, among others, as represented by the average mode of 1 [None]. The model developed revealed that location of the schools and the year of existence were found to be significant, while class and gender were found to be insignificant to ICT adoption in secondary schools. Based on the findings of the study, it is recommended that government and stakeholders should ensure that: schools are provided with the necessary ICT infrastructure; qualified computer teachers are recruited; computer laboratory technicians are rectruited; that there is continuous retooling of both students and staff; and that schools encourage ICT usage at home.
Open Access
Philosophical Review of Artificial Intelligence for Society 5.0
(Springer Nature Singapore Pte Ltd., 2023-02-08) Ggaliwango, Marvin; Tamale, Micheal; Kanagwa, Benjamin; Jjingo, Daudi
Artificial intelligence has come a long way since its inception 60 years ago, and it continues to evolve and change the world in ways we couldn’t have imagined. Today, AI has reached new heights and has a wide range of applications, from playing complex games to language processing, speech recognition, and facial recog nition [1–3]. With its exponential growth and its increasing presence in an ever growing number of sectors, AI is well on its way to becoming a source of significant economic prosperity. But as AI continues to evolve, it poses major policy questions for policymakers, investors, technologists, scholars, and students. AI ethics are crit ical to its development, and it is essential that ethical standards be established to ensure that AI meets a certain standard of public justification and supports citizens’ rights, promoting substantively fair outcomes when deployed [4–7]. The use of AI in everyday life also raises ethical collisions, and human rights principles and legislation must play a key role in addressing these ethical challenges [8–10]. The rapid devel opment of AI presents many opportunities and challenges for the human race. As AI becomes more autonomous and intelligent, it has the potential to greatly improve the performance of manufacturing and service systems, as well as contribute to social development and human life [2, 11, 12, 13]. However, the hardware and software of a fully autonomous, learning, reasoning AI system must mimic the processes and subsystems that exist within the human brain [14, 15].