RP-Department of Computing & Information Technology

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The Type of Knowledge Being Shared and its Effect on Organizational Growth at Kenyatta National Hospital, Nairobi County Kenya
(Journal of Multidisciplinary Engineering Science and Technology, 2022-11) Nyambaso, Elvinah Kerubo; Namande, Wekalao
Organizational growth of health sector in Kenya have not been exceptional in the challenges that face human resource management which among them is the patients complains on poor service delivery, lack of medical equipment, shortage of drugs, poor reputation that arises from malfunctioning Knowledge sharing strategies among others on the staff members. This study thus seeks to investigate the effect of knowledge sharing on the organizational growth of Kenyatta National Hospital. The study research objective was to determine the effect of the types of knowledge shared on organizational growth at Kenyatta National Hospital. The study was guided by Healthcare Knowledge-Sharing Model. The study used descriptive survey research design. The target population of the study was 200 respondents and sample size of 133 was used. The respondents of the study include 100 members of the staff who work at the hospital (5 doctors, 70 nurses, 5 laboratory technicians, 6 pharmacists, 7 clinical officers, 3 human resource staff & 4 nutritionists) and 100 patients. Quantitative data were analysed using both descriptive and inferential statistics. Descriptive statistics included frequencies, percentages, means, and standard deviation. Inferential statistics was analyzed using correlation and multiple linear regressions. Qualitative data was analysed thematically. Pearson Product Moment Correlation Coefficient and regression analysis was used in order to test the relationship between the dependent and independent variables. The study is significant as it helps address knowledge sharing, which is one of the most pertinent issues affecting organizational growth for hospitals and in particular Kenyatta National Hospital. The study is also important as it helps incorporate theories related to knowledge sharing and organizational growth. The study results showed that the types of knowledge commonly used at the Hospital is explicit knowledge sharing, organizational knowledge sharing, and provider knowledge sharing. In conclusion the type of knowledge being shared has a positive and statistically significant effect on organizational growth.
Network Intrusion Detection Using Extreme Machine Learning Algorithm with Extreme Gradient Boosting for Feature Selection
(Journal of the Kenya National Commission for UNESCO, 2024) Ntwiga, Alex; Araka, Eric
This study addresses the challenge of improving the performance of the Extreme Learning Machine model, particularly in accurately identifying minority classes in unbalanced datasets like UNSW-NB15 and NSLKDD. The research question guiding this study is: How can we improve the ELM model's performance for better accuracy and minority class recognition in network intrusion detection? The methodology includes balancing the dataset to address the issue of poor minority class identification, using XGBoost for feature selection to reduce the curse of high data dimensionality, Particle Swarm Optimization finally used to optimize the model. The results show that the proposed approach outperformed other models when tested on the NSL-KDD dataset, achieving accuracies of 94.29% for binary classification and 89.02% for multiclass classification. However, on the UNSW-NB15 dataset, the model achieved a binary accuracy of 90.79%, which was lower than the performance of Random Forest (93.02%) and Decision Tree (92.76 In the multiclass classification the accuracy achieved was 78.79%, indicating underperformance compared to the other state-of-the-art models. The study concludes that although the suggested approach performs well in binary classification, future studies need to focus on improving detection accuracies of datasets that are heavily unbalanced with multiple classes like UNSW-NB15 dataset.
Integrating Generative Artificial Intelligence in Assessment Generation for Higher Education: Computer Science Use Case
(IST-Africa, 2025) Kituku, Benson; Araka, Eric; Muuro, Elizaphan
The overburdened lecturers today face the dual challenge of monitoring online attendance and ensuring active student engagement, while also receiving instant feedback on concept comprehension during live lectures. They are further pressed to extend practical problem-solving experiences beyond the classroom and deliver higher-order thinking assessments - all without increasing their workload. In response, this paper presents a one-year experiment involving 590 students across seven computer science units. The study integrated generative AI-generated questions into live lectures, weekly discussion forums, and both formative and summative exams. The findings reveal that, with ethical use and proper human-inthe-loop, AI can significantly boost student engagement, promptly rectify misconceptions, and foster collaborative learning outside traditional settings and offer diverse question styles. However, given potential pitfalls such as low-quality outputs and overreliance, instructors must adhere to best practices and maintain rigorous oversight to ensure that assessments remain balanced, engaging, and of high quality, eventually benefiting both educators and learners.
Students’ Perceptions on the Use of Generative AI in Enhancing Teaching and Learning Computer Science Courses
(IST-Africa, 2025) Mwaniki, Susan; Araka, Eric; Kituku, Benson; Maina, Elizaphan
This research study examines the perception of third-year computer science students in Kenya towards the use of Generative Artificial Intelligence tools in their studies. The researchers used descriptive research design to understand student attitudes, the perceived usefulness of Generative Artificial Intelligence, and the challenges they face. The study finds that students generally see Generative Artificial Intelligence tools as beneficial for learning, especially in areas like coding and research. However, they also identify concerns about over-reliance on Generative Artificial Intelligence, accuracy of information, and ethical considerations, such as plagiarism. The study concludes that Generative Artificial Intelligence can be valuable in computer science education, however, it should be used reliably and balanced with traditional teaching methods to ensure critical thinking and creativity.
Promoting University Students’ Self-Regulated Learning Skills on E-Learning Platforms Using Educational Data Mining
(IST-Africa, 2025) Araka, Eric; Wario, Ruth; Maina, Elizaphan
Current e-learning platforms used by higher education institutions for open and distance learning often lack automation tools that offer personalized support to students. As a result, learners must take an active role in managing their learning process, as emphasized by the self-regulated learning theory. Given the flexibility of online learning, where students are not bound by fixed schedules, they must develop self-discipline and autonomy to effectively navigate and control their learning experience. This study examines the potential of educational data mining techniques to enhance students' self-regulated learning skills. A true experimental research design was deployed to assess the effectiveness of the developed educational data mining techniques -based interventions in fostering self-regulated learning. The study sample consisted of University students enrolled in a 12-week Data Science with Python course. Results from an independent t-test indicated that educational data mining interventions positively influenced students’ self-regulatory skills by strengthening their cognitive and behavioral learning strategies. However, no significant difference was found in final academic performance between students who received educational data mining interventions and those who received instructor-led support. By providing data-driven support, the study contributes to efforts aimed at improving student retention, reducing dropout rates, and enhancing academic performance in online learning environments. These findings provide a foundation for future research on integrating intelligent automation to support SRL in higher education.
Big data and personal information privacy in developing countries: insights from Kenya
(frontiers, 2025) Masinde Johnson; Mugambi Franklin; Muthee Daniel Wambiri
The present study examined the correlation between big data and personal information privacy in Kenya, a developing nation which has experienced a significant rise in utilization of data in the recent past. The study sought to assess the effectiveness of present data protection laws and policies, highlight challenges that individuals and organizations experience while securing their data, and propose mechanisms to enhance data protection frameworks and raise public awareness of data privacy issues. The study employed a mixed-methods approach, which included a survey of 500 participants, 20 interviews with key stakeholders, and an examination of 50 pertinent documents. Study findings show that the regulatory and legal frameworks though present are not enforced, demonstrating a gap between legislation and implementation. Furthermore, there is a lack of understanding about the risks posed by sharing personal information, and that more public education and awareness activities are required. The findings also demonstrate that while people are prepared to trade their personal information for concrete benefits, they are concerned about how their data is utilized and by whom. The study proposes the establishment of a National Data Literacy Training and Capacity Building Framework (NADACA), that should mandate the training of government officials in best practices for data governance and enforcement mechanisms, educate the public on personal data privacy and relevant laws, and ensure the integration of data literacy into the curriculum, alongside the provision of regular resources and workshops on data literacy. The study has significant implications for policymakers, industry representatives, and civil society organizations in Kenya and globally.
Trends and Advances on The K-Hyperparameter Tuning Techniques In High-Dimensional Space Clustering
(IJAIDM, 2023-09) Gikera, Rufus Kinyua; Mwaura, Jonathan; Maina, Elizaphan; Mambo, Shadrack
Clustering is one of the tasks performed during exploratory data analysis with an extensive and wealthy history in a variety of disciplines. Application of clustering in computational medicine is one such application of clustering that has proliferated in the recent past. K-means algorithms are the most popular because of their ability to adapt to new examples besides scaling up to large datasets. They are also easy to understand and implement. However, with k-means algorithms, k-hyperparameter tuning is a long standing challenge. The sparse and redundant nature of the high-dimensional datasets makes the k-hyperparameter tuning in high-dimensional space clustering a more challenging task. A proper k-hyperparameter tuning has a significant effect on the clustering results. A number of state-of-the art k-hyperparameter tuning techniques in high-dimensional space have been proposed. However, these techniques perform differently in a variety of high-dimensional datasets and data-dimensionality reduction methods. This article uses a five-step methodology to investigate the trends and advances on the state of the art k-hyperparameter tuning techniques in high-dimensional space clustering, data dimensionality reduction methods used with these techniques, their tuning strategies, nature of the datasets applied with them as well as the challenges associated with the cluster analysis in high-dimensional spaces. The metrics used in evaluating these techniques are also reviewed. The results of this review, elaborated in the discussion section, makes it efficient for data science researchers to undertake an empirical study among these techniques; a study that subsequently forms the basis for creating improved solutions to this k-hyperparameter tuning problem.
K-Hyperparameter Tuning in High-Dimensional Space Clustering: Solving Smooth Elbow Challenges Using an Ensemble Based Technique of a Self-Adapting Autoencoder and Internal Validation Indexes
(Tech Science Press, 2023-10-26) Gikera, Rufus; Mwaura, Jonathan; Muuro, Elizaphan; Mambo, Shadrack
k-means is a popular clustering algorithmbecause of its simplicity and scalability to handle large datasets.However, one of its setbacks is the challenge of identifying the correct k-hyperparameter value. Tuning this value correctly is critical for building effective k-means models. The use of the traditional elbow method to help identify this value has a long-standing literature. However, when using this method with certain datasets, smooth curves may appear, making it challenging to identify the k-value due to its unclear nature.Onthe other hand, various internal validation indexes, which are proposed as a solution to this issue, may be inconsistent. Although various techniques for solving smooth elbow challenges exist, k-hyperparameter tuning in high-dimensional spaces still remains intractable and an open research issue. In this paper, we have first reviewed the existing techniques for solving smooth elbow challenges. The identified research gaps are then utilized in the development of the new technique. The new technique, referred to as the ensemble-based technique of a self-adapting autoencoder and internal validation indexes, is then validated in high-dimensional space clustering. The optimal k-value, tuned by this technique using a voting scheme, is a trade-off between the number of clusters visualized in the autoencoder’s latent space, k-value from the ensemble internal validation index score and one that generates a value of 0 or close to 0 on the derivative f ___ (k)(1+f _ (k)2)−3 f __ (k)2f __ ((k)2f _ (k), at the elbow. Experimental results based on theCochran’sQtest,ANOVA, andMcNemar’s score indicate a relativelygoodperformanceof thenewlydevelopedtechnique ink-hyperparameter tuning.
Hybrid Machine Learning Techniques for Comparative Opinion Mining
(IJAIDM, 2023-08) Ondara, Bernard; Waithaka, Stephen; Kandiri, John; Muchemi,Lawrence
Comparative opinion mining has lately gained traction among individuals and businesses due to its growing range of applications in brand reputation monitoring and consumer decision making among others. Past research in sub-field of opinion mining have mostly explored single-entity opinion mining models and the mining of comparative sentences suing single classifiers. Most of these studies relied on a limited number of comparative opinion labels and datasets while applying the techniques in limited domains. Consequently, the reported performances of the techniques might not be optimal in some cases like working with big data. In this study, however, we developed four hybrid machine learning techniques, with which we performed multi-class based comparative opinion mining using three datasets from different domains. From our results, the best-performing hybrid machine learning technique for comparative opinion mining using a multi-layer perceptron as the base estimator was the Multilayer Perceptron + Random Forest (MLP + RF). This technique had an average accuracy of 93.0% and an F1-score of 93.0%. These results show that our hybrid machine learning techniques could reliably be used for comparative opinion mining to support business needs like brand reputation monitoring.
The Competencies of Fashion Design Teachers in Public Institutions of Higher Learning in Nairobi County, Kenya
(IJSBAR, 2016) Isika, Juliet Kaindia; Mburugu, Keren; Nguku, Everlyn; Obere, Almadi
‘Real’ fabric draping involves the use of sample textile, fabric or cloth to make patterns or garments on a model or dress form stands manually. The technique is suitable for ready-to-wear and couture garment designs and has numerous advantages, including satisfaction with garment fit, accurate proportions of fabric division and reduced time waste. Numerous studies in Kenya have been carried out on the subject of Home Science. However, little documentation exists on ‘real’ fabric draping for design in Kenya. This paper anchors its discussion on the findings of a study that sought to assess the usage of ‘real’ fabric in draping by teachers in public institutions of higher learning and fashion designers in Nairobi County, Kenya, and assesses the competencies of fashion design teachers in Nairobi County, Kenya. It also examines the relationship between the use of ‘real’ fabric draping for design, on the one hand, and the teachers’ area of training on the other hand. The study was guided by the activity theory and pedagogic activity system structure. Employing a crosssectional survey research design, five public institutions of higher learning were purposively selected. The sample size comprised five heads of department, 32 teachers and 266 students. The data was collected using questionnaires and interview schedules. Both qualitative and quantitative data analysis techniques were used. The results revealed that very few public institutions of higher learning use ‘real’ fabric draping for design. Majority of the teachers were not trained in the area of fashion design. Chi-square analysis results yielded a fairly strong relationship between use of ‘real’ fabric draping for design and pattern development technique taught (V= 0 .646; p < 0.0001*) and sources of curriculum (V= 0.623; p < 0.0001*). Use of ‘real’ fabric draping for design had a weak association with teachers’ area of training (V = 0. 018; p < 0.006). It was concluded that the teachers area of training was not highly associated with the use of ‘real’ fabric draping. This may be due to the fact that most fashion design teachers were trained in clothing / garment design and are able to understand the technique. Pattern development technique taught and sources of curriculum and teachers’ area of training are the key issues associated with the use of ‘real’ fabric draping for design in public institutions of higher learning. This paper recommends that public institutions of higher learning should ensure that teachers engaged have the adequate skills to teach ‘real’ fabric draping for design as a practical unit. This would ensure that the students acquire pertinent skills imparted as prescribed in the curriculum.
Social Media Influence on Personal Security among the Youth in Nairobi City County, Kenya
(EANSO, 2023) Soita, Sally; Njoroge, Harrison
This study examined positive uses of social media that include warning and preventing individuals from violence resulting from negative uses of social media and user victimisation. The study was guided by Space transition theory which states that criminals are more likely to commit crimes in cyberspace more than in physical space due to anonymity and identity flexibility. The objective of the study was to determine the forms of social media use among the youths in Nairobi County. The target population were members of the Professional Criminologists Association of Kenya (PCAK). Purposive sampling was used to select 155 youth respondents from a population of 15000 youths and 145 law enforcement informant interviewees drawn from 2,000 law enforcement officers in PCAK in Nairobi County. Piloting of the questionnaire was disseminated among 30 PCAK youths Nakuru chapter. The research instruments were verified by the supervisor for content validity. Statistical Packages for Social Sciences, SPSS and Microsoft Excel software were used in data entry and descriptive statistics were used to analyse the data. Qualitative data were analysed using content analysis, coding, classification, and text inferencing. This study was significant to academic research, criminal justice practitioners and the private sector to assist in goal formulation and achievement of cyber security. The results of this research showed that the form of social media that youth mostly prefer +are WhatsApp over other social media platforms. The most preferred social media platforms by both genders were found to be WhatsApp and Twitter. It was recommended that future research could focus on the modern methods of social media as technology is dynamic. This will give direction on the contemporary forms of social media and their relationship to personal security; this, in turn, improves the security settings suitable for the users.
Critical Literature Review on Current State-of-the Art in Predicting Students’ Performance using Machine Learning Algorithm in Blended Learning Environment
(AJOEI, 2023) Ofori, Francis; Matheka, Abraham; Maina, Elizaphan
Background of the study: Predicting and analyzing the performance of the student in a blended learning environment is important to help educators identify poor performing students and improve their academic score. Meanwhile, achieving accurate predictions require selecting machine learning techniques that can produce optimum score. However, there seems to be no critical literature review on current state of art in predicting students’ performance using machine learning algorithms in blended learning environment. Methodology: This critical literature review focuses on, studies on the current state of the art in predicting students’ performance in the blended learning for past 10 years, sources of dataset used by various authors and the machined learning algorithm with high prediction accuracy. Findings: Naïve Bayes was the most frequently used algorithm for predicting students’ performance. Authors mostly used online data for their student’s performance prediction. Finally, artificial neural network was found to give higher prediction accuracy of 98.7%.
Using Educational Data Mining Techniques to Identify Profiles in Self-Regulated Learning: An Empirical Evaluation
(International Review of Research in Open and Distributed Learning, 2022) Araka, Eric; Oboko, Robert; Maina, Elizaphan; Gitonga, Rhoda
With the increased emphasis on the benefits of self-regulated learning (SRL), it is important to make use of the huge amounts of educational data generated from online learning environments to identify the appropriate educational data mining (EDM) techniques that can help explore and understand online learners’ behavioral patterns. Understanding learner behaviors helps us gain more insights into the right types of interventions that can be offered to online learners who currently receive limited support from instructors as compared to their counterparts in traditional face-to-face classrooms. In view of this, our study first identified an optimal EDM algorithm by empirically evaluating the potential of three clustering algorithms (expectation-maximization, agglomerative hierarchical, and k-means) to identify SRL profiles using trace data collected from the Open University of the UK. Results revealed that agglomerative hierarchical was the optimal algorithm, with four clusters. From the four clusters, four SRL profiles were identified: poor self-regulators, intermediate self-regulators, good self-regulators, and exemplary selfregulators. Second, through correlation analysis, our study established that there is a significant relationship between the SRL profiles and students’ final results. Based on our findings, we recommend agglomerative hierarchical as the optimal algorithm to identify SRL profiles in online learning environments. Furthermore, these profiles could provide insights on how to design a learning management system which could promote SRL, based on learner behaviors.
Determinants of Mobile Banking Adoption by Customers of Microfinance Institutions in Nairobi County in Kenya
(International Journal of Science and Research (IJSR), 2015) Wamai, John; Kandiri, John M
Mobile technology usage has had various impacts on individuals and enterprises at different levels. Several factors have been sighted by different researchers as contributing either positively or negatively to the adoption of Mobile banking technology. Banks have implemented this technology to enable them reach more customers due to its ubiquitous nature and to reduce the cost of putting up new branches in their areas of operations. For this effort to be felt and for the technology to be implemented effectively, there is need to understand the factors contributing to its adoption by the customers. This study was carried out to investigate the effects of important factors that affect adoption of mobile banking technology by customers of Microfinance Institutions in Nairobi County, Kenya. A sample of 210 customers were selected randomly and the researcher extended the Technology Acceptance Model (TAM) framework. The study found that both perceived usefulness and perceived ease of use positively correlate and affects adoption of mobile banking technology positively. On the other hand, Perceived Risk and Perceived transaction costs were found to have negative correlation with the adoption of mobile banking technology
Students’Perceived Challenges in an Online Collaborative Learning Environment: A Case of Higher Learning Institutions in Nairobi, Kenya
(\'E}rudit, 2014) Muuro, Maina Elizaphan; Wagacha, Waiganjo Peter; Oboko, Robert; Kihoro, John
Earlier forms of distance education were characterized by minimal social interaction like correspondence, television, video and radio. However, the World Wide Web (WWW) and online learning introduced the opportunity for much more social interaction, particularly among learners, and this has been further made possible through social media in Web 2.0. The increased availability of collaborative tools in Web 2.0 has made it possible to have online collaborative learning realized in Higher Learning Institutions (HLIs). However, learners can perceive the online collaborative learning process as challenging and they fail to utilize these collaborative tools effectively. Although a number of challenges have been mentioned in the literature, considerable diversity exists among countries due to diversity in infrastructure support for e-learning and learners’ background. This motivated this study to investigate components of online collaborative learning perceived as challenging by learners in HLIs in Kenya. Using a questionnaire, a survey was conducted in two public universities and two private universities to identify students’ perceived challenges in an online collaborative learning environment. Through purposive sampling the questionnaire was distributed to 210 students using e-mail and 183 students responded. Based on descriptive analysis the following five major challenges were rated as high: lack of feedback from instructors, lack of feedback from peers, lack of time to participate, slow internet connectivity, and low or no participation of other group members. There was also a relationship between the university type (private or public) with the perceived challenges which included: lack of feedback from the instructor (𝒑=0.046) and work load not shared equally among group members (𝒑=0.000). Apart from slow internet connectivity the rest of the challenges were in line with the observed challenges in the literature.These key challenges identified in this study should provide insight to educators on the areas of collaborative learning that should be improved in order to provide access to quality education that supports effective online collaborative learning in HLIs in Kenya
University Students’ Perception on the Usefulness of Learning Management System Features In Promoting Self-Regulated Learning in Online Learning
(University of Nairobi, 2021) Araka, Eric; Maina, Elizaphan; Gitonga, Rhoda; Oboko, Robert; Kihoro, John
Online learning has increasingly been adopted by most institutions of higher learning to facilitate teaching and learning as a continuum to the traditional face-to-face approach. Most of these institutions utilize Learning Management Systems which contain features that are intended to make students active participants not only by delivering learning resources to learners but also providing the environment for effective interaction in the learning process. Our examination of the literature reveals that there is limited empirical evidence that addresses how these features are being utilized by students in promoting Self-Regulated learning. To realize the usefulness of the features of Learning Management Systems in promoting Self-Regulated Learning, a structured survey was carried out among University students in Kenya. The findings reveal that the features of Learning Management Systems are underutilized by students. The qualitative results of the study illustrate that students face several challenges that obstruct them from being actively involved in online learning. There is lack of individualized feedback on students’ learning habits, lack of instructor guidance, lack of interaction with course instructors, lack of peer interaction and lack of automation tools. This study provides insights for educators and researchers on the areas of focus that can be prioritized towards offering support to students in improving their SelfRegulated learning in online learning environments.
Multiplier Design using Machine Learning Alogorithms for Energy Efficiency
(VLSI, 2023) Juma, Jane; Mdodo, R.M.; Gichoya, David
Designers primary goal is to develop the Adder cell with improved performance viz. speed, fixed rise and fall time, as it the fundamental block in VLSI design process. The dynamic logic circuits are far better than the static logic circuits because it consumes less power and speed performance also increased. But, cascading of several blocks in dynamic logic is found to be a wrong analysis. This drawback of increased complexity with mismatched cascading is overcome by using domino logic circuits. By using domino logic circuits, the reduction of noise margins and increase the speed performance of the circuit is achieved. In this paper, domino logic based Manchester carry chain adder (MCC) is designed using FinFET 18nm technology in Cadence virtuoso. It is noticed that 4-bit and an 8-bit Manchester Carry Chain Adder (MCC) using domino logic design consumes less power and reduction in the delay of the proposed circuit compared with the previous architecture. Implementation results reveal that the 4-Bit MCC Adder has delay of 79.45% less compared to the existed standard design and power consumption also reduced to 94.15%.
Customers Perception on Prior Knowledge of Technology and Its Effect on Usage of Internet Banking in Commercial Banks in Kenya
(Paper Publications, 2015) Waithaka, Stephen Titus; Muthengi, Kilembwa; Nzeveka, Joseph
Internet banking allows banks to provide information and offer services to their customers conveniently using the internet technology. However, studies have shown that customers have perceptions that impact on the uptake and continuous usage of the platform. The purpose of this study is to understand the effect of customer perceptions on usage of internet banking in commercial banks in Kenya. This study used descriptive research design while a stratified random sampling technique was used to select subjects to represent the target population which was made up of 1,837,312 customers of commercial banks within Nairobi County. An estimated 384 respondents were targeted to participate in the study. 272 questionnaires representing a 71% response rate were received and analysed. Based on the findings of the research it was concluded that customers perceptions have an effect on usage of internet banking. Prior knowledge of technology was forund to have an impediment in using internet banking by customers. Not all customers are well versed in using systems used in accessing internet banking- both software and hard ware.
The Effect of Customers Perception on Security and Privacy of Internet Banking on Its Usage in Commercial Banks in Kenya
(Paper Publications, 2015) Waithaka, Stephen Titus; Muthengi, Kilembwa; Nzeveka, Joseph
Internet banking allows banks to provide information and offer services to their customers conveniently using the internet technology. However, studies have shown that customers have perceptions that impact on the uptake and continuous usage of the platform. The purpose of this study is to understand the effect of customer perceptions on usage of internet banking in commercial banks in Kenya. This study used descriptive research design while a stratified random sampling technique was used to select subjects to represent the target population which was made up of 1,837,312 customers of commercial banks within Nairobi County. An estimated 384 respondents were targeted to participate in the study. 272 questionnaires representing a 71% response rate were received and analysed. Based on the findings of the research it was concluded that customers have perception that have an effect on usage of internet banking. Customers both users and potential, are still apprehensive about the security of internet banking transactions and privacy of their sessions while online. Due to increased phishing, on online scams and frauds perpetrated online customers are reluctant to adopt or continue using internet banking. It is the responsibility of commercial banks to sensitize their customers and assure them that it is safe to access internet banking from both a private and public network. They should provide customers with guidelines on how to safe guard their information and secure their log on credential while using both private and public network
Hospital Information Systems Capability and End-User Satisfaction in Hospitals of Nairobi County, Kenya
(IAJIST, 2018) Omune, Onyando George; Kandiri, John M.
Hospitals in Nairobi County, Kenya continue to automate their processes to improve service delivery to their clients by implementing hospital Information Systems (HIS). As studies have revealed, end user satisfaction plays an important role in information systems acceptance, and ultimate success. The study focused on HIS capability and how it affects end user satisfaction in the hospitals by use of descriptive and observations techniques. The scope of this study was hospitals in Nairobi County, Kenya with bed capacity of at least 100 and had used HIS for a period of not less than a year. Stratified sampling method was preferred for sampling of study respondents from the selected ten hospitals of Nairobi County that met inclusion criteria and simple random sampling technique, to select respondents respectively. Semi structured questionnaires were used to collect primary data from a population of 374 respondents. The data collected was analyzed quantitatively using descriptive statistics comprising of the mean, standard deviation and P-values. Statistical Package for Social Sciences (SPSS) version 22, Microsoft Office Excel 2013 and descriptive statistics were the main tools used to analyze the data. The results have shown that systems quality, information quality and service quality of HIS positively affect end user satisfaction.

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