MST-Department of Computing & Information Technology

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Determinants of Knowledge Sharing Through Institutional Repositories among Academic Staff in Selected Public and Private Universities in Kenya
(Kenyatta University, 2025-10) Njogu, Lynette Wambui
In recent times, organizations have experienced changes that have been characterized by the shift from relying on information to the utilization of knowledge. This led to the birth of the Knowledge Management (KM). With organizations investing in embedding KM in their operations, a component of KM being implemented is knowledge sharing. In this study, Knowledge Sharing (KS) is the process by which knowledge generated and stored in an organization is communicated from the source to the recipient. Universities have not been left behind in implementing KS by facilitating their academic staff in this endeavour. They have invested in ICT platforms where respective academic staff share knowledge generated and gained through research. The ICT platforms residing in institutions are referred to as Institutional Repositories (IRs). An evaluation of a number of university institutional repositories, show that academic staff in some faculties have contributed more research and knowledge outputs, while others have little or no contributions. This study led to establishing what determines academic staff’s decision to share their research and knowledge outputs via institutional repositories in selected universities in Kenya. The research objectives that guided the study included: establishing ICT skills of academic staff, the provision of a university ICT policy on knowledge sharing through institutional repositories, the perception of academic staff members in knowledge sharing and the reward systems for knowledge sharing through IRs. This study adopted the Knowledge-Sharing model developed by Cheng et.al. in 2009 as its theoretical model. Descriptive research design was adopted for the study. The study location for the research was the University of Embu, a public university, and St Paul’s University, a private university. The target population in the selected universities was 151 academic staff. A questionnaire was used as the data collection tool. Qualitative data was analysed based on the themes of the study. Descriptive statistics were used to analyse quantitative data and were presented through frequencies, percentages, tables, and graphs. The study major findings included; the academic staff members have a positive perception on knowledge sharing through IRs, self-archiving of knowledge and research outputs through IRs is yet to be embraced, academic staff are not aware if KS through IRs is included in their respective university ICT policy and the academic staff are not satisfied with rewards system in place for awarding knowledge sharing through IRs and suggested monetary and non-monetary rewards as measures to improve on the reward systems. The study recommended that academic staff to be facilitated on self-archiving of their knowledge and research outputs via Irs. Also, universities to include and discuss KS through institutional repositories in their respective ICT policies, conduct user education to academic staff on KS through institutional repositories issues that are discussed in their ICT policies and to evaluate and improve on the rewards system that the respective universities have established.
Adaptive Pedestrian Detection System Based on Deep Learning
(Kenyatta University, 2025-03) Aquino Nyapara Joctum
The existing pedestrian detection algorithms have the potential to improve road safety on a regional level, however their effectiveness in dynamic rural and urban environments remains unexploited. With this potential capability, their efficacy remains uncertain due to infrastructure and operational limitations. Nationally, integration into Kenya’s transport system is still in its infancy, with challenges in policy, infrastructure, and technological readiness limiting real-world deployment. The problem lies in the inability of current systems to provide accurate and timely detection, particularly in complex road topologies such as Type-S roads with sharp curves and frequent occlusions. To address this, this research proposes a YOLO-APD network to enhance detection accuracy and achieve real-time processing. A cost-effective RGB camera in the CARLA simulator was used to generate a custom dataset reflecting diverse traffic scenarios. Enhancements to the YOLOv8 baseline include a novel SimSPPF module for improved feature extraction and speed, a modified detection head with a gather-and-distribute mechanism, and C3Ghost modules for balancing efficiency and accuracy. The model was evaluated through ablation experiments, algorithm comparisons, and robustness tests. Results show YOLO-APD achieved a mean Average Precision (mAP) of 97.8%, with pedestrian detection exceeding 99.5%, outperforming state-of-the-art models. The model demonstrated robust performance with a 94% F1 score, validating its generalization ability in challenging environments. By enhancing detection accuracy and efficiency in Type-S roads, YOLO-APD presents a viable solution for improving autonomous navigation in complex traffic environments.
A Multi-Agent Monitoring System for Computer Networks
(Kenyatta University, 2025-09) Amwayi, Harrison Makokha
In today’s business environment, reliable network infrastructure is critical for day-to-day operations. As networks grow in complexity, efficient monitoring systems are essential to ensure the performance and availability of network devices. This study investigates the use of multi-agents in monitoring computer network devices, focusing on the application of autonomous agents in gathering and analyzing network data using the Simple Network Management Protocol (SNMP). It explores existing multi-agent frameworks and their limitations, followed by the design and implementation of a new model that integrates SNMP agents and Apache Kafka for scalable data ingestion and processing. The developed solution is designed to address concerns of scalability and network congestion, typically associated with centralized monitoring systems. By leveraging on Apache Kafka as a distributed messaging system, polling tasks are dynamically assigned across multiple agents, ensuring load balancing and fault tolerance. Additionally, feature selection is employed to reduce latency and minimize network congestion. The system was tested to evaluate its performance in real-time monitoring scenarios, demonstrating improvements in scalability and efficiency. This research concludes that multi-agent systems, combined with Apache Kafka, provide a robust model for real-time monitoring of network devices, offering enhanced scalability and reduced latency compared to traditional centralized approaches. Future work may involve refining fault-tolerance mechanisms and exploring additional autonomous agent models for other network management tasks.
Digital Literacy Influence on Information Seeking Behavior of Small-Scale Women Farmers in Kenya: Case of Muvuti Kiima Kimwe Ward, Machakos County
(Kenyatta University, 2025-11) Muia, Boniface Kaleli
Information is an essential commodity useful to mankind in all walks of life. As women play a central role in farming activities, they have diversified information needs. Information seeking behavior has dramatically changed with the rapid development of ICTs. Digital literacy is essential as it involves skills, behaviors and knowledge in using technology when communicating, working, learning and leading everyday life. This study purposed to investigate how digital literacy influences information seeking behavior of small-scale women farmers in Kenya. It was guided by the following objectives; establish the information needs of small-scale women farmers, determine the information seeking behavior of small-scale women farmers, assess the ability of small-scale women farmers to seek and retrieve digital information, establish the relationship between digital literacy skills and information seeking behavior of small-scale women farmers and establish the challenges small-scale women farmers encounter when seeking and retrieving information that is in different digital formats. The Big 6 Skills Model served as the theoretical foundation for this study. Descriptive research design was used. The target population was approximately 17059 small-scale women farmers. Nassiuma’s formula was used to calculate a representative sample size of 143 respondents. Questionnaires were used to collect quantitative and qualitative data. Cronbach alpha was used to assess the reliability of the research instrument through the test-retest technique. Quantitative data was cleaned, coded and analysed using Statistical Package for Social Sciences (SPSS) version 28. Descriptive statistics like frequencies, percentages, mean and standard deviations were used to analyse quantitative data. Qualitative data analysis involved three iterative steps of in-depth reading, coding and classification of responses from the questionnaire to identify patterns and themes. The analysed data was presented using charts and tables. The findings established that small-scale women farmers need information to improve their agricultural activities. Small-scale women farmers indicated that the sources of information they prefer most when seeking information are; listening to radio, talking to fellow farmers, watching television, asking friends, neighbours and relatives for information, mobile phone and talking to agricultural input suppliers. The study established that majority of the small-scale women farmers have never attended digital literacy training. Thus, enhancing the digital literacy skills of small-scale women farmers is essential as it equips them with the necessary skills to effectively seek and retrieve digital information. The government, through the Ministry of Information, Communications and Digital Economy, need to formulate clear policy aimed to initiate digital literacy training programs in rural areas targeting small-scale women farmers as a means for improving digital literacy in rural areas.
An Ensemble Feature Selection Model with Machine Learning Model for Detection of Fraudulent Motor Vehicle Insurance Claims
(Kenyatta University, 2025-05) Wambu, Anthony Mwiti
Insurance companies are continuously inventing new competitive insurance products in order to enlarge their market share. This has continuously created opportunities for insurance fraud as well. Despite the insurance industry having extensive motor vehicle policy data and claims information, fraudulent claims remain a significant challenge in motor vehicle insurance. Proper analysis of this data can result in development of more efficient methods for identifying fraudulent claims. The challenge lies on how to extract valuable insights and knowledge from this data. This is because insurance datasets inherently include noisy features or low-quality subsets of data. This study used feature selection techniques to select relevant features from motor vehicle insurance claim dataset. The selected features were then used in training machine learning model. The machine learning model consisted of multiple machine learning algorithms whose individual prediction results were combined by use of a voting method. This helped to improve classification performance. Machine learning model’s performance with feature selected dataset and with full dataset was then evaluated using recall, precision and F1- score. The results indicated that the model trained with feature selected dataset performed better than the model trained with full dataset attaining higher values in recall, precision and F1-score. This indicated improved capability in minimizing false negative and improved overall effectiveness in fraud detection. For feature work the model developed for detecting fraudulent motor vehicle insurance claims can be enhanced by integrating machine learning techniques with nature-inspired optimization algorithms. This will help in better handling of extensive datasets and result to development of more rapid and effective models for identifying false claims.
Archives Management and Service Delivery: The Case of Kenya National Archives and Documentation Services
(Kenyatta University, 2023-06) Isoka, Bathsheba Ratemo M.
Abstract
Adoption of Electronic Medical Record Systems for Health Services Delivery at Moi Teaching and Referral Hospital in Eldoret, Kenya
(Kenyatta University, 2023-06) Manwari, Priscilla K.
Abstract
A Hybrid Model for Detecting Insurance Fraud Using K Means and Support Vector Machine Algorithms
(Kenyatta University, 2024-10) Muthura, Brian Ndirangu
Medical insurance fraud is a significant issue in the healthcare sector, commonly characterized by fraud patterns such as misrepresentation of services, false claims, and identity theft. These patterns contribute to severe data class imbalances, with legitimate claims vastly outnumbering fraudulent ones, complicating effective detection. Current fraud detection methods struggle to address these evolving patterns and manage imbalanced datasets. This study employs a mixed-methods approach, integrating an extensive literature review with quantitative analysis of historical medical claims data. The research develops and evaluates four machine learning models: a standalone Support Vector Machine (SVM), a tuned SVM, a hybrid model combining K-Means clustering with SVM, and a tuned hybrid model. The models were compared using key metrics, including accuracy, precision, recall, and F1 score. Results show that the tuned hybrid model achieved the highest performance with an accuracy of 97.49%, demonstrating its superior ability to detect fraudulent claims compared to the standalone and default hybrid models. Future work will focus on further improving the computational efficiency of the hybrid model and exploring its adaptability to new and evolving fraud patterns in real-time environments. This research significantly advances fraud detection by offering a robust solution that tackles class imbalances and adapts to evolving fraud schemes
Impact of Integrated Mandatory E – Government on Public Service Kenya; Case of Siaya County Government
(Kenyatta University, 2023-08) Miganda, Gloria Amondi
Kenyan citizens have always needed and demanded better service in terms of service provision from their government. Through its ministries and agencies, the government has listened and adopted an integrated e-government system to aid technocrats in providing efficient and effective services. The Government of Kenya adopted and implemented IFMIS (Integrated Financial Management Information System), GHRIS (Government Human Resource Information System), plus IPPD (Integrated Payroll and Personnel Database), which are, Integrated e-government systems that have been in use in government agencies, parastatals, and ministries. These information systems amalgamated different government activities into single units, making the work of public servants easier. Adopting the e-government mentioned above is intended to cure the problems of maladministration, corruption, inefficiency, and ineffective service delivery to the people. The issues discussed above persist to date, necessitating this study. This research sought to examine the Impact of Integrated mandatory e-government in public service; Case of the Siaya County Government. It intended to establish the impact of IFMIS, GHRIS, and IPPD and outline the benefits and challenges of the software systems and how to mitigate them. This study adopted a descriptive survey design, in addition to a sample size of 108 drawn from the population. Seventy-one (71) individuals answered the questions in the questionnaire, representing a reaction frequency of sixty-six (66) percent. Data collection was done using a structured questionnaire, and SPSS software aided in data analysis. Outcomes of the research indicated that the dependent variables of this research had a substantial positive influence on adoption of e-government services in Kenya’s devolved system of governance. However, statistical modelling using the regression analysis while controlling for confounding variable presented that the dependent variables had a positive influence on the adoption of the e-Government. Given the outcomes of this research, it is recommended that County Government of Siaya need to enhance the dependant variables areas in the workplace in order to improve the adoption of e-Government by its staff.
Information Communication Technologies Application For Documenting Indigenous Farming Knowledge for Improved Preservation and Utilization in Public Libraries, Kilifi County, Kenya
(Kenyatta University, 2024-05) Chai,Anderson Kahindi
The study's objective was to evaluate how librarians in Kilifi County, Kenya, used information and communication technologies to document traditional farming knowledge in order to better its application and preservation. Indigenous knowledge (IK) is defined variously. For the purpose of this research will be viewed as locally owned, modified knowledge and wisdom that have been developed and used over times to aid a community maintain or enhance their way of life in a localized rural context. Due to break down of oral transmission routes of knowledge, non-recording forms of capture and transmission of IK basically oral with increased westernization as well as oral transmission of knowledge has been replaced and IK if not carefully planned for is susceptible to extinct yet was and still remains a rich source of knowledge if well integrated with modern knowledge sources. Libraries could be reservoirs of IK if efforts are made to capture IK for preservation and application. Libraries hold a central role and their mandate is to build and maintain reservoirs of needed information for community. The study's purpose was to employ ICT tools to record essential indigenous farming knowledge in Kilifi County, Kenya, for future generations to use and preserve. The study examined the significant role that a library could play in preserving, managing, storing, and disseminating indigenous farming knowledge as well as identifying issues and concerns related to documentation and preservation of indigenous farming knowledge. It also evaluated the awareness and perception of the study community regarding use of ICT tools in preservation and application of indigenous farming knowledge. The study was undertaken across the seven Sub Counties within Kilifi County, namely Malindi, Magarini, Kilifi North, Kilifi South, Ganze, Kaloleni and Rabai. Total population of the study was 162 respondents comprizing 140 Kaya Elders (Farmers), 7 Sub County Agricultural Officers and 15 Library Staff. Utilizing the Krejcie and Morgan formula n=X2NP (1-P)/e2 (N-1) +X2P (1-P), a sample of ninety-eight respondents was determined. For data collection, interviews and questionnaires for Kaya Elders (Farmers), Sub County Agricultural Officers and Library Staff were used. Video recording was used to capture respondents during interview and document reviews including dependable databases, such as the Kilifi County Development Plan, the Kilifi County Agricultural Sector Development Programme, and the Kilifi County Government Demographic Reports were made. Cronbach's alpha was used to assess the instruments' validity. Data analysis techniques included theme content analysis, tables of averages and inferential statistics using the Statistical Package for Social Science (SSPS). Findings revealed indigenous knowledge is extremely valuable, helps the community to ensure food security, which needs to be transmitted to the following generation. Farmers acknowledge the value of information and communication technologies (ICTs) in maintaining indigenous farming knowledge (IFK), and they concur that if it is not recorded, it might vanish with the farmers. Among the ICT instruments in use were radios, TVs, laptops, memory cards, social media, flash drives, memory cards, and iPads. To record and conserve IFK for upcoming generations, the Sub County Librarians and Agricultural Officers can work with the Kaya Elders (Farmers) who own the IFK. The Public Library repository can serve as a valuable resource for managing and preserving documented indigenous farming practices through the inclusion of uploaded video recordings thereby creating a repository of indigenous farming knowledge experiences. Recommendations of the study are incorporation of indigenous farming knowledge to scientific farming knowledge, educating people on value of indigenous farming knowledge, revampment of public libraries to become viable knowledge assets of IFK and the Kenya National Library Services to take responsibility in the capture, preservation and management of indigenous farming knowledge.
Customers’ Perception of Mobile Banking and Financial Performance of Commercial Banks in Nairobi City, Kenya
(Kenyatta University, 2023-11) Munyasia, Nelima Rose; Stephen Titus Waithaka
A Hybrid Model for Text Summarization using Natural Language Processing
(Kenyatta University, 2022) Karanja, James Mugi; Eliud E.O. Obere
Small and medium sized businesses are key aspects of economic progress in every country and their internationalization is thought to be essential to the growth of the economy and firm growth. Micro, small, and medium-sized businesses have endured a great deal of failure and death in Kenya, despite the fact that they are crucial for increasing employment, capital base, and revenue. The performance of the small and medium sized enterprises in Kenya’s internationalization remains dismal because more focus by the government has been on foreign investors. This study sought to establish the key internationalization strategies that impact Micro and SMEs performance in Nairobi City County. The general objective of the study was to investigate effects of internationalization strategies and performance of micro, small and medium sized manufacturing enterprises in Nairobi City County. The specific objectives of this study therefore were to determine the effect of managerial competence, network structure, operating network and international market knowledge on performance of Micro, Small and Medium size Manufacturing Enterprises in Nairobi City County, Kenya. The study was anchored on study: Stakeholders’ Theory, Tradition Foreign Direct Investment Theory, Stage Model Theory and The Network Approach. This study used a descriptive survey design. The target population was 262 exporting micro small and medium sized manufacturing enterprises that are located in Nairobi City, County. A sample size of 53 potential respondents were randomly selected representing 20% of the total population as justified under sampling technique. The key source of data was primary, obtained using structured questionnaires whose reliability and validity were ascertained. Descriptive and inferential analysis were the two methods that were utilized. The study found that managerial competence, operating environment and international marketing knowledge had significant effect on MSMEs performance. Managerial competence is thought to be an important determinant of internationalization for MSMEs. The study recommended global partnership to enhance global experience and innovation capability on SMEs' export success.
Automated Examination Generation using Natural Language Processing and Artificial Neural Network
(Kenyatta University, 2023) Ndirangu, Peter Ndegwa; Elizaphan Maina
The examination process is a key activity in evaluating what the learner has gained from the study. Institutions of Higher Learning (IHL) perform the activity by administering tests which comprises of questions and answers. Cognitive level, weight of the question, and topic coverage are key factors to consider when setting exams. The world today has largely focused on the automation of exam generation which has been ongoing with dire need during the period of the Covid-19 pandemic when education was greatly affected, leading to embracing online learning and examination. The process has taken shape; however, the automation process can be improved by incorporating machine learning algorithms in the process of setting examination. In view of this, the project focused on implementation of a question classification model that uses Neural-Network algorithm (NN) and Natural Language Processing (NLP) to determine questions cognitive levels based on the revised Bloom's Taxonomy. The iterative method of software development was adopted to provide room for continuous improvement. The developed model was put under test with a couple of questions obtained online. The effectiveness of the model was determined by subjecting it into database of 600 questions resulting to an accuracy of about 71%. An Application Programming Interface (API) and Moodle Learning Management System (LMS) plugin were consequently developed to allow integration of the model with an existing system. The deep learning approach was applied to predict cognitive levels of questions based on Bloom’s taxonomy and the resulting questions were made available to the instructor through the LMS interface. Future research should focus on the use of convolutional reinforcement learning to establish its effectiveness in question classification as well as perform comparison with various algorithms.
Improving Effectiveness of Industrial Placement Experience Using a Recommender System
(Kenyatta University, 2023) Ndolo, Daniel Mulinge; J. Kandiri
Different classifier systems have been developed as a result of technological advancement to replace traditional job-search techniques. Finding locations for their industrial attachments is difficult for students in tertiary institutions. By making this training option, they are restricted to open elements like geographic coverage and a limited understanding of industry players. This study used mixed research methodology to conduct its investigation which included survey research and software development methodologies. A multi-criteria classifier called PlacementKe was developed to create user profiles that are used to forecast and provide recommendations while matching a student with the right company for their industrial attachment training. The classifier was based on the hybrid collaborative filtering algorithm. The recommendations generated were anchored on the user profiled interest and weighted ratings. Using a preexisting data set produced by Kaggle, a base model was created using a pre-trained base model. The base model’s accuracy level was 92%. The base model was used to create parameters that were applied in the development of a custom placement recommender system. The model was implemented to a prototype application, which was evaluated using actual users and data. The user comments were recorded on a survey form and examined. According to the analysis, 77.78% of users were happy with the system’s overall performance. The inquiry used in the literature evaluation revealed a need for automation in industrial attachment placements to increase their efficiency and speed. The qualitative and quantitative objectives that guided the system design were both met by the research design. The system designed achieved a high user satisfaction rate. This study suggests that the system be developed and implemented for use in higher education institutions. Future work extensions are also discussed.
Enhancing Asset Security by Integrating Internet of Things on Non-Powered Assets
(Kenyatta University, 2023) Mwema, Joshua Mueke; John Kandiri
Advancements in the field of asset security have emerged as a result of the Internet of Things (IoT)’s explosive expansion. Several innovations have featured systems ranging from smart home automation to asset tracking and monitoring. The systems use different technologies such as Radio Frequency Identification (RFID), Global System for Mobile communications (GSM), General Packet Radio Services (GPRS), Wireless Fidelity (Wi-Fi) among others to secure the assets. As technology improve, intruders also update themselves with intrusion skills and knowledge, and this has led to the emergence of more sophisticated challenges in terms of asset security. To curb these challenges, this project proposes an asset security system that will be comprised of IoT integration, real-time alerts, and power autonomy for remote monitoring of the non-powered assets. Besides, a tamper-proof unit will be used to detect when the asset has been broken into, which will prevent intruders from compromising the security system inside the asset. In that case, a notification will be sent to the asset owner through the GSM SMS functionality. Further, based on the distance from the pre-set distance, the device will map the value to asset security states namely; zero to 0.5, 0.5 to 1 and greater than 1 where they will be interpreted as low, medium, and high-level security threat states respectively. This research project will bring on-board geofencing and remote-control capabilities to asset security systems so that the location of a device can be tracked when it is relocated.
An Intelligent Based System for Supporting Personalised E-Learning
(kenyatta university, 2023) Kivuva, Faith Ngami; Elizaphan Maina
Most traditional e-learning systems fail to provide the intelligence to guide a learner according to their learning style. However, intelligent agents can be created to perform the role of guide to a student depending on a predetermined learning style. In view of this, the study discusses how to design, develop and implement intelligent agents for supporting personalized e-learning based on a predetermined learning style. The main objective of this study was to design and implement an intelligent e-learning system based on intelligent agents for supporting personalized e-learning. The system, which is based on intelligent agents, provides some intelligence and supports dynamic learning. Each learner has different levels of achievement depending on their learning styles and gets personalized feedback/recommendations. Three intelligent agents were developed; a learner agent, a tutor agent, and an information agent. The learner agent, which has an AI engine, uses deep neural networks to provide a recommendation to the learners based on their learning styles. The tutor agent accesses what the learner has accessed and passes this information to the learner agent which then recommends the appropriate materials. The information agent presents the recommendations/feedback of the learners through the Moodle user interface. The learning styles of the students are determined by filling out a Visual, Aural, Read/Write, and Kinesthetic (VARK) questionnaire. The three agents were developed using the Prometheus methodology. They were also tested and integrated into Moodle Learning Management System (LMS). This integration allows learners who are using LMS such as Moodle to learn based on their learning style. The results indicate that it is possible to train a learner agent using deep neural networks and provide personalized learning to the learner based on the learning style. Future studies need to focus on using data collected in a learning management system to identify learner styles instead of using the VARK questionnaire. Additionally, it is necessary to use other learning styles models, such as the Filder-Silverman model, and the Kolb learning style model among others, to identify learning styles and conduct an experimental study to determine their effectiveness in personalized learning with intelligent agents.
Intelligent Conversational Agent for Enhancement of Online Communication in Selected Universities Using Pattern Matching Algorithm
(Kenyatta University, 2022) Kuria, Isaac Njoroge; Harrison Njoroge
University websites and online portals are the primary means through which potential students and other stakeholders find important information about an institution. University websites are essential to these organizations’ marketing and communication efforts. In websites, a lot of information is spread across numerous amounts of web pages. Navigating through these web pages to locate relevant results, according to the user needs, can be non-resulting, time consuming and annoying at times. There is need to complement these websites with the use of an AI Chatbot (UniBot) in order to serve more efficiently. To address this problem, the research project proposes to design, develop and implement such an agent that will engage online users of universities websites and online channels efficiently and in real time. The project initially aims at performing an extensive literature survey on intelligent conversational agents and the feasibility of applying them in enhancing online communication in universities. This will guide the design development, implementation and testing. The project shall utilize an iterative - incremental methodology to aid in design and development of UniBot, using AIML (Artificial Intelligent Markup Language) Pattern matching algorithm on the Pandorabot (AIAAS) platform. This will generate high quality training data, with which, the agents Natural Language Understanding (NLU) model can be trained. This will make the agent (UniBot) capable of handling user requests efficiently at run time. The agent will be integrated to the university website by use of an API. Finally there will be a provision to train and test the agent using data which will be made available by Online Communication/ University Website department at Kenyatta University.
Application of mobile phone in crime prevention within Central division, Nairobi city County
(Kenyatta University, 2017-08) Wambugu, Paul M.
ABSTRACT The study sought to establish application of mobile phones applications by police officers in crime prevention in Central Police Division, Nairobi City County. Application of mobile phone in the police service is underutilized making the organization not to fully benefit from its usage. Understanding use of mobile phone applications by the police officers may assist the organization in crime prevention efforts. Due to its ability to engage consumers in a timely and direct manner at low costs, mobile phone applications are relevant for the police organization. The study was guided by the Mobile Technology Acceptance Model (M-TAM) as the success of the mobile phone implementation depends on perceived usefulness (PU) and perceived ease of use (PEOU).The targeted population in this study was police officers serving at Central Division of Nairobi County (782). A sample of 155 police officers was recruited using random stratified sampling procedure for this study. Questionnaires were used for data collection for both qualitative and quantitative data. Qualitative data was coded and analysed thematically after interpretation of theme while descriptive data was analysed using descriptive statistics by use of Statistical Package for Social Sciences (SPSS version 20.0). Mobile phone was found to be effective tool in prevention of crime in Nairobi Central. Mobile phone applications such as Facebook, Whatsapp, Twitter, emails and short text messages were found through adapted technology acceptance model (TAM) as to effectively contribute to crime prevention based on respondents’ perspective. Police effort to use mobile technology was found to have significant effects on crime prevention. Based on these findings, the study recommends development of customised police mobile phone applications and enhancement of applications use through capacity building among police officers.

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