Organisational Culture Attributes Influencing the Adoption of Agile Practices: A Systematic Literature Review

Organisations have been adopting agile practices to deliver information system projects faster and create business value. Despite its advantages, many organisations battle to successfully adopt agile practices. While there are several challenges for agile adoption, organisational culture has been amongst the challenges on adopting agile practices. The objective of this study was to determine the organisational culture attributes which influence the adoption of agile practices within the organisation. The systematic literature review aimed to explore the organisational culture attributes which influence the adoption of agile practices. The review focused on papers published on organisational culture influencing the adoption of agile practices between January 2015 to December 2020. The search strategy retrieved 204 papers of which nine papers were selected for a detailed analysis. The study revealed five factors of organisational culture that influence the adoption of agile practices in the organization as: management control, team collaboration, market, values and creativity. These factors have a number of organisational culture attributes that influence the adoption of agile practices. This study contributes to the body of knowledge by providing organisational culture attributes which influence the adoption of agile practices. The results of the systematic literature review presented in this study will benefit researchers and practitioners of project management.info:eu-repo/semantics/publishedVersio

A Conceptual Information Technology Project Management Assurance Framework

Over the last decade, information technology (IT) projects have continued to fail at an alarming rate. Project managers are still battling to manage and deliver successful IT projects in organizations. The conceptual information technology project management assurance framework developed consists of project assurance processes. The conceptual framework was validated through a survey of 121 IT project managers from organizations in seven African countries. The purpose of this paper is to present research findings on how well project assurance processes are implemented and their importance in achieving a successful IT project outcome in organizations. The findings indicate that most project assurance processes are implemented better in successful IT projects than in challenged and failed IT projects. The findings also indicate that in successful, challenged, and failed IT projects, project assurance processes were perceived to be important in achieving a successful IT project outcome. This paper contributes to the body of knowledge on project auditing and assurance. Practitioners and project managers can use the conceptual information technology project management assurance framework to deliver successful IT projects in organizations

Combining Clinical Symptoms and Patient Features for Malaria Diagnosis: Machine Learning Approach

This research article published by Taylor & Francis Online, 2022Presumptive treatment and self-medication for malaria have been used in limited-resource countries. However, these approaches have been considered unreliable due to the unnecessary use of malaria medication. This study aims to demonstrate supervised machine learning models in diagnosing malaria using patient symptoms and demographic features. Malaria diagnosis dataset extracted in two regions of Tanzania: Morogoro and Kilimanjaro. Important features were selected to improve model performance and reduce processing time. Machine learning classifiers with the k-fold cross-validation method were used to train and validate the model. The dataset developed a machine learning model for malaria diagnosis using patient symptoms and demographic features. A malaria diagnosis dataset of 2556 patients’ records with 36 features was used. It was observed that the ranking of features differs among regions and when combined dataset. Significant features were selected, residence area, fever, age, general body malaise, visit date, and headache. Random Forest was the best classifier with an accuracy of 95% in Kilimanjaro, 87% in Morogoro and 82% in the combined dataset. Based on clinical symptoms and demographic features, a regional-specific malaria predictive model was developed to demonstrate relevant machine learning classifiers. Important features are useful in making the disease prediction

Organisational Culture Attributes Influencing the Adoption of Agile Practices: A Systematic Literature Review

This research article was published by the Journal of Information Systems Engineering and Management, 2022, Volume 7, Issue 1Organisations have been adopting agile practices to deliver information system projects faster and create business value. Despite its advantages, many organisations battle to successfully adopt agile practices. While there are several challenges for agile adoption, organisational culture has been amongst the challenges on adopting agile practices. The objective of this study was to determine the organisational culture attributes which influence the adoption of agile practices within the organisation. The systematic literature review aimed to explore the organisational culture attributes which influence the adoption of agile practices. The review focused on papers published on organisational culture influencing the adoption of agile practices between January 2015 to December 2020. The search strategy retrieved 204 papers of which nine papers were selected for a detailed analysis. The study revealed five factors of organisational culture that influence the adoption of agile practices in the organization as: management control, team collaboration, market, values and creativity. These factors have a number of organisational culture attributes that influence the adoption of agile practices. This study contributes to the body of knowledge by providing organisational culture attributes which influence the adoption of agile practices. The results of the systematic literature review presented in this study will benefit researchers and practitioners of project management

An Optimal Smart Tank Juice-level Monitoring System for Beverage Industries: A Case Study of Raha Beverages Company Limited, Arusha, Tanzania

This research article was published by IEIESPC(IEIE Transactions on Smart Processing and Computing), Vol. 11, No. 03, 2021Poor monitoring of levels in juice tanks is among the challenges that beverage industries face when pumping liquid from one tank to another. This leads to spilling fluids, faulty juice tests, and industrial accidents. To keep track of the liquid level in a tank, various approaches have been used. Existing technologies are costly and not interactive, and the majority do not benefit individuals with physical disabilities when manual monitoring is needed. The purpose of this paper is to present an optimal smart tank juice-level monitoring system that can be used in beverage industries. The system is able to monitor the juice level within a tank and regulate a pump using voice commands via Alexa and the Amazon Echo Dot. The proposed system was tested and validated, with key findings being that the developed prototype prevented overflowing, accidents, and changes in juice flavor during the dilution process. This paper contributes to the body of knowledge for food and beverage industries in that engineers and operators of beverage industries can monitor the level of juice in a tank, as well as enhance communication when pumping juice from one tank to another in real time

Characterisation of Malaria Diagnosis Data in High and Low Endemic Areas of Tanzania

This research article was published by East African Health Research Journal 2022, Volume 6, Number 2Background: Malaria remains a significant cause of morbidity and mortality, especially in the sub-Saharan African region. Malaria is considered preventable and treatable, but in recent years, it has increased outpatient visits, hospitalisation, and deaths worldwide, reaching a 9% prevalence in Tanzania. With the massive number of patient records in the health facilities, this study aims to understand the key characteristics and trends of malaria diagnostic symptoms, testing and treatment data in Tanzania’s high and low endemic regions. Methods: This study had retrospective and cross-sectional designs. The data were collected from four facilities in two regions in Tanzania,i.e., Morogoro Region (high endemicity) and Kilimanjaro Region (low endemicity). Firstly, malaria patient records were extracted from malaria patients’ files from 2015 to 2018. Data collected include (i) the patient’s demographic information, (ii) the symptoms presented by the patient when consulting a doctor, (iii) the tests taken and results, (iv) diagnosis based on the laboratory results and (v) the treatment provided. Apart from that, we surveyed patients who visited the health facility with malaria-related symptoms to collect extra information such as travel history and the use of malaria control initiatives such as insecticide-treated nets. A descriptive analysis was generated to identify the frequency of responses. Correlation analysis random effects logistic regression was performed to determine the association between malaria-related symptoms and positivity. Significant differences of p < 0.05 (i.e., a Confidence Interval of 95%) were accepted. Results: Of the 2556 records collected, 1527(60%) were from the high endemic area, while 1029(40%) were from the low endemic area. The most observed symptoms were the following: for facilities in high endemic regions was fever followed by headache, vomiting and body pain; for facilities in the low endemic region was high fever, sweating, fatigue and headache. The results showed that males with malaria symptoms had a higher chance of being diagnosed with malaria than females. Most patients with fever had a high probability of being diagnosed with malaria. From the interview, 68% of patients with malaria-related symptoms treated themselves without proper diagnosis. Conclusions: Our data indicate that proper malaria diagnosis is a significant concern. The majority still self-medicate with anti-malaria drugs once they experience any malaria-related symptoms. Therefore, future studies should explore this challenge and investigate the potentiality of using malaria diagnosis records to diagnose the disease

A Machine Learning Model for detecting Covid-19 Misinformation in Swahili Language

The recorded cases of corona virus (COVID-19) pandemic disease are millions and its mortality rate was maximized during the period from April 2020 to January 2022. Misinformation arose regarding this threat, which spread through social media platforms, and especially Twitter, often spreading confusion, social turmoil, and panic to the public. To identify such misinformation, a machine learning model is needed to detect whether the given information is true (true information) or not (misinformation). The aim of this paper is to present a machine-learning model for detecting COVID-19 misinformation in the Swahili language in tweets. The five machine learning algorithms that were trained for detecting Swahili language misinformation related to COVID-19 are Logistic Regression (LR), Support Vector Machine (SVM), Bagging Ensemble (BE), Multinomial Naïve Bayes (MNB), and Random Forest (RF). The study used the qualitative research method because non-numerical data, i.e. text, were used. Python programming language was used for data analysis due to its powerful libraries such as pandas and numpy. Four metrics were used to evaluate the model performance. The results revealed that SVM achieved the highest accuracy of 83.67% followed by LR with 82.47%. MNB achieved the best precision of 92.00% and in terms of recall and F1-score, RF, and SVM achieved the best results with 84.82% and 81.45%, respectively. This study will enable the public to easily identify Swahili language misinformation related to COVID-19 that is circulated on Twitter social media platform

Rule-Based Engine for Automatic Allocation of Smallholder Dairy Producers in Preidentified Production Clusters

This research article was published by Hindawi, 2022Smallholder dairy producers account for around half of all African livestock ventures; nevertheless, they face challenges in producing more milk due to an insufficient framework and infrastructure to maximize their output. Smallholder dairy producers in this scenario use a variety of tactics to boost milk output. However, the attempts need multiple heuristics, time, and financial investment. Furthermore, because of a lack of extension officers, smallholder dairy producers become trapped in failure cycles, unsuccessful attempts, and a diminished motivation to continue farming. Therefore, the interventions were more straightforward as smallholder dairy producers with comparable characteristics grouped. This research aimed to create a rule-based engine that automatically assigns smallholder dairy producers to predefined clusters. About 78 stakeholders were interviewed, including 69 smallholder dairy producers and 9 extension officers from Meru-Arusha, Tanzania. The 10 production features and 6 predefined clusters were adopted from the previous study. Therefore, a rule-based engine used the selected 10 production features. As a result, the rule-based engine automatically assigns the smallholder dairy producers to their respective clusters. Therefore, smallholder dairy producers share their farming skills and experience to increase milk output through these clusters. Furthermore, extension officers in the system provide timely assistance to smallholder dairy producers with farming concerns

Towards an Artificial Intelligence Readiness Index for Africa

This research article was published by Springer Nature in 2023The applications and benefits of Artificial Intelligence (AI) for socio-economic development are immense. AI is projected to contribute approximately USD 15.7 trillion to the global Gross Domestic Product (GDP) by 2030. However, countries need to be prepared to harness such benefits. Hence, assessing the AI readiness of a country is paramount. Africa is currently the only continent without an AI readiness index tailored to its needs. It relies on the existing global indices, which may not accurately measure the progress attained by individual African countries because of the different levels of development and unique context. This paper proposes an AI readiness index for Africa. It starts by exploring what the AI readiness index needs of Africa are, examines the extent to which existing AI readiness indices meet the needs, and then looks at indicators that should constitute the AI readiness index for Africa. The study employed a systematic literature review that aimed to explore the AI readiness needs for Africa and the extent existing indices meet these. The review focused on papers published on the AI readiness index between January 2018 to August 2022. The search strategy retrieved 301 papers, of which seven papers were selected for a detailed analysis. The study revealed that the existing indices partially meet AI readiness needs for Africa. The study also found that AI readiness index dimensions pertinent to Africa’s requirements are: Vision, Governance and Ethics, Digital Capacity, Size of the Technology Sector, Research and Development, Education, Infrastructure, Data Availability, general level of employment, employment in Data Science and AI roles, and Gross Domestic Product-Per Capita Purchasing Power Parity. This study contributes to the knowledge of AI readiness for Africa and globally. The results of this study will benefit governments, researchers, and practitioners of AI and its applications

A conceptual information technology project management assurance framework

Ph.D.Abstract: Most organisations continue to align project activities with their business strategy to achieve strategic objectives and create business value. Most of these organisations have adopted information technology (IT) to increase performance and productivity, improve service delivery to customers and create competitive advantage in the market. Some of these initiatives are managed as IT projects. Despite this investment in IT, IT projects still fail at an alarming rate. These failed IT projects resulted in organisations wasting huge amounts of money and not realising a return on their IT investments. The purpose of this study was fourfold. Firstly, it explored the reasons why information technology (IT) projects are still failing. Secondly, it determined the factors influencing project success. Thirdly, it determined whether a positive relationship between project auditing and project success exists. Fourthly, it examined how project assurance can effectively mitigate IT project failure. These focus areas were addressed by developing a conceptual information technology project management assurance framework to successfully deliver IT projects in organisations. The research methodology employed a mixed-methods design which combined both qualitative and quantitative research methods. First, the qualitative research method was used to validate the conceptual framework through focus group discussion which was composed of IT project managers from South Africa. The results of qualitative data analysis were used to build an instrument to collect data in the follow-up quantitative research. The quantitative research method used survey questionnaires to validate the conceptual framework amongst IT project managers from Africa. The data were analysed using Atlas.ti 7.0 and SPSS 24.0. Factor analysis was conducted to determine possible correlations between the variables and factors as well as to determine how the conceptual information technology project management assurance framework fits the data. Structural Equation Modelling (SEM) was used to construct the conceptual framework. The key findings of this research are that project assurance can effectively mitigate IT project failure and the following factors influence IT project success: project auditing, top management involvement, project management methodology adherence, secure project deliverables, support and maintenance, and benefits realisation. The research also reveals that there is a strong correlation between the identified factors that influence IT project success. These factors were important in the development of a conceptual information technology project management assurance framework..