Business & Management

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    Can Retaining Older Workers in the Workplace Benefit Organisations in South Africa?
    (Dublin Business School, 2024) Schroeder, Julinda; Browne, Andrew
    The aim of the study was to explore whether retaining older workers in the workplace could benefit organisations in South Africa, against the backdrop of increasing numbers of people around the world being forced to work longer, or return to the workforce after retirement. This was due to socio-economic factors, which were currently being further exacerbated by the global inflationary impact on pensions and savings. Apprehension about losing meaning and purpose in life after retirement were further factors that emerged during the research for this dissertation. The objectives were thus to explore the transitioning of older, senior employees in an organisation to a coaching and mentoring role to potentially help mitigate the serous skills shortage in South Africa. Data collection took the form of seven semi-structured, online interviews with the identified participants in South Africa. The data was analysed using an inductive thematic analysis to describe and interpret the information. The interviewees confirmed that older members of the workforce proved to be a rich source of talent and experience for the organisations that engage them and have, in many instances, become catalysts for change and intergenerational diversity in the workplace. Despite the positive potential of retaining more mature members of the workforce, however, the reality of the current socio-economic situation in South Africa, proved to be far more complex than was anticipated at the outset of the study. To this end, the objectives of this study had to be revisited and some harsh truths had to be acknowledged. Thus, it was found that transitioning more mature members of the workforce to a coaching and mentoring role would require a significant mindset shift and more structured human resource management guidelines and processes to facilitate it.
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    Perform the Stock Prediction Using the Sentiment Analysis and Time Series Forecasting Approaches to Determine the Optimal One
    (Dublin Business School, 2024) Thimmaiah, Kavya; Laiho, Heikki
    Stock returns are affected by a variety of factors, among which the social media remarks of public figures are one of the more important aspects on the stock market trend. On top of that, latest news about the product of the stock also matters. In this paper, we determine the sentiment type of public figures' social media remarks from the perspective of textual sentiment, and compare them with the stock chart of the day to analyse the connection between the two. Specifically, we first construct a dataset of public figures' social remarks and classify the sentiment types, and then we use the network model BERT for training to be able to judge the sentiment type of a new remark when it is inputted, which serves as a basis for stock prediction. The experiment shows that the public figure's speech and the news will have a strong impact on the stock trading on the same day, but the impact is small for a long time, at the same time, the more influential the public figure is, the more obvious the impact on the stock. The development and wealth of countries depend heavily on the stock market. Data mining and artificial intelligence methods are required to analyse stock market data. The financial success of particular businesses is one of the important factors that has a significant impact on stock price volatility. However, news reports also have a significant impact on how the stock market moves. In this research, we use sentiment classification to use non-measurable data, such as financial news articles, to forecast a company's future stock trend. We seek to cast light on the effect of news reports on the stock market by analysing the connection between news and stock movement. Our study seeks to advance knowledge of the function of news sentiment in forecasting stock market trends. The dataset used in this study consists of news headlines from the financial news website, Financial Times, and the prediction task is to classify the direction of the stock price changes as either positive or negative. The purpose of this study is to evaluate the effectiveness of sentiment analysis for stock prediction and to compare the performance of different algorithms.
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    Traditional Machine Learning Algorithms and Deep Learning for ODI Cricket Prediction
    (Dublin Business School, 2024) Tirthe, Priti; Hoare, Terry
    A comparative study between traditional machine learning and a deep neural network approach is presented for predicting winning teams in for One Day International (ODI) cricket games. Data is extracted from the espncricinfo website covering the years 1971 to 2022 for model training. Features include team performance and match conditions. Model performance is evaluated on 2023 match results. Both small (2010–2022) and large datasets (1971-2022) are used for training for comparative purposes. The deep neural ANN achieves an accuracy of 85.4%, outperforming the conventional techniques including ensemble techniques such as random forests and gradient boosting. The deep neural ANN model is shown to outperform in identifying nuances and intricate patterns, demonstrating an ability to use large amounts of historical data to increase accuracy. This study builds upon earlier work to add significant insights to improve ODI cricket result predictions.
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    How to Influence People to Migrate to Cloud Computing Systems in Developing Virtual Environment to Store Customer Data in Bangladesh
    (Dublin Business School, 2024) Kamal, Asif; Ibomo, Kingsley
    The opening section emphasizes how Bangladesh has embraced cloud computing to enhance the efficiency and security of data storage, in establishments. The adoption of cloud technology has streamlined information management, reduced costs. Transformed the way organizations operate through technology. The background section discusses the importance of using cloud computing in environments highlighting its affordability and facilitation of business. The problem statement addresses the challenges associated with data storage and advocates for migrating to the cloud. The research aims to explore why Bangladesh should adopt cloud computing as the challenges it may face and recommendations for implementation. One hypothesis suggests that storing customer data in the country's cloud is both secure and efficient. The methodology section explains the research approach, design and strategy used to investigate how cloud computing impacts Bangladesh. Employing a philosophy this study takes an approach with a focus on collecting quantitative data through descriptive research design. Action research strategy is employed to explore migration methods while primary quantitative data collection, from professionals ensures insights. This methodology emphasizes objectivity, logical analysis and minimizes personal bias to comprehensively examine how cloud computing is evolving in Bangladesh. In the discussion and conclusion chapters, this study interprets findings to assess the implications of adopting cloud computing in Bangladesh. It explores the impact of migration recognizes the obstacles involved and offers suggestions, for putting it into action. The final remarks underscore the importance of cloud computing highlighting its ability to revolutionize the landscape and drive growth, in the country.
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    Enhacing Air Quality Index Prediction: A Comparatice Analysis of Transformer Models, Graph Neural Networks, and Traditional Approaches
    (Dublin Business School, 2024) Sharma, Aditya; Izima, Obinna
    This study presents a comprehensive comparative analysis of air quality index (AQI) prediction models, focusing on Transformer Models, Graph Neural Networks (GNN), and traditional approaches such as Linear Regression, Naive Bayes, and Long Short-Term Memory (LSTM). Evaluation metrics include Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). Among the models, Graph Neural Networks exhibit superior performance with an MSE of 1601.86 and an RMSE of 40.02. The Hybrid Model follows closely with an MSE of 2057.53 and an RMSE of 45.36. Traditional methods like Linear Regression and Naive Bayes demonstrate moderate accuracy, while the LSTM model exhibits higher errors. Notably, the Transformer Model records the highest errors, suggesting challenges in accurately predicting AQI using this approach. These findings offer valuable insights for selecting optimal models to enhance air quality predictions in environmental research and monitoring.