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DBS eSource is an online service hosting full content materials produced by Dublin Business School staff and students. It contains the full text of articles, theses, conference papers, book chapters and more. DBS eSource is an open access repository, with the aim of making all content as widely accessible as possible. Use the Browse functions on the right for an overview of relevant materials. For an advanced search click here
Recent Submissions
Item Impact of funding and geographical factors on software startups' success(Dublin Business School, 2024-05)The purpose of this study was to investigate and adress two important questions regarding the success of software startups. To begin, it investigated the level of impact that different types of funding structures and geographical factors have on the success of the software startup companies. It explored various machine learning models to predict the outcomes of startup ventures, taking into account important features, model performance and cost-effectiveness. As demonstrated in this report, the research provided answers to these questions. The study identified the primary factors that contribute to over 64 per cent of the success or failure of a software startup company. Location-region accounts for 18%, Time initial funding has received accounts for 16%, timing of Final Funding accounts for 15%, Access to Venture Capital accounts for 11%, and Location - Country accounts for 5%. Logistic regression emerged as the most suitable model for deployment, achieving an accuracy of 96.58%, precision of 96.51%, recall of 100%, and an F1 score of 98.22%. This is by utilising the CRISP-DM methodology, Python code, and Power BI for data scrutiny and analysis. In addition, this model provides significant cost savings, which amount to 73.81 million dollars. The study does, however, acknowledge that there are challenges associated with limitations in the dataset scope and timeframe. These findings highlight the significance of conducting a comprehensive analysis of a startup, which should include aspects such as financial evaluation, geographical analysis, and predictive modelling.Item Comparing machine learning algorithm for predicting loan application for performance enahncement(Dublin Business School, 2024-05)This study evaluates various machine learning algorithms for predicting bank loan status using the CRISP-DM methodology. The research utilizes a dataset from Kaggle, focusing on algorithms such as Decision Trees, Support Vector Machines, AdaBoost, Random Forests, K-Nearest Neighbours, and Logistic Regression. Models were optimized using GridSearchCV, with recall as the primary metric to highlight the importance of accurately detecting loan repayment patterns. The findings demonstrate that the Support Vector Machine model was the most effective, achieving a recall score of 0.99and an F1 score of 0.96. Ensemble methods, which combine multiple models, notably improved prediction accuracy while maintaining interpretability. This study identifies the most effective algorithms and provides insights into factors influencing loan decisions, offering practical recommendations for banks to reduce bad loan rates and promote sustainable lending practices through advanced machine learning techniques.Item Navigating Identities: exploring the dynamics behind the religious and sexual identities amongst gay Latino men(Dublin Business School, 2024-03)Throughout the last century, traditional religious values have been challenged and, matters of how these values have been used to hide prejudiced attitudes are being questioned. This study sought to extend previous findings by interviewing 5 gay Latino men and exploring the relationship between their religious and sexual identities. From the semi-structured interviews and qualitative thematic analysis of the data, 4 main themes emerged: Conflict of identities, Resolution of conflict, Compassion to self and others and Factors behind a favorable outlook on religion. The findings indicate that religion can negatively impact the journey of self-discovery of LGBTQ-identifying people, but that various strategies can help resolve this internal conflict. Participants’ responses also suggested that a more flexible religious upbringing can foster a more positive outlook on religion later in life. These findings contain important social implications as well as relevant suggestions for clinicians working with spiritual and LGBTQ-identifying individuals.Item Teacher gender bias in the recognition of ADHD symptoms: a study with Irish teachers(Dublin Business School, 2024-03)This study endeavoured to investigate levels of gender bias among Irish teachers in the recognition of ADHD symptoms. Four potential influencing factors on levels of gender bias were also explored. These factors included: teacher knowledge of ADHD, teacher self-efficacy, experience teaching students with ADHD and participation in continuous professional development about ADHD. For this quantitative study, a cross-sectional, non-manipulative approach was taken, with participating teachers (n=100) engaging with an anonymous online questionnaire. The results indicated that there were significant levels of gender bias among participants towards the recognition of ADHD symptoms in boys. Significant negative relationships were also recorded between gender bias and teacher knowledge of ADHD, gender bias and teacher self-efficacy and gender bias and participation in continuous professional development about ADHD. These findings provide support for previous research carried out in the field, as well as the emerging picture of the under diagnosis of girls with ADHD in Ireland.Item The impact of dysmenorrhea on burnout & quality of life in Ireland(Dublin Business, 2024-03)This study aims to understand the relationship between dysmenorrhea, occupational burnout and health-related quality of life (HRQL) of people who menstruate in Ireland, and whether the type of dysmenorrhea impacts HRQL. A quantitative correlational and cross-sectional analysis was carried out. 82 participants (M=32.13) completed an online questionnaire relating to severity of dysmenorrhea, burnout and six aspects of HRQL. Regression analysis found that dysmenorrhea did not predict burnout (P =.232), however severity of dysmenorrhea does predict worse physical, social, and psychological aspects of HRQL (P<.001). An independent samples t – test found that secondary dysmenorrhea leads to poorer HRQL then primary dysmenorrhea (P<.001). Longitudinal and qualitative research is needed to understand the psychological impacts of the menstrual cycle and secondary dysmenorrhea. This analysis highlights the need for governments, organisations, and society to tackle the stigma surrounding menstruation to provide adequate care to people who suffer from dysmenorrhea.
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