/library/oar/handle/123456789/2579 Sat, 07 Nov 2026 12:35:18 GMT 2026-11-07T12:35:18Z /library/oar/handle/123456789/150739 Title: How machine learning based analysis of financial ratios can assist beginner investors Abstract: This study addresses the gap between the increase in retail investors joining the market, but the financial literacy has not kept pace. This subject is referred to as the information and action gap, where beginners struggle to interpret complex financial data. This research aims to investigate how Explainable Artificial Intelligence can be applied to simplify fundamental stock analysis. A quantitative experimental approach was adopted using a dataset of US companies, and being processed within WEKA, a machine learning platform. Fourteen key financial ratios were analysed using a 2 method, including supervised and unsupervised machine learning. The J48 Decision Tree was applied for supervised classification and achieved an accuracy of 65.71%, and also generated a transparent tree of how its logic classifies the data. Simple K-Means clustering was used as an unsupervised machine learning algorithm that identifies natural groupings within the data, and this machine learning algorithm achieve 80% alignment with the pre-defined company classifications. The findings prove that machine learning models can effectively structure and simplify financial data into interpretable outputs and support user understanding. However, the moderate predictive performance highlights the limitations of the models in real-world investing scenarios. In conclusion, this study demonstrates the potential of explainable machine learning as an educational tool that enhances financial understanding, rather than replacing human decision-making. The results support a shift toward FinTech systems that prioritise transparency, user learning, and informed decision support. Description: B.Sc. (Hons) Bus.& IT(Melit.) Thu, 01 Jan 2026 00:00:00 GMT /library/oar/handle/123456789/150739 2026-01-01T00:00:00Z /library/oar/handle/123456789/150738 Title: How Maltese boutique hotels exploit AI agents to enhance customer experience Abstract: This dissertation examines how decision-makers in Maltese boutique hotels perceive, evaluate, and potentially adopt AI chatbots and AI agents to enhance customer experience across the guest journey. The study addresses a context in which hospitality is becoming increasingly digital, while boutique hotels continue to compete through intimacy, personalisation, trust, and visible human service rather than scale and standardisation. An interpretivist qualitative approach was adopted. The research was exploratory in design and based on eight semi-structured in-depth interviews with boutique hotel decision-makers in Malta. All interviews were analysed using thematic analysis. The study focused on managerial perceptions of AI capability, service suitability across the pre-stay, in-stay, and post-stay journey, operational feasibility, trust, integration, and governance. The findings indicate that Maltese boutique hotels do not reject AI-enabled guest service in principle. Most participants regarded AI as potentially useful when deployed selectively for repetitive, low-emotion, and administratively burdensome communication tasks, particularly routine pre-stay and in-stay enquiries such as FAQs, logistical questions, and multilingual support. However, participants also drew clear boundaries around automating arrival, complaint handling, service recovery, and other emotionally sensitive guest interactions. The findings further show that adoption depends not only on favourable attitudes, but also on PMS and API integration, organisational readiness, governance confidence, data and privacy concerns, and clear human handover protocols. The dissertation concludes that the most credible path for Maltese boutique hotels is carefully governed augmentation rather than replacement. The main implication is that boutique hotels should adopt AI cautiously and selectively, using it to support bounded service tasks while preserving human-led interaction at the trust-intensive moments most central to the boutique experience. Description: B.Sc. (Hons) Bus.& IT(Melit.) Thu, 01 Jan 2026 00:00:00 GMT /library/oar/handle/123456789/150738 2026-01-01T00:00:00Z /library/oar/handle/123456789/150737 Title: Assessing AI and automation readiness for productivity growth in Maltese SMEs Abstract: This study examines the maturity and appetite for adopting artificial intelligence and automation technologies among Maltese small and medium-sized enterprises, with particular attention to their potential to improve productivity and operational efficiency. The research addresses a gap in Malta-specific academic literature by investigating current digital practices, readiness for adoption, and the technological, organisational, and environmental conditions influencing implementation. A qualitatively driven mixed-methods approach was adopted. Primary data were collected through semi-structured interviews with Maltese SME owners, managers, and senior decision makers, together with a structured survey aligned to the EU Digital Maturity Assessment (EU DMA) and the Technology, Organisation, Environment (TOE) framework. The qualitative data were analysed using thematic analysis, while the quantitative findings were used to strengthen and support the overall interpretation of the results. The findings show that Maltese SMEs are not digitally inactive, but are uneven in their maturity, readiness, and depth of AI adoption. Although many SMEs already use some form of digital tools and basic AI applications, readiness for deeper AI integration remains uneven. The results indicate that openness to AI is generally stronger than actual implementation. Key barriers include limited time, resource constraints, skills gaps, fragmented data environments, and uneven access to relevant guidance and support. At the same time, SMEs can identify clear areas where AI and automation could improve productivity, particularly in administrative work, reporting, customer communication, finance-related tasks, workflow coordination, and marketing. Description: B.Sc. (Hons) Bus.& IT(Melit.) Thu, 01 Jan 2026 00:00:00 GMT /library/oar/handle/123456789/150737 2026-01-01T00:00:00Z /library/oar/handle/123456789/150736 Title: Adoption of AI and CRM technologies in Malta’s gaming sector Abstract: This research explores the adoption of Artificial Intelligence (AI) and Customer Relationship Management (CRM) technologies within Malta’s gaming sector, focusing on their practical applications, organizational impact, and implementation dynamics. As one of the leading global iGaming hub operating in a highly competitive and regulate environment, Malta presents a strong case for examining technological innovation. However, limited academic attention has been given to the local experiences and challenges of firms adopting these technologies, highlighting the need for context-specific analysis of their influence on operations and customer engagement. A qualitative research design was employed, using semi-structured, in-depth interviews with seven industry experts, including senior managers, data engineers, and CTO from leading gaming companies in Malta. This was complemented by secondary sources such as industry reports and academic literature, with thematic analysis used to identify key patterns and insights. Findings indicate that adoption is primarily driven by the need to improve player retention, enhance operational efficiency through automation, and deliver highly personalised customer experiences. A broader shift towards data-driven organisational cultures was also evident, with evidence-based decision-making increasingly replacing managerial intuition. While AI and CRM technologies improve service responsiveness and fraud detection, their integration is often constrained by high costs, skills shortages, and regulatory requirements such as GDPR. Description: B.Sc. (Hons) Bus.& IT(Melit.) Thu, 01 Jan 2026 00:00:00 GMT /library/oar/handle/123456789/150736 2026-01-01T00:00:00Z