/library/oar/handle/123456789/5581 Sun, 02 Nov 2025 15:29:22 GMT 2025-11-02T15:29:22Z /library/oar/handle/123456789/139952 Title: Optimising monetisation through social media platforms : a case study on MaltaDaily Abstract: This study investigates the monetisation strategies employed by social media platforms, using MD as a case study. MD has rapidly become a leading digital media outlet in Malta, adapting traditional media approaches to a social media-first environment. Adopting a qualitative methodology, nine semi-structured interviews were conducted with internal stakeholders, clients, and external experts. Thematic analysis revealed six key themes: authenticity and trust, monetisation models, social media-first content strategy, engagement and performance metrics, challenges in a small, saturated market, and innovation and future growth opportunities. Findings show that MD relies heavily on direct advertising but is actively diversifying its revenue streams through events, subscription models, and service-based income. Authenticity, relatable content, and ethical brand partnerships were identified as critical to sustaining engagement and trust. However, Malta’s limited geographic and demographic size presents distinct challenges, requiring continuous innovation and the adoption of data-driven strategies. The research concludes that diversified monetisation, investment in audience analytics, and technological innovation are essential for MD’s long-term sustainability. Additional to these findings, it provides practical recommendations and contributes to the growing academic discourse on social media entrepreneurship in niche markets, highlighting the unique dynamics of operating within smaller economies. Description: B.Sc. Bus.& IT(Melit.) Wed, 01 Jan 2025 00:00:00 GMT /library/oar/handle/123456789/139952 2025-01-01T00:00:00Z /library/oar/handle/123456789/139951 Title: Using emerging technologies to help secure sponsorships for mixed ventures Abstract: Securing sponsorships remains one of the most persistent challenges for emerging ventures, particularly those pursuing unconventional initiatives or operating within academic environments such as university based teams. These ventures often lack the visibility, financial capital and established networks enjoyed by larger organizations, making it difficult to attract and retain sponsor interest. Traditional sponsorship acquisition methods rely heavily on personal networking, customized proposal writing and time-intensive outreach efforts all of which, although sometimes effective, are rarely scalable or efficient for resource constrained teams. This study investigates the potential of artificial intelligence (AI) and internet-based tools to transform the sponsorship acquisition process by enhancing efficiency, targeting precision and personalization quality. Specifically, the research compares the time and effort required to generate and send personalized outreach emails using a manual approach versus an AI-assisted one. By integrating web scraping, sentiment analysis, keyword extraction and automated email generation, a replicable end-to end framework was developed. This framework was applied to two real world sponsorship efforts:A Road trip Between Friends similar to the Mongol Rally (ZOB to Osh) and the University of Malta Rocketry (UM Rocketry) team. The system utilized Python based tools to scrape sponsor data from public sources, which was then processed using Google’s Gemini large language model to generate personalized sponsorship emails based on company specific context and branding. A second layer of automation handled bulk email dispatch, while additional scripts cleaned up AI generated inconsistencies such as bracketed placeholders and narrowed down more likely potential sponsors to reduce bulk. The dual implementation approach allowed for the evaluation of AI’s effectiveness in both adventurous and academic domains. Despite challenges such as GDPR constraints, inconsistencies in scraped contact data and limitations within the generative AI model itself, the AI-driven methodology demonstrated significant advantages in speed, scale and personalization when compared to traditional outreach techniques. Preliminary results indicate measurable reductions in outreach time per email, increased scalability in sponsor targeting and a higher level of initial sponsor engagement. However, findings also reveal the continued necessity of human oversight in post processing AI outputs and maintaining message quality. This research contributes a practical and adaptable methodology that emerging ventures can apply to sponsor acquisition, especially in domains where traditional approaches are slow, unsustainable, or ineffective. It also highlights broader considerations regarding the ethical deployment of AI in communication workflows, including data privacy, accuracy and the balance between automation and human input. Future work will aim to refine the AI prompt structure, improve the robustness of data validation methods and integrate sponsor engagement tracking into advanced customer relationship management (CRM) platforms for long term outreach optimization. Description: B.Sc. Bus.& IT(Melit.) Wed, 01 Jan 2025 00:00:00 GMT /library/oar/handle/123456789/139951 2025-01-01T00:00:00Z /library/oar/handle/123456789/139948 Title: The role, drivers and inhibitors of technology in advancing sustainability in organisations in Malta Abstract: This study aims to explore impact of technology advancing sustainability in Malta’s business sector, the current state of play, including its drivers and inhibitors, as well as identify the key technologies used. This research intend to fill the gap in local context (Malta), the technology objectives, concerning the topic, which has been previously explored on the small scale, as demonstrated by the literature review. The main aims of this study is to obtain detailed and insightful information regarding to the use of technology in advancing sustainability in local business sector. Moreover, the research aim to determine key technologies used to support sustainability. The study also identified technologies with the greatest potential for the companies, and type of support that would make it easier for them to adopt. The study concludes with the details about plans of implementation sustainability initiatives and technologies. Sustainability can be divided into three main pillars including: economic, social and environmental aspect. Main focus of this research lies on the environmental sustainability. A qualitative research methodology was employed to gather primary data in order to achieve the stated aims. In total 10 companies participated in the study, primary data was collected through semi-structured interviews aimed at exploring the insides about technology, and other initiatives used to advance sustainability. The responses gathered were then analysed and interpreted based on common key themes and insights. The outcomes of this study suggest that while technology is widely recognised as a key enabler of sustainability, its adoption can be affected by both: internal and external drivers. The drivers include cost savings, as well as regulatory frameworks and compliance. Barriers identified included high initial cost, human resistance to change and current infrastructure of the organisations. The research concluded in highlighting future opportunities through AI, cloud solutions, as well as smart infrastructure. Description: B.Sc. Bus.& IT(Melit.) Wed, 01 Jan 2025 00:00:00 GMT /library/oar/handle/123456789/139948 2025-01-01T00:00:00Z /library/oar/handle/123456789/139855 Title: Evaluating machine learning models for cardiac irregularities (early detection, prediction for heart disease) Abstract: The following research’s purpose is to tackle one of the most significant global health challenges, responsible for millions of deaths annually. Heart diseases are a serious and common threat to nowadays world and, despite constant medical advances, predicting cardiovascular diseases remains a problem. To address this issue, after a personal experience regarding heart disease, the researcher focused on understanding and creating four machine learning models with limited domain knowledge. Random Forest, Support Vector Machines, Deep Learning and XGBoost are the models leveraged in this study, which, once applied to public available datasets, provided unexpected results. The datasets used differ in size, quality and feature composition which mirror real-world conditions of clinical practice. Methods such as normalization, encoding, and Synthetic Minority oversampling technique (SMOTE) were implemented directly on the datasets to enhance the model performance when it come to accuracy, recall and precision. The final results showed how XGBoost is the most consistent and reliable model in between datasets. However, Deep learning, while being the second best, it provided unexpected results when working with small datasets. The findings underline how, to improve timing and precision in early detection of cardiovascular diseases, it is important that proper preprocessing and handling of data is performed, together with choosing the right model. This study’s purpose is to show the base of how important the integration of machine learning models with domain experts’ judgement is. Future research is certainly needed, and the thesis suggests practical directions to further improve clinical applicability in cardiac care. Description: B.Sc. Bus.& IT(Melit.) Wed, 01 Jan 2025 00:00:00 GMT /library/oar/handle/123456789/139855 2025-01-01T00:00:00Z