Browsing by Author "Zarrad, Abdelmajid"
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- Artificial intelligence in Tunisian international hotel industryPublication . Zarrad, Abdelmajid; Fernandes, Paula O.; Teixeira, João PauloIn an era defined by rapid technological advancements, the integration of artificial intelligence (AI) is reshaping industries worldwide, and the Tunisian international hotel industry is no exception. This thesis delves into the multifaceted impact of AI on this sector, exploring its potential to revolutionize guest experiences, streamline operations, and enhance sustainability. The research begins by establishing a foundational understanding of AI, tracing its historical development and defining its various types, from narrow AI to the aspirational general AI. It then examines the global landscape of AI adoption, highlighting its growing importance across diverse sectors. The thesis proceeds to investigate the specific applications of AI within the hotel industry, drawing on real-world examples and case studies. It explores how AI-powered chatbots, recommendation systems, and operational tools are transforming the way hotels interact with guests and manage their resources. The study also examines the perceptions of hospitality professionals regarding AI adoption, revealing a mix of excitement and apprehension about its potential impact on jobs and the guest experience. A significant portion of the thesis is dedicated to analyzing the economic dimensions of Tunisia's international hotel industry. It scrutinizes data on customer entries, foreign currency incomes, and GDP contributions over the past two decades. This analysis reveals trends, fluctuations, and the impact of significant events such as the Arab Spring and the COVID-19 pandemic on the industry's performance. Furthermore, the thesis employs artificial neural networks, specifically Multilayer Perceptron (MLP) models, to forecast tourist entries and revenues in Euros for Tunisia. By leveraging historical data and advanced AI techniques, the study demonstrates the accuracy and potential of MLP models in predicting future trends, offering valuable insights for stakeholders in the tourism sector. This thesis concludes that AI is crucial for the Tunisian hotel industry's future. AI enhances guest experiences, streamlines operations, and improves sustainability. By embracing AI and data-driven decisions, Tunisian hotels can lead innovation in global hospitality.