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Sağlık profesyonellerinin stratejik sağlık turizmi değerlendirmeleri: Türkiye çalışması
This study aims at examining the dynamics of health tourism in the Turkish health sector. It captures the actual state of Turkiye's position in health tourism that accelerates in line with the expansion of global health tourism market, advances in technology, and with increasing international patient demands. The study employs data gathered from questionnaires filled out by 52 health professionals from 46 different hospitals in Turkiye. The findings of the study highlight the areas of concern for such issues as the impact of health tourism certifications on foreign patient admissions, strategic development recommendations that impact hospital choices, and technology spending. The study presents the competitive strengths, Turkiye's challenges, and development opportunities for health tourism market. It also emphasizes how Turkiye should pursue strategic steps towards boosting its foreign patient reception for the health tourism sector by outlining policy recommendations.
Yapay zeka düzenlemelerı̇nı̇n karşılaştırmalı hukukı̇ analı̇zı̇: AB ve Türkı̇ye örneklerı̇
This thesis provides a comparative legal analysis of AI regulations, focusing on the EU AI Act and how it would affect the case of Türkiye. The thesis will first elaborate on the driving factors and documents leading to the EU AI Act. It analyzes the possibility of a Brussels effect of the AI Act. By decoding the main pillars of the act, it provides an understanding of the legal nature surrounding the act, and discusses the effects of one of the most important features of the act, which are the risk-based approach and human-centric governance on the strategic autonomy of the European Union. After understanding and analyzing these features, the thesis will further discuss how the concepts of Brussels Effect and Strategic Autonomy can affect Türkiye's regulatory attempts on AI, which could be beneficial to understand what can really shape the future for Türkiye's AI governance.
Yeşil mutabakat ve türkiye'de tekstil ve ayakkabı sektöründeki e-ticaret firmalarının hazırlık durumu
Today, sustainability has become critical not only for environmental policies but also for economic and political issues, and many new regulations or incentives have been prepared. The Green Deal announced by the European Union in 2019 has been one of the most comprehensive examples of these developments. The regulaton directly affects companies and organizations in countries that have commercial relations with the European Union. Türkiye is among these countries affected due to both its economic and geographical location and political relations with the EU. This study examines the awareness and readiness levels of e-commerce companies operating in the footwear and textile sectors in Türkiye regarding these environmental issues. This study aims to assess the companies' knowledge levels, strategic planning and readiness to implement the Green Deal and related regulations. The study uses primary data collected via a survey. Data analyses reveal that many companies do not have sufficient knowledge in the field of sustainability and aren't sufficiently prepared to comply with The EU Green Deal strategeically and operatonally. This study contributes to the literature on understanding the effects of the Green Deal on e-exporting companies and provides implications for decision makers.
Zorunlu ESG açıklamalarıyla birlikte kazanç duyuru toplantılarında ton değişimi
This thesis investigates whether mandatory Environmental, Social, and Governance (ESG) disclosure regulations influence the tone of corporate communication during earnings conference calls (ECCs). Using a large global panel of transcripts from 16,327 firms across 95 countries (2005–2024), the study employs advanced natural language processing tools to quantify tone. Specifically, FinBERT-Tone is used to detect sentence-level sentiment (positive, neutral, negative), while FinBERT-ESG and ESG-BERT models classify ESG-related content across environmental, social, and governance dimensions. For each ECC, the most optimistic ESG-related sentence is extracted to construct ESG_Tone_max, E_Tone_max, S_Tone_max, and G_Tone_max indicators. A Difference-in-Differences (DiD) approach compares tone changes in firms from countries with ESG mandates (treatment group) to those without (control group), while controlling for firm, year, and sector-year fixed effects. Results indicate a statistically significant increase in social tone (S_Tone_max) following ESG mandates, whereas environmental and governance tones show no robust change. Overall ESG tone improves slightly but is sensitive to model specification. Firms with stronger ESG performance consistently use more positive ESG language. This study bridges NLP and causal inference to show that regulation affects not only what firms disclose but also how they speak about ESG, enriching the literature on corporate communication and sustainability.
Economic crises and shifting political attitudes: Impact of eurozone crisis on creditor and debtor countries
The Eurozone crisis had a significant impact on individual political preferences in both creditor and debtor countries. This thesis examines the extent to which the crisis affected Euroscepticism, satisfaction with democracy, and trust in political parties and institutions. Using Eurobarometer micro-data, the sample was divided into debtors (Greece, Spain, Portugal, Italy, and Ireland) and creditors (Germany, France, the UK, Belgium, and the Netherlands) and analyzed for three time periods: pre-crisis (2007), crisis-period (2010), and post-crisis (2014). The findings reveal that the crisis had a negative impact on individuals' preferences in both creditor and debtor countries during the crisis-period. Euroscepticism increased, satisfaction with democracy decreased, and trust in political parties, local and international institutions decreased. However, the situation returned to pre-crisis levels for creditors in the post-crisis period, while debtors continued to experience negative impacts. The results suggest that the Eurozone crisis had a profound and lasting effect on individual political preferences in debtor countries whereas the crisis has a temporary impact on individual preferences in creditor countries. In conclusion, this study highlights the importance of understanding the impact of the Eurozone crisis on individual political preferences, particularly in countries most affected by the crisis.
Selection of a fulfillment center in the US market: The case of exporter SMEs in turkey
Selection of a Fulfillment Center in the US Market: The Case of Exporter SMEs in Turkey For the last couple of decades, globalization carved the path for a more interconnected medium of trade for businesses, ranging between large-size corporations and SMEs, to penetrate into new markets. As a result of this high-paced medium of transactions, businesses have been opting to outsource some of their supply-chain and logistics-related processes to some particular 3rd party firms, such as fulfillment centers. SMEs, however, can achieve even higher competitive advantages by working with fulfillment service providers for their export operations to foreign markets, which is being reinforced by several factors. This survey particularly investigates the Turkey-based SMEs' selection criteria on the appropriate fulfillment service providers for their export operations to the United States of America, through a wide-coverage questionnaire that measures companies' perception on fulfillment centers. Consequently, the presence of locality and the effectiveness of logistics are found to be significant for Turkey-based SMEs that develop export plans and/or operations to the United States, for their fulfillment center selection criteria.
Blok zincir teknolojisi ile fon toplama yöntemleri ve uygulanan kanunlar
This thesis analyzes and compares conventional fundraising methods such as initial public offerings, crowdfunding, venture capital and angel investors with blockchain-based fundraising methods such as initial coin offerings (ICO), security token offerings (STO) and initial exchange offerings (IEO). It then selects countries where blockchain-based fundraising methods are most actively used and compares the legal approaches and regulations in these countries. While this study is an academic contribution to the literature, it also serves as a professional guide for companies and legislators. The thesis uses literature review and encompassing comparative study of laws as research methods. The research questions: Which blockchain-based fundraising method can be used to replace conventional fundraising methods? What should be done to prepare the legal infrastructure for digital assets? Should new laws be created or can existing laws be applied? The results of the research can be listed as follows: STOs are preferable to IPOs, venture capital and angel investors, equity and lending based crowdfunding as they allow investors to become shareholders in return for their investments and provide the opportunity to borrow like bonds, while ICO can replace donation-based and reward-based crowdfunding, and IEO fulfills the function of intermediary platforms where crowdfunding is conducted. Some countries introduced new regulations, while others adopted regulations under existing laws. Common steps taken by all these countries include digital asset classification, anti-market manipulation, anti-money laundering, anti-terrorist financing requirements, licensing and prospectus compliance.
Predicting video popularity of streaming services with machine learning approaches
Live or online video streaming services have gained immense popularity in recent years due to the expansion of the internet and the impact of Covid-19. However, delivering content to an ever-increasing user base poses challenges to online video streaming companies. To respond to user demands and preferences, a prediction model with high accuracy is needed. In this thesis, a predictive model is developed to anticipate a video's popularity as popular or unpopular by applying machine learning algorithms to metadata and textual features of each video from a prominent online video streaming provider in Iran. The study shows that video popularity can be modeled with high accuracy based on video-related attributes and textual features in Persian language. Four classification models are applied, with the Random Forest model achieving the highest accuracy and F1-Score of 86% and 72%, respectively. The Support Vector Machine obtains the most accurate results when new attributes obtained through NLP are combined with metadata. Moreover, the inclusion of word embeddings of the video description as predictive features improves classifiers' performance significantly. The study finds that the number of program episodes, video type, channel, and year of production are the most influential features in predicting video popularity. Predicting video content popularity in advance has enormous benefits for marketing purposes, network usage, and network cost reduction.