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Arşivlenen Tez
COVİD-19 sürecinde hareketlilikte eşitsizlik: Küresel ve yerel analiz
This thesis analyzes mobility patterns during the Covid-19 pandemic from a global and local perspective. The global framework includes 37 European countries and the local framework comprises 81 Turkish cities. The study follows the daily mobility trajectories of people from February 2020 to January 2022. The analyzes are conducted to understand the economic opportunities available in countries -at a macro scale- that facilitate or hinder the "proper" mobility behavior of individuals while focusing on the captive commuters, i.e., the share of the population who need to commute to the work despite the risk of infection and governmental policies. The results indicate that the workforce in regions with higher GDP per capita, education level, and life expectancy at birth was able to reduce their workplace mobility higher than commuters in areas with low income, education level, and life expectancy at birth. Therefore, unprivileged populations were exposed to higher health risks against rapid Covid-19 transmission in Europe and Turkish cities.
Geliştirilmiş RFM modeli ile müşteri segmentasyonu: Bir halı ve kilim üretici firmasında uygulama
Data science has gained enormous importance by contributing to the in-depth understanding and interpretation of information. Especially companies consult on data analysis to make strategic decisions in the competitive market. Much more important than the decisions taken is a determination of the customer or customer groups to which these decisions will be adapted. For that reason, customer segmentation by identifying similarities and differences between customers becomes crucial. In recent times, the RFM model is preferred mostly for customer segmentation. The RFM model is based on the customer's last purchase date, how often they purchase, and how much money contributes to the company. It is an easy model to understand and interpret results in a clear way. Many researchers prefer to apply the RFM method by adding extra variables to the analysis. Thus, customers are evaluated from a broader perspective. This study aims to present a developed RFM model by adding extra variables which are Loyalty, Dependence, and Expectation which are determined by a broad literature review and as a result of a survey relating to 106 dealers. There are some studies that create a segmentation model by using loyalty and the RFM model. However, this study developed a new model by including the dependence and expectation variables, which are not been used previously with the RFM model, besides loyalty. In the study, dealers are analyzed by the K-means clustering method and the optimum number of clusters is indicated as six. Each cluster has its specific customer behavior and this study guides the company to constitute marketing strategies regarding customers' specifications.
Üniversitelerde ar-ge harcamalarının araştırma performansına etkisi
The most significant factor showing that countries are economically developed is the level of technology they produce. Technology, on the other hand, is the application of knowledge produced theoretically through scientific research. In this context, there is a clear connection between the level of economic development and welfare and the point reached in science. The resources that countries allocate to science and research activities are increasing every year, and the effective use of these resources plays a critical role in increasing the welfare level of these countries. This study reveals the effect of the university investment budget, which constitutes an essential part of the financial resources allocated to research and development activities in Turkey, on the research performance of universities. Multiple regression analysis was used to achieve the aim outlined in this thesis. The number of publications per lecturer published in AHCI, SSCI, SCI, and SCI-Expanded indexed journals and the number of Web of Science publications citations were used as dependent variables to represent research performance. The rate of investment budget spent on R&D was used as an independent variable to represent R&D expenditures. In addition, the number of lecturers and the number of YÖK 100/2000 Project doctoral scholars were included in the models as control variables. It was concluded that R&D expenditures affect research performance positively.
COVID-19 döneminde Türkiye ve İsveç'te uygulanan politikaların fırsat eşitliği ve eğitim çıktıları üzerine etkisi
This thesis delves into the impact of COVID-19 policies implemented in Türkiye and Sweden on equal opportunity within higher education and explores the consequential higher education outcomes. The research employs a mixed-methods approach, incorporating both qualitative and quantitative methodologies. Through an examination of socio economic structure, education system, the study elucidates the distinctive approaches taken by Türkiye and Sweden. The findings contribute significantly to a comprehensive understanding of global education policy responses during crises, emphasizing the pivotal role of ensuring equal opportunity. By scrutinizing the specific measures undertaken by both countries, this study not only informs the on education policy during extraordinary times but also provides valuable insights for policymakers, educators, and stakeholders seeking to enhance equal opportunity and foster positive outcomes in higher education.
Istatistiksel ön puanlama bileşeni ile gruplama puanlama modellemesi (GSM) yaklaşımın geliştirilmesi: Yüksek boyutlu transkriptomik veri analizi için bir vaka çalışması
Rapid advancements in transcriptomic technologies have significantly increased the volume of data available for analysis, which presents challenges in terms of efficiency and computational demand. This thesis introduces a Pre-Scoring component to the Grouping-Scoring-Modeling (G-S-M) framework to address inefficiencies caused by the excessive number of gene groups generated by traditional GSM. By selectively prioritizing gene groups based on their statistical significance, this innovation aims to reduce the computational demands associated with scoring these groups using machine learning models, thereby streamlining the analysis process. Assessed across nine diverse Gene Expression datasets, the Pre-Scoring G-S-M framework not only maintained accuracy comparable to the traditional approach but did so with significantly fewer genes. This refinement conserves resources while maintaining the robustness and reliability of the data analysis, crucial for advancing research in personalized medicine and therapeutic strategies. The findings suggest that the modified G-S-M framework serves as a valuable tool in bioinformatics, offering a more efficient approach to handling large-scale genomic datasets. Future work will focus on adapting this enhanced framework to incorporate diverse types of omics knowledge, such as proteomics and metabolomics, further optimizing its performance to broaden its applicability in both clinical and research settings
Ulusal gönüllü değerlendirme raporları üzerinden sürdürülebilir kalkındırma hedeflerine ulaşma analizi
The 2030 Agenda for Sustainable Development sets 17 Sustainable Development Goals (SDGs) to address critical global challenges such as poverty, inequality and climate change. To monitor progress towards these goals, countries submit Voluntary National Reviews (VNRs) that qualitatively and quantitatively assess their SDG implementation. This study comprehensively analyses the VNR reports submitted in 2023 and 2024 and examines the extent to which these reports reflect progress towards the SDGs. Using a multi-layered text classification model supported by advanced machine learning algorithms, the alignment of VNR reports with specific SDGs is categorized and assessed. The study provides data-driven recommendations on regional differences, performance by goal, and the development of reporting frameworks. The findings show that SDG 5 (Gender Equality) and SDG 13 (Climate Action) are prioritized in VNR reports in many countries, while SDG17 (Partnerships for the Goals) is underrepresented in VNR reports. In addition , positive and negative relationships between the goals have been addressed in VNR reports. With these results, recommendations for more effective policy development, international cooperation and data-driven decision-making processes are presented for decision-makers.
Türkiye'de dindarlığın milliyetçilik ve küreselleşme tutumları üzerindeki etkilerinin araştırılması: Dünya değerler araştırması verileri kullanılarak yapılan bir çalışma
This study examines the complex relationships across religiosity, nationalist and globalist attitudes in Türkiye. The study provided a unique conceptual foundation for Türkiye by examining whether religiosity supports nationalist and globalist attitudes. This study analyzes how religion affects nationalism and globalization through the variables of attendance to religious services, national pride, and support for the European Union (EU) and the United Nations (UN). Using the Turkish leg of World Values Survey (WVS) data, and a series of generalized ordered logistic (gologit) regression models, this study aims to provide empirical findings revealing relationships between these variables. Understanding the associations between these variables provides crucial insights into the question what determines participants' sense of national identity and belonging to global institutions. The findings presented that religiosity interacts positively with national pride, and negatively with sonfidence with the EU.
Makine öğrenmesi teknikleri kullanarak moda e-ticaret sektöründe müşteri segmentasyonu
In today's world where technology is developing very rapidly, internet usage is also increasing proportionally. This change has revealed that brands attach importance to the sector. The significance of e-commerce is to the advantage of brands because there have been decreases in some fixed expenses of companies. With the increase in online shopping, personal analyses of customers can also be made by customer relationship management (CRM). It is necessary to divide customers into segments for customer-oriented marketing. Customer segmentation is a widely used form of analysis. There is an increasing demand to develop a deep awareness of individual customer needs and desires. Segmentation, a commonly utilized method for achieving this understanding, has undergone continuous refinement in recent years. This study targets to present a detailed analysis of various segmentation approaches and their evolution. In this study, RFM (Recency, Frequency, Monetary) analysis was used for segmenting the customers. Customers were divided into segments by scoring them on the last shopping time, shopping frequency and total spending. Four customer groups were created with K-means and the values of each segment were analyzed. Churn rate analysis determined customers who did not shop for 90 days as lost. Churn estimation was performed with the LightGBM model using the machine learning technique. In addition, the Predictive CLV (customer lifetime value) model was developed using the 1 machine learning technique Ridge Regression. The accuracy rate was increased and low, medium and high CLV segments were created. As a result; RFM , K-means and CLV estimation were used to optimize customer relationships and increase revenues. E-commerce data of a private brand was analyzed using machine learning techniques. Nowadays, there is an increase in computing power and rapid developments in machine learning/artificial intelligence algorithms. This has recently enabled the application of more advanced techniques.
Reklam değerinin yapay zeka tarafından oluşturulan reklamlara yönelik tutum ve tüketici satın alma niyeti üzerindeki etkisi: Planlı davranış teorisi perspektifi
Artificial intelligence is one of vital technological tools increasingly popular andused by companies in marketing applications such as advertising. This thesis aims toinvestigate relationship between AI-generated ads and Ad value, attitudes, and purchaseintentions, and moderating roles of variables such as gender and self-efficacy. Thetheoretical framework has been designed in line with Theory of Planned Behavior andAdvertising Value Model, which provides a comprehensive perspective on howindividuals evaluate and respond to AI-supported promotional content. Data was collectedthrough an online survey from participants aged 18-64 between August and September2025, yielding 361 responses. IBM SPSS Statistics 25 and Python were used for dataanalysis. Reliability analyses, Factor Analysis (EFA, CFA) were performed to verify theadequacy and robustness of the measurement tools, and hypotheses were analyzed withStructural Equation Modeling (SEM). According to results perceived ad value positivelyaffects both attitudes toward AI-generated ads and purchase intentions. Attitudes towardAI-generated ads notably affect purchase intentions. Attitudes have a mediating role inthe relationship between purchase intentions and perceived advertising value. Whilegender had no moderating impact on perceived ad value and attitude relations, itmoderated ad value and purchase intention relations. Its effect on the relationship betweenattitude and purchase intention is not statistically significant. Self-efficacy significantlynegatively moderates the relationship between attitudes and Ad Value. While it positivelystrengthens the effect of advertising value on purchase intentions, it negatively affects the effect of attitude on purchase intention. Theoretically, this research advances knowledgein AI-enabled marketing communications by integrating two key moderating individualcharacteristics into the relationship between value perceptions, attitudinal responses, andbehavioral outcomes. Also, it provides managerial benefits by highlighting the importanceof personalizing AI ads based on consumer behavior.
Kredi hacmi ve takipteki kredilerin kısa vadeli ve uzun vadeli analizi: Türk bankacılık sektörü perspektifi
This thesis investigates the dynamic relationship between loan volume and NPLs in the Turkish banking sector, focusing on both short-term and long-term perspectives. Using weekly data from January 2014 to May 2024 and employing the DCC model, this study explores how credit growth and economic shocks influence NPLs. The findings reveal a significant correlation between loan volumes and NPLs in the long term. On the other hand, the thesis also shows notable fluctuations during financial instability, such as the 2018 currency crisis and the COVID-19 pandemic. The cross-correlation analysis further highlights the effects of loan growth on NPLs following the importance of proactive risk management. The research provides actionable insights for policymakers, emphasizing the need for balanced credit growth, enhanced regulatory frameworks, and dynamic risk assessment tools. These findings contribute to the literature by offering a nuanced understanding of the relationship between credit dynamics and financial stability in an emerging market context. The study also aligns with Sustainable Development Goals (SDGs) 8 10, and 17, supporting inclusive economic growth and reduced financial inequalities and partnership through improved banking practices. Keywords: Non-Performing Loans, Loan Volume, Banking Sector