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ÖgeAraştırma & geliştirme (Ar-Ge) yatırımlarının hisse fiyatları üzerine etkisi(Lisansüstü Eğitim Enstitüsü, 2024-07-18)Şirketlerin gerçekleştirmiş oldukları Araştırma ve Geliştirme (Ar-Ge) yatırımlarının hisse senedi fiyatları üzerindeki etkisine odaklanan çalışma, şirketlerin Ar-Ge harcamaları ile hisse senedi fiyatları arasındaki ilişkiyi inceleyecektir. Çalışmada, Ar-Ge harcamalarının şirketlerin hisse senedi fiyatları üzerindeki etkisini değerlendirmek için Borsa İstanbul'da işlem gören çeşitli sektörlerden ve iş alanlarından firmaların Ar-Ge yatırım ve hisse senedi verileri kullanılacaktır. Çalışmada farklı sektör ve iş alanlarından firmaların nicel verilerinin kullanılması, çalışma sonucunda elde edilecek bulguların güvenilirliğinin ve tutarlılığının artmasına olanak sağlayacak ve aynı zamanda farklı sektörlerdeki firmaların sektörel bazda Ar-Ge yatırımlarının hisse senedi fiyat performansı üzerindeki etkilerinin anlaşılmasına yardımcı olacaktır. Araştırma nicel bir bakış açısı ile gerçekleştirilecek olup yapılacak olan analizler panel veri analizi yöntemi kullanılarak gerçekleştirilecektir. Çalışmada Ar-Ge harcamalarının şirketlerin finansal performansı üzerindeki etkisi, hisse senedi değerlemelerindeki rolü, yatırımcı davranışları üzerindeki etkileri ve şirket imajı ve müşteri güveni üzerine etkisi gibi konulara odaklanılacaktır. Ayrıca, Ar-Ge harcamalarının hisse senedi fiyatları üzerindeki etkisinin sektörel farklılıkları da firmalar bazında detaylı bir şekilde incelenecektir. Veri analizi sürecinde, bulguların güvenilirliğini ve anlamlılığını değerlendirmek için panel veri analizi yöntemi kullanılacaktır. Elde edilen sonuçlar, literatürdeki mevcut bilginin derinleşmesine katkıda bulunacak ve Ar-Ge harcamalarının şirketlerin hisse senedi fiyatları üzerindeki etkisinin de daha ayrıntılı bir şekilde anlaşılmasına katkı sağlayacaktır. Bu çalışma, Ar-Ge harcamalarının firmaların hisse senedi fiyatları üzerindeki etkisinin derinlemesine bir analizini ortaya koymaya amaçlamaktadır. Literatürde mevcut olan sınırlı ve kısıtlı bilgiyi daha da derinleştirmek ve Ar-Ge harcamalarının hisse senedi fiyatları üzerindeki etkisini daha iyi anlamak için 2016-2023 yılları arasında Borsa İstanbul'da işlem gören farklı sektör ve iş alanlarından firmaların verileri kullanılacaktır. Yapılacak olan analizlerden elde edilecek bulgular, şirketlerin Ar-Ge stratejilerini şekillendirmede ve yatırımcıların yatırım kararı alma süreçlerine ışık tutma açısından da önemli bir rol oynayacaktır. Bu çalışma, Ar-Ge harcamalarının şirketlerin hisse senedi fiyatları üzerindeki etkisine dair daha derinlemesine ve detaylı bir çalışma geliştirmek isteyen araştırmacılar, iş dünyası profesyonelleri ve yatırımcılar için değerli bir kaynak olacaktır.
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ÖgeThe circularization of the textile and ready-to-wear industry in Turkey: An evaluation in the context of the European green deal(Graduate School, 2022)Economic growth has always been one of the main goals for countries. However, the rapid depletion of resources in the face of this desire for growth, the fact that the effects of global warming are beginning to be felt, the environmental problems our world is facing, and the fact that it is a problematic world to be left to the future generations have changed the economic strategy of the EU. As a result, the EU submitted the Green Deal (GD) in late 2019. In conjunction with the Green Deal, the European Union aims to end the resource-dependent economic growth model and achieve net zero greenhouse gas emissions by 2050. The Green Deal affects the European Union (EU) member states as well as the countries which export goods and services to the EU. The transition to a circular economy, one of the six goals of the "Green Deal Action Plan," also plays a vital role in exiting the resource-dependent growth model. This transformation will also affect the textile and ready-to-wear industry, which is one of the nine critical areas in "Turkey's Green Deal Action Plan" and one of the "Key Product Value Chains" in the "EU Circular Economy Action Plan." The textile and ready-to-wear industry is a critical manufacturing sector for the Turkish economy. It is a major contributor to employment demand and as one of the Turkish economy's main export-oriented sectors, it helps reduce the current account deficit. Textile, ready-made clothing, and leather products constitute 16.4% of Turkey's total export revenues. The EU is a critical trading partner for Turkey, with its share of over 40 percent of Turkey's total exports. However, since the EU announced the European Green Deal, these exports have been exposed to risks of additional possible costs through EU's planned environmental taxes. In the context of the EU Green Deal, this study explores the need for circularization for Turkey's textile and apparel industry and its relative position with respect to its potential competitors in global markets. To this end, the study first undertakes RCA, SWOT and PESTLE analyses to assess the current situation of Turkish textiles and ready-to-wear industry at the sectoral level. RCA analysis shows that Turkey's sectoral competitiveness remains high with respcto to its competitors yet this advantage is observed to follow a decreasing trend over time. Additionally, the PESTLE analysis shows that the industry does not only make a contribution to overall labor demand but also does so in a gender balanced manner, helping to increase the female employment rate. 41% of the employees in the sector are women. Reducing greenhouse gas emissions is another goal of the Green Deal. The EU will introduce the Carbon Border Adjustment Mechanism (CBAM) to ensure that export firms to the EU are faced with similar environmental requirements as its own producers. In order to assess the likely costs to Turkish export firms in the textile and ready-to-wear industry under CBAM, the study estimates the carbon emissions on a sectoral basis disaggregated by three scopes. In this analysis, the textile and ready-to-wear sector (Nace C13-C15) is estiated as the sector with the sixth highest greenhouse gas emissions both in terms of Scope 1 (subject to CBAM under current plans) and total emissions (amongst a categorization of 24 economic activity sectors). These sectoral analyses emphasize the necessity for the Turkish textile and ready-to-wear industry for transition to the circular economy. The second part of the study moves onto answering the question of how to make such a transition through development of a strategic roadmap for circularization of the sector. The development of the roadmap is undertaken in several stages. First, through the Fuzzy Cognitive Maps (FCM) method, and using inputs identified through the literature survey and sectoral analyses in the first part, the study assessed a set of obstacles to and drivers of circularization in the Turkish textiles and ready-to-wear industry and the hierarchical causal relations across these factora. According to the findings of the FCM analysis, the key barriers are lack of awareness, short-term priorities, lack of management support, appropriate culture, and demand for the final product of circular economy. Lack of effective legislation and incentives has the most considerable outdegree value, i.e. it affects more concepts than any other particular obstacle. The results of the FCM analysis were then validated by experts using structured in-depth interviews. Finally, the experts answered open-ended questions to elicit their views and insights on the obstacles and drivers and suggested possible solutions. Finally a sectoral strategic roadmap for circularization was developed based on a content analysis of the results of the in-depth interviews.
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ÖgeEffects of internal and external factors on green (environmental) consumer behavior(Graduate School, 2023)In this study, it was aimed to analyze green (environmental) user behaviors by separating them into purchasing, recycling and using parameters and to determine the parameters affecting these behaviors. A comprehensive literature search was conducted and similar studies were examined.
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ÖgeThe effects of online marketing communications and sales promotion types on online television sales(Graduate School, 2022)Over the last decades, many consumer segments have started to shop online, and as a consequence online shopping has become an affluent area of research. The proportion of online shopping in overall retail purchases increases year by year dramatically, and especially after the pandemic, it has had a peak. With the increasing demand for e-commerce, sales promotion campaigns have become appealing to consumers. Drawing upon these trends, the impact of these digital/online marketing communications strategies and the effects of marketing investment on online television sales are analyzed in this thesis. A literature review was conducted on the effects of sales promotion campaigns and marketing communications on customers' online shopping decisions for various television segments. For years, numerous marketing communication strategies have been developed for online shopping. The purpose of this study is to identify the digital marketing strategies, their effects on consumers' purchasing patterns, the difficulties and convenience encountered during the process, and the acquisition on a company/customer basis. In addition, this study aims to investigate into how sales promotion campaigns and digital marketing communications shape online television sales. During the research, data was collected using a quantitative method. The screen size of the televisions that were sold was used to classify it. Consequently, in this thesis, televisions are divided into three categories: small, medium, and large television segments. For these three different television segments, past data on online television sales from April 2020 to April 2021 was examined on a daily basis for one year. This thesis contains a total of 379 daily observations. In order to measure the effects of sales promotion campaigns and digital marketing investments on online television sales, the Polynomial Distributed Lag model was used in this thesis. It was anticipated to reduce multicollinearity issues by using the PDL method. Online campaigns and marketing investments' immediate impact might be reduced in the future. To observe these effects, the PDL method was used. Dataset is run in IBM SPSS Statistics software. The analysis acknowledges significant correlations between digital marketing investment and online television sales for all three television segments. The findings in this thesis demonstrate that marketing expenditure positively impacts online television sales. The maximum influence was seen at medium-size television sales. Meanwhile, sales of large-screen TVs had the most negligible impact among those three different television categories. The effects of sales promotion campaigns and online marketing communications on e-commerce television sales data over a one-year period have been examined in this thesis. However, one of the studies' limitations is that offline television sales were not taken into account. Therefore, these effects on offline television sales can be acknowledged for future investigation areas. Another limitation is that these impacts are only measured in terms of television sales, with no consideration given to sales in other categories. Accordingly, long-term effects of these marketing activities should be investigated in future studies, in addition to offline television sales. Finally, the study did not consider pandemic effects. Therefore, pandemic effects can be studied in future research.
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ÖgeAn intelligent system for ranking e-commerce customer reviews to boost engagement(Graduate School, 2024-06-26)This study introduces an innovative framework employing learning algorithms to effectively rank customer reviews on e-commerce platforms. The approach addresses the inherent ambiguity and subjectivity in customer feedback by leveraging an extensive dataset and sophisticated feature engineering techniques. Central to the methodology is the introduction of an original target variable, the adjusted action rate, which, along with advanced training methods, helps mitigate the prevalent position bias. This is crucial for accurately reflecting the nuances of user behavior and the dynamics of review interaction, ensuring that the most relevant feedback is highlighted for prospective buyers. The framework utilizes Learning to Rank methods specifically designed to tackle the unique challenges of review ranking. These methods prioritize user feedback based on its relevance and helpfulness, enhancing the precision of review rankings. By using advanced machine learning techniques, the framework can discern subtle patterns in user interactions and preferences, providing a more personalized and efficient ranking system. The effectiveness of this approach is evaluated using the Normalized Discounted Cumulative Gain metric, which measures the correlation between user reviews and the AAR. This metric ensures that the ranking system not only improves user satisfaction but also drives engagement performance. The incorporation of regression and classification models further strengthens the framework's ability to handle diverse review data. Regression models predict the adjusted action rate by analyzing various features derived from the reviews, such as length, sentiment, and user credibility. Classification models, on the other hand, help categorize reviews based on their relevance, ensuring that the most significant feedback is prioritized. These models collectively enhance the accuracy and reliability of the review ranking system, making it more robust and adaptive to different user needs. Validation demonstrates significant improvements in user interaction and decision-making efficiency, enhancing the shopping experience by enabling customers to access the most relevant reviews. Detailed analysis reveals substantial increases in key engagement metrics, confirming the model's robustness and reliability. The framework successfully addresses the complexities of review ranking, benefiting both users and vendors. Its ability to adapt to evolving user preferences by continuously learning from new data ensures that the most current and relevant reviews are highlighted, keeping the platform dynamic and user-centric. This adaptability enhances user satisfaction and fosters greater trust in the e-commerce platform, providing accurate and helpful feedback consistently.
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