E-commerce sites have generated large amounts of unstructured data as they allow millions of users to generate product reviews. Thus, although there have been significant improvements in the characteristics of big data, such as speed and volume, developing various analysis techniques to monitor, understand, and extract useful information from this web-based data has become challenging. This study aims to analyze cosmetic products on a Turkish-based e-commerce website with sentiment analysis and to create a new domain-specific Turkish sentiment dictionary model with manual labeling. In the study, a Turkish sentiment dictionary consisting of 65,378 words was created by manually labeling 875,455 product reviews for 24 cosmetic brands sold on the Turkey-based trendyol e-commerce site, and sentiment analysis was performed using this dictionary. The dataset, divided into seven product groups, was analyzed using K-NN, SVM, DT, RF, and LR algorithms to address three classification problems. The algorithms were evaluated with comparative analysis using accuracy, precision, recall, and f-1 score metrics. SVM gave the highest performance result with over 93% accuracy, 92% precision, 93% recall, and a 91% f-1 score in all product groups. The dictionary model created for the cosmetics industry in the study helps businesses and researchers to use their resources more efficiently and save time by performing fast and low-cost analyses on large datasets of product reviews. Moreover, by analyzing customer feedback, brands can offer long-lasting and environmentally friendly products that align with customers’ feelings. Thus, businesses have the opportunity to develop or improve products.

Web temelli verilerin endüstriyi besleyen bir alan olması, bilgisayar bilimlerinin yanı sıra yönetim bilimlerinin araştırma konularına dahil olmasına neden olmuştur. İşletmelerin yeni ürünlerini piyasaya sürmesinde, yeniden şekillenmelerinde, stratejik kararlarında, pazarlama çalışmalarında yardımcı olan büyük veri, kurumların karar alma süreçlerinde oldukça etkilidir. Özellikle e-ticaret sitelerinde yer alan müşteri yorumları işletmeler ve markalar için kıymetli bilgileri içerisinde saklar. Denetimli makine öğrenme algoritmalarına istatiksel olarak dayanan duygu analizi, otomatik olarak bilgi çıkarımında fayda sağlayan önemli yöntemlerden biridir. Bu çalışmada, belirli bir kozmetik markasının trendyol.com e-ticaret sitesinde yer alan ürünlerine yönelik Türkçe müşteri değerlendirmeleri üzerinde duygu analizi gerçekleştirilmiştir. Veri seti pozitif, negatif ve nötr olarak sınıflandırılmış ve ardından K-En Yakın Komşular (K-NN), Destek Vektör Makinesi (SVM) ve Karar Ağacı (DT) algoritmaları kullanarak sınıflandırma performansları ölçülerek değerlendirilmiştir. Karşılaştırmalı analizler sonucunda her üç algoritma için değerlendirme metrikleri %85 oranının üzerinde sonuç vermiştir. Kullanılan algoritmalar içerisinde Destek Vektör Makinesi %91 doğruluk oranı ile en başarılı sonucu vermiştir.

Natural disasters such as earthquakes, anthropogenic hazards such as terrorism, or pandemic diseases such as COVID-19, which affect the whole world in every aspect, have caused businesses and supply chains to be adversely affected throughout history. In this way, unpredictable and unpreventable conditions have forced businesses and decision-makers to develop strategies and act with multiple alternatives to take precautions. With the COVID-19 outbreak, the high dependence of countries, industries and businesses on their suppliers and the extent to which interruptions cause disruptions have shown themselves with brutal consequences. In this study, by focusing on the literature and real case studies, the disruptions experienced by supply chains during the COVID-19 period are discussed, and the measures taken to improve supply-chain resilience against disruptions after the pandemic are evaluated. Thus, by analysing the case studies obtained through six semi-structured interviews with supply-chain businesses from various industries, the lessons learned from the COVID-19 experiences have revealed the practices that can respond quickly to change, make more functional decisions at times of risk, and take more robust steps in the event of a different disruption in the future, on the purpose of developing supply chain resilience. <!----> <!----> <!----> <!---->

Understanding behavioral responses during and immediately after an earthquake is critical to reducing injuries and developing preparedness strategies. This study identifies response patterns under seismic threat using qualitative evidence from the 2020 Elazığ and İzmir earthquakes in Türkiye. Data were collected from 63 participants through semistructured individual interviews and family‐based focus group discussions. Responses were examined in three temporally distinct phases: (1) onset of the tremor, (2) intensification, and (3) immediate post‐tremor period. During the tremor, reported behaviors clustered around four dominant patterns: movement toward exits or open spaces under acute fear, freezing or waiting, improvised protective action, and moving toward vulnerable individuals. These responses were shaped by real‐time threat cues, including perceived shaking intensity, environmental noise, falling objects, perceived building danger, and the visible reactions of others. As the tremor intensified, increased perceived risk was associated with evacuation attempts, continued immobility, or delayed action under uncertainty. In several instances, participants remained in place because they perceived movement through stairs, exits, or enclosed spaces as dangerous. Initial postearthquake responses were characterized by movement toward perceived safe areas, attempts to contact relatives, and spontaneous helping behaviors toward nearby individuals. The findings suggest that earthquake response is shaped by dynamic threat interpretation, fear transmission through social cues, family responsibility, and socially mediated coordination. Interpreted through the Protective Action Decision Model and Emerging Norm Theory, the results highlight how behavioral responses evolve through the interaction between environmental signals, social cues, and emergent coordination. The study provides empirically based insights for improving preparedness messages, promoting contextually sensitive protective actions, strengthening household coordination strategies, and enhancing immediate postearthquake communication planning.