Analysis and Evaluation of Supply Chain Companies with High Brand Value and Annual Reports of Strategic Decisions Made During and After the COVID-19 Pandemic

Öz

Research on sustainability reporting is becoming increasingly important. Despite the growing literature on sustainability reporting, there is still limited knowledge about past trends and uncertainties about how research areas will evolve in the future. Recognizing and understanding the research trend in sustainability reporting enables future researchers to plan and conduct studies that are likely to attract high interest and citations from both readers and references. This study examines a broad literature on environmental, social, and governance (ESG) and sustainability reporting. The study has conducted an analysis of sustainability reports of supply chain companies during the Covid pandemic using Deep Learning with Python software. Uniquely, this study also employed the Bert Model to uncover past ESG research trends and predict how the content of these study areas will evolve in the future. Using a trained model, a Zero Shot Learning classification algorithm has been employed. In this context, text preprocessing was performed on 42 sustainability reports from 20 companies for the years 2019 and beyond, followed by analyses using machine learning with software tools like Python, Anaconda, and Google Colab. The selection of sustainability reports from 2019 onwards was driven by the aim to examine the impact of COVID and the pandemic. The results of the machine learning technique reveal that ESG and sustainability reports now possess a stronger social focus due to their shift towards benefits and environmental impacts, concentrating on benefits and corporate social responsibility (CSR) awards, and elucidating ESG outcomes. This research sheds light for future researchers on planning and designing their research focuses.

Anahtar Kelimeler

• Covid 19
• Supply Chain Management
• Sustainability
• Text Analysis

Marka Değeri Yüksek Tedarik Zinciri Firmalarının COVID-19 Pandemi ve Sonrasında Aldığı Stratejik Kararların ve Yıllık Raporlarının Analiz ve Değerlendirilmesi

Öz

Sürdürülebilirlik raporlaması üzerine yapılan araştırmalar ve önemi giderek artmasına rağmen, geçmiş trendler hakkında çok az şey bilinmekte ve araştırma alanlarının gelecekte nasıl evrileceği konusunda belirsizlikler bulunmaktadır. Sürdürülebilirlik raporlamasıyla ilgili araştırma trendini tanımak ve anlamak, gelecekteki araştırmacıların hem okuyuculuk hem de atıf açısından yüksek ilgi ve etkiye sahip araştırmalar planlamalarına ve yürütmelerine olanak tanır. Bu çalışmada çevresel, sosyal ve yönetişim (ESG) ve sürdürülebilirlik raporlaması üzerine geniş bir literatür incelenmiştir. Çalışma, Python yazılımını kullanarak makine öğrenmesi yaparak sürdürülebilirlik raporlarının pandemi sürecindeki tedarik zincirleri şirketlerinin analizini gerçekleştirmiştir. Bu çalışmada ayrıca geçmiş ESG araştırma trendlerini ve sürdürülebilirlik raporlarını ortaya çıkarmak ve bu çalışma alanlarının içeriğinin gelecekte nasıl evrileceğini tahmin etmek için Bert Modeli kullanılmıştır. Eğitilmiş Model kullanılarak Zero Shot Learning sınıflandırma algoritması kullanılmıştır. Burada 20 şirketin 2019 ve sonrası yıllarındaki 42 sürdürülebilirlik raporlarının metin ön işleme yapıldıktan sonra makine öğrenmesini Python ve Anaconda ve Google Colab gibi yazılım araçları ile analizler gerçekleştirilmiştir. 2019 ve sonrası yıllarındaki sürdürülebilirlik raporlarının seçilmesinin nedeni Covid ve pandemi etkisinin incelenmesidir. Makine öğrenme tekniğinin sonuçları, ESG ve sürdürülebilirlik raporlarının, kurumsal sosyal sorumluluk (CSR) ve sürdürülebilirlik raporlamasının şimdi faydalara ve çevresel etkilere odaklandığı için daha güçlü bir sosyal odağa sahip olduğunu, faydalara ve kurumsal sosyal sorumluluk ödüllerine odaklanmakta, ESG sonuçlarını açıklamaktadır. Araştırma, gelecekteki araştırmacılara araştırma odaklarını planlama ve tasarlama konusunda ışık tutmaktadır.

Anahtar Kelimeler

• COVID-19
• Tedarik Zinciri
• Sürdürebilirlik
• Metin Madenciliği

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