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

Yıl 2024, Cilt: 4 Sayı: 2, 73 - 91, 31.08.2024

Elmas Dündar , Feyza Gürbüz

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

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

Kaynakça

• Aleta, A., Martin-Corral, D., Pastore y Piontti, A., Ajelli, M., Litvinova, M., Chinazzi, M., . . . Merler, S. J. N. H. B. (2020). Modelling the impact of testing, contact tracing and household quarantine on second waves of COVID-19. 4(9), 964-971.
• Amini, M., Bienstock, C. C., Narcum, J. A. J. B. S., & Environment, t. (2018). Status of corporate sustainability: A content analysis of Fortune 500 companies. 27(8), 1450-1461.
• Benites-Lazaro, L. L., Giatti, L., Giarolla, A. J. E. r., & science, s. (2018). Topic modeling method for analyzing social actor discourses on climate change, energy and food security. 45, 318-330.
• Billal, B., Fonseca, A., Sadat, F., & Lounis, H. (2017). Semi-supervised learning and social media text analysis towards multi-labeling categorization. 2017 IEEE international conference on big data (Big Data).
• Bird, S. (2006). NLTK: the natural language toolkit. Proceedings of the COLING/ACL 2006 Interactive Presentation Sessions.
• Brookes, G., & McEnery, T. J. D. S. (2019). The utility of topic modelling for discourse studies: A critical evaluation. 21(1), 3-21.
• Büyüköz, B., Hürriyetoğlu, A., & Özgür, A. (2020). Analyzing ELMo and DistilBERT on socio-political news classification. Proceedings of the Workshop on Automated Extraction of Socio-political Events from News 2020.
• Büyüktahtakın, İ. E., des-Bordes, E., & Kıbış, E. Y. J. E. J. o. O. R. (2018). A new epidemics–logistics model: Insights into controlling the Ebola virus disease in West Africa. 265(3), 1046-1063.
• Calabrese, A., Costa, R., Ghiron, N. L., & Menichini, T. J. E. J. o. S. D. (2017). To be, or not to be, that is the Question. Is Sustainability Report Reliable? , 6(3), 519-519.
• Camacho-Collados, J., & Pilehvar, M. T. J. a. p. a. (2017). On the role of text preprocessing in neural network architectures: An evaluation study on text categorization and sentiment analysis.
• Carlile, P. R., & Rebentisch, E. S. J. M. s. (2003). Into the black box: The knowledge transformation cycle. 49(9), 1180-1195.
• Chmiela, S., Tkatchenko, A., Sauceda, H. E., Poltavsky, I., Schütt, K. T., & Müller, K.-R. J. S. a. (2017). Machine learning of accurate energy-conserving molecular force fields. 3(5), e1603015.
• Chopra, S., & Meindl, P. (2007). Supply chain management. Strategy, planning & operation. Springer.
• Clarke, V., & Braun, V. J. T. j. o. p. p. (2017). Thematic analysis. 12(3), 297-298.
• Davi, A., Haughton, D., Nasr, N., Shah, G., Skaletsky, M., & Spack, R. J. T. A. S. (2005). A review of two text-mining packages: SAS TextMining and WordStat. 89-103.
• Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. J. a. p. a. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding.
• Dubey, R., Altay, N., Gunasekaran, A., Blome, C., Papadopoulos, T., Childe, S. J. J. I. J. o. O., & Management, P. (2018). Supply chain agility, adaptability and alignment: empirical evidence from the Indian auto components industry. 38(1), 129-148.
• Einea, O., Elnagar, A., & Al Debsi, R. J. D. i. b. (2019). Sanad: Single-label arabic news articles dataset for automatic text categorization. 25, 104076.
• Fragapane, G., Eleftheriadis, R., Powell, D., & Antony, J. J. C. i. I. (2023). A global survey on the current state of practice in Zero Defect Manufacturing and its impact on production performance. 148, 103879.
• Halldorsson, A., Kotzab, H., Mikkola, J. H., & Skjøtt‐Larsen, T. J. S. c. m. A. i. j. (2007). Complementary theories to supply chain management. 12(4), 284-296.
• Hinds, P. J. J. J. o. e. p. a. (1999). The curse of expertise: The effects of expertise and debiasing methods on prediction of novice performance. 5(2), 205.
• Hoang, M., Bihorac, O. A., & Rouces, J. (2019). Aspect-Based Sentiment Analysis using BERT. Nordic Conference of Computational Linguistics.
• Hutton, D. W., Tan, D., So, S. K., & Brandeau, M. L. J. A. o. i. m. (2007). Cost-effectiveness of screening and vaccinating Asian and Pacific Islander adults for hepatitis B. 147(7), 460-469.
• İşsever, H., İşsever, T., & Öztan, G. (2020). COVID-19 Epidemiyolojisi [Epidemiology of COVID-19]. Sağlık Bilimlerinde İleri Araştırmalar Dergisi, 3(S1), 1-13. https://dergipark.org.tr/tr/pub/sabiad/issue/54344/738096
• Ivanov, D., & Dolgui, A. J. I. j. o. p. r. (2020). Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak. 58(10), 2904-2915.
• Junior, R. M., Best, P. J., & Cotter, J. J. J. o. b. e. (2014). Sustainability reporting and assurance: A historical analysis on a world-wide phenomenon. 120, 1-11.
• Kang, H., & Kim, J. (2022). Analyzing and Visualizing Text Information in Corporate Sustainability Reports Using Natural Language Processing Methods. Applied Sciences-Basel, 12(11), Article 5614. https://doi.org/10.3390/app12115614
• Kantarcı, Ö., Özalp, M., Sezginsoy, C., Özaşkınlı, O., & Cavlak, C. J. T. Y. (2017). Dijitalleşen dünyada ekonominin itici gücü: E-ticaret.
• Kinra, A., Hald, K. S., Mukkamala, R. R., & Vatrapu, R. (2020). An unstructured big data approach for country logistics performance assessment in global supply chains. International Journal of Operations & Production Management, 40(4), 439-458. https://doi.org/10.1108/IJOPM-07-2019-0544
• Kumar, S., & Chandra, C. J. T. J. (2010). Supply chain disruption by avian flu pandemic for US companies: a case study. 49(4), 61-73.
• Landrum, N. E., Ohsowski, B. J. B. S., & Environment, t. (2018). Identifying worldviews on corporate sustainability: A content analysis of corporate sustainability reports. 27(1), 128-151.
• Linton, J. D., Klassen, R., & Jayaraman, V. J. J. o. o. m. (2007). Sustainable supply chains: An introduction. 25(6), 1075-1082.
• Liu, S. M., & Chen, J.-H. J. E. S. w. A. (2015). A multi-label classification based approach for sentiment classification. 42(3), 1083-1093.
• Lu, H.-m., & Unpingco, J. (2021). How PDFrw and fillable forms improves throughput at a Covid-19 Vaccine Clinic. Proc. of the 20th Python in Science Conference (SCIPY 2021).
• McKie, J. PyMuPDF-the Python bindings for MuPDF (2021). In.
• Medhat, W., Hassan, A., & Korashy, H. J. A. S. e. j. (2014). Sentiment analysis algorithms and applications: A survey. 5(4), 1093-1113.
• Modapothala, J. R., & Issac, B. (2009). Evaluation of corporate environmental reports using data mining approach. 2009 International Conference on Computer Engineering and Technology.
• Modapothala, J. R., Issac, B., & Jayamani, E. (2009, Dec 04-12). Appraising the Corporate Sustainability Reports - Text Mining and Multi-Discriminatory Analysis. [Innovations in computing sciences and software engineering]. International Joint Conferences on Computer, Information, and Systems Sciences, and Engineering (CISSE), Bridgeport, CT.
• Mokoatle, M., Marivate, V., Mapiye, D., Bornman, R., & Hayes, V. M. J. B. b. (2023). A review and comparative study of cancer detection using machine learning: SBERT and SimCSE application. 24(1), 112.
• Moroney, L. (2020). Ai and machine learning for coders. O'Reilly Media.
• Nakiboğlu, G. (2020). COVID-19 Küresel Tedarik Zincirlerinde Yaşananlar ve Dönüşüm [Global Supply Chains and Transformation in the COVID-19 Era]. Çağ Üniversitesi Sosyal Bilimler Dergisi, 17(2), 1-16. https://dergipark.org.tr/tr/pub/cagsbd/issue/58680/818244
• Nidumolu, R., Prahalad, C. K., & Rangaswami, M. R. J. H. b. r. (2009). Why sustainability is now the key driver of innovation. 87(9), 56-64.
• Norrman, A., Wieland, A. J. I. J. o. P. D., & Management, L. (2020). The development of supply chain risk management over time: revisiting Ericsson. 50(6), 641-666.
• Olivares-Aguila, J., & ElMaraghy, H. J. J. o. M. S. (2020). Co-development of product and supplier platform. 54, 372-385.
• Oral, O., Aylak, B. L., & Yazici, K. (2021). Yapay Zeka ve Makine Öğrenmesi Tekniklerinin Lojistik Sektöründe Kullanımı [Using Artificial Intelligence and Machine Learning Applications in Logistics]. El-Cezeri, 8(1), 74-93. https://doi.org/10.31202/ecjse.776314
• Ozgur, C., Colliau, T., Rogers, G., & Hughes, Z. J. J. o. d. S. (2017). MatLab vs. Python vs. R. 15(3), 355-371. Prager, F., Beeler Asay, G. R., Lee, B., & von Winterfeldt, D. J. R. A. A. I. J. (2011). Exploring reductions in London underground passenger journeys following the July 2005 bombings. 31(5), 773-786.
• Reimers, N., & Gurevych, I. J. a. p. a. (2019). Sentence-bert: Sentence embeddings using siamese bert-networks.
• Reyes-Menendez, A., Saura, J. R., Alvarez-Alonso, C. J. I. j. o. e. r., & health, p. (2018). Understanding# WorldEnvironmentDay user opinions in Twitter: A topic-based sentiment analysis approach. 15(11), 2537.
• Salem, R. W., & Haouari, M. J. I. J. o. P. R. (2017). A simulation-optimisation approach for supply chain network design under supply and demand uncertainties. 55(7), 1845-1861.
• Sarkar, D. (2019). Text analytics with Python: a practitioner's guide to natural language processing. Springer. Seuring, S., & Müller, M. J. J. o. c. p. (2008). From a literature review to a conceptual framework for sustainable supply chain management. 16(15), 1699-1710.
• Shahi, A. M., Issac, B., & Modapothala, J. R. (2012). Intelligent Corporate Sustainability report scoring solution using machine learning approach to text categorization. 2012 IEEE Conference on Sustainable Utilization and Development in Engineering and Technology (STUDENT).
• Short, J. C., & Palmer, T. B. J. O. r. m. (2008). The application of DICTION to content analysis research in strategic management. 11(4), 727-752.
• Sinha, K. K., & Van de Ven, A. H. J. O. s. (2005). Designing work within and between organizations. 16(4), 389-408.
• Sotiriadou, P., Brouwers, J., & Le, T.-A. J. A. o. l. r. (2014). Choosing a qualitative data analysis tool: A comparison of NVivo and Leximancer. 17(2), 218-234.
• Székely, N., & Vom Brocke, J. J. P. o. (2017). What can we learn from corporate sustainability reporting? Deriving propositions for research and practice from over 9,500 corporate sustainability reports published between 1999 and 2015 using topic modelling technique. 12(4), e0174807.
• Tata, S., & Patel, J. M. J. A. S. R. (2007). Estimating the selectivity of tf-idf based cosine similarity predicates. 36(2), 7-12.
• Tausczik, Y. R., Pennebaker, J. W. J. J. o. l., & psychology, s. (2010). The psychological meaning of words: LIWC and computerized text analysis methods. 29(1), 24-54.
• Tay, H. L., & Loh, H. S. (2022). Digital transformations and supply chain management: a Lean Six Sigma perspective. Journal of Asia Business Studies, 16(2), 340-353. https://doi.org/10.1108/jabs-10-2020-0415
• Te Liew, W., Adhitya, A., & Srinivasan, R. J. C. i. I. (2014). Sustainability trends in the process industries: A text mining-based analysis. 65(3), 393-400.
• Thanaki, J. (2017). Python natural language processing. Packt Publishing Ltd.
• Turian, J., Ratinov, L., & Bengio, Y. (2010). Word representations: a simple and general method for semi-supervised learning Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, Uppsala, Sweden.
• Wang, W., Wei, F., Dong, L., Bao, H., Yang, N., & Zhou, M. J. A. i. N. I. P. S. (2020). Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers. 33, 5776-5788.
• Wang, X., Yuen, K. F., Wong, Y. D., Li, K. X. J. T. R. P. D. T., & Environment. (2020). How can the maritime industry meet Sustainable Development Goals? An analysis of sustainability reports from the social entrepreneurship perspective. 78, 102173.
• Weng, T., Liu, W., Xiao, J. J. I. M., & Systems, D. (2020). Supply chain sales forecasting based on lightGBM and LSTM combination model. 120(2), 265-279.
• Williamson, B. N., Feldmann, F., Schwarz, B., Meade-White, K., Porter, D. P., Schulz, J., . . . Pérez-Pérez, L. J. N. (2020). Clinical benefit of remdesivir in rhesus macaques infected with SARS-CoV-2. 585(7824), 273-276.
• Wood, L. C., Reiners, T., & Srivastava, H. S. (2014). Sentiment analysis in supply chain management. In Encyclopedia of Business Analytics and Optimization (pp. 2147-2158). IGI Global.
• Zhou, Z., Cheng, S., Hua, B. J. C., & Engineering, C. (2000). Supply chain optimization of continuous process industries with sustainability considerations. 24(2-7), 1151-1158.
• Zlojutro, A., Rey, D., & Gardner, L. J. S. r. (2019). A decision-support framework to optimize border control for global outbreak mitigation. 9(1), 2216. https://tusiad.org/tr/yayinlar/raporlar/item/7254-7-adimda-surdurulebilir-tedarik-zinciri