Abstract

Background: The importance and market share of e-commerce has been increasing with the COVID-19 pandemic in recent days. Employees sometimes cannot go to the workplace due to epidemics such as COVID-19 that is spreading rapidly around the world, natural disasters and accidents. Companies can continue to serve their customers with the internet infrastructure and computer technologies they will provide to their employees. Thus, e-commerce companies can provide a sustainable competitive advantage in the sector. Working with the right suppliers is one of the important decisions that will improve the service quality of the firms and affect the sustainability of the enterprise. Methods: This study aims to select the best laptop for a company in the online trade industry using Entropy-based EDAS, CODAS and TOPSIS methods. In the study, 6 alternative laptops have been evaluated according to hard disk capacity, ram, battery power, processor speed, weight, price criteria. The Entropy method has been used to identify the weights of the criteria in the study. These criteria weights have been used in EDAS, CODAS and TOPSIS methods. TOPSIS, EDAS and CODAS methods have been used to determine the best alternative. Also, the correlation between the results of the TOPSIS, EDAS and CODAS methods has been examined with the Spearman Correlation approach. Results: As a result of the Entropy method, it has been determined that the most important criterion is the hard disk capacity criterion. TOPSIS, EDAS and CODAS method results have been compared and the most suitable alternative has been selected. According to the results of the study, the best alternative has been selected as A5. Spearman Correlation analysis results show that there was a strong positive relationship between the methods used and the results obtained. Conclusions: The study differs from existing studies in the literature in that it is the first study in which laptop selection was made using TOPSIS, EDAS and CODAS methods together. The results of this study can be compared with the results of future studies that will be carried out using different MCDM methods and different data.

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