Optimizing E-Commerce Selection Under Uncertainty: A New Framework for Decision-Making With Extended Fuzzy TOPSIS

Abstract

Te COVID-19 pandemic signifcantly transformed consumer habits and the landscape of e-commerce. Tis research assesses the performance of various e-commerce platforms during this unprecedented period by employing the extended technique for order of preference similarity to ideal solution(TOPSIS) methodology. Data pertinent to e-commerce websites during the pandemic was sourced from various channels, including web analytics, marketing reports, and customer feedback surveys. Key performance indicators (KPIs) were established, focusing on website trafc, conversion rates, marketing efectiveness, customer satisfaction, and operational efciency. Tese indicators were then normalized and weighted according to their signifcance. Tis study investigates the utility of the extended fuzzy TOPSIS method in selecting e-commerce platforms, pinpointing essential criteria that afect consumer preferences and satisfaction. Te fndings provide critical insights into how website characteristics relate to consumer behavior, thereby assisting online retailers in improving their digital strategies. Te extended TOPSIS method was utilized to determine how closely each website aligns with the ideal and anti-ideal solutions, resulting in a ranking based on this proximity. To ensure the robustness of the extended TOPSIS approach, a sensitivity analysis was performed by adjusting the weights assigned to the criteria and monitoring the resulting shifts in website rankings. Tis analysis revealed the top-performing and least-performing e-commerce websites during the pandemic, as determined by the extended TOPSIS rankings. Te sensitivity analysis indicated that the rankings produced by extended TOPSIS remained relatively stable despite changes in criteria weights, underscoring its reliability and applicability. Tis study highlights the efectiveness of the extended TOPSIS method in evaluating e-commerce performance amid the unique challenges posed by COVID-19. By incorporating a range of criteria and conducting sensitivity analyses, the research establishes extended TOPSIS as a robust and valuable framework for identifying high-performing e-commerce websites during periods of disruption. Te originality of this paper lies in its ability to tackle signifcant uncertainties in e-commerce selection and to present a practical case study utilizing extended TOPSIS for the frst time.