Using a Multi-Criteria Decision-Making Model to Evaluate and Select an E-Commerce Platform

Thanh, Vu Duc; Van, Luu Huu; Tuyết, Nguyễn Thị; Tuan, Hoang Minh

doi:10.51594/ijmer.v4i1.285

Int. j. manag. entrep. res

2022

DOI: 10.51594/ijmer.v4i1.285

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Using a Multi-Criteria Decision-Making Model to Evaluate and Select an E-Commerce Platform

Vu Duc Thanh¹,

Luu Huu Van²,

Nguyễn Thị Tuyết³

et al.

Abstract: The COVID-19 pandemic has led to disruptions in consumers' lifestyles and purchases, as well as businesses' online business models. Online platforms are increasingly used for shopping purposes. To evaluate and choose an e-commerce platform requires using many criteria and decision makers. Therefore, the process of evaluating and selecting an e-commerce platform is viewed as a multi-criteria decision-making problem. The objective of this study is to develop a multi-criteria decision-making model to help consume… Show more

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2024

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Evaluation of Virtual Commerce Applications for the Metaverse Using Spherical Linear Diophantine‐Based Modeling Approach

Bilquise,

Shaalan,

AlKhatib

2024

Human Behavior and Emerging Technologies

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The rise of the metaverse has ignited a surge of interest among researchers and decision‐makers, seeking to develop effective virtual commerce (v‐commerce) applications that cater to business demands and customer preferences. v‐commerce, an emerging concept, redefines the future of shopping experiences and customer‐product interactions. While businesses are actively exploring the potential of immersive technologies to deliver captivating and engaging shopping experiences, there remains a lack of consensus on what constitutes an ideal v‐commerce experience and how to identify optimal v‐commerce stores effectively. Considering this, benchmarking v‐commerce applications for the metaverse is crucial for its development. This endeavor falls within the realm of multiple‐criteria decision‐making, given various critical issues such as the multitude of design attributes, uncertainty regarding their relative importance, and data variability. This study proposes an innovative approach that extends the fuzzy‐weighted zero‐inconsistency (FWZIC) method with spherical linear Diophantine fuzzy sets (FSs) (SLDFSs) to determine the weights of v‐commerce attributes. The obtained weights are integrated with the ranking alternatives by trace median index (RATMI) method to select the optimal v‐commerce application for the metaverse. Criterion weighting results reveal that “ease of navigation” and “recommendation agents” are the most significant criteria in assessing v‐commerce solutions. Based on these results, 24 v‐commerce solutions were evaluated. Additionally, sensitivity analysis and comparative evaluation were used to assess the robustness and validity of the proposed framework. This research provides essential insights for decision‐makers and practitioners to facilitate business growth, consumer satisfaction, and further research in this domain.

show abstract

Evaluation of Virtual Commerce Applications for the Metaverse Using Spherical Linear Diophantine‐Based Modeling Approach

Bilquise,

Shaalan,

AlKhatib

2024

Human Behavior and Emerging Technologies

View full text Add to dashboard Cite

show abstract

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

Edward,

Kaliyaperumal

2024

International Journal of Mathematics and Mathematical Sciences

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The COVID‐19 pandemic significantly transformed consumer habits and the landscape of e‐commerce. This 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 traffic, conversion rates, marketing effectiveness, customer satisfaction, and operational efficiency. These indicators were then normalized and weighted according to their significance. This study investigates the utility of the extended fuzzy TOPSIS method in selecting e‐commerce platforms, pinpointing essential criteria that affect consumer preferences and satisfaction. The findings provide critical insights into how website characteristics relate to consumer behavior, thereby assisting online retailers in improving their digital strategies. The 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. This analysis revealed the top‐performing and least‐performing e‐commerce websites during the pandemic, as determined by the extended TOPSIS rankings. The sensitivity analysis indicated that the rankings produced by extended TOPSIS remained relatively stable despite changes in criteria weights, underscoring its reliability and applicability. This study highlights the effectiveness 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. The originality of this paper lies in its ability to tackle significant uncertainties in e‐commerce selection and to present a practical case study utilizing extended TOPSIS for the first time.

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Using a Multi-Criteria Decision-Making Model to Evaluate and Select an E-Commerce Platform

Cited by 2 publications

References 2 publications

Evaluation of Virtual Commerce Applications for the Metaverse Using Spherical Linear Diophantine‐Based Modeling Approach

Evaluation of Virtual Commerce Applications for the Metaverse Using Spherical Linear Diophantine‐Based Modeling Approach

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

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