Integrating the PARETO – FUZZY – AHP – TOPSIS Model in Analyzing Consumer Behavior in Online Purchase Decisions on C2C E-Commerce Websites toward Green Consumption

© The Author(s) 2025. Published by AMO Publisher. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https:// creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. Integrating the PARETO – FUZZY – AHP – TOPSIS Model in Analyzing Consumer Behavior in Online Purchase Decisions on C2C E-Commerce Websites toward Green Consumption Tran Trung Dung  Hanoi University of Natural Resources and Environment, Viet Nam Article History: Received: 12.10.2025 Revised: 11.11.2025 Accepted: 14.11.2025 Published: 14.11.2025 Abstract In the era of the digital economy, online shopping has become an indispensable part of daily life. Particularly in the modern context, the Consumer-to-Consumer (C2C) e-commerce model is increasingly prevalent. However, the enormous amount of information on C2C platforms makes it difficult for consumers to make decisions that match their needs and budgets. To support consumers in selecting optimal products, this study applies an integrated Pareto – Fuzzy – AHP – TOPSIS model to analyze consumer behavior on C2C e-commerce websites toward green consumption, thereby helping users optimize online purchasing decisions. Keywords: E-commerce, consumer behavior, online shopping, green consumption, C2C transaction model, Pareto principle, Fuzzy-AHP model, TOPSIS model, Fuzzy-AHP-TOPSIS model. Suggested citation: Dung, T.T. (2025). Integrating the PARETO – FUZZY – AHP – TOPSIS Model in Analyzing Consumer Behavior in Online Purchase Decisions on C2C E-Commerce Websites toward Green Consumption. European Journal of Theoretical and Applied Sciences, 3(6), 122-127. https://doi.org/10.59324/ejtas.2025.3(6).11 Introduction Amid the rapid development of e-commerce, online consumer purchasing behavior is increasingly influenced by green consumption trends. Understanding the factors that affect purchasing decisions on C2C platforms is essential for businesses and policymakers. However, these factors are qualitative, vague, and difficult to measure directly. Therefore, this study applies an integrated PARETO – FUZZY – AHP – TOPSIS model to analyze influencing factors and rank six typical C2C websites based on consumer preference levels. The results provide implications for developing sustainable and environmentally friendly e-commerce. Theoretical Background The Pareto principle, also known as the 80/20 rule, was discovered by Italian economist Vilfredo Pareto (1897) when studying the distribution of wealth in society. In management science, the Pareto principle serves as an initial qualitative analysis tool that helps researchers identify and select core factors with the greatest impact on the phenomenon under investigation. Fuzzy logic theory was first introduced by L.A. Zadeh in 1965. This theory addresses problems in a manner close to human reasoning. In this study, Fuzzy Logic is used to standardize and quantify subjective consumer or expert evaluations into fuzzy numbers, enhancing the accuracy and objectivity of the criterion- www.ejtas.com European Journal of Theoretical and Applied Sciences (ISSN 2786-7447) 2025 | Volume 3 | Number 6 123 weighting process before applying the AHP– TOPSIS model. AHP (Analytic Hierarchy Process) is a multicriteria decision-making method developed by Professor Thomas L. Saaty in the 1970s. This method helps analyze, evaluate, and select C2C websites based on multiple criteria. TOPSIS is another multi-criteria decisionmaking method designed to rank alternatives based on their similarity to an ideal solution. In this study, TOPSIS is used to rank C2C websites by priority of selection. Research Methodology The study was conducted over five months, surveying 1,000 consumers and 8 experts, including 4 academic researchers and lecturers in e-commerce, 1 e-commerce business operator, 1 digital marketing specialist, 1 marketing manager, and 1 website developer. Step 1: Collect information and data. Step 2: Identify core factors. Step 3: Develop evaluation criteria. Step 4: Construct the hierarchical structure. Step 5: Build evaluation matrices. Step 6: Assess the impact levels of criteria on purchasing decisions. Step 7: Rank priority levels of C2C websites for online purchasing. Research Findings Survey results reveal that the occupational structure of the 1,000 respondents is diverse, reflecting the characteristics of current online consumers. Among them, students account for 45%, forming the largest group. This indicates that young consumers—who are technologically adept and frequent users of C2C platforms— have a high propensity for online shopping. Other occupations also represent significant proportions, including freelancers (20%), office workers (12%), factory workers (8%), lecturers (5%), police officers (4%), nurses (3%), sales staff (2%), and other groups (1%) such as high school students, auditors, lawyers, tourism management workers, tattoo artists, etc. Consumers on C2C websites tend to prioritize fast-moving, easy-to-trade products of moderate value. Essential and commonly used product groups have significantly higher purchase rates than niche or infrequently used items. Electronics (90.8%) are the most purchased category, reflecting strong demand for technology products and increasing consumer trust in buying high-value goods on C2C platforms. Home décor (67.5%), cosmetics and perfumes (65.3%), jewelry (58.7%), and beautyhealth products (55.4%) demonstrate high demand for personal care and lifestyle enhancement items. These products are easy to trade online and require minimal warranty conditions. Moderate-purchase categories include books (52%), clothing and footwear (49.4%), and kitchen tools (49.4%). Consumers often seek lower prices or second-hand goods in these categories. Low-purchase categories include food (24%), tours/tickets (27.7%), and mother-and-baby products (8.1%), due to high risks related to quality, expiration dates, or warranty needs. The “Other” category (14%) indicates a small proportion of consumers buying niche items. The study also shows clear differentiation in factors influencing consumer interest during online purchases. Product price (90%) and product quality (90%) are the most important factors, reflecting common consumption patterns in Vietnam. Product information (70.8%) and seller credibility (62%) are also highly valued, highlighting the importance of transparency and trust. Transaction-support factors such as return policies (49.1%), promotions (45.8%), brand identity and flexible payment (57.6%) are also significant, showing consumer expectations for safe and convenient transactions. However, website brand reputation (24%), advertising (39.1%), and green commerce (22.9%) receive lower attention, indicating that awareness of sustainable and eco-friendly online consumption remains limited. Based on consumer surveys and expert interviews, the study initially identified 18 criteria for the evaluation model. After applying the Pareto 80/20 principle, 13 key influencing factors were selected for inclusion in the final evaluation model. www.ejtas.com European Journal of Theoretical and Applied Sciences (ISSN 2786-7447) 2025 | Volume 3 | Number 6 124 Table 1. Evaluation Criteria for C2C E-Commerce Websites No. Evaluation Criteria Name Criteria Code 1 Green product brand TC1 2 Green pricing TC2 3 Green product quality TC3 4 Green product images TC4 5 Green product information TC5 6 Green online brand reputation TC6 7 Green product categories TC7 8 Green shipping cost TC8 9 Green promotional programs TC9 10 Green payment methods TC10 11 Green delivery time TC11 12 Green return policy TC12 13 Green delivery partners TC13 Source: Proposed by the author The selected criteria are those that have the strongest impact on consumer behavior when making online purchase decisions on C2C websites. Table 2. Linguistic Variables and Corresponding Fuzzy Numbers Linguistic Variable Symbol Linguistic Code Corresponding Triangular Fuzzy Numbers Inverse Triangular Fuzzy Numbers Equally important BN 1 (1, 1, 3) (1/3, 1/1, 1/1) More important TH 3 (1, 3, 5) (1/5, 1/3, 1/1) Much more important NH 5 (3, 5, 7) (1/7, 1/5, 1/3) Very important RT 7 (5, 7, 9) (1/9, 1/7, 1/5) Extremely important CT 9 (7, 9, 9) (1/9, 1/9, 1/7) Source: Proposed by the author In this study, the 1–9 scale (Sodhi & Prabhakar, 2012) is used to convert linguistic variables into fuzzy numbers as shown in Table 2. Five linguistic levels were selected to perform pairwise comparisons among the fuzzy parameters. The websites selected for evaluation are Shopee, Tiki, Lazada, Sendo, Chotot, and Taobao, denoted as W1, W2, W3, W4, W5, and W6, respectively. The results of the normalized matrix are presented in Table 3 (Appendix). The core and essential criteria include: TC1 (Green product brand), TC3 (Green product quality), TC5 (Green product information), TC7 (Green product categories), and TC11 (Green delivery time). These are decisive factors in consumer selection behavior and in their evaluation of “green” products and services. The group of important–supporting criteria includes: TC2 (Green pricing), TC4 (Green product images), TC6 (Green online brand reputation), TC9 (Green promotional programs), and TC10 (Green payment methods). These factors enhance competitiveness and improve user experience. The group of criteria that require maintenance and improvement includes: TC8 (Green shipping cost), TC12 (Green return policy), and TC13 (Green delivery partners). These factors are related to service and operational processes that need cost control to enhance reliability and transparency. © The Author(s) 2025. Published by AMO Publisher. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https:// creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. Table 4. Distance Matrix between Alternatives and Closeness Coefficients Distance Alternatives W1 W2 W3 W4 W5 W6 𝑑𝑖 + 0.012 0.023 0.049 0.068 0.073 0.041 𝑑𝑖 − 0.067 0.039 0.018 0.010 0.009 0.041 𝑑𝑖 ++ 𝑑𝑖 − 0,079 0.062 0.067 0.078 0.082 0.082 CCi 0.848 0.629 0.268 0.128 0.109 0.500 Ranking 1 2 4 5 6 3 The results indicate that Shopee.vn is considered the most optimal C2C website for green online purchasing behavior in Vietnam, as it successfully integrates both economic benefits and sustainable values, aligning with modern consumer trends. Based on these findings, it is expected that C2C platforms can build competitive strategies that better meet customer needs. Conclusions and Recommendations Green product quality: C2C platforms need to focus on controlling product quality by establishing seller verification systems and requiring proof of product origin. Green pricing: C2C websites should implement reasonable pricing strategies accompanied by green-focused incentives. Green product information: There is a need to enhance product information transparency by requiring sellers to provide detailed and accurate data. Green product categories: Platforms must ensure that product categories are updated, diversified, transparent, and verified for quality. Green product images: C2C platforms should require sellers to upload high-quality images with detailed descriptions of green attributes. Green product brand: Platforms need to build promotional strategies and maintain strict control over green-brand integrity to strengthen competitive advantage. Green shipping cost: Policies should be optimized to reduce shipping costs for green products; delivery partners should be encouraged to use energy-efficient transportation. Green delivery time: Platforms must develop optimal delivery solutions that are both fast and environmentally friendly, with transparent tracking for consumers. Green delivery partners: Partnerships should be established with reputable logistics providers committed to meeting green standards. Green return policy: Flexible return policies should be developed, such as requiring customers to return recyclable packaging or offering guidelines for eco-friendly product disposal. Green payment methods: Platforms should diversify cashless payment options and encourage electronic payments to support digital transformation. Green promotional programs: C2C websites need to design promotional campaigns specifically for green products. Green website interface: Platforms should design user-friendly, intuitive interfaces incorporating green colors, icons, and visual elements aligned with sustainability criteria. Green online brand reputation: C2C platforms must build and maintain online brand credibility through strict product quality control, transparent information, and professional customer service. References Gefen, D. (2000). E-commerce: The role of familiarity and trust. Omega, 28(6), 725-737. https://doi.org/10.1016/S03050483(00)00021-9 Gürel, Y. (2022). A hybrid fuzzy-AHP-TOPSIS model for evaluation of manufacturing relocation decisions. Operations Management Research, 16(1), 164-191. https://doi.org/10.1007/s12063-022-00284-6 ResearchGate+1 www.ejtas.com European Journal of Theoretical and Applied Sciences (ISSN 2786-7447) 2025 | Volume 3 | Number 6 126 Hwang, C. L., & Yoon, K. (1981). Multiple attribute decision making: Methods and applications. Springer-Verlag. Laudon, K. C., & Traver, C. G. (2022). 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Journal of Theoretical and Applied Information Technology, 96(13), 4065-4073. www.ejtas.com European Journal of Theoretical and Applied Sciences (ISSN 2786-7447) 2025 | Volume 3 | Number 6 127 Appendix Table 3. Decision Matrix Based on Evaluation Criteria Criteria Decision matrix Normalized matrix Weighted normalized matrix W1 W2 W3 W4 W5 W6 W1 W2 W3 W4 W5 W6 W1 W2 W3 W4 W5 W6 TC1 5.75 6,75 4.25 4.125 4.25 5.50 0.451 0.530 0.333 0.324 0.333 0.432 0.194 0.228 0.143 0.139 0.143 0.186 TC2 6.25 7,25 5.25 6.00 6.75 6.00 0.406 0.471 0.341 0.389 0.438 0.389 0.157 0.183 0.132 0.151 0.170 0.151 TC3 8.25 6.00 5.50 6.00 4.50 5.50 0.555 0.403 0.370 0.403 0.302 0.370 0.205 0.149 0.136 0.149 0.111 0.136 TC4 5.50 6.00 4.25 4.50 4.75 6.25 0.426 0.465 0.329 0.349 0.368 0.484 0.206 0.225 0.159 0.168 0.178 0.234 TC5 6.00 6.50 5.00 4.50 4.50 7.00 0.432 0.468 0.360 0.324 0.324 0.504 0.217 0.235 0.181 0.163 0.163 0.254 TC6 7.00 6.25 5.50 4.75 6.25 7.00 0,462 0.413 0.363 0.313 0.413 0.462 0.213 0.190 0.167 0.144 0.190 0.213 TC7 7.00 5.00 4.00 4.00 4.50 6.50 0.539 0.385 0.308 0.308 0.346 0.500 0.269 0.192 0.154 0.154 0.173 0.250 TC8 6.50 5.00 5.00 5.25 5,75 5.75 0.476 0.366 0.366 0.385 0.421 0.421 0,200 0.154 0.154 0.162 0.177 0.177 TC9 6.75 5.75 6.00 4.00 3.50 5.50 0.513 0.437 0.456 0.304 0.266 0.418 0.214 0.182 0.190 0.127 0.111 0.174 TC10 7.50 6.25 5.50 5.00 4.25 5.25 0.535 0.446 0.392 0.356 0.303 0.374 0,200 0.166 0.146 0.133 0.113 0.139 TC11 6.25 6.25 4.75 4.00 4.00 5.75 0.485 0.485 0.368 0.310 0.310 0.446 0.216 0.216 0.164 0.138 0.138 0.198 TC12 7.00 5.50 5.00 4.00 3.75 5.50 0.545 0.428 0.389 0.311 0.292 0.428 0.233 0.183 0.166 0.133 0.124 0.183 TC13 6.00 6.50 5.00 4.50 4.50 5.50 0.454 0.492 0.379 0.341 0.341 0.416 0.188 0.204 0.157 0.141 0.141 0.173 Source: Proposed by the author