Article: An explainable decision intelligence framework for consumer packaging evaluation in e-commerce: a hybrid entropy-weighted grey MCDM approach Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.97 pp.21 - 40 Abstract: With the rapid application of transformative technologies such as federated learning and generative AI in consumer terminals and digital commerce, there is a growing demand for decision-support frameworks that are intelligent, interpretable, and privacy-protective. In the e-commerce field, consumer satisfaction is increasingly dependent on data-driven product evaluation mechanisms rather than isolated design attributes. This study proposes a grey multi-criteria decision-making (G-MCDM) framework integrated with entropy weighting to evaluate consumer satisfaction with various types of cosmetic packaging, including bottles, jars, hoses, and aerosol cans. The model integrates entropy-based objective weighting methods and grey system theory, effectively addressing the uncertainty, heterogeneity, and information incompleteness present in large-scale consumer feedback data. Empirical analysis based on e-commerce platform data shows that tube packaging achieves the highest overall consumer satisfaction among all tested packaging forms. Beyond the specific application scenario, this framework can serve as an interpretable decision-making module, embedded in the federated learning architecture to support decentralised consumer data analysis and generative AI-assisted packaging design optimisation. This research improves the transparent and scalable evaluation mechanism suitable for AI-enabled consumer scenarios, and clarifies the core role of hybrid decision intelligence models in supporting the next-generation digital e-commerce ecosystem. Inderscience Publishers - linking academia, business and industry through research

Title: An explainable decision intelligence framework for consumer packaging evaluation in e-commerce: a hybrid entropy-weighted grey MCDM approach

Authors: Wenchao Pan; Lianhua Liu; Peng Qiang; Fangting Zhang; Luanling Tan; Xingyu Zou; Xiao Wang

Addresses: School of Management, Guangzhou Huashang College, Guangzhou, 511300, China; School of Economics and Management, Hunan University of Science and Engineering, Yongzhou, 425199, China ' Faculty of Humanities and Social Sciences, Macao Polytechnic University, Macau, 999078, China ' School of Management, Guangzhou Huashang College, Guangzhou, 511300, China; Office of Human Resources, Guangzhou Huashang College, Guangzhou, 511300, China ' School of Economics and Management, Hunan University of Science and Engineering, Yongzhou, 425199, China ' School of Economics and Management, Hunan University of Science and Engineering, Yongzhou, 425199, China ' School of Economics and Management, Hunan University of Science and Engineering, Yongzhou, 425199, China ' School of Economics and Management, Hunan University of Science and Engineering, Yongzhou, 425199, China

Abstract: With the rapid application of transformative technologies such as federated learning and generative AI in consumer terminals and digital commerce, there is a growing demand for decision-support frameworks that are intelligent, interpretable, and privacy-protective. In the e-commerce field, consumer satisfaction is increasingly dependent on data-driven product evaluation mechanisms rather than isolated design attributes. This study proposes a grey multi-criteria decision-making (G-MCDM) framework integrated with entropy weighting to evaluate consumer satisfaction with various types of cosmetic packaging, including bottles, jars, hoses, and aerosol cans. The model integrates entropy-based objective weighting methods and grey system theory, effectively addressing the uncertainty, heterogeneity, and information incompleteness present in large-scale consumer feedback data. Empirical analysis based on e-commerce platform data shows that tube packaging achieves the highest overall consumer satisfaction among all tested packaging forms. Beyond the specific application scenario, this framework can serve as an interpretable decision-making module, embedded in the federated learning architecture to support decentralised consumer data analysis and generative AI-assisted packaging design optimisation. This research improves the transparent and scalable evaluation mechanism suitable for AI-enabled consumer scenarios, and clarifies the core role of hybrid decision intelligence models in supporting the next-generation digital e-commerce ecosystem.

Keywords: federated learning applications; privacy-preserving computation; intelligent packaging design; grey multi-criteria decision making; hybrid decision intelligence.

DOI: 10.1504/IJICT.2026.156199

International Journal of Information and Communication Technology, 2026 Vol.27 No.97, pp.21 - 40

Received: 26 Mar 2026
Accepted: 30 Apr 2026
Published online: 07 Sep 2026 *

Title: An explainable decision intelligence framework for consumer packaging evaluation in e-commerce: a hybrid entropy-weighted grey MCDM approach

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