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Managing knowledge for sustainability: AI-Enhanced retail strategies for real-time consumer segmentation
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DOI：10.1080/14778238.2026.2647264.png)
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Abstract
En 中文
Sustainability is transforming how organisations capture, share, and update knowledge related to consumer behaviour in retail environments. Firms require dynamic knowledge management (KM) systems to track preferences for eco-friendly products such as certified organic goods and fully recyclable packaging while aligning operational practices with sustainability objectives. This study develops a continuously adaptive framework that integrates machine learning (ML) methods, including Self-Organising Incremental Neural Networks, Principal Component Analysis, and Variational Autoencoders, with organisational learning and knowledge economy principles. The model identifies evolving consumer segments influenced by affordability, brand credibility, and recyclability, and translates these insights into actionable knowledge for pricing, inventory planning, and communication strategies. Empirical application within sustainable retail demonstrates the framework's ability to anticipate emerging green segments and embed knowledge within decision-making systems. The contribution advances theory by linking knowledge lifecycle processes - creation, sharing, use, and updating - with sustainability-driven retail practice to enhance managerial agility.
Keywords:
Knowledge management
organizational learning
adaptive segmentation
self-organizing neural networks
sustainable consumer behavior
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