Customer-based sustainable product design: a data-driven approach

Purpose Sustainable product design (SPD) is essential for promoting environmental and economic sustainability by integrating sustainability principles throughout the product design process. However, existing approaches often fail to effectively incorporate consumer values, leading to a misalignment between sustainable products and consumer expectations. Challenges such as the attitude–behaviour gap, difficulties in quantifying subjective consumer perceptions and the lack of robust methodologies to align diverse preferences with design attributes hinder sustainable product adoption. This study contributes to SPD research by identifying key consumer values, sustainable product attributes, and data-driven methodologies, culminating in an integrated framework for customer-based SPD. Design/methodology/approach A Systematic Literature Review of Scopus-indexed studies – selected for its multidisciplinary peer-reviewed coverage – refined 2,256 articles to 39 empirical studies using Boolean search strings combining sustainable product design and consumer-centric terms, followed by multi-criteria abstract and full-text screening. Qualitative content analysis was conducted in NVivo 14 using an a priori coding framework, iteratively refined to capture key themes. The study examines consumer assessments of consumption values, product attributes, and SPD methodologies. By integrating established theories and data-driven techniques, it systematically maps the interconnections between design dimensions, consumer values and product attributes. Findings The review identifies six consumer values (functional, social, emotional, epistemic, conditional and green) and seven product attribute groups (sustainability cues, eco-labels, country of origin, quality, price, after-sales services and design) influencing sustainable product adoption. The analysis reveals no universal hierarchy: functional value operates as a non-negotiable gatekeeper in durables and automotive contexts, while merging indistinguishably with green value in agri-food settings. Existing SPD research relies heavily on structured surveys and hypothetical scenarios, limiting real-world applicability. To address these gaps, this study proposes a data-driven framework integrating choice-based experiments, machine learning algorithms and sentiment analysis to optimise SPD by aligning product attributes with consumer expectations. Research limitations/implications The framework integrates consumer values with Mishra's (2016) design perception dimensions but treats sustainability as a separate element, potentially limiting holistic integration of aesthetics, functionality and sustainability in design decision-making. The SLR relies solely on Scopus, excluding built-environment and product–service system studies visible in other databases. The cross-sectional evidence base limits the tracking of dynamic consumer preference shifts over time. Cultural variation in value hierarchies and the magnitude of the attitude–behaviour gap across different regulatory and infrastructural contexts remains unaddressed. Longitudinal studies, multi-database searches and cross-cultural empirical validation across industries are recommended to enhance the framework's robustness and adaptability. Practical implications From a managerial standpoint, this framework provides an actionable blueprint for a demand-led innovation strategy, de-risking green investment. It iteratively maps qualitative insights and quantitative analytics to tailor offerings for specific market segments, identifying attributes for which customers will pay a premium or switch brands. This approach closes the attitude–behaviour gap by ensuring every sustainable feature delivers tangible consumer value, making the sustainable choice the superior choice. Beyond industry, these insights inform targeted public policy, helping firms convert regulatory compliance – such as the EU Right to Repair directive – into a competitive advantage and fostering a market-based sustainability transition. Originality/value This study's primary value is a holistic framework fusing consumer psychology, SPD attributes and data analytics into a demand-led innovation strategy. Drawing on 39 empirical studies, it provides a novel value-to-attribute logic revealing that value hierarchies and attribute distributions are highly context-dependent, varying systematically across product categories. Its systematic process moves from identifying values and linking them to attributes, to quantifying their relative importance through continuous improvement loops. Acting as a practical front-end to existing SPD toolkits such as LCA and design for sustainable behaviour, it advances prior reviews from descriptive cataloguing toward an explicit, actionable logic for customer-based SPD.