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.

Sustainable product design (SPD) is a central strand of eco-innovation (Rennings, 2000; Xavier et al., 2017) that integrates environmental, economic, and social concerns across the product life cycle (Ahmad et al., 2018; He et al., 2020; Mengistu et al., 2024), with growing emphasis on consumer values and adoption behaviours as triggers of product uptake (Pinkse and Bohnsack, 2021). Rising consumer demand for sustainability (Dyllick and Rost, 2017) and the broader transition from linear to circular economic models (Bocken et al., 2016; Baldassarre et al., 2020) jointly underscore the need for data-driven, user-centred SPD methodologies that systematically translate consumer insights into product decisions (Bakker et al., 2014) in driving sustained behavioural change (Horani, 2020; Kramer, 2012).

Despite progress in SPD, consumer values remain difficult to integrate and measure during product design. A central challenge is the attitude–behaviour gap (Carrington et al., 2010), which reflects the disjunction between consumers' pro-environmental attitudes and their actual purchasing decisions. The extent of this gap is itself contested. Research using actual purchasing data suggests that relying on self-reported intentions can exaggerate it: Moser (2016) found that environmental attitudes predicted self-reported green purchases but had no significant effect on real purchase behaviour across several product categories. Yet other work upholds the gap's robustness even with improved data — Auger and Devinney (2007) observed that even highly eco-conscious consumers often fail to translate attitudes into action, underscoring a genuine “word-deed” disconnect. Crucially, its magnitude is context-dependent: product availability, price, and social influence moderate the attitude–behaviour linkage, and making sustainable options more accessible has been shown to narrow the divide (Vermeir and Verbeke, 2006). The gap has also been linked to greenwashing scepticism, doubts about product effectiveness, and price sensitivity (Aibar-Guzmán and Somohano-Rodríguez, 2021). Theoretically, classic frameworks — the Expectancy-Value Model (Lee and Holden, 1999), the Theory of Reasoned Action (Fishbein and Ajzen, 1975), and the Theory of Planned Behaviour (Ajzen, 1991) — each illuminate part of this dynamic, yet evidence shows that personal values can bypass attitudes entirely and directly shape habitual purchase choices (Hauser et al., 2013; Schäufele and Janssen, 2021), with ethical, environmental, and social values significantly enhancing green purchase propensity (Zhuo et al., 2023; Ghazali et al., 2018). Together, these strands indicate that narrowing the attitude–behaviour gap depends as much on the value structures consumers bring to purchase decisions as on the environmental credentials of the products themselves.

Equally important is a critical examination of methodologies for SPD and their treatment of consumer insights. Traditional eco-design tools — such as Life Cycle Assessment (LCA) and CAD-integrated ecodesign frameworks — are effective in quantifying environmental impacts but often treat consumer behaviour as secondary (Rossi et al., 2016; Briem et al., 2019). User-centred methods such as Kansei Engineering, Design Thinking, and Quality Function Deployment incorporate consumer data to shape product features and design choices, but are frequently criticised for lacking robust quantitative links to design outcomes; relatively few approaches connect consumer perceptions of sustainability with product features in a measurable way (Montecchi and Becattini, 2020; Daae and Boks, 2015; Gong et al., 2022). This methodological divide has prompted calls for more data-driven and interdisciplinary approaches, while also raising the legitimate concern that purely data-centric models may overlook experiential and social dimensions of sustainable consumption (Montecchi and Becattini, 2020). In practice, persistent challenges in quantifying subjective perceptions, aligning diverse preferences with specific design attributes, and collecting robust data for modelling (Amjad et al., 2023) reinforce the case for evidence-based approaches grounded in customer feedback as a means of reconciling sustainability priorities with consumer demands (Maior et al., 2022).

In response to these research gaps, this study conducts a Systematic Literature Review (SLR) of Scopus-indexed articles focussing on SPD, consumer perceptions of sustainable products, and consumer value integration in product design. Three closely related aims guide the review: to clarify how sustainable product attributes and consumer value dimensions are conceptualised and operationalised in the SPD literature; to examine how they are distributed across different product and market contexts; and to use these insights to articulate an integrated, data-driven framework for customer-based SPD. The framework's distinctive contribution lies in its explicit value-to-attribute logic — translating multi-dimensional consumer values into concrete, marketable product attributes. While the underlying value dimensions are well established in consumer research, their integration with sustainable product attributes and design decision-making remains underdeveloped, and the study advances prior reviews (Geng et al., 2020; Camilleri et al., 2023; Marcon et al., 2022) by moving beyond descriptive cataloguing toward an actionable design logic. In doing so, it offers a practical, supply-side route to addressing the attitude–behaviour gap and points to future research directions for extending the framework across industries.

This study uses an SLR to examine consumer perceptions of SPD. Scopus was chosen as the primary database due to its extensive coverage of peer-reviewed journals across multiple disciplines (Mongeon and Paul-Hus, 2016). The search strategy followed guidelines from Camilleri et al. (2023) and Marcon et al. (2022), combining eco-design and sustainable product terms — “ecodesign,” “sustainable product,” “green product innovation” — with consumer-centric terms — “customer perception,” “consumer preference,” “purchase intention” — using Boolean operators. Articles were included based on three criteria: (1) explicit focus on consumer perceptions of sustainable product design, rather than sustainable products in general; (2) investigation of consumer values and external drivers incorporated into product design; and (3) use of data-driven methodologies to understand consumer perception and sustainable product attributes. Figure 1 illustrates the step-by-step screening strategy. The initial search identified 2,256 documents, refined to 2,218 after excluding non-English publications and to 1,711 after limiting to journal articles. Abstract and full-text screening for consumer-focused empirical methods reduced the pool to 185 documents, and applying the three inclusion criteria in full identified 39 studies for the final analysis.

Figure 1
A flowchart illustrating the systematic literature review process and protocols.The flowchart illustrates the systematic literature review process and protocols. The process begins with the identification phase, where studies are identified from Scopus, totaling 2256 studies. Studies excluded before screening amount to 545, due to not being in English language or not being peer-reviewed articles. The next phase is screening, where 1711 studies are screened based on title and abstract review. Studies excluded at this stage total 1524, due to not being consumer-centric, absence of a relevant methodology, or being systematic literature review or review articles. The subsequent phase is eligibility assessment, where 185 studies are assessed for eligibility through full text review. Studies excluded at this stage total 146, due to focusing on sustainable products rather than sustainable product development or being irrelevant articles. The final phase includes 39 studies in the review.

SLR process and protocols

Figure 1
A flowchart illustrating the systematic literature review process and protocols.The flowchart illustrates the systematic literature review process and protocols. The process begins with the identification phase, where studies are identified from Scopus, totaling 2256 studies. Studies excluded before screening amount to 545, due to not being in English language or not being peer-reviewed articles. The next phase is screening, where 1711 studies are screened based on title and abstract review. Studies excluded at this stage total 1524, due to not being consumer-centric, absence of a relevant methodology, or being systematic literature review or review articles. The subsequent phase is eligibility assessment, where 185 studies are assessed for eligibility through full text review. Studies excluded at this stage total 146, due to focusing on sustainable products rather than sustainable product development or being irrelevant articles. The final phase includes 39 studies in the review.

SLR process and protocols

Close modal

This section presents findings derived from the SLR and qualitative content analysis of the 39 selected scholarly papers, focussing specifically on consumer perceptions toward SPD. Qualitative content analysis was conducted using NVivo 14, facilitating systematic coding and organisation of data (Jackson and Bazeley, 2019). Figure 2 summarises the temporal distribution of the 39 studies included in the review. An a priori coding framework, guided by the research objectives, was developed and iteratively refined to capture key themes and patterns from the selected literature. The findings are categorised into three main thematic areas: (1) consumer assessment of sustainable product attributes, (2) consumer value dimensions influencing SPD, and (3) evaluation of data-driven approaches for measuring consumer perceptions. Each category is discussed in turn below.

Figure 2
A bar graph showing the distribution of publications by year from 2004 to 2025.The bar graph compares the number of publications by year from 2004 to 2025. The x-axis represents the years, and the y-axis represents the number of publications. There are 13 vertical bars, each representing a different year. The bars show an increase in the number of publications over time. The years 2004, 2011, 2012, 2015, 2016, and 2019 each have 1 publication. The years 2020 and 2021 each have 3 publications. The year 2022 has 1 publication. The year 2023 has 4 publications. The year 2024 has 6 publications. The year 2025 has 16 publications. The color scheme is blue. All values are approximated.

Distribution of publications by year in the systematic literature review (n = 39). Note(s): The chart shows the temporal evolution of research in sustainable consumption and green product design from 2004 to 2025. The data reveals three distinct periods: an early foundational period (2004–2016) with sporadic publications, a middle expansion period (2019–2022) with moderate growth, and a recent acceleration period (2023–2025) showing exponential increase in research output, with 2025 accounting for 41% of all publications in the review

Figure 2
A bar graph showing the distribution of publications by year from 2004 to 2025.The bar graph compares the number of publications by year from 2004 to 2025. The x-axis represents the years, and the y-axis represents the number of publications. There are 13 vertical bars, each representing a different year. The bars show an increase in the number of publications over time. The years 2004, 2011, 2012, 2015, 2016, and 2019 each have 1 publication. The years 2020 and 2021 each have 3 publications. The year 2022 has 1 publication. The year 2023 has 4 publications. The year 2024 has 6 publications. The year 2025 has 16 publications. The color scheme is blue. All values are approximated.

Distribution of publications by year in the systematic literature review (n = 39). Note(s): The chart shows the temporal evolution of research in sustainable consumption and green product design from 2004 to 2025. The data reveals three distinct periods: an early foundational period (2004–2016) with sporadic publications, a middle expansion period (2019–2022) with moderate growth, and a recent acceleration period (2023–2025) showing exponential increase in research output, with 2025 accounting for 41% of all publications in the review

Close modal

The attributes of SPD significantly shape consumer acceptance and purchasing decisions (Ghazali et al., 2023). Attributes such as quality, eco-labels, price, and country of origin are frequently decisive for environmentally conscious consumers (Aibar-Guzmán and Somohano-Rodríguez, 2021). Key product attributes identified from the literature that influence consumer adoption of sustainable products include sustainability cues, eco-labels and packaging, country of origin, quality and functional attributes, price sensitivity, after-sales services and design and aesthetic features. Tables 1–7 synthesise this evidence by grouping first-order attributes from the reviewed studies into broader second-order categories. This structure shows that consumer evaluation of sustainable products is shaped not only by environmental performance itself, but also by how that performance is communicated, validated, priced, supported, and embedded in the overall product experience. The following subsections discuss each attribute group and use the tables to clarify how individual design features contribute to customer-based SPD.

3.1.1 Sustainability cues

Sustainability cues such as durability, reparability, and eco-friendly materials drive consumer acceptance by extending product life, reducing environmental impact, and enhancing perceived sustainability (Koszewska et al., 2020). Moreover, intrinsic sustainability cues, such as organic or eco-friendly ingredients, enhance perceived value by linking the product to environmental and personal health benefits (Seo et al., 2016). Additionally, consumer acceptance improves when sustainable choices are seamlessly integrated into everyday routines, suggesting that practical ease of adoption significantly influences consumer behaviour toward sustainable products (Nath and Agrawal, 2023). In the reviewed studies, five sustainability-cue groups emerge: circularity, environmental metrics, material choices, production integrity, and certification. Table 1 summarises the attributes within each group.

Table 1

Sustainability cues, circularity and ethical production attributes

Second order attributeFirst order attributesSource papers
Circularity and end-of-life managementRecyclability, Material Recovery and Recyclable Design; Garment Life and Recycling; Longevity of Products; Eco-friendliness; Biodegradability; Reusability; Menstrual-cup; Recycling-based SolutionBovea and Vidal (2004), Koszewska et al. (2020), Narassima et al. (2025), Balcıoğlu et al. (2025), Indrawati et al. (2025) 
Environmental impact assessment and metricsGraded Colour-Coded Environmental Impact Rating (A–G); Composite LCA-Based Environmental Impact Metric; Environmental Impact Disclosure; Carbon Footprint Label; Corporate Sustainability Signalling and CommunicationDihr et al. (2021), Li (2025), Paffarini et al. (2025), Zhang and Chen (2025), D'Adamo et al. (2024) 
Material selection and resource efficiencySurface Treatment Substitution (e.g. Powder Coating over Solvent-Based Coating); Biodiversity-friendly Product Assortment; Resource Efficiency Claims; Sustainable Water Use, Preserving Soil Fertility and Protecting Biodiversity; Material and Process Sustainability; Material Type (e.g. traditional, synthetic, or vegan leather)Bovea and Vidal (2004), Foti et al. (2019), Wurster et al. (2020), Villanueva et al. (2025), Zhang and Chen (2025), D'Adamo et al. (2024) 
Production process integrity and methodsFishing Technique Disclosure; Lower Energy Consumption; Production Method (Organic vs. Conventional/Traditional); Production Process Circularity; Organic and Sustainability Labelling (Present vs. Absent); Environmental Impact and Animal Ethics; Reduced Carbon Footprint; Sustainability Label Type; Plastic Waste, Biowaste, GHG Emissions; Ethical Sourcing, Fair Production; Green Features and Eco-design of the VehicleForleo et al. (2023), Bovea and Vidal (2004), Eldesouky et al. (2020), Koszewska et al. (2020), Paffarini et al. (2025), Chaloupkova et al. (2025), Narassima et al. (2025), Vaikma et al. (2025), Villanueva et al. (2025), Balcıoğlu et al. (2025), Zhang and Chen (2025), Wijayatunga et al. (2024), Wallmo and Kosaka (2025) 
Sustainability certification and standardsEco-Label Presence; Eco-Friendly Ingredients Certification; Animal Welfare Label; Eco-label Claim; Certified; Sustainability Standard Integration; Eco-scoreDihr et al. (2021), Seo et al. (2016), Eldesouky et al. (2020), Li (2025), Huang et al. (2024), Wijayatunga et al. (2024), Shaikh et al. (2024) 
Source(s): Authors' own elaboration

3.1.2 Eco-labels, packaging, and certifications

Consumers rely on eco-labels as trusted markers of environmental responsibility and product quality, positively influencing their purchasing decisions (Eldesouky et al., 2020). Additionally, sustainable packaging contributes to consumer perceptions by visibly demonstrating environmental commitment and enhancing perceived product value (Seo et al., 2016). The reviewed studies identify six groups of labelling and packaging attributes, covering: how eco-information is displayed and made accessible on pack; how claims are verified and made trustworthy; whether labelling follows recognised standards; how visual elements such as colour and icons aid quick comprehension; what the packaging material itself is made of; and how the physical design and size of packaging reinforces or undermines the sustainability message. Table 2 details the specific attributes within each group.

Table 2

Packaging, eco-labels and information transparency attributes

Second order attributeFirst order attributesSource papers
Information display and communication systemsCross-Category Normalised Comparison Capability; Scale Granularity (Multiple Tiers); Front-of-Pack Placement and Visibility; Label Presence (Label vs. Label-Free); Logo Design Format; Information System Granularity; Sustainability Information Dimensions; Information Accessibility and Clarity; Blockchain-based Traceability Label; Descriptive Carbon Footprint; Award LogoDihr et al. (2021), Kato et al. (2023), Borin et al. (2011), Wurster et al. (2020), Li (2025), Huang et al. (2024), Edenbrandt et al. (2025) 
Information transparency and verificationClaim Specificity and Verifiability; Eco-label Presence and InformativenessNath and Agrawal (2023), Kumar and Basu (2023) 
Labelling standards and complianceGeographical Indication (PGI) Label; Environmental Labels and Certification on Clothing; Presence vs. Absence of Labels; Official Organic CertificationLambarraa-Lehnhardt et al. (2021), Koszewska et al. (2020), Edenbrandt et al. (2025), Shaikh et al. (2024) 
Visual communication and graphic designColour-Coded Environmental and Carbon CommunicationDihr et al. (2021), Forleo et al. (2023), Edenbrandt et al. (2025) 
Sustainability of packaging materialsReduced Packaging; Recycled Packaging; Returnable/Refillable ContainersBovea and Vidal (2004), Seo et al. (2016), Narassima et al. (2025), Vaikma et al. (2025), Czine et al. (2025), Wijayatunga et al. (2024) 
Packaging design elements and aestheticsCap and Bottle Shape and Style; Appropriate vs. Excessive Packaging LevelKato et al. (2023), Seo et al. (2016), Wijayatunga et al. (2024) 
Source(s): Authors' own elaboration

3.1.3 Country of origin and local ecosystem

Products carrying strong local identities — such as Protected Designation of Origin labels — are often preferred for their authenticity and support of local economies (Lambarraa-Lehnhardt et al., 2021). Additionally, biodiversity-friendly production practices can positively influence consumer preferences by signalling ecological stewardship (Foti et al., 2019). Table 3 indicates that origin-related attributes operate through two closely linked mechanisms: geographic identity and local production systems. These attributes matter because they connect sustainability claims to place-based authenticity, local economic support, and perceived ecological stewardship.

Table 3

Origin, local and ecosystem attributes

Second order attributeFirst order attributesSource papers
Geographic origin and regional identityCountry/Region of Origin Label; Geographic Origin Information; Territorial Identity and Regional Branding; Geographical Indication (PDO/PGI) Certification; Contribution to Rural Economy; Geographic Authenticity and Quality ClaimsForleo et al. (2023), Foti et al. (2019), Eldesouky et al. (2020), Li (2025), Paffarini et al. (2025), Villanueva et al. (2025), Czine et al. (2025), Wallmo and Kosaka (2025), D'Adamo et al. (2024) 
Local production systems and manufacturingLocal/Domestic Production Claim or Label; Support for Local Economy and Small Producers; Local Production Systems; Locally SourcedLambarraa-Lehnhardt et al. (2021), Foti et al. (2019), Narassima et al. (2025), Wijayatunga et al. (2024) 
Source(s): Authors' own elaboration

3.1.4 Quality (functional attributes)

Sustainable products face a notable perception challenge: to gain adoption, they must match or exceed the functional performance of traditional alternatives, because consumers treat quality as a threshold condition rather than a dimension, they will trade off for sustainability (Nath and Agrawal, 2023). This challenge further extends into contexts where eco-labelled goods influence purchase decisions through perceived quality enhancements linked to environmental and health attributes (Eldesouky et al., 2020). Four groups of functional quality attributes are identified: overall performance and reliability; physical composition and intrinsic properties; safety and structural durability; and sensory qualities such as taste, texture, and freshness. Together these show that consumers assess functional quality across multiple dimensions, not simply whether a product “works”. Table 4 details the specific attributes within each group.

Table 4

Functional performance and safety quality attributes

Second order attributeFirst order attributesSource papers
Performance quality and effectivenessFunctional Quality and Performance Delivery; Quality, Effectiveness and Efficiency; Tyre Performance; Nutritional Value and Impact on Health; Durability (paint quality, material durability, component longevity); Usage Convenience DesignYuan et al. (2022), Nath and Agrawal (2023), Tsai et al. (2012), Wurster et al. (2020), Chaloupkova et al. (2025), Mitrache et al. (2025), Zhang et al. (2025), Balcıoğlu et al. (2025), Zhang and Chen (2025) 
Physical properties and compositionHigh Juice-Content Metric (above threshold); Fruit Pulp Texture (Crisp-Firm); Presence of Pesticides, Formaldehyde, Plasticisers, and Recycled Materials; Food Ingredients (origin/base)Lambarraa-Lehnhardt et al. (2021), Borin et al. (2011), Vaikma et al. (2025) 
Safety, durability and structural integrityHealth and Food-Safety Emphasis for Biodiversity-Friendly Vegetables; Functional Durability and Quality; Absence of Harmful ElementsFoti et al. (2019), Koszewska et al. (2020), Narassima et al. (2025), D'Adamo et al. (2024) 
Sensory quality and experienceSensory Quality and Freshness Cues (“tasty”, “fresher”, “less treated”); Intrinsic Sensory Quality (Taste/Texture); Skin Colour (Dark Red)Chaloupkova et al. (2025), Foti et al. (2019) 
Source(s): Authors' own elaboration

3.1.5 Price

While some consumers pay a premium for sustainability features, broader market penetration is constrained by price sensitivity, which can override environmental concerns for cost-sensitive segments (Eldesouky et al., 2020; Kato et al., 2023). Strategies to attract price-sensitive consumers might include not only setting competitive prices but also highlighting the long-term cost savings associated with purchasing sustainable products, such as lower energy costs or reduced waste (e.g. Eldesouky et al., 2020). As shown in Table 5, price-related attributes in the literature extend beyond simple cost levels to include market positioning and the communication of long-term economic benefits. This pattern shows that consumers evaluate sustainable products through both immediate affordability and perceived long-term value. Pricing is therefore not external to design strategy; it shapes whether sustainability is interpreted as accessible value or as an unjustified premium.

Table 5

Price and economic attributes

Second order attributeFirst order attributesSource papers
Pricing strategy and market positioningCheaper; Promotions/Low Prices; Premium Pricing and Market Positioning; Price Point Accessibility; Price Competitiveness; Economic Value and Positioning StrategyFoti et al. (2019), Eldesouky et al. (2020), Paffarini et al. (2025), Chaloupkova et al. (2025), Narassima et al. (2025), Balcıoğlu et al. (2025), Czine et al. (2025), Wallmo and Kosaka (2025), D'Adamo et al. (2024) 
Value communication and economic benefitsPrice Level/Premium; Value Proposition Communication; Price/Cost PropositionNath and Agrawal (2023), Li (2025), Mitrache et al. (2025), Zhang et al. (2025) 
Source(s): Authors' own elaboration

3.1.6 Service including warranty and after-sales

Robust after-sales services and warranties are essential for consumer trust in sustainable products, and their absence is a notable barrier to adoption, as consumers fear potential maintenance and support issues (Nath and Agrawal, 2023). Three groups of service attributes are identified: after-sales support and warranties that reduce consumer risk; distribution and availability that determine whether sustainable options are practically accessible; and ease of use that supports integration into everyday routines. Table 6 details the specific attributes within each group.

Table 6

Services, warranties and convenience attributes

Second order attributeFirst order attributesSource papers
Customer service and technical supportAfter-Sales Service and Maintenance Support; Technical Support ServicesNath and Agrawal (2023), Zhang et al. (2025), D'Adamo et al. (2024) 
Distribution networks and product availabilityProduct Availability and Distribution Accessibility; Delivery Quality (speed, condition, reliability, arrival time)Nath and Agrawal (2023), Li (2025), Chaloupkova et al. (2025), Zhang et al. (2025) 
Usage convenience and user experienceEase of Integration/Use Convenience; Digital Features for User Convenience; User Experience DesignNath and Agrawal (2023), Wurster et al. (2020), Balcıoğlu et al. (2025) 
Source(s): Authors' own elaboration

3.1.7 Design, aesthetic, form and sensory symbolism

Visual design elements often act as powerful, non-verbal cues for quality; for example, the colour saturation of a fruit peel can signal ripeness (Lambarraa-Lehnhardt et al., 2021), while the overall aesthetic form and visual appeal of a product and its packaging can significantly influence choice (Foti et al., 2019; Mitrache et al., 2025; Seo et al., 2016). In identity-relevant categories such as sustainable luxury fashion, the “Aesthetic Style/Fashionability” is a prerequisite for adoption, often taking precedence over sustainability credentials (Nath and Agrawal, 2023), with “Aesthetic Design Excellence” being used to justify a premium market position (D'Adamo et al., 2024). The tactile and ergonomic qualities are also critical, as sensory design features like material finish enhance the user experience and can create a premium feel (Yuan et al., 2022). Finally, design has a powerful symbolic dimension, using “Symbolic Eco-Signalling Design Cues” like leaf motifs to appeal to a consumer's environmental identity (Kato et al., 2023), or incorporating cultural and heritage motifs to create a deeper, more resonant brand story (Kato et al., 2023; Zhang and Chen, 2025). Table 7 summarises the design, aesthetic, and symbolic attributes associated with consumer perceptions of sustainable products.

Table 7

Design, aesthetic, form and sensory symbolism attributes

Second order attributeFirst order attributesSource papers
Sensory experience and tactilitySensory Properties (texture, shape, colour); Sensory/Tactile DesignD'Adamo et al. (2024), Indrawati et al. (2025) 
Symbolic design and cultural meaningSymbolic Design CuesZhang and Chen (2025) 
Visual design elements and aestheticsPeel Colour Tone Saturation (Deep Orange); Playful and Aesthetic Design; Visual Design Appeal and Aesthetic IntegrationLambarraa-Lehnhardt et al. (2021), Tsai et al. (2012), Mitrache et al. (2025), Zhang et al. (2025), Balcıoğlu et al. (2025) 
Source(s): Authors' own elaboration

3.1.8 Attribute distribution across product contexts

The content analysis shows clear patterns in how attributes are used across product and market contexts, with some clusters receiving sustained attention and others largely overlooked. Cross-category studies on green consumer products cover the widest range of attributes but tend to emphasise generic levers — circularity, production processes, information display — rather than context-specific features. In Agri-Food and Beverages, the emphasis shifts strongly towards production methods, certification, origin, and price, supported by on-pack information and packaging materials. In Fashion, Textiles and Luxury Accessories, sustainability is more often framed through transparency, value communication, and basic service assurances, with less systematic attention to packaging or end-of-life design. Technology-intensive categories such as Green Household Appliances and Mobility/Automotive place more weight on performance, durability, and price, reflecting the higher perceived risk of these purchases, while studies on Sustainable Packaging concentrate almost exclusively on information and labelling, largely ignoring service and use-phase dimensions.

Across all contexts (Figure 3), a consistent hierarchy emerges. On the sustainability side, Production Process Integrity and Methods stands out as the most frequently examined attribute, alongside environmental metrics, material choices, and formal certifications. On the informational side, Information Display and Communication Systems is especially prominent, followed by sustainable packaging materials and labelling standards. Geographic origin, functional quality, and pricing strategy also rank highly, confirming that consumers assess sustainable products through a combined lens of provenance, performance, and price. By contrast, sensory experience, service and support, availability and convenience, and especially aesthetic style and symbolic design cues receive far less attention, even in categories where they are likely to matter most — fashion, premium food.

Figure 3
A table comparing product attributes categories across different product markets.A table comparing product attributes categories across different product markets. The table has 7 columns and 6 rows, including a summary row for all products. The columns are labeled as follows: A1: Sustainability Cues, A2: Information Transparency Features (Eco-labels, Packaging, and Certifications), A3: Country of Origin and Local Ecosystem, A4: Quality (Functional Attributes), A5: Price and Economic Attributes, A6: Services, warranty, aftersales, convenience in availability, and A7: Design, Aesthetic, Form & Sensory Symbolism. The rows are labeled with different product categories: Agri-Food & Beverages, Cross-Category / General Green Consumer Products, Fashion, Textiles & Luxury Accessories, Green Household Appliances & Home Energy Technologies, Mobility & Automotive (NEVs & Sustainable Transport), Sustainable Packaging, and All Products. Each cell contains numerical values representing the attributes for each product category.

Product/Market context vs product attributes categories. Source: Authors' own elaboration

Figure 3
A table comparing product attributes categories across different product markets.A table comparing product attributes categories across different product markets. The table has 7 columns and 6 rows, including a summary row for all products. The columns are labeled as follows: A1: Sustainability Cues, A2: Information Transparency Features (Eco-labels, Packaging, and Certifications), A3: Country of Origin and Local Ecosystem, A4: Quality (Functional Attributes), A5: Price and Economic Attributes, A6: Services, warranty, aftersales, convenience in availability, and A7: Design, Aesthetic, Form & Sensory Symbolism. The rows are labeled with different product categories: Agri-Food & Beverages, Cross-Category / General Green Consumer Products, Fashion, Textiles & Luxury Accessories, Green Household Appliances & Home Energy Technologies, Mobility & Automotive (NEVs & Sustainable Transport), Sustainable Packaging, and All Products. Each cell contains numerical values representing the attributes for each product category.

Product/Market context vs product attributes categories. Source: Authors' own elaboration

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These patterns reflect a concentration of scholarly attention rather than direct evidence of their substantive market importance, and are partly explained by the fact that technical and informational attributes are more readily standardised and tested than service-related, sensory, and symbolic dimensions. The resulting skew points to a clear opportunity: future research should give more systematic attention to services, use experience, and meaning-laden design as drivers of sustainable product adoption, and should extend the attribute framework to two notably absent contexts — green buildings and product–service systems — that are well developed in the broader sustainability literature but largely absent from consumer-perception studies of SPD.

Building on the analysis of product attributes, it is essential to explore how consumers interpret those attributes through underlying value dimensions. While attributes such as quality, eco-labels, and price are crucial in shaping purchase decisions, their influence depends on the values consumers assign to them (ElHaffar et al., 2020). These consumer values encompass the benefits sought throughout the product lifecycle, from pre-purchase considerations to post-purchase experiences (Sheth et al., 1991; Sweeney and Soutar, 2001). Given the previously discussed attitude-behaviour gap — where pro-environmental attitudes do not always translate into sustainable purchasing (Sivapalan et al., 2021) — understanding these values is critical. By aligning sustainable product attributes with the six value dimensions consumers bring to purchase decisions, businesses can more effectively address the gap between positive attitudes and actual behaviours (Biswas and Roy, 2015; Lai, 1995; Sivapalan et al., 2021). Table 8 summarises the six value dimensions identified in the reviewed studies and the main benefits consumers associate with each. These cover: whether a product performs well and offers good value for money (functional); whether it is genuinely better for the environment (green); how it makes the consumer feel (emotional); how it affects their standing with others (social); how well they understand what they are buying (epistemic); and whether it is practically available and affordable in their specific situation (conditional). These dimensions underpin the framework developed in Section 3.4. The following subsections detail each in turn.

Table 8

Consumer value dimensions in relation to SPD

Value categoryKey contentsSource papers
Functional valueCore performance, reliability and quality (performance utility, “genuine/fresh/less-treated”, safe formulation); economic and cost utility (price fairness, durability, savings, cost-per-use, transaction utility); health and safety utility; service and risk reduction (warranty, after-sales support); functional assurance bundled with eco-attributes (energy efficiency, low operating cost, safe resource use, origin/quality reassurance)Dihr et al. (2021), Waris et al. (2021), Yuan et al. (2022), Nath and Agrawal (2023), Lambarraa-Lehnhardt et al. (2021), Essiz and Senyuz (2024), Borin et al. (2011), Bovea and Vidal (2004), Foti et al. (2019), Wurster et al. (2020), Eldesouky et al. (2020), Koszewska et al. (2020), Li (2025), Paffarini et al. (2025), Chaloupkova et al. (2025), Narassima et al. (2025), Vaikma et al. (2025), Mitrache et al. (2025), Zhang et al. (2025), Villanueva et al. (2025), Edenbrandt et al. (2025), Balcıoğlu et al. (2025), Xu et al. (2025), Zhang and Chen (2025), Wallmo and Kosaka (2025), Aggarwal et al. (2024), Shaikh et al. (2024), D'Adamo et al. (2024) 
Green valueEnvironmental impact reduction and eco-benefits (lower footprint, waste, resource use; biodiversity and climate protection); altruistic and biospheric concern; trust in eco-labels/certification; animal welfare and humane treatment; circularity and recyclability; green consumption values and nature relatednessWaris et al. (2021), Li et al. (2021), Yuan et al. (2022), Kumar and Basu (2023), Forleo et al. (2023), Lambarraa-Lehnhardt et al. (2021), Essiz and Senyuz (2024), Borin et al. (2011), Tsai et al. (2012), Bovea and Vidal (2004), Bossle et al. (2015), Seo et al. (2016), Foti et al. (2019), Wurster et al. (2020), Eldesouky et al. (2020), Koszewska et al. (2020), Li (2025), Paffarini et al. (2025), Chaloupkova et al. (2025), Huang et al. (2024), Narassima et al. (2025), Vaikma et al. (2025), Zhang et al. (2025), Villanueva et al. (2025), Edenbrandt et al. (2025), Balcıoğlu et al. (2025), Xu et al. (2025), Zhang and Chen (2025), Indrawati et al. (2025), Wijayatunga et al. (2024), Wallmo and Kosaka (2025), Aggarwal et al. (2024), Shaikh et al. (2024), D'Adamo et al. (2024) 
Emotional valueWarm-glow and moral satisfaction (“doing the right thing”, reduced guilt); reduced anxiety and greater confidence/assurance; pride, joy, and ethical gratification (status from responsible choices; pride in region or brand); hedonic enjoyment and aesthetics (taste, sensory pleasure, design beauty, minimalism); affective attitudes toward eco-products and futures (children, future generations)Yuan et al. (2022), Kato et al. (2023), Essiz and Senyuz (2024), Borin et al. (2011), Tsai et al. (2012), Bovea and Vidal (2004), Eldesouky et al. (2020), Li (2025), Paffarini et al. (2025), Chaloupkova et al. (2025), Narassima et al. (2025), Vaikma et al. (2025), Mitrache et al. (2025), Zhang et al. (2025), Villanueva et al. (2025), Balcıoğlu et al. (2025), Xu et al. (2025), Zhang and Chen (2025), Indrawati et al. (2025), Aggarwal et al. (2024), D'Adamo et al. (2024) 
Social valueSocial norms and approval (descriptive and injunctive norms, marketplace fairness); status and signalling (eco-self, prestige/heritage, responsible parenting, circular/eco-luxury image); community and local support (supporting local farmers, regions, rural development); identity and belonging (regional pride, green self-identity, community/influencer signalling); moral and fairness concerns toward others (workers, producers, society)Waris et al. (2021), Yuan et al. (2022), Kumar and Basu (2023), Lambarraa-Lehnhardt et al. (2021), Essiz and Senyuz (2024), Tsai et al. (2012), Bovea and Vidal (2004), Bossle et al. (2015), Foti et al. (2019), Wurster et al. (2020), Eldesouky et al. (2020), Koszewska et al. (2020), Paffarini et al. (2025), Chaloupkova et al. (2025), Narassima et al. (2025), Vaikma et al. (2025), Balcıoğlu et al. (2025), Xu et al. (2025), Zhang and Chen (2025), D'Adamo et al. (2024) 
Epistemic valueKnowledge gain and learning (understanding labels, footprints, biodiversity, production methods, technology); information sufficiency, transparency and verifiability (blockchain, detailed panels, carbon data, clear eco-attributes); curiosity and novelty (innovative materials/tech, circular solutions, configurators); confidence from knowing enough to choose correctly and update mental modelsWaris et al. (2021), Yuan et al. (2022), Lambarraa-Lehnhardt et al. (2021), Essiz and Senyuz (2024), Bossle et al. (2015), Foti et al. (2019), Wurster et al. (2020), Li (2025), Paffarini et al. (2025), Chaloupkova et al. (2025), Zhang et al. (2025), Villanueva et al. (2025), Edenbrandt et al. (2025), Balcıoğlu et al. (2025), Xu et al. (2025), Wallmo and Kosaka (2025), D'Adamo et al. (2024) 
Conditional valueContext- and situation-dependent utility: category or risk context (food vs. non-food, high-risk categories); segment- or occasion-specific amplifiers (EV owners, gifting, special editions, promotions, policy incentives, high petrol prices); availability and convenience in specific situations; price and budget constraints as situational filters; religion or norms as enabling/blocking conditions; opt-out or “no choice” when greener substitutes are availableWaris et al. (2021), Nath and Agrawal (2023), Kumar and Basu (2023), Essiz and Senyuz (2024), Wurster et al. (2020), Eldesouky et al. (2020), Paffarini et al. (2025), Chaloupkova et al. (2025), Balcıoğlu et al. (2025), Czine et al. (2025), Wallmo and Kosaka (2025), D'Adamo et al. (2024) 
Source(s): Authors' own elaboration

3.2.1 Green value

Green value encompasses the consumer's valuation of products based on their environmental benefits, with consumers increasingly willing to pay a premium for a reduced carbon footprint or sustainable sourcing (Bovea and Vidal, 2004). Environmental consciousness and ethical motivations shape these perceptions and, in turn, satisfaction and loyalty toward green products (Bolsunovskaya et al., 2023).

3.2.2 Functional value

Functional value pertains to the intrinsic utility of a product — its quality, reliability, and performance (Yuan et al., 2022). For sustainable products, functional value often intertwines with durability and efficiency, aligning short-term performance expectations with long-term sustainability goals (Essiz and Senyuz, 2024).

3.2.3 Social value

Social value reflects the benefits consumers derive from products in social settings, including enhanced social image and peer approval (Essiz and Senyuz, 2024). For sustainable products, this value drives green behaviour through peer influence and social norms, but its effect varies sharply across cultural and social groups.

3.2.4 Emotional value

Emotional value is associated with the feelings and psychological satisfaction a consumer derives from purchasing or using a product (Bossle et al., 2015; Essiz and Senyuz, 2024). In SPD, it can be amplified by aligning product attributes with personal identities — for example, pride in making environmentally responsible choices — strengthening the consumer–product relationship (Essiz and Senyuz, 2024).

3.2.5 Conditional value

Conditional value depends on the specific circumstances in which the product is used or purchased (Bolsunovskaya et al., 2023). In SPD, situational factors such as product availability, promotional incentives, and environmental-crisis contexts can significantly shift consumer choices (Essiz and Senyuz, 2024).

3.2.6 Epistemic value

Epistemic value involves the arousal of curiosity, provision of novelty, and the satisfaction of knowledge-seeking behaviour related to a product (Bolsunovskaya et al., 2023; Essiz and Senyuz, 2024; Yuan et al., 2022). Sustainable products often fulfil this value by offering innovative features and new technologies that are environmentally friendly (Essiz and Senyuz, 2024).

3.2.7 Category-specific value hierarchies

A critical finding from this analysis is that no universal value hierarchy applies across sustainable products — the relative importance of the six dimensions shifts dramatically by product category. Figure 4 provides a synthesised overview of how each value type varies across the key product categories represented in the research, assessed by the strength and frequency of drivers identified in the data.

Figure 4
A table comparing sustainable value drivers across product categories.The table compares sustainable value drivers across six product categories: Food and Beverage, Consumer Durables, Automotive, Fashion and Luxury, and Novel Tech Circular Products. It has six rows and seven columns, including headers for Green Value, Functional Value, Emotional Value, Social Value, Epistemic Value, and Conditional Value. Each cell indicates the level of influence of these values on the respective product category, with labels such as Primary Driver, Key Influencer, Minor Factor, Prerequisite, and Baseline. Notable trends include the primary influence of Green Value on Food and Beverage, Automotive, and Novel Tech Circular Products, while Functional Value is a primary driver for Consumer Durables and Fashion and Luxury.

Context-dependency matrix of sustainable value drivers. Source: Authors' own elaboration

Figure 4
A table comparing sustainable value drivers across product categories.The table compares sustainable value drivers across six product categories: Food and Beverage, Consumer Durables, Automotive, Fashion and Luxury, and Novel Tech Circular Products. It has six rows and seven columns, including headers for Green Value, Functional Value, Emotional Value, Social Value, Epistemic Value, and Conditional Value. Each cell indicates the level of influence of these values on the respective product category, with labels such as Primary Driver, Key Influencer, Minor Factor, Prerequisite, and Baseline. Notable trends include the primary influence of Green Value on Food and Beverage, Automotive, and Novel Tech Circular Products, while Functional Value is a primary driver for Consumer Durables and Fashion and Luxury.

Context-dependency matrix of sustainable value drivers. Source: Authors' own elaboration

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Functional value emerges as a non-negotiable prerequisite rather than a dimension consumers are willing to trade off. In Consumer Durables, performance shortfalls trigger strong negative reactions regardless of environmental credentials; sustainability considerations only become relevant once functional adequacy is secured. A similar pattern holds in Mobility and Automotive, where range, reliability, and cost form hard adoption thresholds that green benefits cannot override, even though context-dependent factors such as policy incentives and fuel prices can relax or tighten those thresholds. Agri-Food and Beverages departs from this sequence: here, environmental and functional concerns — organic certification, production methods, safety, taste — act as joint primary drivers. Because many food attributes are credence in nature, claims such as “pesticide-free” simultaneously signal environmental impact and personal health, making green and functional value effectively inseparable. Novel technologies and circular products face the most demanding configuration: they must prove functional viability while overcoming distrust and perceived risk, and simultaneously deliver convincing environmental benefits, with conditional value often decisive — religious compliance, situational availability, and promotional timing can make otherwise attractive solutions either adoptable or untenable. As products move further away from familiar consumption patterns, more value dimensions must meet minimum thresholds simultaneously, substantially raising the design bar.

Social value closely tracks product visibility and identity relevance — central in high-visibility categories such as Mobility and Automotive and Fashion, where status signalling and identity expression are key, but largely peripheral in low-visibility domains such as Consumer Durables and privately consumed food. Green value plays two distinct roles: in Novel Technologies and explicitly green product lines, environmental performance is the core value proposition without which the product has little reason to exist; in established categories such as Fashion or Consumer Durables, sustainability operates as a secondary differentiator or premium justification once functional and aesthetic expectations have been met. Emotional value — feelings of pride, moral satisfaction, or reduced guilt — appears consistently as a meaningful amplifier rather than a stand-alone driver, strengthening decisions already justified on functional or environmental grounds rather than compensating for weaknesses in those domains. Conditional value functions less as an additional benefit and more as a contextual gatekeeper, determining when otherwise attractive sustainable options are practically viable for specific segments and situations. Taken together, these patterns suggest that emotional and social benefits are best treated as reinforcing layers built on a solid functional and green foundation, rather than as substitutes for it.

This section examines the quantitative methods used in the 39 reviewed studies. Tables 9 and 10 offer a comprehensive overview of the empirical techniques used. Specifically, Table 9 details the assessment methods for consumer perceptions of sustainable product attributes, summarising data collection techniques, analytical tools, and the independent and dependent variables considered. Meanwhile, Table 10 outlines the approaches for evaluating consumer values, presenting similar methodological details. Across both tables, there is a methodological concentration around structured surveys, choice experiments, and variance-based modelling. While these approaches are valuable for estimating stated preferences, they privilege pre-specified, cognitively accessible attributes and capture less effectively the tacit, embodied, relational, and post-purchase dimensions of sustainable product use. As a result, the current evidence base is stronger on what consumers can report in standardised settings than on how sustainability is experienced in routine practice, which helps explain the weaker coverage of service, sensory, and symbolic design dimensions in the SPD literature.

Table 9

Assessment methods of consumer perception towards attributes

AttributeData collectionAnalytical methodIndependent variablesDependent variablesReferences
Sustainability cuesOnline survey; Lab-based experiment; Choice-based conjoint experimentANOVA; Logistic regression; Hierarchical Bayes estimationSustainability, compromise, and confidence attributesConsumer attitudes toward circular economy; Preferences in transportation scenarios; User preferences in electric vehicle features; Most/least desirable attributes in purchase contextKoszewska et al. (2020), Wurster et al. (2020) 
Country of origin and local ecosystemOnline survey; Face-to-face interviewsBinary logistic regression; Choice Experiment (CE); Conditional logit; Cluster analysis; Social Network Analysis (SNA)Frequency of canned tuna consumption; Consumer preferences for meat attributes; Motivations leading to agri-food product choiceSignificance of attributes; Consumer choices; Consumer profiles and indirect relationshipsEldesouky et al. (2020), Forleo et al. (2023), Foti et al. (2019) 
Quality (functional attributes) and service including warranty and after-salesSelf-administered surveyPLS-SEMFunctional and service-related attributes of sustainable productsPurchase IntentionsNath and Agrawal (2023) 
PriceOnline survey; CEConditional logit; Cluster analysisCountry of origin, production method, labels, and price (beef attributes); Consumer beliefs, attitudes, and environmental concernsConsumer preferences; Consumer segmentationEldesouky et al. (2020) 
Eco-labels, packaging and certificationsOnline survey with quota sampling; Structured questionnaire; Face-to-face interviewsChi-square test; Cronbach's alpha; One-way ANOVA; SEM; PLS; Factor analysis; Principal component analysisPresence/absence of labels; Consumer attitudes toward environmental certification; Environmental message levels; Knowledge of eco-labelsConsumer attraction to product; Perception of safety and environmental impact; Purchase intention for green and energy-efficient products; Consumer choice of environmentally friendly food productsDihr et al. (2021), Eldesouky et al. (2020), Kato et al. (2023), Koszewska et al. (2020), Lambarraa-Lehnhardt et al. (2021), Waris et al. (2021) 
DesignOnline survey; Web-scraping; Multiple surveys and questionnairesMCDA; K-means clustering; Ordinal regression; KeyBERT; Word2Vec; Kano modelProduct aesthetics; Appearance design; Visual appeal; Demographic factors; Design preferencesPurchase intentions; Consumer satisfaction; Willingness to pay; Customer experience ratings; Aesthetic evaluation indicatorsD'Adamo et al. (2024), Mitrache et al. (2025), Zhang et al. (2025) 
Source(s): Authors' own elaboration
Table 10

Consumer value assessment methods

ValueData collectionAnalytical methodIndependent variablesDependent variablesReferences
Conditional valueStructured online surveyPLS-SEM; ANN; IPMAConditional Value; GAR; CESPurchase IntentionsEssiz and Senyuz (2024) 
Emotional valueStructured online survey; QuestionnairePLS-SEM; ANN; IPMA; Simple Regression; SEM; CFAEmotional Value; GAR; CES; Firm Environmental Strategy; Customer Environmental Consciousness; Consumption ValuesPurchase Intentions; Green Product DevelopmentEssiz and Senyuz (2024), Tsai et al. (2012), Yuan et al. (2022) 
epistemic valueStructured online survey; QuestionnaireExploratory Factor Analysis; PLS-SEM; ANN; IPMA; SEM; CFAEpistemic Value; GAR; CES; Firm Environmental Strategy; Customer Environmental Consciousness; Consumption ValuesPurchase Intentions; Structure of Values and AttitudesBossle et al. (2015), Essiz and Senyuz (2024), Yuan et al. (2022) 
Functional valueStructured online survey; QuestionnairePLS-SEM; ANN; IPMA; SEM; CFA; Simple RegressionFunctional Value; GAR; CES; Firm Environmental Strategy; Customer Environmental Consciousness; Consumption ValuesPurchase Intentions; Structure of Values and Attitudes; Green Product DevelopmentEssiz and Senyuz (2024), Tsai et al. (2012), Yuan et al. (2022) 
Green valueStructured online survey; QuestionnairePLS-SEM; ANN; IPMA; SEM; CFA; Simple RegressionGreen Value; GAR; CES; Willingness to Pay (WTP); Customer Environmental Consciousness; Consumption ValuesAverage Variance Extracted (AVE); Declared vs. Revealed WTP; Purchase IntentionsBovea and Vidal (2004), Essiz and Senyuz (2024), Li et al. (2021), Yuan et al. (2022) 
Social valueFace-to-face interviews; Structured online survey; QuestionnaireExploratory Factor Analysis; PLS-SEM; ANN; IPMA; SEM; CFA; Simple RegressionSocial Value; GAR; CES; Firm Environmental Strategy; Customer Environmental Consciousness; Consumption ValuesDemand-Side Analysis for Sustainable Products; Green Product Development; Purchase IntentionsBossle et al. (2015), Essiz and Senyuz (2024), Tsai et al. (2012), Yuan et al. (2022) 
Source(s): Authors' own elaboration

In response to the research gaps identified and insights gained from the SLR, this section proposes a six-stage framework for customer-based SPD (Figure 5). Drawing on established theories and methodologies, it systematically maps the interconnections between design dimensions, consumer values, and product attributes. The framework moves from value identification through perceived benefits, attribute selection, preference ranking, and design-dimension mapping to customer valuation modelling and iterative refinement.

Figure 5
A diagram of a data-driven framework for customer-based SPD.A diagram of a data-driven framework for customer-based SPD showing six sequential stages. Stage 1 identifies consumer values using methods like surveys and sentiment analysis, highlighting six value dimensions. Stage 2 identifies perceived benefits through methods such as Means-End Chain laddering and behavioral analysis. Stage 3 determines product attributes using literature synthesis and expert review, listing seven attribute groups. Stage 4 weights and ranks attributes with methods like conjoint analysis and machine learning. Stage 5 maps attributes onto Mishra's design dimensions using A/B testing and pilot launches, identifying five design perception dimensions. Stage 6 focuses on customer valuation models and continuous improvement using predictive modeling and real-time sentiment analytics. A feedback loop indicates refining values, benefits, and attributes through continuous improvement.

Data-driven framework for customer-based SPD. Source: Authors' own elaboration. Note(s): Six sequential stages move from identifying consumer values, through perceived benefits and product attributes, to attribute weighting, design-dimension mapping, and customer valuation modelling. Each stage names the key methods drawn from the SLR. Bordered chips indicate the substantive content of each stage: six consumer value dimensions (Stage 1), seven product attribute groups (Stage 3), and Mishra's (2016) five design perception dimensions (Stage 5). The dashed line on the left indicates that valuation results iterate back to refine values, benefits, and attributes through continuous improvement

Figure 5
A diagram of a data-driven framework for customer-based SPD.A diagram of a data-driven framework for customer-based SPD showing six sequential stages. Stage 1 identifies consumer values using methods like surveys and sentiment analysis, highlighting six value dimensions. Stage 2 identifies perceived benefits through methods such as Means-End Chain laddering and behavioral analysis. Stage 3 determines product attributes using literature synthesis and expert review, listing seven attribute groups. Stage 4 weights and ranks attributes with methods like conjoint analysis and machine learning. Stage 5 maps attributes onto Mishra's design dimensions using A/B testing and pilot launches, identifying five design perception dimensions. Stage 6 focuses on customer valuation models and continuous improvement using predictive modeling and real-time sentiment analytics. A feedback loop indicates refining values, benefits, and attributes through continuous improvement.

Data-driven framework for customer-based SPD. Source: Authors' own elaboration. Note(s): Six sequential stages move from identifying consumer values, through perceived benefits and product attributes, to attribute weighting, design-dimension mapping, and customer valuation modelling. Each stage names the key methods drawn from the SLR. Bordered chips indicate the substantive content of each stage: six consumer value dimensions (Stage 1), seven product attribute groups (Stage 3), and Mishra's (2016) five design perception dimensions (Stage 5). The dashed line on the left indicates that valuation results iterate back to refine values, benefits, and attributes through continuous improvement

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3.4.1 Identifying and categorising consumer values

The framework begins by identifying and categorising consumer values into six key dimensions — functional, social, emotional, epistemic, conditional, and green — drawing on consumption value theory and its extensions in sustainable consumption research (Sheth et al., 1991; Sweeney and Soutar, 2001; Sivapalan et al., 2021; Essiz and Senyuz, 2024). Because quantitative surveys tend to overlook nuanced or context-specific perspectives, this step is strengthened by integrating qualitative exploratory methods — such as in-depth interviews or ethnographic studies — with traditional survey-based approaches. Such mixed-method designs allow researchers to surface consumer values that standardised instruments may not fully reveal.

3.4.2 Identifying perceived benefits

The second stage identifies the benefits consumers associate with each value dimension. The theoretical foundation for this connection draws on Means-End Chain (MEC) theory, which proposes that consumers link product attributes to consequences and, ultimately, to personal values through a chain of associations guiding purchase decisions (Reynolds and Gutman, 1988). In the context of green consumption, Sivapalan et al. (2021) illustrate how value categories mediate the relationship between product attributes and perceived benefits. Since traditional methods of eliciting perceived benefits rely on self-reported data prone to social desirability bias, this stage is strengthened by experimental designs — such as discrete choice experiments — and real-world behavioural analyses, which validate whether the benefits consumers claim to value in principle also drive their actual purchasing behaviour.

3.4.3 Determining product attributes

Drawing on the identified consumer values and perceived benefits, the framework specifies which product attributes warrant emphasis in the design. A key risk at this stage is concentrating on a pre-specified attribute set and overlooking emerging consumer concerns. Integrating sentiment analysis from social media and digital platforms mitigates this risk by capturing real-time consumer feedback and surfacing attributes that structured survey instruments may not have initially considered, ensuring that the attribute portfolio remains aligned with evolving consumer preferences.

3.4.4 Evaluating the weight of attributes and preference ranking

Once the relevant product attributes have been identified, the framework determines their relative importance and preference ranking (Horsky and Rao, 1984). Advanced analytical techniques — conjoint analysis and Multi-criteria Decision Analysis (Soota, 2014) — are employed to quantify the influence of each attribute on consumer decision-making. These approaches can be further enhanced by machine learning algorithms, which enable the processing of large datasets and more accurate predictions of consumer preferences while accommodating ongoing data streams rather than relying on static, one-time assessments.

3.4.5 Mapping attributes against consumer preferences

With attribute weights established, this stage validates whether the prioritised attributes genuinely resonate with consumer behaviour beyond stated intentions. Quasi-experimental designs and real-world interventions — such as pilot launches or A/B testing — reduce the biases inherent in hypothetical choice scenarios and provide a behavioural reality check before full-scale design commitments are made. Building on these validation strategies, the framework incorporates Mishra's (2016) model of design perception to structure how validated attributes are translated into design decisions. The model identifies five dimensions of the product experience: the visual dimension covers aesthetic appeal and initial attraction; functional addresses utilitarian performance and reliability; kinaesthetic focuses on ergonomic and tactile qualities; interface encompasses ease of interaction, particularly salient for technologically complex products; and information concerns the clarity and accessibility of product-related details.

3.4.6 Customer valuation models and continuous improvement

The final stage develops customer valuation models using predictive modelling and machine learning algorithms to quantify the value consumers assign to specific product attributes, thereby informing pricing strategies, marketing communications, and product development priorities (Ahmad et al., 2018; Codini et al., 2020; Hanley et al., 2008). To ensure that these models do not become static as consumer preferences evolve, the framework integrates continuous feedback loops using real-time analytics and sentiment analysis. Longitudinal tracking complements this real-time capability by revealing how the weight of specific attributes shifts as consumers gain direct experience with the product — enabling the design process to remain adaptive rather than anchored to a single moment of data collection.

This review set out to clarify how sustainable product attributes and consumer value dimensions are conceptualised and distributed across the SPD literature, and to use those insights to develop an integrated, data-driven framework for customer-based SPD. The findings advance prior reviews in a consequential direction. Where earlier syntheses — notably Camilleri et al. (2023) and Marcon et al. (2022) — catalogued green product attributes and consumer responses to them, the present analysis reveals that the relationship between attributes and consumer values is neither straightforward nor universal: the same attribute can function as a primary purchase driver in one product category and be effectively irrelevant in another. This context-dependency is compounded by a methodological pattern in the literature — the dominance of structured surveys and choice experiments — that systematically privileges technical and informational attributes while leaving tacit, experiential, and post-purchase dimensions largely unmeasured. Understanding what this means for SPD theory and practice, and how the proposed framework addresses it, is the focus of the sections that follow.

Two theoretical constructs are sharpened by the findings. First, the attitude–behaviour gap is refined in an important way: its magnitude is not fixed but varies systematically with product category and with the nature of the environmental attributes involved. Where attributes are performance-based, as in consumer durables, the gap is wide because functional shortfalls disqualify a product before sustainability credentials are even considered. Where attributes are credence-based, as in agri-food, environmental and functional concerns fuse rather than compete, and the gap narrows accordingly. This category-dependence has been noted in the broader consumer behaviour literature but has not previously been integrated into SPD frameworks in a way that connects it directly to design decisions. Second, the review extends consumption-value theory by demonstrating that value dimensions do not operate as additive contributors to purchase decisions but as context-dependent filters, with different dimensions assuming gatekeeping or amplifying roles depending on the product category. Functional value functions as a prerequisite in durables and automotive contexts; social value becomes central where product use is publicly visible; emotional value consistently amplifies decisions already justified on functional or environmental grounds rather than substituting for them. These refinements jointly open empirical questions — taken up in Section 4.2 — about how value hierarchies stabilise or shift as consumers gain direct experience with sustainable products across different cultural and regulatory settings.

The proposed framework complements, rather than displaces, established SPD toolkits. Where Life Cycle Assessment provides rigorous environmental accounting, it offers limited purchase on dynamic consumer preferences; the proposed framework acts as a practical front-end to LCA and to models such as Design for Sustainable Behaviour and Circular Design (Ceschin and Gaziulusoy, 2016), unifying dispersed tools under a single, value-centric process with a real-time feedback capability that static traditional assessments lack.

For practitioners, the six framework stages compress into three, summarised in Figure 6. Diagnosis identifies which values dominate the focal category — establishing, for instance, whether functional and green value operate as joint drivers or whether one gates access to the other before sustainability is considered at all. Prioritisation then quantifies trade-offs between candidate sustainability features, concentrating R&D investment on the attributes that carry the greatest combined environmental and consumer value. Validation moves beyond stated preferences to revealed choices, testing whether prioritised attributes genuinely drive behaviour rather than simply attracting positive survey responses. The pay-off of this sequence is that sustainability enters the product as a component of the core value proposition rather than as an added-on credential. When sustainable features are designed to deliver on the values consumers already hold — reliability, cost savings, or sensory quality, depending on the category — sustainability becomes part of the quality promise rather than a separate, potentially compromising feature. This directly reduces the cognitive and motivational conflict that underpins the attitude–behaviour gap.

Figure 6
A flowchart illustrating a three-stage process for customer-based sustainable product development.The flowchart outlines a three-stage process for customer-based sustainable product development. The process begins with the Diagnosis stage, which includes in-depth interviews, ethnographic studies, and sentiment analysis to identify the value hierarchy per product category. This stage is represented by stages 1 and 2. The next stage is Prioritisation, involving discrete choice experiments, machine learning, and conjoint/multi-criteria decision analysis (MCDA) to rank attribute trade-offs and set research and development (R&D) investment priorities. This stage is represented by stages 3 and 4. The final stage is Validation, which includes A/B experiments, behavioral pilots, and real-time analytics to refine consumer choices continuously. This stage is represented by stages 5 and 6. The overall pay-off is that sustainability becomes a core value proposition, narrowing the attitude-behavior gap.

Three-stage practitioner sequence for customer-based SPD. Source: Authors' own elaboration

Figure 6
A flowchart illustrating a three-stage process for customer-based sustainable product development.The flowchart outlines a three-stage process for customer-based sustainable product development. The process begins with the Diagnosis stage, which includes in-depth interviews, ethnographic studies, and sentiment analysis to identify the value hierarchy per product category. This stage is represented by stages 1 and 2. The next stage is Prioritisation, involving discrete choice experiments, machine learning, and conjoint/multi-criteria decision analysis (MCDA) to rank attribute trade-offs and set research and development (R&D) investment priorities. This stage is represented by stages 3 and 4. The final stage is Validation, which includes A/B experiments, behavioral pilots, and real-time analytics to refine consumer choices continuously. This stage is represented by stages 5 and 6. The overall pay-off is that sustainability becomes a core value proposition, narrowing the attitude-behavior gap.

Three-stage practitioner sequence for customer-based SPD. Source: Authors' own elaboration

Close modal

The framework equally offers policymakers a route beyond purely informational interventions — labels, awareness campaigns — toward targeted measures grounded in category-specific consumer drivers. Diagnosing the value hierarchy of a given category reveals whether a subsidy, infrastructure investment, or regulatory instrument is most likely to shift behaviour, and where each is likely to fail. The EU's Right to Repair directive illustrates this alignment potential: firms that design repair services to deliver on consumer values of reliability and long-term cost savings, rather than merely meeting the directive's minimum requirements, convert a compliance obligation into a source of loyalty and repeat purchase. The bottom-up market validation that the framework provides complements top-down regulation by revealing which sustainable features consumers will actually adopt and sustain in use.

Several limitations of the present study warrant acknowledgement and point to productive directions for future inquiry. First, the framework integrates consumer values with Mishra's (2016) design perception dimensions while treating sustainability as a separate element, which may restrict its capacity to capture the inherently holistic nature of SPD. Integrating LCA techniques directly within Mishra's dimensions — rather than alongside them — would yield a more unified analytical structure in which consumer perceptions and environmental impacts are evaluated through a common lens.

Second, the cross-sectional nature of the evidence base constrains the framework's capacity to track preference shifts driven by emerging societal norms, technological change, or evolving environmental awareness. Longitudinal study designs and real-time data collection — incorporating continuous sentiment analysis and panel methodologies — would substantially enhance the framework's adaptability to dynamic market conditions.

Third, the framework remains to be validated empirically across industries, cultures, and regulatory contexts. Value hierarchies and attribute preferences are known to vary with cultural setting, as do the norms, infrastructural conditions, and regulatory environments that moderate the attitude–behaviour gap. The theoretical proposition that the gap's magnitude depends systematically on whether environmental attributes are credence-based or performance-based similarly awaits direct empirical examination. Cross-industry case studies, experimental designs, and structured industry partnerships are therefore needed to establish the framework's external validity and practical scope.

Fourth, important limitations characterise the corpus this review synthesises. The concentration of extant research on technical and informational attributes, at the expense of experiential and relational dimensions, partly reflects the methodological conventions that dominate the field. Beyond these thematic gaps, two substantive domains that figure prominently in the broader sustainable design literature — green building design and product–service systems — are effectively absent from the consumer-perception studies synthesised here, a lacuna the present review necessarily inherits. The restriction of the search to Scopus and to English-language peer-reviewed sources compounds this absence. Extending coverage to Web of Science, Google Scholar, and grey literature would bring built-environment and PSS-based research into view; extending the attribute framework itself to these domains, alongside methodologies suited to capturing experiential and symbolic design dimensions, represents a clear priority for future synthesis.

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