Value-aligned algorithms strengthen commitment to sustainable consumption

The study finds that consumers face high cognitive and emotional barriers when deciding whether to dispose of luxury goods. These barriers include uncertainty about authenticity, concern over resale value, fear of fraud and sentimental attachment to items. Traditional policy interventions and sustainability campaigns have struggled to address these concerns because they operate at a distance from the real decision-making context. According to the authors, AI tools built into online resale platforms intervene directly in the moment of choice, reshaping how users perceive complexity, risk and social expectations.

Artificial intelligence (AI) is emerging as a decisive force in shifting consumer attitudes toward sustainable consumption, according to a new study that examines how algorithmic decision systems influence participation in high-value circular economy markets. Researchers argue that consumers are increasingly guided not by traditional social pressures or environmental appeals but by the empowering effects of AI-driven recommendation, trust-building and decision-support tools built into major resale platforms.

The findings come from the study Algorithmic Empowerment and Its Impact on Circular Economy Participation: An Empirical Study Based on Human–Machine Collaborative Decision-Making Mechanisms, published in the Journal of Theoretical and Applied Electronic Commerce Research.

The authors analyze how human–AI collaborative systems within Chinese secondhand luxury platforms reshape user motivations, trust dynamics and perceptions of risk, ultimately altering willingness to donate, resell or recycle high-value goods. Their research builds on a data set of 1,396 platform users and positions algorithmic systems as active participants in decision-making, rather than passive informational tools.

AI systems reduce psychological barriers that prevent consumers from entering circular markets

The study finds that consumers face high cognitive and emotional barriers when deciding whether to dispose of luxury goods. These barriers include uncertainty about authenticity, concern over resale value, fear of fraud and sentimental attachment to items. Traditional policy interventions and sustainability campaigns have struggled to address these concerns because they operate at a distance from the real decision-making context. According to the authors, AI tools built into online resale platforms intervene directly in the moment of choice, reshaping how users perceive complexity, risk and social expectations.

The researchers identify three dimensions of what they call algorithmic empowerment. The first, algorithmic connectivity, reflects the ability of AI systems to link users efficiently to valuation services, authentication tools, market price histories and relevant communities. This connectivity reduces the information deficit that often prevents consumers from taking part in resale or recycling. By presenting accurate comparisons, personalized suggestions and ongoing updates, the system creates a sense of competence and situational control, making participation in the circular economy feel less daunting.

The second dimension, human–agent symbiotic trust, describes how consumers develop confidence in the algorithm as a decision-making partner. Rather than viewing AI merely as a technical feature, users experience it as a reliable assistant that simplifies complicated tasks. The study shows that trust emerges from perceived competence, consistency and alignment with user interests. Trust in the algorithm reduces perceived risk, especially in markets where authenticity verification and secure transactions are crucial.

The third dimension, algorithmic value alignment, examines how AI systems reinforce social and environmental values. When a platform emphasizes sustainability, responsible consumption or community norms, users who share those values feel their identities reflected in the system. This alignment increases intention to donate or resell because the platform’s messaging appears personally meaningful rather than generic. The authors argue that algorithmic value alignment turns sustainability from an abstract moral concept into a personalized motivation embedded directly within the platform experience.

Across all three dimensions, algorithmic empowerment helps dismantle the cognitive and emotional barriers that typically inhibit participation in high-value circular commerce. Instead of relying on external incentives, the platform uses AI to make sustainable behavior feel intuitive and frictionless.

Algorithms influence social norms and decision fluency, reshaping sustainable consumption at scale

Traditional social influence relies on peer behaviour, advertising or public messaging, but digital platforms deploy algorithms that curate what users see, shaping perceptions of what the broader community considers normal or desirable. When users encounter high participation signals, such as recycled product listings, popular resale trends or sustainability scores, the platform signals that circular behaviour is socially endorsed.

According to the authors, algorithmic social norms have a stronger and more immediate impact on user behaviour than conventional social influence channels. They are embedded into the platform interface and appear in real time, creating a feedback mechanism that is both personalized and scalable. The study finds that algorithmic connectivity and algorithmic trust significantly reinforce this perceived social endorsement, which in turn increases consumer willingness to dispose of luxury items in sustainable ways.

The second mechanism, AI-driven decision fluency, plays an equally critical role. Decision fluency refers to the ease and smoothness with which a person can make a choice. High-value circular economy participation traditionally involves complex steps: authenticating goods, estimating resale value, selecting a platform, arranging shipping and negotiating terms. AI reduces this friction by guiding consumers through each stage with predictive suggestions, automated pricing estimates and real-time risk assessments.

The authors show that when decision fluency increases, behavioural resistance decreases. Users begin to see resale and recycling as routine rather than burdensome. This shift in perception is crucial because circular markets depend on consistent and widespread participation; if only highly motivated users take part, the ecological and economic benefits remain limited. The study presents evidence that enhanced decision fluency significantly improves participation intentions across luxury resale markets.

Together, these two mechanisms, algorithm-curated social norms and AI-driven decision fluency, form the core of the study’s proposed model. They reveal how AI shapes both the social meaning and practical experience of circular behaviour, enabling large-scale behavioural change without direct intervention from regulators or policymakers.

Platforms can build dual-core AI systems to accelerate circular economy growth

Businesses seeking to expand circular economy participation should redesign their algorithmic systems around two strategic priorities: social motivation and cognitive efficiency. They describe this approach as a dual-core model capable of driving sustainable behaviour at scale.

On the social side, platforms can strengthen algorithm-curated norms to highlight circular participation as mainstream, desirable and responsible. This includes emphasizing sustainability themes, showcasing user success stories and foregrounding community engagement metrics. AI can tailor these signals to individual users, ensuring that the most influential norms appear in the right contexts.

On the cognitive side, platforms must refine decision-support tools to enhance fluency. This includes improving authentication technologies, providing transparent price forecasts, simplifying transaction steps and reducing uncertainty about quality or resale value. The more seamless the process, the more likely users are to develop habitual circular behaviour.

Trust remains a foundational component of both cores. The study shows that algorithmic trust influences users’ acceptance of social norms and their perception of decision ease. Platforms must therefore design AI interactions that demonstrate reliability, safety and fairness. Transparent data practices, consistent performance and user-controlled settings can strengthen this symbiotic trust relationship.