Table 5 The overview of future research...

Table 5

The overview of future research recommendations

AI domain in sustainable food consumption	Research gaps/future directions
AI for Product Discovery and Choice Navigation (e.g. smart filters, recommender systems, recipe engines)	To develop and compare ethically guided and commercially driven recommendation systems To explore how diversity in recommendations affects habit formation, disruption, and exploration of sustainable options
AI for Food Label Interpretation and Sustainability Signaling (e.g. eco-feedback, carbon footprint visualization, XAI)	To evaluate deep cognitive load and trust associated with different AI solutions in product packaging To conduct longitudinal studies and monitoring experiments to measure real-life learning effects from repeated exposure to such solutions To investigate whether consumers trust AI-generated sustainability labels
AI for Health Nudging and Dietary Guidance (e.g. nutrition chatbots, meal planners, feedback dashboards)	To explore perceptions and trust in AI as a dietary authority? To explore the intersections between personal identity, reported habits, and developed AI solutions To test motivational AI solutions in the longitudinal experimental design to grasp the real value of such beahviour change inventions
AI for Smart Household Food Management (e.g. smart bins, fridge monitors, inventory alerts)	To assess how smart home systems affect long-term sustainability outcomes, such as the reduction of food waste and the use of sustainable products To investigate barriers to the adoption of AI tools among households with diverse socio-economic backgrounds and an aging population To address user perception and feelings related to automated alerts
AI for Social Engagement and Value Co-Creation (e.g. gamified challenges, citizen science, peer benchmarking)	To uncover basic notions, such as whether these platforms create sustainable habits or just short-term engagement, we will examine the effect of gamified AI features (badges, challenges, streaks) on habit retention To test whether social comparison tools increase pro-environmental behavior across different social groups

AI domain in sustainable food consumption	Research gaps/future directions
AI for Product Discovery and Choice Navigation (e.g. smart filters, recommender systems, recipe engines)	To develop and compare ethically guided and commercially driven recommendation systems To explore how diversity in recommendations affects habit formation, disruption, and exploration of sustainable options
AI for Food Label Interpretation and Sustainability Signaling (e.g. eco-feedback, carbon footprint visualization, XAI)	To evaluate deep cognitive load and trust associated with different AI solutions in product packaging To conduct longitudinal studies and monitoring experiments to measure real-life learning effects from repeated exposure to such solutions To investigate whether consumers trust AI-generated sustainability labels
AI for Health Nudging and Dietary Guidance (e.g. nutrition chatbots, meal planners, feedback dashboards)	To explore perceptions and trust in AI as a dietary authority? To explore the intersections between personal identity, reported habits, and developed AI solutions To test motivational AI solutions in the longitudinal experimental design to grasp the real value of such beahviour change inventions
AI for Smart Household Food Management (e.g. smart bins, fridge monitors, inventory alerts)	To assess how smart home systems affect long-term sustainability outcomes, such as the reduction of food waste and the use of sustainable products To investigate barriers to the adoption of AI tools among households with diverse socio-economic backgrounds and an aging population To address user perception and feelings related to automated alerts
AI for Social Engagement and Value Co-Creation (e.g. gamified challenges, citizen science, peer benchmarking)	To uncover basic notions, such as whether these platforms create sustainable habits or just short-term engagement, we will examine the effect of gamified AI features (badges, challenges, streaks) on habit retention To test whether social comparison tools increase pro-environmental behavior across different social groups

Note(s): The table synthesizes research gaps identified across AI mechanisms in food consumption, outlining directions for studies that can advance theory, improve consumer engagement, and strengthen sustainability outcomes

Source(s): Authors’ own

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