Purpose: The purpose of this paper is to explore a hybrid model of the consumption of organic foods, combining the use of exploratory factor analysis (EFA) and an artificial neural network (ANN).Design/methodology/approach: The study has three phases. In the first phase, the Delphi method is employed, and 15 motives for the consumption of organic food are identified; these motives are used to develop the model in the second phase. Finally, in the last phase, an ANN is used to rank the motives to determine their priority.Findings: The EFA model explored includes four factors that have a positive effect on the level of organic food consumption. These are naturalness, trust, sanitariness and marketing. Results from the use of an ANN indicate that the main variables in organic food consumption are claims, psychological variables and doubt. From the results of the EFA model it is clear these three variables are components of the factor of trust.Practical implications: Marketers can use the model developed in this paper to satisfy the needs of their customers and hence enhance their market share and profitability. This study shows that improvements in truth in the claims made for organic products, perceived security from using these products and doubts about the safety of other foods can lead marketers to their goal. Informative advertisements can inculcate trust and naturalness among consumers as main factors.Originality/value: The main contribution of this study is the light it sheds on how consumers think about organic foods. It develops a model incorporating motives for consuming organic food and determining the priorities held by consumers of organic foods.

This study maps how Natural Language Processing (NLP) contributes to sustainability. Using a PRISMA‐guided review of 131 English‐language articles from Web of Science (2018–2025), we combine bibliometric co‐word mapping with BERTopic to derive complementary structural and semantic views. Four themes emerge: Topic 0—Climate Change Discourse and Energy Innovation (SDG 13, SDG 7); Topic 1—Sustainability Policy and Public Engagement (SDG 11, SDG 16); Topic 2—Tech‐Driven Environmental Solutions (SDG 6, SDG 9, SDG 15); and Topic 3—Corporate Sustainability and Sustainable Infrastructure (SDG 8, SDG 9, SDG 12). Social equity remains comparatively under‐covered, with limited attention to SDG 5 and SDG 10. Theoretically, we position NLP as a bridge that supports sensemaking, coordination, and accountability across the environmental, social, and economic pillars. Practically, our synthesis highlights deployable uses of NLP for real‐time narrative monitoring, environmental, social, governance (ESG) claim assessment, and horizon scanning for emerging risks and innovation niches. For policy, coupling narrative analytics with policy‐text tracking can create feedback loops that enhance communication, legitimacy, and policy‐mix coherence for SDG implementation. By jointly leveraging network‐based and embedding‐based evidence, the review reveals thematic frontiers, makes SDG linkages explicit, and offers a topic‐based agenda with testable propositions.