Artificial Intelligence Driven Sustainable Consumption Decisions of Generation Z: A Systematic Literature Review

Supriya Sahu; Sushil Kumar Sahoo; Bibhuti Bhusan Choudhury; Prasant Ranjan Dhal; Ipsita Dhar; Monika Imreh-Tóth

doi:10.31181/dma0002026171

Authors

Supriya Sahu Department of Mechanical Engineering, Indira Gandhi Institute of Technology, Sarang 759146, Odisha, India https://orcid.org/0000-0002-1823-9568
Sushil Kumar Sahoo Department of Mechanical Engineering, Indira Gandhi Institute of Technology, Sarang 759146, Odisha, India https://orcid.org/0000-0002-8551-7353
Bibhuti Bhusan Choudhury Department of Mechanical Engineering, Indira Gandhi Institute of Technology, Sarang 759146, Odisha, India https://orcid.org/0000-0002-2990-3911
Prasant Ranjan Dhal Department of Mechanical Engineering, Indira Gandhi Institute of Technology, Sarang 759146, Odisha, India https://orcid.org/0000-0002-7402-2187
Ipsita Dhar Department of Mechanical Engineering, Indira Gandhi Institute of Technology, Sarang 759146, Odisha, India https://orcid.org/0009-0007-4086-8137
Monika Imreh-Tóth Széchenyi István University, Győr, Hungary https://orcid.org/0000-0002-0094-5827

DOI:

https://doi.org/10.31181/dma0002026171

Keywords:

Decision Support Systems, MCDM, Green Consumer Behaviour, Sustainable Consumption, Artificial Intelligence, Generation Z

Abstract

The need to engage in sustainable consumption is increasing, and extrapolating. Consumer decision-making on artificial intelligence (AI) is changing how people make decisions and consume products, providing better and more personalized advice based on data. Being a digital-native and sustainability-sensitive generation, Generation Z is one of the key factors that determine the future consumption trends; nevertheless, current studies tend to view AI and sustainable consumption in isolation. The given study attempts to fill this gap by applying systematic literature review with the help of the Dimensions.ai database. An initial list of 2,606 records was then found and 876 publications were selected based on PRISMA-based screening. The thematic and bibliometric analysis demonstrate such topics such as AI-based recommendation systems, influence on behaviour, trust and explainability, and connection with multi-criteria decision-making (MCDM). The research reveals that there exist substantial gaps in the combined framework and real-life application, and suggests a theoretical framework of the connection between AI, decision-making and sustainable consumption. The results provide useful information that researchers and practitioners can use to design smart systems that can spur sustainable consumption among the generation z.

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