Human-Centric Consumption Intelligence and Explainable AI (HCCI-XAI): Framework for Aspirational Consumption and Responsible Consumer Behaviour in Industry 5.0

Abstract

The connection between purchase behaviour, social identity, financial decision making and psychosocial well-being in the lives of young adults has changed due to the digitally mediated consumption. Young buyers consider several factors in the product evaluation process which include: words of the influencers, Brand image, Social media trends, product impressions, Social bandwagon effect and peer feedback as well. These are the advancements driven by automation and have been supported by Artificial Intelligence and advanced ML techniques to motivate consumers to make informed purchase decisions. From the consumers' perspectives these are all opportunities leading to “Self-expression” however raising questions related to financial pressure, consumer autonomy and the over interference of digital technologies in consumer’s purchase decision. In the new paradigm of Industry 5.0 consumers and machines interacted and coordinated with each other to facilitate the purchase process. However in the upcoming evolution of industry 6.0 three core elements: Human centric, Sustainability and Resilience shape up the entire digital ecosystem. Thus this study aims to create a Human-Centric Consumption Intelligence framework with Explainable Artificial Intelligence (HCCI-XAI) to explore the behavioural determinants of aspirational purchasing among the post-graduates students of Bengaluru city, India. The research was based on structured primary data collected in the period of March-July 2026 with the participation of 386 survey respondents and 35 recorded variables. The six behavioural constructs studied in this article include: Masstige Perception, Symbolic Consumption Orientation, Value Consciousness, Consumer Orientation, Digital Influence and Ethical Orientation. Statistical tools such as descriptive statistics, Spearman's rank correlation, Ridge regression, Random Forest regression, repeated cross-validation and K-means clustering have been used.  Findings show that the most strongly endorsed construct is Value Consciousness (M = 4.210), and the construct with the highest positive correlation with Purchase Intention is Ethical Orientation (ρ = 0.475, p < 0.001). Ridge regression has a mean cross-validated coefficient of determination value of 0.3151 and a Random Forest of 0.2805 under the reported model configurations. K means clustering finds two clusters with a silhouette coefficient of 0.2485, indicating that there is moderate behavioural differentiation. The results show that the intention to purchase is linked to ethical concerns, digital influence and symbolic motives and affordability assessments. The study further contributes to Industry 5.0 research by showing the applicability of Machine Learning with Human-Centric Consumer Intelligence. It does not include validated mental health outcomes and its contribution to psychological wellbeing as it remains conceptual rather than diagnostic. The research creates an empirical basis to further explore the prime constructs such as: consumption behavior, financial well-being, psychosocial development, and preventive digital health promotion.