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This paper intends to contribute to the evolving understanding of Indian adolescents as consumers by examining their unique relationships with food brands, focusing specifically on brand love. It further investigates the key drivers that cultivate brand love among adolescents and explores the resulting outcomes of brand love.

About 37 in-depth interviews, including three exercises, were conducted with adolescents aged 11–16. The first and second exercises used projective techniques to explore respondents’ culture-bound love relationships with their favorite brands. Using the laddering technique, the third exercise investigated the critical drivers of respondents’ brand love.

The study reveals that adolescents derive value through attribute-benefit-value linkages from the consumption experience, leading to brand love. The customer value–brand love dynamics result in adolescents’ customer engagement behavior. Additionally, Indian adolescent customers seek brand consumption as a medium to instate their social identity and achieve hedonic pleasure from the experience. The study highlights the role of socialization and attitudinal autonomy in shaping adolescent–brand interactions.

The study could be relevant for both academicians and practitioners as they unveil the consumer psychology of contemporary adolescents in emerging countries like India and how similar or different they are from adult consumers. Also, there are very few adolescent–brand relationship studies in the past that have been deliberated in the context of food brands. Brand managers may design their product development and communication appeals around higher levels of abstraction in the attribute-benefit-value linkages discovered by this study.

This study aims to investigate factors that influence individuals’ continuance intention to use massive open online courses (MOOCs) by using machine learning models.

Data was collected from 702 MOOC users from major metropolitan cities in India through a network-based sampling and recruitment via various social media outlets (e.g. LinkedIn and Facebook). Various machine learning algorithms along with explainable artificial intelligence (XAI) were employed using Python PyCaret.

Results confirm that pedagogy value, content value, interface value, ubiquity value, teacher presence and learning satisfaction have positive effects on the continuous intention of MOOCs. Furthermore, pedagogy value is a chief driving force of CI of MOOCs. XAI helps clarify intricate patterns in learner data, thus allowing more appropriate interventions.

The findings of the study would be useful for MOOC developers to formulate better value propositions for ensuring a sustainable business and higher growth rate in the market.

This study bridges the gap in the existing literature by providing a novel approach. To the best of the authors’ knowledge, this is the first study using machine learning and XAI earlier in identifying factors leading to continuous intentions of MOOCs, so this research adds value to the existing method of exploring factors enhancing retention rates among learners.