F1000Research (2025) — Predicting Luxury Product Adoption via Instagram Marketing (ML approach)

Summary:

This study looks at what drives people to adopt luxury products through Instagram marketing.

It asks a simple question: do basic Instagram activity signals matter most, or do deeper things like trust, prestige, and personalization matter more?

It was written by researchers from Indian universities. The authors wanted to test which factors link with luxury product adoption, and they also wanted to see whether machine learning could predict who is more likely to adopt.

They used a mixed-methods survey study.

The team collected primary data from 205 respondents in India using an online Google Forms questionnaire.

The sample was purposive, aimed at people who use Instagram in the context of luxury product purchases.

The survey asked closed questions on Instagram use, trust, prestige, uniqueness, loyalty, and demographics, plus open-ended questions for sentiment. The authors then ran several models, including linear regression, logistic regression, path analysis, support vector machines, decision trees, and lexicon-based sentiment analysis.

The biggest takeaway is that simple engagement measures did not predict luxury product adoption in their regression model. Time spent on Instagram, influencer marketing, sponsored ads, story ads, and interactive content were all not significant there.

In contrast, personalized recommendations were one of the strongest predictors of purchase intention. Brand trust was also a strong predictor, and brand prestige mattered too. Brand uniqueness pointed in a positive direction but was not statistically significant in that model.