| Project ID |
BITS-SRIP/823039/2026 |
| Project Title |
Generative Ai and changing Indian consumer behaviour |
| Project Description |
Brief Description:
The rapid proliferation of Generative AI (ChatGPT, Midjourney, Gemini, etc.) has created a paradigm shift in digital consumption. While global discourse focuses on adoption rates, there is a significant research gap regarding the specific behavioral shifts within the Indian consumer demographic—a market characterized by unique cultural nuances, price sensitivity, and a mobile-first digital ecosystem.
This research project aims to empirically investigate how Indian consumers perceive, trust, and interact with GenAI-enabled products and services. Instead of traditional surveys, this study will employ Computational Social Science methods. We will utilize secondary data analysis by mining unstructured data from social media platforms (X/Twitter, Reddit, Quora), consumer review portals, and tech forums.
The study will focus on:
Sentiment Decomposition: Moving beyond positive/negative binary classification to identify specific emotions (anxiety, excitement, skepticism, trust) associated with GenAI adoption in India.
Topic Modeling: Using techniques like Latent Dirichlet Allocation (LDA) or BERTopic to extract latent themes in consumer discourse (e.g., privacy concerns vs. productivity hacks).
Conceptual Framework: Developing a theoretical model that links these mined insights to established marketing theories (such as the Technology Acceptance Model or Trust-Commitment Theory) to predict future behavioral patterns.
Scope of Work for the Intern:
Phase 1 (Weeks 1-2): Targeted literature review to identify current gaps in AI perception literature and refining search queries/keywords.
Phase 2 (Weeks 3-4): Developing and executing Python-based web scrapers (using libraries like Selenium, Scrapy, or official APIs) to curate a dataset of Indian consumer discussions regarding GenAI.
Phase 3 (Weeks 5-6): Data Pre-processing and Natural Language Processing (NLP). The intern will perform cleaning, tokenization, and apply sentiment analysis/topic modeling algorithms.
Phase 4 (Weeks 7-8): Visualizing insights and co-developing the Conceptual Model based on the data findings.
Expected Outcomes:
Novel Dataset: A cleaned, structured dataset of Indian consumer sentiment toward GenAI (approx. 10k-50k data points).
Conceptual Model: A scientifically grounded framework illustrating the drivers and barriers of GenAI acceptance in India.
Research Manuscript: A high-quality research paper draft targeting Q1/Q2 Scimago-ranked journals (e.g., Journal of Consumer Behaviour, Computers in Human Behavior, or Journal of Business Research).
Skill Acquisition: The student will gain hands-on experience in Text Mining, NLP, Academic Writing, and Marketing Analytics.
Prerequisites:
Strong proficiency in Python (specifically NLP libraries like NLTK/Spacy/Pandas) is preferred. An interest in Consumer Psychology or Marketing Science is essential. |
| Project Discipline |
Any but having an interest in Marketing and Consumer Behaviour |
| Faculty Name |
Achint Nigam |
| Department |
Department of Management |
