The Relationship Between Artificial Intelligence and Consumer Decision Making in the Context of Personalized Cosmetic Products
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2025.16.9.10Keywords:
Artificial Intelligence, Consumer Decision-Making, Personalized Marketing, Cosmetic Industry, Digital Literacy, Consumer Trust, Consumer PreferencesDimensions Badge
Issue
Section
License
Copyright (c) 2025 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Artificial intelligence, or AI, creates entirely new dimensions in combining consumer experiences via personal marketing instruments. This objective of the study is to explore the causal relationship between AI-based personalization and consumer behavior within the cosmetics sector. Further, the investigation looks into how AI acceptance and effectiveness in influencing purchase behaviour are dependent on factors such as digital literacy, demographic attributes, and trust. This study used a quantitative method with structured questionnaires, targeting women in Pune who have interacted with AI-based beauty applications. Data were analyzed on SPSS software by applying descriptive statistics, Cronbach’s Alpha for reliability, regression analysis, and ANOVA testing. The findings indicated a significant influence of AI personalization on consumer purchasing intent and trust. Digital literacy and ease of use were crucial for consumer engagement. Ethical and data privacy concerns were some of the barriers to hasty AI acceptance. The tendency of the cosmetic company to encourage and provide customer satisfaction and loyalty in a digital marketplace would be with transparency about ethical artificial intelligence use and user-centric personalization strategies.Abstract
How to Cite
Downloads
Similar Articles
- Sowmiya M, Banu Rekha B, Malar E, Ensemble classifiers with hybrid feature selection approach for diagnosis of coronary artery disease , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Nilesh Anute, Geetali Tilak, Revolutionizing e-Learning with AR, VR, And AI , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- Kunal Lanjekar, Prashant Kalshetti, Joe C. Lopez, Role of social media in lead generation , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- M. Deepika, I Antonitte Vinoline, Optimization of an Advanced Integrated Inventory Model Considering Shortages and Deterioration across Varying Demand Functions , The Scientific Temper: Vol. 16 No. 09 (2025): The Scientific Temper
- Pravin P. Adivarekar1, Amarnath Prabhakaran A, Sukhwinder Sharma, Divya P, Muniyandy Elangovan, Ravi Rastogi, Automated machine learning and neural architecture optimization , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Thangatharani T, M. Subalakshmi, Development of an adaptive machine learning framework for real-time anomaly detection in cybersecurity , The Scientific Temper: Vol. 16 No. 08 (2025): The Scientific Temper
- Kalyani K., Praveen Kumar T. D., Roopa A. N., AI-based tools for enhancing reflective practice and self-efficacy in pre-service teachers , The Scientific Temper: Vol. 16 No. 04 (2025): The Scientific Temper
- Merlin Sofia S, D. Ravindran, G. Arockia Sahaya Sheela, Clean Balance-Ensemble CHD: A Balanced Ensemble Learning Framework for Accurate Coronary Heart Disease Prediction , The Scientific Temper: Vol. 16 No. 10 (2025): The Scientific Temper
- Shantanu Kanade, Anuradha Kanade, Secure degree attestation and traceability verification based on zero trust using QP-DSA and RD-ECC , The Scientific Temper: Vol. 15 No. spl-2 (2024): The Scientific Temper
- Tassar Aniam, Sneha Kanade, A study on the inventory management of perishable products , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
<< < 4 5 6 7 8 9 10 11 12 13 > >>
You may also start an advanced similarity search for this article.

