Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Mukherjee, Ranit; Natarajan, Arul Kumar Title A name given to the resource A Comprehensive Analysis of the Impact of Demographic, Socioeconomic Factors, and Social Media Advertisements on Consumers Online Shopping Behavior Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Electrical Engineering;Volume;1269;pp.407-424 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-97-9515-4_31" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-9515-4_31</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105003895394?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105003895394?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Mukherjee R., Christ University, Bengaluru, India; Natarajan A.K., Samarkand International University of Technology, Samarkand, Uzbekistan Description An account of the resource This research comprehensively explores the intricate relationship between social media, demographic factors, socioeconomic status, and online shopping behavior. It investigates how various elements, such as brand, quality, price, and reviews, intersect to influence consumers virtual shopping experiences. Recognizing the diversity among consumer populations, the study considers demographic traits like age, gender, and socioeconomic status. It identifies a research gap concerning the moderating effects of these factors on virtual shopping behavior and the influence of different advertisement sources. The research proposes a methodology involving machine learning approaches to address these gaps. Using a dataset collected from 331 respondents in Kolkata, the study employs statistical tests and machine learning models to analyze the correlations and predict online shopping behavior. The results underscore the importance of understanding consumer behavior in the evolving e-commerce landscape. Logistic Regression emerges as the most accurate algorithm for predicting online shopping decisions. Additionally, the research investigates the reasons limiting consumers use of online shopping platforms, providing practical insights for tailored marketing strategies. These findings empower businesses to adapt their marketing strategies to the evolving landscape of consumer behavior. Overall, the study enhances our understanding of the multifaceted variables shaping customers decisions in online marketplaces. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Consumers; E-Commerce; Machine learning; Marketing; Neural network; Online shopping; Polychoric correlation Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 18761100; ISBN: 978-981979514-7; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online