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Big Data, the perfect instrument to study today's consumer behavior

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  • Cristina STOICESCU

    (University of Economic Studies, Bucharest, Romania)

Abstract

Consumer behavior study is a new, interdisciplinary and emerging science, developed in the 1960s. Its main sources of information come from economics, psychology, sociology, anthropology and artificial intelligence. If a century ago, most people were living in small towns, with limited possibilities to leave their community, and few ways to satisfy their needs, now, due to the accelerated evolution of technology and the radical change of life style, consumers begin to have increasingly diverse needs. At the same time the instruments used to study their behavior have evolved, and today databases are included in consumer behavior research. Throughout time many models were developed, first in order to analyze, and later in order to predict the consumer behavior. As a result, the concept of Big Data developed, and by applying it now, companies are trying to understand and predict the behavior of their consumers.

Suggested Citation

  • Cristina STOICESCU, 2016. "Big Data, the perfect instrument to study today's consumer behavior," Database Systems Journal, Academy of Economic Studies - Bucharest, Romania, vol. 6(3), pages 28-42, January.
    • Handle: RePEc:aes:dbjour:v:6:y:2016:i:3:p:28-42
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    File URL: http://www.dbjournal.ro/archive/21/21_4.pdf
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    References listed on IDEAS

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    1. Daniel Kahneman & Richard H. Thaler, 2006. "Anomalies: Utility Maximization and Experienced Utility," Journal of Economic Perspectives, American Economic Association, vol. 20(1), pages 221-234, Winter.
    2. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    3. Mircea Răducu TRIFU & Mihaela Laura IVAN, 2014. "Big Data: present and future," Database Systems Journal, Academy of Economic Studies - Bucharest, Romania, vol. 5(1), pages 32-41, May.
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    Cited by:

    1. Thi Mai Le & Shu-Yi Liaw, 2017. "Effects of Pros and Cons of Applying Big Data Analytics to Consumers’ Responses in an E-Commerce Context," Sustainability, MDPI, vol. 9(5), pages 1-19, May.
    2. Muhammad Imran & Waseem ul Hameed & Adnan ul Haque, 2018. "Influence of Industry 4.0 on the Production and Service Sectors in Pakistan: Evidence from Textile and Logistics Industries," Social Sciences, MDPI, vol. 7(12), pages 1-21, November.
    3. Ian Sutherland & Youngseok Sim & Seul Ki Lee & Jaemun Byun & Kiattipoom Kiatkawsin, 2020. "Topic Modeling of Online Accommodation Reviews via Latent Dirichlet Allocation," Sustainability, MDPI, vol. 12(5), pages 1-15, February.

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