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Big Data Analysis in Consumer Behavior: Evidence from Social Media and Mobile Payment
- 1 Sichuan University
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Abstract
Recently, along with the continuous progress of science and technology, big data analytics has started to be applied in all works of life. From the perspective of consumer behavior studies, big data analytics is a tool that can effectively predict future consumer behavior. Consumer behavior studies is a discipline that focuses on understanding the behavior and motivations of consumers during the process of purchasing products or services. In the past, researchers mainly relied on traditional research methods such as questionnaire surveys and field observations to understand consumer behavior and needs. When the Internet era arrives, the amount of data generated by consumers has exploded, providing vast opportunities for the application of big data analytics. This article provides a brief overview of the application of big data analytics in analyzing consumer behavior in social media and mobile payment, and briefly reveals the conclusions drawn from these analyses. It aims to provide a small contribution to further understanding the application of big data analytics in the field of consumer behavior studies.
Keywords
Big data analytics, consumer behavior, social media, mobile payment
[1]. Liu, H., Huang, F., and Ye, R. (2020). Field experiments in consumer behavior research: concept, operational introduction and implementation suggestions. Foreign Economics and Management, 42(3), 35-56.
[2]. Corea, F. (2016). Big data analytics: A management perspective (Vol. 21). Switzerland: Springer.
[3]. Erevelles, S., Fukawa, N., and Swayne, L. (2016). Big Data consumer analytics and the transformation of marketing. Journal of business research, 69(2), 897-904.
[4]. Chaudhary, K., Alam, M., Al-Rakhami, M.S., and Gumaei, A. (2021). Machine learning-based mathematical modelling for prediction of social media consumer behavior using big data analytics. Journal of Big Data, 8(1), 1-20.
[5]. Chu, S.C., Chen, H.T., and Gan, C. (2020). Consumers’ engagement with corporate social responsibility (CSR) communication in social media: Evidence from China and the United States. Journal of Business Research, 110, 260-271.
[6]. Bailey, A.A., Bonifield, C.M., and Elhai, J.D. (2021). Modeling consumer engagement on social networking sites: Roles of attitudinal and motivational factors. Journal of Retailing and Consumer Services, 59, 102348.
[7]. Hu, Y., Xu, A., Hong, Y., Gal, D., Sinha, V., and Akkiraju, R. (2019). Generating business intelligence through social media analytics: Measuring brand personality with consumer-, employee-, and firm-generated content. Journal of Management Information Systems, 36(3), 893-930.
[8]. Salehan, M., and Kim, D.J. (2016). Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics. Decision Support Systems, 81, 30-40.
[9]. Teng, S., and Khong, K.W. (2021). Examining actual consumer usage of E-wallet: A case study of big data analytics. Computers in Human Behavior, 121, 106778.
[10]. Oliveira, T., Thomas, M., Baptista, G., and Campos, F. (2016). Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology. Computers in human behavior, 61, 404-414.
[11]. Bailey, A.A., Bonifield, C. M., Arias, A., and Villegas, J. (2022). Mobile payment adoption in Latin America. Journal of Services Marketing, 36(8), 1058-1075.
[12]. Karimi, S., and Liu, Y.L. (2020). The differential impact of “mood” on consumers’ decisions, a case of mobile payment adoption. Computers in Human Behavior, 102, 132-143.
Cite this article
Li,Y. (2023). Big Data Analysis in Consumer Behavior: Evidence from Social Media and Mobile Payment. Advances in Economics, Management and Political Sciences,64,269-275.
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Volume title: Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
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