Education, Science, Technology, Innovation and Life
Open Access
Sign In

Consumer Behavior Based on Network Integration Data in the Context of Big Data

Download as PDF

DOI: 10.23977/infse.2025.060110 | Downloads: 21 | Views: 428

Author(s)

Chanjuan Li 1

Affiliation(s)

1 College of Digital Business, Jiangsu Vocational Institute of Commerce, Nanjing, 211168, Jiangsu, China

Corresponding Author

Chanjuan Li

ABSTRACT

In the modern society with the prevalence of online consumption behavior and Internet technology, online big data has penetrated into various industries. As people gradually enter the era of data explosion, the consumption mode of consumer behavior has changed to some extent, and there are more diversified ways. Based on the era background of Internet information explosion, this study studies the consumer behavior based on online consumption under the condition of big data integration from the perspective of big data integration, combined with the current consumption situation of online consumer behavior. Based on the literature research at home and abroad, combined with the specific practice of online consumption, this paper focuses on the factors of website platform affecting online consumption behavior. Through the empirical research on the relationship between individual differences of consumers, product factors, safety factors, convenience factors, interaction factors and third-party evaluation factors and consumer behavior, this paper finds out the main factors that consumers pay attention to and worry about in the process of online consumption, as well as the importance of these factors. Then it provides the corresponding theoretical basis for the marketing strategy formulated by the e-commerce website platform, and promotes the development of online shopping and online marketing. The experimental results of this paper show that the factors evaluated by others play a very important role in the impact of online consumer behavior. After the analysis and summary of the collected survey samples, the average values of sample identity are calculated, which are 4.51, 4.41 and 4.35 respectively, and the number of identity is more than 100. The second is security and interactivity, which is also important factors affecting consumer behavior.

KEYWORDS

Big Data Analysis, Network Integration, Consumer Behavior, Online Consumption, Intentional Factors

CITE THIS PAPER

Chanjuan Li, Consumer Behavior Based on Network Integration Data in the Context of Big Data. Information Systems and Economics (2025) Vol. 6: 63-71. DOI: http://dx.doi.org/10.23977/infse.2025.060110.

REFERENCES

[1] Bardhi F, Eckhardt G M. Liquid Consumption - why the consumer increasingly prefers access to goods and services over ownership[J]. Journal of Consumer Research, 2017, 44(3): 582-597. 
[2] Aagerup U, Nilsson J. Green consumer behavior: being good or seeming good?[J]. Journal of Product & Brand Management, 2016, 25(3): 274-284.
[3] Ribeiro R, HM Pinheirosant, J G Pádua, et al. Consumer behavior and the effects of the supply of french cultivars of potatoes [J]. Bioscience Journal, 2016, 32(2): 308-318.
[4] Gao L, Xiao J. Big Data Credit Report in Credit Risk Management of Consumer Finance[J]. Wireless Communications and Mobile Computing, 2021, 2021(4): 1-7.
[5] Ghose A, V Todri-Adamopoulos. Toward A Digital Attribution Model: Measuring The Impact Of Display Advertising On Online Consumer Behavior[J]. Mis Quarterly, 2016, 40(4): 889-910.
[6] Si Y. Research on the Balanced Relationship between Online Consumer Behavior and E-Commerce Service Quality Based on 5G Network[J]. Mobile Information Systems, 2021, 2021(5): 1-12.
[7] Zhang X, Liu H, Yao P. Research Jungle on Online Consumer Behaviour in the Context of Web 2.0: Traceability, Frontiers and Perspectives in the Post-Pandemic Era[J]. Journal of Theoretical and Applied Electronic Commerce Research, 2021, 16(5): 1740-1767.
[8] Demchenko M. Quantitative methods of studying the consumer needs and behavior in the context of modern marketing communications[J]. Communications and Communicative Technologies, 2019(19): 41-47.
[9] Isaac P, Cleverly J, Mchugh I, et al. OzFlux data: network integration from collection to curation[J]. Biogeosciences, 2017, 14(12): 1-41
[10] Wen Z. Analysis of the Impact of Interest Rate Changes on Chinese Consumer Behavior[J]. Asian Agricultural Research, 2019, 11(01): 30-32+36.
[11] Paynter N P, Ridker P M, Chasman D I. Are Genetic Tests for Atherosclerosis Ready for Routine Clinical Use?[J]. Circulation Research, 2016, 118(4): 607.
[12]Huang Q, Li X W. Research on the Design of Government Affairs Platform in the Context of Big Data[J]. Scientific Programming, 2021, 2021(12): 1-13.
[13] Krca M, Akku M. Internet Usage, Economic Growth And Electricity Consumption: The Case Of Eu-15[J]. Ekonomi Politika & Finans Araştırmaları Dergisi, 2020, 5(3): 576-594.
[14] Nobre G C, Tavares E. Scientific literature analysis on big data and internet of things applications on circular economy: a bibliometric study[J]. Scientometrics, 2017, 111(1): 463-492.
[15] Mahmoud M S, Mohamad A. A Study of Efficient Power Consumption Wireless Communication Techniques/ Modules for Internet of Things (IoT) Applications[J]. Advances in Internet of Things, 2016, 06(2): 19-29. 
[16] Yuan C, Wu C, Wang D, Yao S, & Feng Y. Review of Consumer-to-Consumer E-Commerce Research Collaboration. Journal of Organizational and End User Computing, 2021, 33(4): 167-184. 
[17] Xu A, Li Y, & Donta P K. Marketing decision model and consumer behavior prediction with deep learning. Journal of Organizational and End User Computing, 2024, 36(1): 1-25.

Downloads: 19144
Visits: 450613

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.