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Analysis of users’ impulse purchase behavior based on data mining for e-commerce live broadcast

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Abstract

Based on e-commerce live broadcast data, this paper, with the support of big data technology, aims to explore the impact of Internet celebrities on consumers' impulse purchase behavior, analyzing the relevant factors. According to big data technology, this paper carries out e-commerce live broadcast big data processing and constructs the Internet celebrity marketing model. This paper, with the support of the model, analyzes the impact of Internet celebrities on consumers' impulse purchase behavior. Through the data collected, this paper, from both positive and negative aspects, analyzes the impact of Internet celebrities on consumers. Judging by the experimental research, the data mining approaches proposed here can play a certain effect in the analysis of the impact of Internet celebrities on consumer impulse purchase behavior. According to the experimental analysis of mathematical statistics, Internet celebrity consumption has become one of the important consumption forms at present.

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All data generated or analyzed during this study are included in the manuscript.

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References

  1. Malthouse, E. C., & Li, H. (2017). Opportunities for and pitfalls of using big data in advertising research. Journal of Advertising, 46(2), 227–235.

    Article  Google Scholar 

  2. Lakhan, G. R. (2021). Factors affecting consumer purchase intention: live streaming commerce. Psychology and Education Journal, 58(5), 1110–1120.

    Google Scholar 

  3. Aggarwal, K., Khoa, B. T., Sagar, K. D., Agrawal, R., Dhingra, M., & Dhingra, J. (2023). Marketing information system based on unsupervised visual data to manage transportation industry using signal processing. Expert Systems. https://doi.org/10.1111/exsy.13384

    Article  Google Scholar 

  4. Ye, D., Muthu, B., & Kumar, P. M. (2022). Identifying buying patterns from consumer purchase history using big data and cloud computing. International Journal of Distributed Systems and Technologies (IJDST)., 13(7), 1–9.

    Article  CAS  Google Scholar 

  5. Niu, Y., Ying, L., Yang, J., Bao, M., & Sivaparthipan, C. B. (2021). Organizational business intelligence and decision making using big data analytics. Information Processing & Management., 58(6), 102725.

    Article  Google Scholar 

  6. Wei, W., Sivaparthipan, C. B., & Kumar, P. M. (2022). Online shopping behavior analysis for smart business using big data analytics and blockchain security. International Journal of Modeling, Simulation, and Scientific Computing., 13(04), 2250053.

    Article  Google Scholar 

  7. Truby, J., & Brown, R. (2021). Human digital thought clones: The Holy Grail of artificial intelligence for big data[J]. Information & Communications Technology Law, 30(2), 140–168.

    Article  Google Scholar 

  8. Ying, Y. (2020). A study on the influence of cognitive on repurchase intension of new e-commerce system: Focused on the mediation effect of consumer satisfaction and quasi social relations. Journal of The Korea Society of Computer and Information, 25(8), 189–196.

    Google Scholar 

  9. Ali, B. J., & Anwar, G. (2021). Marketing strategy: Pricing strategies and their influence on consumer purchasing decisions. International Journal of Rural Development, Environment and Health Research, 5(2), 26–39.

    Article  MathSciNet  Google Scholar 

  10. Belk, R. W. (2017). Qualitative research in advertising. Journal of Advertising, 46(1), 36–47.

    Article  Google Scholar 

  11. Villamediana-Pedrosa, J. D., Vila-Lopez, N., & Küster-Boluda, I. (2019). Secrets to design an effective message on facebook: An application to a touristic destination based on big data analysis. Current Issues in Tourism, 22(15), 1841–1861.

    Article  Google Scholar 

  12. Sharma, R., Ahuja, V., & Alavi, S. (2018). The future scope of netnography and social network analysis in the field of marketing. Journal of Internet Commerce, 17(1), 26–45.

    Article  ADS  Google Scholar 

  13. Wachyuni, S. S., & Priyambodo, T. K. (2020). The influence of celebrity endorsement in restaurant product purchase decision-making. International Journal of Management, Innovation & Entrepreneurial Research, 6(2), 45–54.

    Article  Google Scholar 

  14. Sudha, M., & Sheena, K. (2017). Impact of influencers in consumer decision process: The fashion industry. SCMS Journal of Indian Management, 14(3), 14–30.

    Google Scholar 

  15. Liu, X., Burns, A. C., & Hou, Y. (2017). An investigation of brand-related user-generated content on Twitter. Journal of Advertising, 46(2), 236–247.

    Article  Google Scholar 

  16. Sunarjo, W. A., Nurhayati, S., & Muhardono, A. (2021). Consumer impulse purchase behavior toward adoption of mobile payment: A case study in Indonesia during the COVID-19 pandemic. The Journal of Asian Finance, Economics and Business, 8(4), 581–590.

    Google Scholar 

  17. Liu, Y., Qu, Y., Lei, Z., et al. (2017). Understanding the evolution of sustainable consumption research. Sustainable Development, 25(5), 414–430.

    Article  Google Scholar 

  18. Ali, B. J. (2020). Impact of COVID-19 on consumer buying impulse purchase behavior toward online shopping in Iraq. Economic Studies Journal, 18(42), 267–280.

    MathSciNet  Google Scholar 

  19. Schmidt, A. L., Zollo, F., Del Vicario, M., et al. (2017). Anatomy of news consumption on facebook. Proceedings of the National Academy of Sciences, 114(12), 3035–3039.

    Article  ADS  CAS  Google Scholar 

  20. Alfredsson, E., Bengtsson, M., Brown, H. S., et al. (2018). Why achieving the Paris agreement requires reduced overall consumption and production. Sustainability: Science, Practice and Policy, 14(1), 1–5.

    Google Scholar 

  21. Ness, D. A., & Xing, K. (2017). Toward a resource-efficient built environment: A literature review and conceptual model. Journal of Industrial Ecology, 21(3), 572–592.

    Article  Google Scholar 

  22. Bruel, A., Kronenberg, J., Troussier, N., et al. (2019). Linking industrial ecology and ecological economics: A theoretical and empirical foundation for the circular economy. Journal of Industrial Ecology, 23(1), 12–21.

    Article  Google Scholar 

  23. Lee, C. H., & Chen, C. W. (2021). Impulse buying behaviors in live streaming commerce based on the stimulus-organism-response framework [J]. Information, 12(6), 241.

    Article  MathSciNet  Google Scholar 

  24. Yi, Q., Khan, J., Su, Y., Tong, J., & Zhao, S. (2023). Impulse buying tendency in live-stream commerce: The role of viewing frequency and anticipated emotions influencing scarcity-induced purchase decision[J]. Journal of Retailing and Consumer Services, 75, 103534.

    Article  Google Scholar 

  25. Min, Y., & Tan, C. C. (2022). Research and application of compulsive buying behaviors of consumers in e-commerce livestreaming on big data. Human-Centric Intelligent Systems, 2(3–4), 113–123.

    Article  Google Scholar 

  26. Lin, S. T., & Niu, H. J. (2018). Green consumption: Environmental knowledge, environmental consciousness, social norms, and purchasing impulse purchase behavior. Business Strategy and the Environment, 27(8), 1679–1688.

    Article  Google Scholar 

  27. Evans, D. M. (2019). What is consumption, where has it been going, and does it still matter? The Sociological Review, 67(3), 499–517.

    Article  Google Scholar 

  28. Dwyer, T., & Martin, F. (2017). Sharing news online: E-commerce live broadcast news analytics and their implications for media pluralism policies. Digital journalism, 5(8), 1080–1100.

    Article  Google Scholar 

  29. Iqbal, S., Bilal, A. R., Nurunnabi, M., et al. (2021). It is time to control the worst: Testing the COVID-19 outbreak, energy consumption, and CO2 emission. Environmental Science and Pollution Research, 28(15), 19008–19020.

    Article  CAS  PubMed  Google Scholar 

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Funding

The Project of Excellent Innovation Team of Soochow University “Local Government and Social Governance” (project number: NH33710921) and “Collaborative Innovation Center for New Urbanization and Social Governance of Universities in Jiangsu” (project number: ISX10200118).

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Authors and Affiliations

  1. Business School, Suzhou University of Science and Technology, Suzhou, 215009, China

    Yumei Wang

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  1. Yumei Wang
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All Authors contributed to the design and methodology of this study, the assessment of the outcomes, and the writing of the manuscript.

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Correspondence to Yumei Wang.

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Wang, Y. Analysis of users’ impulse purchase behavior based on data mining for e-commerce live broadcast. Electron Commer Res (2024). https://doi.org/10.1007/s10660-024-09820-x

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