Open Access Journal Article

Discovering online Chinese consumers’ impulse buying in live streaming by the theory of planned behavior

by Lifu Li a,* orcid  and  Kyeong Kang a orcid
a
School of Professional Practice and Leadership, University of Technology Sydney, Sydney, Australia
*
Author to whom correspondence should be addressed.
JEA  2024 3(2):60; https://doi.org/10.58567/jea03020008
Received: 7 July 2023 / Accepted: 25 July 2023 / Published Online: 15 June 2024
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Abstract

The study explores online consumers’ impulse buying intentions and behaviors on live streaming platforms. Unlike traditional shopping modes, the development of real-time video streaming provides online consumers with a distinct approach to interacting with live streamers and browsing online products in real-time, potentially causing their impulse buying intentions. To understand online consumers’ impulse buying intentions and behaviors, the paper establishes the research model based on the theory of planned behavior (TPB) model and analyses influencing factors from attitude, subject norm and perceived control aspects. Through the data analysis based on the partial least squares path modelling and variance-based structural equation modelling (PLS-SEM), the research results show that, three factors positively affect online consumers’ impulse buying intentions and lead to their final behaviors. Meanwhile, control variables, including gender, age, and income level, demonstrate insignificant effects across the model. Unlike existing literature, the current study displays the distinct features of live streaming platforms and discovers online consumers’ impulse buying intention based on the TPB model. The results are helpful for related scholars and departments to pay more attention to the live shopping environment and understand online consumers’ impulse buying issues.

Keywords: Live streaming platform; online Chinese consumers; impulse buying intention; TPB model; live shopping environment;
Copyright: © 2024 by Li and Kang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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ACS Style
Li, L.; Kang, K. Discovering online Chinese consumers’ impulse buying in live streaming by the theory of planned behavior. Journal of Economic Analysis, 2024, 3, 60. https://doi.org/10.58567/jea03020008
AMA Style
Li L, Kang K. Discovering online Chinese consumers’ impulse buying in live streaming by the theory of planned behavior. Journal of Economic Analysis; 2024, 3(2):60. https://doi.org/10.58567/jea03020008
Chicago/Turabian Style
Li, Lifu; Kang, Kyeong 2024. "Discovering online Chinese consumers’ impulse buying in live streaming by the theory of planned behavior" Journal of Economic Analysis 3, no.2:60. https://doi.org/10.58567/jea03020008
APA style
Li, L., & Kang, K. (2024). Discovering online Chinese consumers’ impulse buying in live streaming by the theory of planned behavior. Journal of Economic Analysis, 3(2), 60. https://doi.org/10.58567/jea03020008

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References

  1. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
  2. Aminu, I. M., & Shariff, M. N. M. (2014). Strategic orientation, access to finance, business environment and SMEs performance in Nigeria: Data screening and preliminary analysis. European Journal of Business and Management, 6(35), 124-132. https://core.ac.uk/reader/234626040
  3. Apasrawirote, D., & Yawised, K. (2022). Factors Influencing the Behavioral and Purchase Intention on Live-streaming Shopping. Asian Journal of Business Research, 12(1), https://doi.org/39. 10.14707/ajbr.220119
  4. Asghar, M. Z., Arif, S., Iqbal, J., & Seitamaa-Hakkarainen, P. (2022). Social Media Tools for the Development of Pre-Service Health Sciences Researchers during COVID-19 in Pakistan. International Journal of Environmental Research and Public Health, 19(1), 581. https://doi.org/10.3390/ijerph19010581
  5. Baker Qureshi, P. A., Murtaza, F., & Kazi, A. G. (2019). The impact of social media on impulse buying behavior in Hyderabad Sindh Pakistan. International Journal of Entrepreneurial Research, 2(2), 8-12. https://orcid.org/0000-0002-6354-7391
  6. Chan, T. K., Cheung, C. M., & Lee, Z. W. (2017). The state of online impulse-buying research: A literature analysis. Information & Management, 54(2), 204-217. https://doi.org/10.1016/j.im.2016.06.001
  7. Chen, H. S., Liang, C. H., Liao, S. Y., & Kuo, H. Y. (2020). Consumer attitudes and purchase intentions toward food delivery platform services. Sustainability, 12(23), 10177. https://doi.org/10.3390/su122310177
  8. Cheng, H. H., & Huang, S. W. (2013). Exploring antecedents and consequence of online group-buying intention: An extended perspective on theory of planned behavior. International Journal of Information Management, 33(1), 185-198. https://doi.org/10.1016/j.ijinfomgt.2012.09.003
  9. Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information Systems Research, 14(2), 189-217. https://doi.org/10.1287/isre.14.2.189.16018
  10. Chin, W.W. (1998). Commentary: Issues and opinion on structural equation modeling. JSTOR. https://www.jstor.org/stable/249674
  11. CNNIC. (2020). The 46th China statistical report on internet development. China Internet Network Information Center. https://www.cnnic.com.cn/IDR/ReportDownloads/202012/P020201201530023411644.pdf
  12. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104
  13. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. (2010). Multivariate data analysis (7th ed.). Pearson Education Limited. https://www.drnishikantjha.com/papersCollection/Multivariate%20Data%20Analysis.pdf
  14. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (1998). Multivariate data analysis. Prentice Hall. https://books.google.com.au/books/about/Multivariate_Data_Analysis.html?id=-ZGsQgAACAAJ&redir_esc=y
  15. Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24. https://doi.org/10.1108/EBR-11-2018-0203
  16. Kang, K., Li, L., & Sohaib, O. (2023). Graduates' intention to develop live commerce: The educational background perspective using multi-group analysis. Entrepreneurial Business and Economics Review, 11(1), 113-126. https://doi.org/10.15678/EBER.2023.110106
  17. Kang, K., Lu, J., Guo, L., & Li, W. (2021). The dynamic effect of interactivity on customer engagement behavior through tie strength: Evidence from live streaming commerce platforms. International Journal of Information Management, 56, 102251. https://doi.org/10.1016/j.ijinfomgt.2020.102251
  18. Li, L., & Kang, K. (2022). Impact of opportunity and capability on e-entrepreneurial motivation: a comparison of urban and rural perspectives. Journal of Entrepreneurship in Emerging Economies. https://doi.org/10.1108/JEEE-06-2022-0178
  19. Li, L., & Kang, K. (2022). The role of cultural attractors in live streaming content: regional cultural perspective using multi-group analysis. PACIS 2022 Proceedings. 49. https://aisel.aisnet.org/pacis2022/49
  20. Li, L., & Kang, K. (2022). Understanding the real-time interaction between middle-aged consumers and online experts based on the COM-B model. Journal of Marketing Analytics, 1-13. https://doi.org/10.1057/s41270-022-00196-1
  21. Li, L., & Kang, K. (2023). Why ethnic minority groups’ online-startups are booming in China’s tight cultural ecosystem? Journal of Entrepreneurship in Emerging Economies, 15(2), 278-300. https://doi.org/10.1108/JEEE-08-2021-0322
  22. Li, L., Feng, Y., & Zhao, A. (2023). An interaction–immersion model in live streaming commerce: the moderating role of streamer attractiveness. Journal of Marketing Analytics, 1-16. https://doi.org/10.1057/s41270-023-00225-7
  23. Li, L., Kang, K., & Sohaib, O. (2023). Analysing younger online viewers’ motivation to watch video game live streaming through a positive perspective. Journal of Economic Analysis, 2(2), 56-69. https://doi.org/10.58567/jea02020004
  24. Li, L., Kang, K., & Sohaib, O. (2023). Investigating factors affecting Chinese tertiary students’ online-startup motivation based on the COM-B behavior changing theory. Journal of Entrepreneurship in Emerging Economies, 15(3), 566-588. https://doi.org/10.1108/JEEE-08-2021-0299
  25. Li, L., Kang, K., Feng, Y., & Zhao, A. (2022). Factors affecting online consumers’ cultural presence and cultural immersion experiences in live streaming shopping. Journal of Marketing Analytics, 1-14. https://doi.org/10.1057/s41270-022-00192-5
  26. Li, L., Kang, K., Zhao, A., & Feng, Y. (2022). The impact of social presence and facilitation factors on online consumers' impulse buying in live shopping–celebrity endorsement as a moderating factor. Information Technology & People. https://doi.org/10.1108/ITP-03-2021-0203
  27. Liu, Q. (2017, January 4). Old people feed many liars. Health.People.cn. http://health.people.com.cn/n1/2017/0104/c406173-28998579.html
  28. Ming, J., Zeng, J., Bilal, M., Akram, U., & Fan, M. (2021). How social presence influences impulse buying behavior in live streaming commerce? The role of SOR theory. International Journal of Web Information Systems. https://doi.org/10.1108/IJWIS-02-2021-0012
  29. Muruganantham, G., & Bhakat, R. S. (2013). A review of impulse buying behavior. International Journal of Marketing Studies, 5(3), 149. http://dx.doi.org/10.5539/ijms.v5n3p149
  30. Nuseir, M. T. (2020). The extent of the influences of social media in creating'impulse buying'tendencies. International Journal of Business Innovation and Research, 21(3), 324-335. https://doi.org/10.1504/IJBIR.2020.105925
  31. Peña-García, N., Gil-Saura, I., Rodríguez-Orejuela, A., & Siqueira-Junior, J. R. (2020). Purchase intention and purchase behavior online: A cross-cultural approach. Heliyon, 6(6), e04284. https://doi.org/10.1016/j.heliyon.2020.e04284
  32. Rehman, S. U., Bhatti, A., Mohamed, R., & Ayoup, H. (2019). The moderating role of trust and commitment between consumer purchase intention and online shopping behavior in the context of Pakistan. Journal of Global Entrepreneurship Research, 9(1), 1-25. https://doi.org/10.1186/s40497-019-0166-2
  33. Rowley, J. (2014). Designing and using research questionnaires. Management Research Review. https://doi.org/10.1108/MRR-02-2013-0027
  34. Saravanakumar, M., & Lakshmi, T. S. (2012). Social media marketing. Life Science Journal, 9(4), 4444-4451. http://www.lifesciencesite.com/lsj/life0904/670_13061life0904_4444_4451.pdf
  35. Sarstedt, M., & Cheah, J.-H. (2019). Partial least squares structural equation modeling using SmartPLS: a software review. Journal of Marketing Analytics, 7(3), 196-202. https://doi.org/10.1057/s41270-019-00058-3
  36. Sorce, P., Perotti, V., & Widrick, S. (2005). Attitude and age differences in online buying. International Journal of Retail & Distribution Management, 33(2), 122-132. https://doi.org/10.1108/09590550510581458
  37. Sun, Y. (2020). Analysis of impulsive buying behavior in live broadcast scenarios. Education Reform and Development, 2(2). http://ojs.bbwpublisher.com/index.php/erd/article/view/2135
  38. Sun, Y., Shao, X., Li, X., Guo, Y., & Nie, K. (2019). How live streaming influences purchase intentions in social commerce: An IT affordance perspective. Electronic Commerce Research and Applications, 37, 100886. https://doi.org/10.1016/j.elerap.2019.100886
  39. Sun, Y., Shao, X., Li, X., Guo, Y., & Nie, K. (2020). A 2020 perspective on “How live streaming influences purchase intentions in social commerce: An IT affordance perspective”. Electronic Commerce Research and Applications, 40, 100958. https://doi.org/10.1016/j.elerap.2019.100886
  40. Tarigan, E. D., Putri, Y. S., & Sabrina, H. (2021). Green Buying Behavior Using Theory of TPB in Online Shop in Medan City. International Journal of Science, Technology & Management, 2(3), 604-607. https://doi.org/10.46729/ijstm.v2i3.223
  41. Tariq, A., Wang, C., Tanveer, Y., Akram, U., & Bilal, M. (2019). Online impulse buying of organic food: A moderated (website personality) mediation (social appeal) process. International Journal of Information Systems and Change Management, 11(1), 3-24. https://doi.org/10.1504/IJISCM.2019.101646
  42. Toh, E. B., & Selvan, P. (2015). Application of the Theory of Planned Behavior on impulse buying behavior in an internationalized shopping centre. https://core.ac.uk/download/pdf/148366816.pdf
  43. Wang, O., Somogyi, S., & Charlebois, S. (2020). Food choice in the e-commerce era: A comparison between business-to-consumer (B2C), online-to-offline (O2O) and new retail. British Food Journal. https://doi.org/10.1108/BFJ-09-2019-0682
  44. Wongkitrungrueng, A., & Assarut, N. (2020). The role of live streaming in building consumer trust and engagement with social commerce sellers. Journal of Business Research, 117, 543-556. https://doi.org/10.1016/j.jbusres.2018.08.032
  45. Wu, L., Chen, K-W., & Chiu, M-L. (2016). Defining key drivers of online impulse purchasing: A perspective of both impulse shoppers and system users. International Journal of Information Management, 36(3), 284-296. https://doi.org/10.1016/j.ijinfomgt.2015.11.015
  46. Xiang, L., Zheng, X., Lee, M. K., & Zhao, D. (2016). Exploring consumers’ impulse buying behavior on social commerce platform: The role of parasocial interaction. International Journal of Information Management, 36(3), 333-347. https://doi.org/10.1016/j.ijinfomgt.2015.11.002
  47. Xiong, Y. (2022). Personalized marketing of agricultural products based on digital economy environment. Academic Journal of Business & Management, 4(6), 36-40. https://doi.org/10.25236/AJBM.2022.040606.
  48. Xu, X., Wu, J-H., & Li, Q. (2020). What drives consumer shopping behavior in live streaming commerce? Journal of Electronic Commerce Research, 21(3), 144-167. http://ojs.jecr.org/jecr/sites/default/files/2020vol21no3_Paper1.pdf
  49. Yang, S., Li, L., & Zhang, J. (2018). Understanding consumers’ sustainable consumption intention at China’s double-11 online shopping festival: An extended theory of planned behavior model. Sustainability, 10(6), 1801. https://doi.org/10.3390/su10061801
  50. Zafar, A. U., Qiu, J., Li, Y., Wang, J., & Shahzad, M. (2021). The impact of social media celebrities' posts and contextual interactions on impulse buying in social commerce. Computers in Human Behavior, 115, 106178. https://doi.org/10.1016/j.chb.2019.106178
  51. Zhang, X., Cheng, X., & Huang, X. (2022). “Oh, My God, Buy It!” Investigating impulse buying behavior in live streaming commerce. International Journal of Human-Computer Interaction, 1-14. http://hdl.handle.net/10125/79502
  52. Zhong, Y., Zhang, Y., Luo, M., Wei, J., Liao, S., Tan, K-L., & Yap, S. S-N. (2022). I give discounts, I share information, I interact with viewers: A predictive analysis on factors enhancing college students' purchase intention in a live-streaming shopping environment. Young Consumers. https://doi.org/10.1108/YC-08-2021-1367