EXAMINE THE FACTORS AFFECTING CUSTOMERS’ IMPULSIVE BUYING BEHAVIORS IN LIVE STREAMING COMMERCE
Tập 133 Số 5C (2024), Nghiên cứu
Tập 133 Số 5C (2024)

EXAMINE THE FACTORS AFFECTING CUSTOMERS’ IMPULSIVE BUYING BEHAVIORS IN LIVE STREAMING COMMERCE

Nghiên cứu
https://doi.org/10.26459/hueunijed.v133i5C.7386
Đã xuất bản 2024-09-30
Tran Thi Nhat Anh
University of Economics, Hue University, 99 Ho Dac Di St., Hue, Vietnam
Tran Van Hoa
University of Economics, Hue University, 99 Ho Dac Di St., Hue, Vietnam

Tóm tắt

The rise of livestreaming trends has created a new business model known as livestreaming commerce, which is popular in Vietnam these days. However, little research has been done on impulsive buying behaviors in live streaming commerce, especially in the context of Vietnam. Using the Stimulus-Organism-Response (S-O-R) and Flow theories as foundations, this study develops a conceptual framework to examine the impact of livestreaming on consumers’ impulsive purchasing behavior. In this study, Partial Least Square – Structural Equation Modeling (PLS-SEM) was used to analyze data from the questionnaire survey of 363 customers who are familiar with live streaming platforms in Vietnam. By using SmartPLS for data empirical evaluation and hypotheses testing, research shows that the social presence of a live streaming platform and scarcity have a positive impact on consumer’s flow states, hence inducing consumers’ impulsive buying behaviors. E-tailers and live streamers may find this research useful in understanding customer’s buying behaviors and efficiently developing an action strategy for the long-term growth of live streaming commerce.

https://doi.org/10.26459/hueunijed.v133i5C.7386
PDF (English)

Tài liệu tham khảo

  1. Vietnam’s Economy Forecast to Grow 7.5% in 2022, New World Bank Report Says, World Bank. Accessed: Mar. 29, 2024. [Online]. Available: https://www.worldbank.org/en/news/press-release/2022/08/08/vietnam-s-economy-forecast-to-grow-7-5-in-2022-new-world-bank-report-says.
  2. Decision Lab, Vietnam: live stream watching duration 2022, Statista. Accessed: Mar. 29, 2024. [Online]. Available: https://www.statista.com/statistics/1301396/vietnam-average-time-spent-watching-a-live-stream-session/.
  3. Li, L., Kang, K., Zhao, A., and Feng, Y. (2023), The impact of social presence and facilitation factors on online consumers’ impulse buying in live shopping – celebrity endorsement as a moderating factor, Inf. Technol. People, 36(6), 2611–2631, doi: 10.1108/ITP-03-2021-0203.
  4. Ming, J., Jianqiu, Z., Bilal, M., Akram, U., and Fan, M. (2021), How social presence influences impulse buying behavior in live streaming commerce? The role of S-O-R theory, Int. J. Web Inf. Syst., 17(4), 300–320, doi: 10.1108/IJWIS-02-2021-0012.
  5. Zhang, J., Jiang, N ., Turner, J. J., and Pahlevan-Sharif, S. (2022), The Impact of Scarcity on Consumers’ Impulse Buying Based on the S-O-R Theory, Front. Psychol., 13, 792419, doi: 10.3389/fpsyg.2022.792419.
  6. Huang, Y. and Suo, L. (2021), Factors Affecting Chinese Consumers’ Impulse Buying Decision of Live Streaming E-Commerce, Asian Soc. Sci., 17(5), 16, doi: 10.5539/ass.v17n5p16.
  7. Liu, C., Zhang, Y., and Zhang, J. (2020), The impact of self-congruity and virtual interactivity on online celebrity brand equity and fans’ purchase intention, J. Prod. Brand Manag., 29(6), 783–801.
  8. Li, M., Wang, Q., and Cao, Y. (2022), Understanding Consumer Online Impulse Buying in Live Streaming E-Commerce: A Stimulus-Organism-Response Framework, Int. J. Environ. Res. Public. Health, 19(7), 4378, doi: 10.3390/ijerph19074378.
  9. T. Q. Tran (2021), Factors influence on customer purchase intention via livetream: Case of vietnam, Int. J. Manag. Entrep. Res., 3(7), 253–265, doi: 10.51594/ijmer.v3i7.245.
  10. Trung P. Q. and Ha N. N. H. (2017), Factor Affecting Impulsive Online Buying Behavior of Consumer in Ho Chi Minh city, Ho Chi Minh city Open University Journal of Science, 12(3).
  11. Karbasivar, A. and Yarahmadi, H. (2011), Evaluating Effective Factors on Consumer Impulse Buying Behavior.
  12. Parboteeah, D. V., Valacich, J. S., and Wells, J. D. (2009), The Influence of Website Characteristics on a Consumer’s Urge to Buy Impulsively, Inf. Syst. Res., 20(1), 60–78.
  13. Stern, H. (1962), The significance of impulse buying today, J. Mark., 26(2), 59–62.
  14. Beatty, S. E. and Elizabeth Ferrell, M. (1998), Impulse buying: Modeling its precursors, J. Retail., 74(2), 169–191, doi: 10.1016/S0022-4359(99)80092-X.
  15. Badgaiyan, A. J. and Verma, A. (2015), Does urge to buy impulsively differ from impulsive buying behaviour? Assessing the impact of situational factors, J. Retail. Consum. Serv., 22, 145–157, doi: 10.1016/j.jretconser.2014.10.002.
  16. Verhagen, T. and Van Dolen, W. (2011), The influence of online store beliefs on consumer online impulse buying: A model and empirical application, Inf. Manage., 48(8), 320–327, doi: 10.1016/j.im.2011.08.001.
  17. Lombard, M. and Ditton, T. (1997), At the heart of it all: The concept of presence., J. Comput.-Mediat. Commun., 3(2), p. No Pagination Specified-No Pagination Specified.
  18. Xu, X., Huang, D., and Shang, X. (2021), Social presence or physical presence? Determinants of purchasing behaviour in tourism live-streamed shopping, Tour. Manag. Perspect., 40, 100917, doi: 10.1016/j.tmp.2021.100917.
  19. Woodworth, R. S. (1929), Psychology (revised edition), Henry Holt and Co.
  20. Russell, J. A. and Mehrabian, A. (1974), Distinguishing anger and anxiety in terms of emotional response factors., J. Consult. Clin. Psychol., 42(1), 79–83, doi: 10.1037/h0035915.
  21. Jacoby, J. (2002), Stimulus‐organism‐response reconsidered: an evolutionary step in modeling (consumer) behavior, J. Consum. Psychol., 12(1), 51–57.
  22. Gao, W., Liu, Y., Liu, Z., and Li, J. (2018), How does presence influence purchase intention in online shopping markets? An explanation based on self-determination theory, Behav. Inf. Technol., 37(8), 786–799, doi: 10.1080/0144929X.2018.1484514.
  23. Muhammad, A., S., Adeshola, I., and Isiaku, L. (2023), A mixed study on the ‘wow’ of impulse purchase on Instagram: insights from Gen-Z in a collectivistic environment, Young Consum., doi: 10.1108/YC-04-2023-1728.
  24. Lin, S.-C., Tseng, H.-T., Shirazi, F., Hajli, N., and Tsai, P.-T. (2023), Exploring factors influencing impulse buying in live streaming shopping: a stimulus-organism-response (SOR) perspective, Asia Pac. J. Mark. Logist., 35(6), 1383–1403, doi: 10.1108/APJML-12-2021-0903.
  25. Yu, C., Cheah, J.-H., and Liu, Y. (2022), To stream or not to stream? Exploring factors influencing impulsive consumption through gastronomy livestreaming, Int. J. Contemp. Hosp. Manag., 34(9), 3394–3416, doi: 10.1108/IJCHM-09-2021-1137.
  26. M. Koufaris (2002), Applying the Technology Acceptance Model and Flow Theory to Online Consumer Behavior, Inf. Syst. Res., 13(2), 205–223, doi: 10.1287/isre.13.2.205.83.
  27. Ghani, J. A., Supnick, R., and Rooney, P., The experience of flow in computer-mediated and in face-to-face groups.
  28. Huang, Z., Zhu, Y., Hao, A., and Deng, J. (2023), How social presence influences consumer purchase intention in live video commerce: the mediating role of immersive experience and the moderating role of positive emotions, J. Res. Interact. Mark., 17(4), 493–509, doi: 10.1108/JRIM-01-2022-0009.
  29. Bründl, S., Matt, C., and Hess, T. (2017), Consumer use of social live streaming services: the influence of co-experience and effectance on enjoyment.
  30. Li, B., Hou, F., Guan, Z., and Chong, A. Y.-L. (2018), What Drives People to Purchase Virtual Gifts in Live Streaming? The Mediating Role of Flow.
  31. Guo, Y., Zhang, K., and Wang, C. (2022), Way to success: Understanding top streamer’s popularity and influence from the perspective of source characteristics, J. Retail. Consum. Serv., 64, 102786, doi: 10.1016/j.jretconser.2021.102786.
  32. Xu, X., Wu, J. -H., and Li, Q. (2020), What drives consumer shopping behavior in live streaming commerce?, 21(3).
  33. Chen, H., Zhang, S., Shao, B., Gao, W., and Xu, Y. (2022), How do interpersonal interaction factors affect buyers’ purchase intention in live stream shopping? The mediating effects of swift guanxi, Internet Res., 32(1), 335–361, doi: 10.1108/INTR-05-2020-0252.
  34. Lv, X., Zhang, R., Su, Y., and Yang, Y. (2022), Exploring how live streaming affects immediate buying behavior and continuous watching intention: A multigroup analysis, J. Travel Tour. Mark., 39(1), 109–135, doi: 10.1080/10548408.2022.2052227.
  35. Guo, Y., Zhang, K., and Wang, C. (2022), Way to success: Understanding top streamer’s popularity and influence from the perspective of source characteristics, J. Retail. Consum. Serv., 64, 102786, doi: 10.1016/j.jretconser.2021.102786.
  36. Lo, P.-S., Dwivedi, Y. K., Wei-Han Tan, G., Ooi, K.-B., Cheng-Xi Aw, E., and Metri, B. (2022), Why do consumers buy impulsively during live streaming? A deep learning-based dual-stage SEM-ANN analysis, J. Bus. Res., 147, 325–337, doi: 10.1016/j.jbusres.2022.04.013.
  37. Zhang, J., Jiang, N., Turner, J. J., and Pahlevan-Sharif, S. (2022), The Impact of Scarcity on Consumers’ Impulse Buying Based on the S-O-R Theory, Front. Psychol., 13, 792419, doi: 10.3389/fpsyg.2022.792419.
  38. Li, X., Guo, M., and Huang, D. (2023), The role of scarcity promotion and cause-related events in impulse purchase in the agricultural product live stream, Sci. Rep., 13(1), 3800, doi: 10.1038/s41598-023-30696-8.
  39. Adeshola, A. S. I., and Isiaku, L. (2023), A mixed study on the ‘wow’ of impulse purchase on Instagram: insights from Gen-Z in a collectivistic environment, Young Consum., doi: 10.1108/YC-04-2023-1728.
  40. Statista, Social Media Report in Vietnam 2020 | Market Report IO. Accessed: Mar. 28, 2024. [Online]. Available: https://marketreport.io/social-media-report-vietnam-2020.
  41. Social Media Report in Vietnam 2020 | Market Report IO, Accessed: Mar. 28, 2024. [Online]. Available: https://marketreport.io/social-media-report-vietnam-2020.
  42. Sun, Y., Shao, X., Li, X., Guo, Y., and Nie, K. (2019), How live streaming influences purchase intentions in social commerce: An IT affordance perspective, Electron. Commer. Res. Appl., 37, 100886, doi: 10.1016/j.elerap.2019.100886.
  43. Ye, S., Lei, S. I., Shen, H ., and Xiao, H. (2020), Social presence, telepresence and customers’ intention to purchase online peer-to-peer accommodation: A mediating model, J. Hosp. Tour. Manag., 42, 119–129, doi: 10.1016/j.jhtm.2019.11.008.
  44. Verplanken, B. and Herabadi, A. (2001), Individual differences in impulse buying tendency: feeling and no thinking, Eur. J. Personal., 15, 1_suppl, S71–S83, doi: 10.1002/per.423.
  45. Hair, J., Hult, G. T. M., Ringle, C., and Sarstedt, M. (2016), A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 2nd edition.
  46. Henseler, J., Ringle, C., and Sinkovics, R. (2009), The Use of Partial Least Squares Path Modeling in International Marketing, in Advances in International Marketing, 20, 277–319. doi: 10.1108/S1474-7979(2009)0000020014.
  47. Wetzels, M. and Odekerken, G. (2009), Using PLS Path Modeling for Assessing Hierarchical Construct Models: Guidelines and Empirical Illustration, Manag. Inf. Syst. Q. - MISQ, 33, doi: 10.2307/20650284.
  48. Zheng, Y., Zhao, K., and Stylianou, A. (2013), The impacts of information quality and system quality on users’ continuance intention in information-exchange virtual communities: An empirical investigation, Decis. Support Syst., 56, 513–524, doi: 10.1016/j.dss.2012.11.008.
  49. Chin, W. W., Marcolin, B. L., and 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., Inf. Syst. Res., 14(2), 189–217, doi: 10.1287/isre.14.2.189.16018.
  50. Hair, J. and Alamer, A. (2022), Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: Guidelines using an applied example, Res. Methods Appl. Linguist., 1(3), 100027, doi: 10.1016/j.rmal.2022.100027.
  51. Fornell, C. and Larcker, D. F. (1981), Evaluating Structural Equation Models with Unobservable Variables and Measurement Error, J. Mark. Res., 18(1), 39–50, doi: 10.1177/002224378101800104.
  52. Lee, C.-H. and Chen, C.-W. (2021), Impulse Buying Behaviors in Live Streaming Commerce Based on the Stimulus-Organism-Response Framework, Information, 12(6), 241, doi: 10.3390/info12060241.
  53. Hu, M., and Chaudhry, S. S. (2020), Enhancing consumer engagement in e-commerce live streaming via relational bonds, Internet Res., 30(3), 1019–1041, doi: 10.1108/INTR-03-2019-0082.
  54. Zhang, X., Cheng, X. and Huang, X. (2023), Oh, My God, Buy It!’ Investigating Impulse Buying Behavior in Live Streaming Commerce, Int. J. Human–Computer Interact., 39(12), 2436–2449, doi: 10.1080/10447318.2022.2076773.
  55. Chen, C.-C. and Yao, J.-Y. (2018), What drives impulse buying behaviors in a mobile auction? The perspective of the Stimulus-Organism-Response model, Telemat. Inform., 35(5), 1249–1262, doi: 10.1016/j.tele.2018.02.007.
  56. Koufaris, M. (2002), Applying the Technology Acceptance Model and Flow Theory to Online Consumer Behavior, Inf. Syst. Res., 13, 205–223, doi: 10.1287/isre.13.2.205.83.
Creative Commons License

công trình này được cấp phép theo Creative Commons Ghi công-Chia sẻ tương tự 4.0 License International .

Bản quyền (c) 2024 Array