Critical Factors Impacting Consumer Behavior in Douyin E-commerce

Research Article
Open access

Critical Factors Impacting Consumer Behavior in Douyin E-commerce

Lixuan Wei 1*
  • 1 University of Melbourne    
  • *corresponding author lixuan.wei@student.unimelb.edu.au
Published on 13 September 2023 | https://doi.org/10.54254/2754-1169/21/20230260
AEMPS Vol.21
ISSN (Print): 2754-1177
ISSN (Online): 2754-1169
ISBN (Print): 978-1-915371-85-0
ISBN (Online): 978-1-915371-86-7

Abstract

After the global public health emergency, changes in consumer behavior are subtly affecting people's lives. Based on the Douyin e-commerce platform, and marketing concepts, including the Stimulus-Organism-Response model (SOR), Elaboration Likelihood Model (ELM), and consumer decision-making process, this paper studies the transformation of consumer behavior through statistical methods. From the research results, it can be concluded that consumer purchasing aspirations can increase by 2.919 times when the total assessment of the recommended goods in the Douyin e-commerce platform increases by one unit. Based on this, changes in consumer behavior will help scholars find future research directions. At the same time, suggestions based on the results can also outline a future blueprint for the development of short video e-commerce companies and industries.

Keywords:

consumer behavior, Douyin e-commerce, logistic regression, ridge regression, correlation analysis

Wei,L. (2023). Critical Factors Impacting Consumer Behavior in Douyin E-commerce. Advances in Economics, Management and Political Sciences,21,254-262.
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References

[1]. Michael K. (2021). Global Ecommerce Explained: Stats and Trends to Watch in 2021. Shopify. Retrieved from https://www.shopify.com/enterprise/global-ecommerce-statistics.

[2]. World Health Organization. (2021). Update on Omicron. WHO News. Retrieved from https://www.who.int/news/item/28-11-2021-update-on-omicron.

[3]. Kenrick D. (2018). The App That Launched a Thousand Memes. Sixth Tone. Retrieved from https://www.sixthtone.com/news/1001728/the-app-that-launched-a-thousand-memes.

[4]. ByteDance Establishes E-commerce. (2020). ByteDance Establishes E-commerce Business Group. PingWest. Retrieved from: https://en.pingwest.com/w/6969.

[5]. Man Deng, Pei Deng, Baike Chen, Qirong Liang, Gaoxu Deng. (2021). Research on Tik Tok platform live streaming e-commerce to help rural revitalization based on SOR model. Academic Journal of Business & Management, 3(6), 91-94. DOI: https://doi.org/10.25236/AJBM.2021.030614.

[6]. Chu Tan Ming. (2018). [Research on consumers' willingness to choose online shopping channels based on SOR theory]. Beijing University of Posts and Telecommunications, (In Chinese).

[7]. Petty, R., Cacioppo, J. & Goldman, R. (1981). Journal of Personality and Social Psychology, 41(5), 847-855.

[8]. Shi, J., Hu, P., Lai, K. K., & Chen, G. (2018). Determinants of users’ information dissemination behavior on social networking sites. Internet Research, 28(2), 393-418. DOI: http://dx.doi.org/10.1108/IntR-01-2017-0038.

[9]. Srivastava, M., & Saini, G. K. (2022). A bibliometric analysis of the elaboration likelihood model (ELM). Journal of Consumer Marketing, 39(7), 726–743. DOI: https://doi.org/10.1108/JCM-12-2021-5049.

[10]. Choi, J., Lee, A. and Ok, C. (2013), “The effects of consumers’ perceived risk and benefit on attitude and behavioral intention: a study of street food”, Journal of Travel and Tourism Marketing, 30(3), 222-237. DOI: 10.1080/10548408.2013.774916.

[11]. Zeyen Loh, & Hasnah Hassan, S. (2022). Consumers’ attitudes, perceived risks and perceived benefits towards repurchase intention of food truck products. British Food Journal, 124(4), 1314–1332. DOI: https://doi.org/10.1108/BFJ-03-2021-0216.

[12]. Yoon, B. and Chung, Y. (2018), “Consumer attitude and visit intention toward food-trucks: targeting Millennials”, Journal of Foodservice Business Research, 21(2), 187-199.

[13]. Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. DOI: https://doi.org/10.2307/41410412.

[14]. Kleijnen, M., De Ruyter, K., & Wetzels, M. (2007). An assessment of value creation in mobile service delivery and the moderating role of time consciousness. Journal of Retailing, 83(1), 33–46. DOI: https:// doi.org/10.1016/j.jretai.2006.10.004.

[15]. Rithika D., Kazuo F. & Taro K. (2021). Quantitative decision-making model to ananlyze the post-disaster consumer behavior. International Journal of Disaster Risk Reduction, 61, 102329, ISSN 2212-4209, DOI: https://doi.org/10.1016/j.ijdrr.2021.102329.

Cite this article

Wei,L. (2023). Critical Factors Impacting Consumer Behavior in Douyin E-commerce. Advances in Economics, Management and Political Sciences,21,254-262.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Management Research and Economic Development

ISBN：978-1-915371-85-0(Print) / 978-1-915371-86-7(Online)
Editor：Canh Thien Dang, Javier Cifuentes-Faura
Conference website: https://2023.icmred.org/
Conference date: 28 April 2023
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.21
ISSN：2754-1169(Print) / 2754-1177(Online)

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References

[1]. Michael K. (2021). Global Ecommerce Explained: Stats and Trends to Watch in 2021. Shopify. Retrieved from https://www.shopify.com/enterprise/global-ecommerce-statistics.

[2]. World Health Organization. (2021). Update on Omicron. WHO News. Retrieved from https://www.who.int/news/item/28-11-2021-update-on-omicron.

[3]. Kenrick D. (2018). The App That Launched a Thousand Memes. Sixth Tone. Retrieved from https://www.sixthtone.com/news/1001728/the-app-that-launched-a-thousand-memes.

[4]. ByteDance Establishes E-commerce. (2020). ByteDance Establishes E-commerce Business Group. PingWest. Retrieved from: https://en.pingwest.com/w/6969.

[5]. Man Deng, Pei Deng, Baike Chen, Qirong Liang, Gaoxu Deng. (2021). Research on Tik Tok platform live streaming e-commerce to help rural revitalization based on SOR model. Academic Journal of Business & Management, 3(6), 91-94. DOI: https://doi.org/10.25236/AJBM.2021.030614.

[6]. Chu Tan Ming. (2018). [Research on consumers' willingness to choose online shopping channels based on SOR theory]. Beijing University of Posts and Telecommunications, (In Chinese).

[7]. Petty, R., Cacioppo, J. & Goldman, R. (1981). Journal of Personality and Social Psychology, 41(5), 847-855.

[8]. Shi, J., Hu, P., Lai, K. K., & Chen, G. (2018). Determinants of users’ information dissemination behavior on social networking sites. Internet Research, 28(2), 393-418. DOI: http://dx.doi.org/10.1108/IntR-01-2017-0038.

[9]. Srivastava, M., & Saini, G. K. (2022). A bibliometric analysis of the elaboration likelihood model (ELM). Journal of Consumer Marketing, 39(7), 726–743. DOI: https://doi.org/10.1108/JCM-12-2021-5049.

[10]. Choi, J., Lee, A. and Ok, C. (2013), “The effects of consumers’ perceived risk and benefit on attitude and behavioral intention: a study of street food”, Journal of Travel and Tourism Marketing, 30(3), 222-237. DOI: 10.1080/10548408.2013.774916.

[11]. Zeyen Loh, & Hasnah Hassan, S. (2022). Consumers’ attitudes, perceived risks and perceived benefits towards repurchase intention of food truck products. British Food Journal, 124(4), 1314–1332. DOI: https://doi.org/10.1108/BFJ-03-2021-0216.

[12]. Yoon, B. and Chung, Y. (2018), “Consumer attitude and visit intention toward food-trucks: targeting Millennials”, Journal of Foodservice Business Research, 21(2), 187-199.

[13]. Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. DOI: https://doi.org/10.2307/41410412.

[14]. Kleijnen, M., De Ruyter, K., & Wetzels, M. (2007). An assessment of value creation in mobile service delivery and the moderating role of time consciousness. Journal of Retailing, 83(1), 33–46. DOI: https:// doi.org/10.1016/j.jretai.2006.10.004.

[15]. Rithika D., Kazuo F. & Taro K. (2021). Quantitative decision-making model to ananlyze the post-disaster consumer behavior. International Journal of Disaster Risk Reduction, 61, 102329, ISSN 2212-4209, DOI: https://doi.org/10.1016/j.ijdrr.2021.102329.

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