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A Study on the Impact of Linguistic Persuasive Styles on the Sales Volume of Live Streaming Products in Social E-Commerce Environment

Author

Listed:
  • Hanyang Luo

    (Institute of Big Data Intelligent Management and Decision, College of Management, Shenzhen University, Shenzhen 518060, China)

  • Sijia Cheng

    (College of Management, Shenzhen University, Shenzhen 518060, China)

  • Wanhua Zhou

    (College of Management, Shenzhen University, Shenzhen 518060, China)

  • Sumin Yu

    (Institute of Big Data Intelligent Management and Decision, College of Management, Shenzhen University, Shenzhen 518060, China)

  • Xudong Lin

    (Institute of Big Data Intelligent Management and Decision, College of Management, Shenzhen University, Shenzhen 518060, China)

Abstract

Live-stream shopping is developing rapidly, but the sales levels of live streaming products vary by different hosts. How to increase the sales volume of live streaming products has become a problem. Consumers’ purchase behavior in live streaming is determined by some subjective factors, and the persuasiveness of linguistic style affects this subjective judgment to a certain extent. Therefore, the persuasiveness of the hosts’ linguistic style will lead to changes in consumers’ purchase intentions, which will affect the sales volume of products sold in the live streaming. Based on Hovland’s persuasion model, Aristotle’s rhetoric skills, text analysis, Latent Dirichlet Allocation (LDA) topic extraction model and grounded theory, this study divides the host’s linguistic persuasive style in the social e-commerce environment into five types: appealing to personality, appealing to logic, appealing to emotion, appealing to reward, and appealing to exaggeration. Combined with the sales volume of the product, we establish a regression model, and obtain the influence results of the host’s various linguistic persuasive styles on the sales of live streaming products. The results show that: the linguistic persuasive style of appealing to personality has the greatest positive impact on the sales volume of live broadcast products, but the linguistic style of appealing to logic has a negative impact. Interestingly, the same linguistic style has different effects for different types of products: the linguistic style of appealing to exaggeration has a negative effect on the sales volume of apparel products, but it has a positive influence on the sales volume of digital electrical products. Therefore, different linguistic styles should be used for different product types.

Suggested Citation

  • Hanyang Luo & Sijia Cheng & Wanhua Zhou & Sumin Yu & Xudong Lin, 2021. "A Study on the Impact of Linguistic Persuasive Styles on the Sales Volume of Live Streaming Products in Social E-Commerce Environment," Mathematics, MDPI, vol. 9(13), pages 1-21, July.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:13:p:1576-:d:588407
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    References listed on IDEAS

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    1. Mingyang Zhang & Chao Chen & Wuyang Hu & Lijun Chen & Jintao Zhan, 2016. "Influence of Source Credibility on Consumer Acceptance of Genetically Modified Foods in China," Sustainability, MDPI, vol. 8(9), pages 1-16, September.
    2. Krishnan, Balaji C. & Dutta, Sujay & Jha, Subhash, 2013. "Effectiveness of Exaggerated Advertised Reference Prices: The Role of Decision Time Pressure," Journal of Retailing, Elsevier, vol. 89(1), pages 105-113.
    3. Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2011. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Management Science, INFORMS, vol. 57(8), pages 1485-1509, August.
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    Cited by:

    1. Zhang, Cong & Pan, Siyu & Zhao, Yanhui, 2024. "More is not always better: Examining the drivers of livestream sales from an information overload perspective," Journal of Retailing and Consumer Services, Elsevier, vol. 77(C).
    2. Liao, Miyan & Fang, Jiaming & Han, Lintong & Wen, Ling & Zheng, Qiqi & Xia, Guoen, 2023. "Boosting eCommerce sales with livestreaming in B2B marketplace: A perspective on live streamers’ competencies," Journal of Business Research, Elsevier, vol. 167(C).
    3. Hao, Caixia & Yang, Lei, 2023. "Resale or agency sale? Equilibrium analysis on the role of live streaming selling," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1117-1134.
    4. Hu, Hai-hua & Ma, Fang, 2023. "Human-like bots are not humans: The weakness of sensory language for virtual streamers in livestream commerce," Journal of Retailing and Consumer Services, Elsevier, vol. 75(C).
    5. Zhang, Peilin & Chao, Chih-Wei (Fred) & Chiong, Raymond & Hasan, Najmul & Aljaroodi, Hussain M. & Tian, Feng, 2023. "Effects of in-store live stream on consumers’ offline purchase intention," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    6. Xiaojun Mai & Fauziah Sheikh Ahmad & Jiayi Xu, 2023. "A Comprehensive Bibliometric Analysis of Live Streaming Commerce: Mapping the Research Landscape," SAGE Open, , vol. 13(4), pages 21582440231, December.
    7. Luo, Xi & Cheah, Jun-Hwa & Hollebeek, Linda D. & Lim, Xin-Jean, 2024. "Boosting customers’ impulsive buying tendency in live-streaming commerce: The role of customer engagement and deal proneness," Journal of Retailing and Consumer Services, Elsevier, vol. 77(C).
    8. Lei Yang & Cong Zheng & Caixia Hao, 2024. "Optimal platform sales mode in live streaming commerce supply chains," Electronic Commerce Research, Springer, vol. 24(2), pages 1017-1070, June.
    9. Minqin Yi & Ming Chen & Jilang Yang, 2024. "Understanding the self-perceived customer experience and repurchase intention in live streaming shopping: evidence from China," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.
    10. Zhang, Yanfen & Xu, Qi & Zhang, Guoqing, 2023. "Optimal contracts with moral hazard and adverse selection in a live streaming commerce market," Journal of Retailing and Consumer Services, Elsevier, vol. 74(C).

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