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Open AccessOpen Access JUSTC Management 30 June 2023

Live-streaming selling strategies for competitive firms

  • School of Management, University of Science and Technology of China, Hefei 230026, China

Cite this:
https://doi.org/10.52396/JUSTC-2022-0171
More Information
  • Author Bio:

    Quan Du is currently a graduate student under the tutelage of Prof. Jie Wu at the University of Science and Technology of China. His research mainly focuses on operations management and marketing science

    Xiang Ji is currently an Associate Professor at the School of Management, University of Science and Technology of China (USTC). He received the Ph.D. degree in Management Science and Engineering from USTC in 2017. His research mainly focuses on operations management and marketing science

  • Corresponding author: E-mail: signji@mail.ustc.edu.cn
  • Received Date: 25 November 2022
  • Accepted Date: 23 March 2023
    • Available Online: 30 June 2023
    Abstract

  • Abstract

    The booming live-streaming commerce has significantly changed the traditional e-commerce model, thus attracting much attention from both industry and academia. In recent years, an increasing number of scholars have applied analytical models to explore live-streaming strategies for firms in different scenarios. However, the previous literature mainly considers monopolists, while in the real world, competition is not rare. To fill this gap between the literature and practical observations, this paper applies a game theoretical model to study live-streaming adoption and pricing strategy for firms under competitive environments. The results show that, for competitive firms, the equilibrium strategy depends on the relation between the commission rate and the intensity of the market expansion effect. Additionally, compared to the case in which no firm adopts live-streaming, competitive firms do not always benefit from the adoption of live-streaming selling. The paper also shows that competition plays a negative role in inducing a firm to adopt live-streaming.

    Graphical abstract

    Competitive supply chain structure and key research issues.

    Abstract

    The booming live-streaming commerce has significantly changed the traditional e-commerce model, thus attracting much attention from both industry and academia. In recent years, an increasing number of scholars have applied analytical models to explore live-streaming strategies for firms in different scenarios. However, the previous literature mainly considers monopolists, while in the real world, competition is not rare. To fill this gap between the literature and practical observations, this paper applies a game theoretical model to study live-streaming adoption and pricing strategy for firms under competitive environments. The results show that, for competitive firms, the equilibrium strategy depends on the relation between the commission rate and the intensity of the market expansion effect. Additionally, compared to the case in which no firm adopts live-streaming, competitive firms do not always benefit from the adoption of live-streaming selling. The paper also shows that competition plays a negative role in inducing a firm to adopt live-streaming.

    • Public Summary

    • For competitive firms, the equilibrium strategy depends on the relation between the commission rate and the intensity of the market expansion effect.
    • Compared to the case in which no firm adopts live-streaming, competitive firms do not always benefit from the adoption of live-streaming selling.
    • Competition plays a negative role in inducing a firm to adopt live-streaming.

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    • References(20)
  • [1]
    Lu B, Chen Z. Live streaming commerce and consumers’ purchase intention: An uncertainty reduction perspective. Information & Management, 2021, 58 (7): 103509. doi: 10.1016/j.im.2021.103509
    [2]
    Hou J, Shen H, Xu F. A model of livestream selling with online influencers. Available at: http://dx.doi.org/10.2139/ssrn.3896924
    [3]
    Pan R, Feng J, Zhao Z. Fly with the wings of live-stream selling-channel strategies with/without switching demand. Production and Operations Management, 2022, 31 (9): 3387–3399. doi: 10.1111/poms.13784
    [4]
    Wang Q, Zhao N, Ji, X. Reselling or agency selling? The: strategic role of live streaming commerce in distribution contract selection. Electronic Commerce Research, 2022: DOI: 10.1007/s10660-022-09581-5.
    [5]
    Jiang Y, Lu W, Ji X, Wu, J. How livestream selling strategy interacts with product line design. Electronic Commerce Research, 2022: DOI: 10.1007/s10660-022-09648-3.
    [6]
    Soberman D A. Research note: Additional learning and implications on the role of informative advertising. Management Science, 2004, 50 (12): 1744–1750. doi: 10.1287/mnsc.1040.0288
    [7]
    Xu X, Wu J H, Li Q. What drives consumer shopping behavior in live streaming commerce? Journal of Electronic Commerce Research, 2020, 21 (3): 144–167.
    [8]
    Wongkitrungrueng A, Assarut N. The role of live streaming in building consumer trust and engagement with social commerce sellers. Journal of Business Research, 2020, 117: 543–556. doi: 10.1016/j.jbusres.2018.08.032
    [9]
    Kang K, Lu J, Guo L, et al. The dynamic effect of interactivity on customer engagement behavior through tie strength: Evidence from live streaming commerce platforms. International Journal of Information Management, 2021, 56: 102251. doi: 10.1016/j.ijinfomgt.2020.102251
    [10]
    Sun Y, Shao X, Li X, et al. How live streaming influences purchase intentions in social commerce: An IT affordance perspective. Electronic Commerce Research and Applications, 2019, 37: 100886. doi: 10.1016/j.elerap.2019.100886
    [11]
    Qi A, Sethi S, Wei L, Zhang J. Top or regular influencer? Contracting in live-streaming platform selling. Contracting in live-streaming platform selling. Available at: http://dx.doi.org/10.2139/ssrn.3668390
    [12]
    Li G, Nan G, Wang R, et al. Retail strategies for e-tailers in live streaming commerce: When does an influencer marketing channel work? Available at: http://dx.doi.org/10.2139/ssrn.3998665
    [13]
    Godes D, Mayzlin D. Using online conversations to study word-of-mouth communication. Marketing Science, 2004, 23 (4): 545–560. doi: 10.1287/mksc.1040.0071
    [14]
    Tereyaǧoǧlu N, Veeraraghavan S. Selling to conspicuous consumers: Pricing, production, and sourcing decisions. Management Science, 2012, 58 (12): 2168–2189. doi: 10.1287/mnsc.1120.1545
    [15]
    Godes D. Product policy in markets with word-of-mouth communication. Management Science, 2017, 63 (1): 267–278. doi: 10.1287/mnsc.2015.2330
    [16]
    Chong A Y L, Ch’ng E, Liu M J, et al. Predicting consumer product demands via Big Data: the roles of online promotional marketing and online reviews. International Journal of Production Research, 2017, 55 (17): 5142–5156. doi: 10.1080/00207543.2015.1066519
    [17]
    Choi T M. Incorporating social media observations and bounded rationality intofashion quick response supply chains in the big data era. Transportation Research Part E: Logistics and Transportation Review, 2018, 114: 386–397. doi: 10.1016/j.tre.2016.11.006
    [18]
    Kuksov D, Liao C. Opinion leaders and product variety. Marketing Science, 2019, 38 (5): 812–834. doi: 10.1287/mksc.2019.1179
    [19]
    Orji I J, Kusi-Sarpong S, Gupta H. The critical success factors of using social media for supply chain social sustainability in the freight logistics industry. International Journal of Production Research, 2020, 58 (5): 1522–1539. doi: 10.1080/00207543.2019.1660829
    [20]
    Ji X, Li G, Sethi S P. How social communications affect product line design in the platform economy. International Journal of Production Research, 2022, 60 (2): 686–703. doi: 10.1080/00207543.2021.2013562
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    Figure  1.  Model timeline.

    Figure  2.  Firm’s response to the rival’s strategy.

    Figure  3.  Equilibrium strategy for competitive firms.

    Figure  4.  Profit implication for competitive firms.

    Figure  5.  Equilibrium strategy for a monopoly firm.

    Figure  6.  Equilibrium comparison between the monopoly and duopoly cases.

    [1]
    Lu B, Chen Z. Live streaming commerce and consumers’ purchase intention: An uncertainty reduction perspective. Information & Management, 2021, 58 (7): 103509. doi: 10.1016/j.im.2021.103509
    [2]
    Hou J, Shen H, Xu F. A model of livestream selling with online influencers. Available at: http://dx.doi.org/10.2139/ssrn.3896924
    [3]
    Pan R, Feng J, Zhao Z. Fly with the wings of live-stream selling-channel strategies with/without switching demand. Production and Operations Management, 2022, 31 (9): 3387–3399. doi: 10.1111/poms.13784
    [4]
    Wang Q, Zhao N, Ji, X. Reselling or agency selling? The: strategic role of live streaming commerce in distribution contract selection. Electronic Commerce Research, 2022: DOI: 10.1007/s10660-022-09581-5.
    [5]
    Jiang Y, Lu W, Ji X, Wu, J. How livestream selling strategy interacts with product line design. Electronic Commerce Research, 2022: DOI: 10.1007/s10660-022-09648-3.
    [6]
    Soberman D A. Research note: Additional learning and implications on the role of informative advertising. Management Science, 2004, 50 (12): 1744–1750. doi: 10.1287/mnsc.1040.0288
    [7]
    Xu X, Wu J H, Li Q. What drives consumer shopping behavior in live streaming commerce? Journal of Electronic Commerce Research, 2020, 21 (3): 144–167.
    [8]
    Wongkitrungrueng A, Assarut N. The role of live streaming in building consumer trust and engagement with social commerce sellers. Journal of Business Research, 2020, 117: 543–556. doi: 10.1016/j.jbusres.2018.08.032
    [9]
    Kang K, Lu J, Guo L, et al. The dynamic effect of interactivity on customer engagement behavior through tie strength: Evidence from live streaming commerce platforms. International Journal of Information Management, 2021, 56: 102251. doi: 10.1016/j.ijinfomgt.2020.102251
    [10]
    Sun Y, Shao X, Li X, et al. How live streaming influences purchase intentions in social commerce: An IT affordance perspective. Electronic Commerce Research and Applications, 2019, 37: 100886. doi: 10.1016/j.elerap.2019.100886
    [11]
    Qi A, Sethi S, Wei L, Zhang J. Top or regular influencer? Contracting in live-streaming platform selling. Contracting in live-streaming platform selling. Available at: http://dx.doi.org/10.2139/ssrn.3668390
    [12]
    Li G, Nan G, Wang R, et al. Retail strategies for e-tailers in live streaming commerce: When does an influencer marketing channel work? Available at: http://dx.doi.org/10.2139/ssrn.3998665
    [13]
    Godes D, Mayzlin D. Using online conversations to study word-of-mouth communication. Marketing Science, 2004, 23 (4): 545–560. doi: 10.1287/mksc.1040.0071
    [14]
    Tereyaǧoǧlu N, Veeraraghavan S. Selling to conspicuous consumers: Pricing, production, and sourcing decisions. Management Science, 2012, 58 (12): 2168–2189. doi: 10.1287/mnsc.1120.1545
    [15]
    Godes D. Product policy in markets with word-of-mouth communication. Management Science, 2017, 63 (1): 267–278. doi: 10.1287/mnsc.2015.2330
    [16]
    Chong A Y L, Ch’ng E, Liu M J, et al. Predicting consumer product demands via Big Data: the roles of online promotional marketing and online reviews. International Journal of Production Research, 2017, 55 (17): 5142–5156. doi: 10.1080/00207543.2015.1066519
    [17]
    Choi T M. Incorporating social media observations and bounded rationality intofashion quick response supply chains in the big data era. Transportation Research Part E: Logistics and Transportation Review, 2018, 114: 386–397. doi: 10.1016/j.tre.2016.11.006
    [18]
    Kuksov D, Liao C. Opinion leaders and product variety. Marketing Science, 2019, 38 (5): 812–834. doi: 10.1287/mksc.2019.1179
    [19]
    Orji I J, Kusi-Sarpong S, Gupta H. The critical success factors of using social media for supply chain social sustainability in the freight logistics industry. International Journal of Production Research, 2020, 58 (5): 1522–1539. doi: 10.1080/00207543.2019.1660829
    [20]
    Ji X, Li G, Sethi S P. How social communications affect product line design in the platform economy. International Journal of Production Research, 2022, 60 (2): 686–703. doi: 10.1080/00207543.2021.2013562
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