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Online dynamic group-buying community analysis based on high frequency time series simulation

Author

Listed:
  • Qing Zhu

    (Shaanxi Normal University
    Xi’an Jiaotong University)

  • Renxian Zuo

    (Shaanxi Normal University)

  • Shan Liu

    (Xi’an Jiaotong University)

  • Fan Zhang

    (Shaanxi Normal University)

Abstract

Group-buying often fails even when there are satisfactory quantities as not enough consumers join in the required time, which can waste seller, purchaser, and platform operator time resources; therefore, the group buying features require further research. Over a 3 weeks period, around 700 million click-stream records from 1,061,770 users from a stable and continuous time series were allocated to groups of 5 min frequency, and a hybrid neural network model developed to simulate group-buying behavior in four experiments, from which it was found that adding to the cart and adding as a favorite were significant group-buying behavior features, and shopping depth was the main demographic feature, but age was not. Compared with previous ambiguous online consumer feature conclusions on gender, the results revealed that the commodity feature was the main determinant for gender feature significance. The college student feature was found to be a pseudo feature, and should connect with other fixed effects such as low income or education level. This paper is the first to construct an online dynamic group-buying community, which is a new type of social network and could provide a new perspective for social commerce research. A big data neural network-based method for examining group-buying community behavior over time is proposed that can offer novel insights to online vendors for the development of targeted marketing campaigns.

Suggested Citation

  • Qing Zhu & Renxian Zuo & Shan Liu & Fan Zhang, 2020. "Online dynamic group-buying community analysis based on high frequency time series simulation," Electronic Commerce Research, Springer, vol. 20(1), pages 81-118, March.
    • Handle: RePEc:spr:elcore:v:20:y:2020:i:1:d:10.1007_s10660-019-09380-5
    DOI: 10.1007/s10660-019-09380-5
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    References listed on IDEAS

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    1. Cao, Jian & Li, Zhi & Li, Jian, 2019. "Financial time series forecasting model based on CEEMDAN and LSTM," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 127-139.
    2. Zhang, Xun & Yu, Lean & Wang, Shouyang & Lai, Kin Keung, 2009. "Estimating the impact of extreme events on crude oil price: An EMD-based event analysis method," Energy Economics, Elsevier, vol. 31(5), pages 768-778, September.
    3. Ke Gong & Yi Peng & Yong Wang & Maozeng Xu, 2018. "Time series analysis for C2C conversion rate," Electronic Commerce Research, Springer, vol. 18(4), pages 763-789, December.
    4. Li, Chaoshun & Xiao, Zhengguang & Xia, Xin & Zou, Wen & Zhang, Chu, 2018. "A hybrid model based on synchronous optimisation for multi-step short-term wind speed forecasting," Applied Energy, Elsevier, vol. 215(C), pages 131-144.
    5. Lee, Richard J. & Sener, Ipek N. & Mokhtarian, Patricia L. & Handy, Susan L., 2017. "Relationships between the online and in-store shopping frequency of Davis, California residents," Transportation Research Part A: Policy and Practice, Elsevier, vol. 100(C), pages 40-52.
    6. Ying Liu & Hong Li & Geng Peng & Benfu Lv & Chong Zhang, 2015. "Online purchaser segmentation and promotion strategy selection: evidence from Chinese E-commerce market," Annals of Operations Research, Springer, vol. 233(1), pages 263-279, October.
    7. Prateek Kalia, 2017. "Does Demographics Affect Purchase Frequency in Online Retail?," International Journal of Online Marketing (IJOM), IGI Global, vol. 7(2), pages 42-56, April.
    8. Xiao-Liang Shen & Kem Z.K. Zhang & Sesia J. Zhao, 2016. "Herd behavior in consumers’ adoption of online reviews," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(11), pages 2754-2765, November.
    9. Wu, Yu-Xi & Wu, Qing-Biao & Zhu, Jia-Qi, 2019. "Improved EEMD-based crude oil price forecasting using LSTM networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 114-124.
    10. Bangwool Han & Minho Kim, 2019. "Hofstede’s Collectivistic Values and Sustainable Growth of Online Group Buying," Sustainability, MDPI, vol. 11(4), pages 1-15, February.
    11. Zhuoxi Yu & Yanqing Wu & Zhiwen Zhao, 2016. "Quality Evaluation of Group-Buy Websites," Journal of Electronic Commerce in Organizations (JECO), IGI Global, vol. 14(1), pages 1-10, January.
    12. Zhu, Jiaming & Wu, Peng & Chen, Huayou & Liu, Jinpei & Zhou, Ligang, 2019. "Carbon price forecasting with variational mode decomposition and optimal combined model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 140-158.
    13. Gerlach, Jin & Eling, Nicole & Wessels, Nora & Buxmann, Peter, 2019. "Flamingos on a Slackline: Companies’ Challenges of Balancing the Competing Demands of Handling Customer Information and Privacy," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 106582, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    14. Chenxu Ke & Bo Yan & Ruofan Xu, 2017. "A group-buying mechanism for considering strategic consumer behavior," Electronic Commerce Research, Springer, vol. 17(4), pages 721-752, December.
    15. Janice Y. Tsai & Serge Egelman & Lorrie Cranor & Alessandro Acquisti, 2011. "The Effect of Online Privacy Information on Purchasing Behavior: An Experimental Study," Information Systems Research, INFORMS, vol. 22(2), pages 254-268, June.
    16. Yuansheng Huang & Shijian Liu & Lei Yang, 2018. "Wind Speed Forecasting Method Using EEMD and the Combination Forecasting Method Based on GPR and LSTM," Sustainability, MDPI, vol. 10(10), pages 1-15, October.
    17. Punj, Girish, 2011. "Effect of Consumer Beliefs on Online Purchase Behavior: The Influence of Demographic Characteristics and Consumption Values," Journal of Interactive Marketing, Elsevier, vol. 25(3), pages 134-144.
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