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Improved Bass model for predicting the popularity of product information posted on microblogs

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  • Han, Zhongya
  • Tang, Zhongjun
  • He, Bo

Abstract

Companies use celebrity accounts on microblogs to promote product information. Predicting the popularity of product information before publication is important for celebrity account selection and microblog account management. Previous studies mainly considered the characteristics of information content, context, and information sources while ignoring the impact of the heterogeneity of user retweeting decision-making processes on the popularity of information. In this study, we analyze the retweeting decision processes of two types of users with different information sources and build a two-stage process model of product information diffusion. Based on this model, we explore the interest decline rate of users and propose two improved Bass models: the exponential- and power-function improved models. The experiment results and model comparisons show that the exponential-function improved model outperforms the Bass, Gompertz and power-function improved models, and is suitable for the pre-release prediction of product information popularity. The interest decline rate of users in retweeting product information follows an exponential function, and product information diffusion on microblogs is mainly driven by the celebrity effect. Our research reveals the mechanism of the interest attenuation effect, the celebrity effect, and product information quality affecting product information diffusion on microblogs, which can aid further research on product information popularity.

Suggested Citation

  • Han, Zhongya & Tang, Zhongjun & He, Bo, 2022. "Improved Bass model for predicting the popularity of product information posted on microblogs," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
  • Handle: RePEc:eee:tefoso:v:176:y:2022:i:c:s0040162521008933
    DOI: 10.1016/j.techfore.2021.121458
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    References listed on IDEAS

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    1. Frank M. Bass & Trichy V. Krishnan & Dipak C. Jain, 1994. "Why the Bass Model Fits without Decision Variables," Marketing Science, INFORMS, vol. 13(3), pages 203-223.
    2. Wang Jing & Li Min & Wang Ya-Qi & Zhou Zi-Chen & Zhang Li-Qiong, 2019. "The influence of oblivion-recall mechanism and loss-interest mechanism on the spread of rumors in complex networks," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 30(09), pages 1-21, September.
    3. Karniouchina, Ekaterina V., 2011. "Impact of star and movie buzz on motion picture distribution and box office revenue," International Journal of Research in Marketing, Elsevier, vol. 28(1), pages 62-74.
    4. Vijay Mahajan & Robert A. Peterson, 1978. "Innovation Diffusion in a Dynamic Potential Adopter Population," Management Science, INFORMS, vol. 24(15), pages 1589-1597, November.
    5. Hermann Simon & Karl-Heinz Sebastian, 1987. "Diffusion and Advertising: The German Telephone Campaign," Management Science, INFORMS, vol. 33(4), pages 451-466, April.
    6. Xielin Liu & Feng-Shang Wu & Wen-Lin Chu, 2009. "Innovation Diffusion: Mobile Telephony Adoption In China," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 13(02), pages 245-271.
    7. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    8. Singh, Sanjay Kumar, 0. "The diffusion of mobile phones in India," Telecommunications Policy, Elsevier, vol. 32(9-10), pages 642-651, October.
    9. Moldovan, Sarit & Steinhart, Yael & Lehmann, Donald R., 2019. "Propagators, Creativity, and Informativeness: What Helps Ads Go Viral," Journal of Interactive Marketing, Elsevier, vol. 47(C), pages 102-114.
    10. Frank M. Bass, 2004. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 50(12_supple), pages 1825-1832, December.
    11. Goodwin, Paul & Meeran, Sheik & Dyussekeneva, Karima, 2014. "The challenges of pre-launch forecasting of adoption time series for new durable products," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1082-1097.
    12. Meade, Nigel & Islam, Towhidul, 2006. "Modelling and forecasting the diffusion of innovation - A 25-year review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 519-545.
    13. Wu, Bo & Shen, Haiying, 2015. "Analyzing and predicting news popularity on Twitter," International Journal of Information Management, Elsevier, vol. 35(6), pages 702-711.
    14. Dan Horsky & Leonard S. Simon, 1983. "Advertising and the Diffusion of New Products," Marketing Science, INFORMS, vol. 2(1), pages 1-17.
    15. Guidolin, Mariangela & Guseo, Renato, 2014. "Modelling seasonality in innovation diffusion," Technological Forecasting and Social Change, Elsevier, vol. 86(C), pages 33-40.
    16. Ramírez-Hassan, Andrés & Montoya-Blandón, Santiago, 2020. "Forecasting from others’ experience: Bayesian estimation of the generalized Bass model," International Journal of Forecasting, Elsevier, vol. 36(2), pages 442-465.
    17. Christopher J. Easingwood & Vijay Mahajan & Eitan Muller, 1983. "A Nonuniform Influence Innovation Diffusion Model of New Product Acceptance," Marketing Science, INFORMS, vol. 2(3), pages 273-295.
    18. Frank M. Bass, 2004. "Comments on "A New Product Growth for Model Consumer Durables The Bass Model"," Management Science, INFORMS, vol. 50(12_supple), pages 1833-1840, December.
    19. Zhang, Yuchi & Moe, Wendy W. & Schweidel, David A., 2017. "Modeling the role of message content and influencers in social media rebroadcasting," International Journal of Research in Marketing, Elsevier, vol. 34(1), pages 100-119.
    20. Ioannis Arapakis & Berkant Barla Cambazoglu & Mounia Lalmas, 2017. "On the feasibility of predicting popular news at cold start," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(5), pages 1149-1164, May.
    21. Velickovic, Stevan & Radojicic, Valentina & Bakmaz, Bojan, 2016. "The effect of service rollout on demand forecasting: The application of modified Bass model to the step growing markets," Technological Forecasting and Social Change, Elsevier, vol. 107(C), pages 130-140.
    22. Dimara, Efthalia & Skuras, Dimitris, 2003. "Adoption of agricultural innovations as a two-stage partial observability process," Agricultural Economics, Blackwell, vol. 28(3), pages 187-196, May.
    23. Jain, Dipak C & Rao, Ram C, 1990. "Effect of Price on the Demand for Durables: Modeling, Estimation, and Findings," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(2), pages 163-170, April.
    24. Shlomo Kalish, 1985. "A New Product Adoption Model with Price, Advertising, and Uncertainty," Management Science, INFORMS, vol. 31(12), pages 1569-1585, December.
    25. Fan, Zhi-Ping & Che, Yu-Jie & Chen, Zhen-Yu, 2017. "Product sales forecasting using online reviews and historical sales data: A method combining the Bass model and sentiment analysis," Journal of Business Research, Elsevier, vol. 74(C), pages 90-100.
    26. Parra, Carlos M. & Gupta, Manjul & Mikalef, Patrick, 2021. "Information and communication technologies (ICT)-enabled severe moral communities and how the (Covid19) pandemic might bring new ones," International Journal of Information Management, Elsevier, vol. 57(C).
    27. Zhao, Laijun & Wang, Qin & Cheng, Jingjing & Chen, Yucheng & Wang, Jiajia & Huang, Wei, 2011. "Rumor spreading model with consideration of forgetting mechanism: A case of online blogging LiveJournal," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(13), pages 2619-2625.
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