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

Zhongya Han, Zhongjun Tang and Bo He

Technological Forecasting and Social Change, 2022, vol. 176, issue C

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.

Keywords: Forecasting; Two-stage process model; Improved bass model; Product information popularity (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:176:y:2022:i:c:s0040162521008933

DOI: 10.1016/j.techfore.2021.121458

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