A TWO-PATH BAYESIAN PROBABILITY MODEL FOR EXAMINING INDIVIDUAL AND PROCESS HETEROGENEITY IN THE AISAS FRAMEWORK

Hui-Hsin Huang

doi:10.37075/JOMSA.2026.1.12

Authors

Hui-Hsin Huang Fu Jen Catholic University, Taiwan https://orcid.org/0009-0000-3442-3114

DOI:

https://doi.org/10.37075/JOMSA.2026.1.12%20

Keywords:

AISAS model, Bayesian probability model, Weibull distribution

Abstract

The AISAS (Attention-Interest-Search-Action-Share) model is widely utilized to explore the influence of social media on information diffusion and marketing campaign applications. However, traditional approaches often overlook the concurrent variations across individual consumers and transition processes. This paper constructs a Bayesian probability model to predict both consumer individual differences and process-specific variations across the AISAS steps. Utilizing empirical data from diverse social media platforms, including individual media behaviors and the specific duration of each AISAS stage, we estimate model parameters and evaluate the goodness of fit. The empirical results demonstrate satisfactory predictive power, offering critical insights and managerial implications for future marketing applications.

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References

Atara, & Dentsu. (2015). Atara develops ‘Dual Aisas ModelTM’, a new consumer behavior models in the network era. https://www.atara.co.jp/news/dual-aisas-2.html

Dentsu. (2008). The change of consumer action model: From AIDMA to AISAS. http://www.dentsu.com.tw/

Dentsu. (2015). Buying behavior data, models: Capturing free online and offline movement of consumers. http://www.dentsu.com/business/japan/promotions/buying_behavior.html

Erkan, I., & Evans, C. (2016). The influence of EWOM in social media on consumers’ purchase intentions: An extended approach to information acceptance. Computers in Human Behavior, 61, 47–55.

Gerbarg, D. (2009). The Economics of Information Communication and Entertainment: The Impacts Of Digital Technology in the 21st Century. Springer: New York, USA.

Huang, M. C., Li, J. L., Huang, H. L., Xiang, S, B, Wang, A. G. & Xian, L. (2022). Tourism AIDMA Model for the Construction of Artificial Intelligence Sensors of IoT Intelligent System. International Journal of Innovative Application on Social Science and Engineering Technology, 3(4), pp. 1-9

Javed, S., Md. Salamun Rashidin & Xiao, Y. (2022). Investigating the impact of digital influencers on consumer decision-making and content outreach: using dual AISAS model. Economic Research-Ekonomska Istraživanja, 35:1, 1183-1210.

Li, H. & Pan, Y. (2023) Impact of Interaction Effects between Visual and Auditory Signs on Consumer Purchasing Behavior Based on the AISAS Model. Journal of Theoretical and Applied Electronic Commerce Research, 18, pp.1548–1559.

Rogers, E. M. (1962). Diffusion of innovations (1st ed.). New York: Free Press of Glencoe.

Rogers, E. M. (1995). Diffusion of innovations (4th ed.). New York: The Free Press.

Tadayuki, Y. (2014). Comparative Study of Social Media Marketing in Japan and Taiwan: Based on AISAS and SIPS Model. National Taichung University of Science and Technology, Department of Applied Japanese, Japanese Market and Business Strategy Master's Program, dissertation.

Tao, K. (2024). The Communication Effect of Elevator Multimedia Advertisement Based on the AIDMA Model–Taking a commercial plaza in Shanghai as an example. SHS Web of Conferences, 1 April 2024, 188, pp.1-5.

Xiong, X. & Su, Z. (2023). The Analysis of Consumer Behavior Based on SIPS Model: Taking the Avengers Series as Examples. 4th International Conference on World Economy and Project Management (WEPM 2023), 22, 159-163.