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Consumers' Bayesian learning under Knightian uncertainty: An eye‐tracking analysis
Author(s) -
Song Hongjuan,
Jiang Yushi
Publication year - 2018
Publication title -
journal of consumer behaviour
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.811
H-Index - 43
eISSN - 1479-1838
pISSN - 1472-0817
DOI - 10.1002/cb.1740
Subject(s) - knightian uncertainty , bayesian inference , bayesian probability , quality (philosophy) , process (computing) , computer science , eye tracking , artificial intelligence , machine learning , psychology , economics , ambiguity , epistemology , programming language , philosophy , operating system
In recent years, risky decisions and the “gambling paradigm” have been mainstream topics in economics and psychology research. However, the choice of Knightian uncertainty, especially as it relates to the potential cognitive processes of consumers in marketing, has not drawn the attention of scholars. This study introduced Bayesian learning theory to reproduce the consumer learning process and conducted a two‐stage experiment to explore the process of how perceived price and uncertain quality information influence consumer learning and decisions. The results were validated via an eye‐tracking experiment with dynamic (updated) advertising information to observe the learning mechanism of consumers under Knightian uncertainty. The results showed that consumers conduct irregular Bayesian learning based on different perceived price levels in different information state spaces. Belief changes follow the first‐order Markov learning rule, and Bayesian learning is a dual‐system process. Overall, the results are in line with the belief‐integration principle of the Bayesian learning model under Knightian uncertainty.