PUB - Publikationen an der Universität Bielefeld

When consumers are uncertain whether they have sufficient and unambiguous information regarding a new product, they might delay the adoption decision to a later point in time. This “ambiguity effect” can have a major impact on the product’s market diffusion. Prior models that account for consumers’ belief updating in such a setting usually capture uncertainty by resorting to some form of Bayesian learning and, in so doing, typically assume that the distribution of information with respect to the attributes of the new product is normal and that the information points received by individual consumers are independent draws from this distribution. Consequently, consumers’ uncertainty regarding their beliefs decreases with more information (e.g., after talking with a peer). This strong assumption is convenient as it makes models of opinion dynamics analytically tractable, and it works in most instances. However, when inconsistent information is provided from two opposing sources and, thus, receiving additional information potentially increases uncertainty of individual consumers, a different approach is required. We propose such an approach and demonstrate its value added through computational simulation experiments, in which the ambiguity effect arises. In further research, this novel approach can serve as a starting point for investigating related topics such as the impact of disinformation campaigns.