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 “uncertainty 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 that effect 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. Corresponding markets, for example, can be found for radically new products for which consumers have strong opinions or as a result of a disinformation campaign by some competitor. We propose an approach that can deal with such a setting and we demonstrate the value added of this novel approach (in contrast to the traditional Bayesian approach) through computational simulation experiments with an agent-based market model of innovation diffusion.