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3163. Innovation diffusion in the presence of opposing information sources: An agent-based simulation approach

Invited abstract in session TA-43: Simulation in innovation, stream Agent-based Models in Management, Economic and Organisation Sciences.

Tuesday, 8:30-10:00
Room: 99 (building: 306)

Authors (first author is the speaker)

1. Frederik Tolkmitt
Department of Business Administration and Economics, Bielefeld University
2. Christian Stummer
Department of Business Administration and Economics, Bielefeld University

Abstract

The market introduction of radically new products and their respective innovation diffusion can considerably be affected by consumers’ increased uncertainty due to diverging information received from opposing sources. Such information can originate, for example, from strong general attitudes being prevalent in certain consumer groups (e.g., regarding the pros and cons of integrating AI capabilities in products) or it may even be spread by a competitor employing a negative-word-of-mouth campaign (e.g., when questioning the eco friendliness of electric vehicles). In such a setting, consumers receive ambiguous signals, which should increase their uncertainty and potentially delay product adoption. However, commonly used belief updating models (such as Bayesian learning), in a counterfactual manner, assume a decrease of uncertainty whenever new information is received even if this information substantially deviates from previous information. In our research, we demonstrate this effect by means of a computational simulation experiment based on a straightforward agent-based model of innovation diffusion and we propose a novel approach that more realistically captures consumer uncertainty with respect to insufficient or ambiguous information.

Keywords

Status: accepted

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