Into the Mind of the Machine: Implications of Fixed and Adaptive Algorithms on Consumer Perceptions and Behavior

Algorithms are the backbone of the new cutting-edge technologies of the digital age including autonomous products, artificial intelligence (AI) or the internet of things. Many different kinds of algorithms exist that are lively discussed in the computer science literature. For consumers, however, the inside of a machine typically constitutes a black box. Integrating research streams from consumer behavior and computer science, we open the box and inform consumers about two major categories of algorithms: fixed (following parameters pre-determined by a programmer) and adaptive (following parameters that can be altered in the course of operation). Since former consumer-centered research on perceptions of algorithms and AI typically treats algorithms as a generic concept, this work is the first to examine the effect of informing consumers about the specific type and working style of algorithms implemented in products and technologies. The main objective of this project is to investigate how information about the characteristics of the underlying algorithm alters consumer reactions directed to a technology (e.g., a smart product). Extending former research on consumers’ perceptions of algorithms and building on our own former research that adaptive (vs. fixed) algorithms are perceived as more creative and human-like, I propose that information about the implemented algorithm will significantly influence consumers’ behavior directed towards technologies and evaluations of their produced outcome. In a mixture of behavioral lab and online experimental studies during the anticipated research stay at the Columbia University, I aim to collect complementary behavioral evidence for our initial work and extend our research focus.