Bots for Research: Minimising the Experimenter Effect
Senuri Wijenayake, Niels van Berkel, Jorge Gonçalves · VBN Forskningsportal (Aalborg Universitet) · 2020
Experimenter-induced influences can trigger biased responses from research participants. We evaluate how digital bots can be used as an alternative research tool to mitigate these biases, as based on existing literature. We note that the conversational interactivity provided by bots can significantly reduce biased responses and satisficing behaviour, while simultaneously enhancing disclosure and facilitating scalability. Bots can also build rapport with participants and explain tasks at hand as well as a human experimenter, with the added benefit of anonymity. However, bots often follow a predetermined script when conversing and therefore may not be able to handle complex and unstructured conversations, which could frustrate users. Studies also imply that bots with human-like features may induce experimenter effects as similar to humans. We conclude with a discussion on how bots could be designed for optimal utilisation in research.