Users in a target audience are likely to experience the bandwagon effect because they rely on others' assessment of information. In some domains , it is expected that some users are less likely to be influenced by similar phenomena. However, some users such as clinicians are being influenced by the new hype of novel diagnostic solutions, promoting the bandwagon effect. For instance, Artificial Intelligence (AI) is rapidly evolving into solutions for clinical practice , but the reality is that the field has not yet fully embraced to clinical practice.
Based on this question, UX research must take into account the bandwagon effect to mitigate the associated cognitive bias. Some of our research problems are concerning with the bias answers that clinicians may provide during these studies. For instance, we study usability (e.g., SUS) and workload (e.g., NASA-TLX) to understand some workflow changes, but we felt like the clinician's answer could be biased. Because now they are must likely to be influenced by the clinical community, who are seeing this as a hype.
Thus, the following question arise:
How to avoid the bandwagon effect on users of the clinical domain for UX research purposes?
 Luckhoff C. (2021) Bandwagon Effect. In: Raz M., Pouryahya P. (eds) Decision Making in Emergency Medicine. Springer, Singapore. https://doi.org/10.1007/978-981-16-0143-9_9
 Francisco Maria Calisto, Carlos Santiago, Nuno Nunes, Jacinto C. Nascimento, Introduction of human-centric AI assistant to aid radiologists for multimodal breast image classification, International Journal of Human-Computer Studies, Volume 150, 2021, 102607, ISSN 1071-5819, https://doi.org/10.1016/j.ijhcs.2021.102607