People in a target audience are likely to experience the bandwagon effect [1] because they rely on others' assessment of information. Overall, taking advantage of the bandwagon effect can be beneficial in a wide range of situations [2]. However, reverse-bandwagon behaviors are not always driven by the associated cognitive bias, which go against what is done by others.
A practical example of how the bandwagon effect can spread, appears in the case of the medical sciences, which are generally viewed as objective, rigorous, and empirically-driven. Thus, less likely to be influenced by similar phenomena. Although the medical sciences are not commonly influenced by these phenomena, the new hype of a novel medical concept can promote the bandwagon effect. As a result of a large-scale bandwagon effect, some studies [3, 4] have demonstrated how a new medical concept can gain momentum and become mainstream. For instance, Artificial Intelligence (AI) is rapidly evolving into solutions for clinical practice [5], but the reality is that the field has not yet fully embraced to clinical practice. Still, doctors and more and more open [6] for the introduction of AI in their clinical daily basis. Making it apparent the presence of the bandwagon effect, while denoting other research problems.
For research purposes, the following question arise:
What underlying psychological mechanisms could cause a doctor or a group of doctors to experience the bandwagon effect over the AI hype?
References
[1] 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
[2] Howard J. (2019) Bandwagon Effect and Authority Bias. In: Cognitive Errors and Diagnostic Mistakes. Springer, Cham. https://doi.org/10.1007/978-3-319-93224-8_3
[3] Whelehan, D.F., Conlon, K.C. & Ridgway, P.F. Medicine and heuristics: cognitive biases and medical decision-making. Ir J Med Sci 189, 1477–1484 (2020). https://doi.org/10.1007/s11845-020-02235-1
[4] Landucci, F., Lamperti, M. A pandemic of cognitive bias. Intensive Care Med 47, 636–637 (2021). https://doi.org/10.1007/s00134-020-06293-y
[5] 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
[6] Nadarzynski, T. et al. (2019) ‘Acceptability of artificial intelligence (AI)-led chatbot services in healthcare: A mixed-methods study’, DIGITAL HEALTH. https://doi.org/10.1177/2055207619871808