Abstract
PurposeThis study strives to better understand resistance to chatbots in the banking sector. To achieve this, it proposes a model based on the paradigm of resistance to innovation and the complexity theory. In addition, it explores the role of gender in relation to chatbot resistance.Design/methodology/approachData are collected in France using a snowball sampling technique. The sample is composed of 385 participants. FsQCA is used to identify all possible combinations of usage, value, risk, tradition and image barriers, as well as two gender conditions that predict resistance to chatbots.FindingsThe results reveal that the sample provides four possible solutions/combinations that may explain resistance to chatbots. These are: (i) a combination of usage, value, risk and tradition barriers, (ii) a combination of value, risk, tradition and image barriers, (iii) a combination of usage, value, risk and image barriers, along with the male gender and (iv) a combination of usage, value, tradition and image barriers, along with the female gender.Research limitations/implicationsThis study provides valuable and straightforward theoretical and managerial implications. The proposed solutions suggest a deep understanding of chatbot resistance. Chatbot developers and marketers can highly benefit from these findings to enhance user acceptance.Originality/valueIn this study, barriers are envisioned within the larger context of innovation resistance. The interactions among barriers causing resistance to chatbots are examined through the lens of the complexity theory, while the data analysis employs the fsQCA approach. Furthermore, this study sheds light on the role of gender in explaining chatbot resistance in the banking sector.