In this article, the authors examine the factors influencing the adoption of chatbots within the fintech industry. By employing the Interpretive Structural Modeling (ISM) and Matrice d’Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) methodologies, the study identifies key drivers and barriers to fintech chatbot adoption.
Authors
Sonal Ahuja, Doctoral Research Scholar, Jindal Global Business School, O.P. Jindal Global University, Sonipat, India.
Vaibhav Sharma, Doctoral Research Scholar, Jindal Global Business School, O.P. Jindal Global University, Sonipat, India.
Simarjeet Singh, Great Lakes Institute of Management, Gurugram, India.
Summary
The financial services sector experiences significant effects from digitalization. A crucial factor is that financial offerings heavily rely on information. Notably, fintech chatbots enhance the exchange of information by being integrated into messaging platforms, websites, and mobile apps. This integration enables users to engage with these chatbots using written or spoken conversations.
While the literature has explored different enablers that facilitate the adoption of fintech chatbots, the interplay between these enablers is currently intricate and multifaceted. Due to the rising complexity, academics and practitioners have become increasingly interested in understanding the proper hierarchy of fintech chatbots’ enablers. The SPAR-4-SLR protocol of systematic literature review is used to identify the contextual enablers of fintech chatbots, which are further finalized by integrating experts’ recommendations. In response, this study applies interpretive structural modeling (ISM) and creates a five-level hierarchical structure.
The dependence and driving power of the selected key enablers were then assessed using Matrice d’ Impacts Croises- Multiplication Applique a classement (MICMAC) analysis. Our results show that human-like interaction and social influence are placed at the bottom level of the ISM hierarchy model and have the highest deriving power based on the MICMAC classification. On the other hand, pleasure and adaptability rises to the highest level and has the strongest dependence power.
The findings of this study could help practitioners in the finance sector and academics in the field better understand key enablers and how they interact. This can help practitioners create better strategies for accelerating the use of fintech chatbots. This study is an early attempt to identify the important fintech chatbot enablers and rank them according to their dependence and deriving power.
Published in: Quality and Quantity
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