With the popularity of financial technology (fintech) chatbots equipped with artificial intelligence, understanding the user's response mechanism can help bankers formulate precise marketing strategies, which is a crucial issue in the social science field. Nevertheless, the user's response mechanism towards financial technology chatbots has been relatively under-investigated. To fill these literature gaps, latent growth curve modeling was adopted by the present research to survey Taiwanese users of fintech chatbots. The present study proposed a customer continuance model to predict continuance intention for fintech chatbots and that cognitive and emotional dimensions positively influence the growth in a user's attitude toward fintech chatbots, which in turn, positively influences continuance intention over time. In total, 401 customers of fintech chatbots were surveyed through three time points to examine the relationship between these variables over six months. The results support the theoretical model of this research and can advance the literature of fintech chatbots and the information technology adoption model.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8433661 | PMC |
http://dx.doi.org/10.3390/s21175687 | DOI Listing |
Sensors (Basel)
August 2021
Department of Finance, Chihlee University of Technology, New Taipei 220, Taiwan.
With the popularity of financial technology (fintech) chatbots equipped with artificial intelligence, understanding the user's response mechanism can help bankers formulate precise marketing strategies, which is a crucial issue in the social science field. Nevertheless, the user's response mechanism towards financial technology chatbots has been relatively under-investigated. To fill these literature gaps, latent growth curve modeling was adopted by the present research to survey Taiwanese users of fintech chatbots.
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