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This paper analyzes automated advisory services, a new business model developed by Fintech start-ups in the USA. The markets for automated advisory services (AAS) in the USA, where it is most established, and in Germany, where the market is still in the infancy stage, are examined and compared. The aim of the paper is to identify the market conditions determining user acceptance of automated advisory services in the USA and comparing the German market conditions to the US market in order to predict consumer acceptance in Germany. The markets are examined using the PEST analysis and Porter´s five forces framework. Recent market data, mainly from online media like online newspapers and studies, is collected and applied to the models. The data reveals that the market conditions for AAS are similar in both markets but in Germany there are some negative factors restraining growth. The second part of the paper is a technology acceptance analysis of automated advisory services using the UTAUT2 model. The UTAUT2 model is first applied to and extended for the automated advisory services market. Afterwards, the market data is applied to the model, confirming that the market data and theoretical framework of the model are conform. Finally, an outlook into the future of AAS in Germany and the USA is given, showing that the German market has the potential to develop similar to the US market once the negative influences on technology acceptance are diminished Further research into the aspects individualization of automated advisory services, web 3.0 applications and security of the algorithms and user data should be conducted.
Purpose: The goal of this paper is to develop a technology acceptance model for digital anamnesis and explore the factors that influence individual adoption behavior.
Methods: Through a literature review we identified important factors that influence acceptance. We then conduct an empirical study among patients with 115 respondents. Subsequently, we test the model using partial least squares path modelling.
Results: We found that performance expectancy, social influence, and trust are the most have significant influence on behavioral intention. A group comparison reveals significant differences between young and old patients. The model explained 57.5 % of the variance of behavioral intention.
Conclusions: This study helps us understand the key determinants of patient acceptance behavior, and enables us to give advice to businesses in the early stages of development.