Determinanten der Akzeptanz automatisierter Anlageberatungssysteme – Eine theoretische und empirisch-experimentelle Analyse

Mischel, Roman GND

Automated Investment Advisory Systems (AIBS), also known as Robo-Advice or Robo-Advisor, are discussed in the financial industry as another innovation that competes with traditional personal investment advice. Digital distribution and their functioning offer numerous advantages both for customers (e.g. lower fees, constant availability) and for AIBS providers (e.g. cost reduction, opening up of new markets), which means that AIBS is predicted to have great growth potential.
However, both current user numbers and customer surveys indicate that customers are reserved towards AIBS. Diverging results – on the one hand, AIBS have great potential, but on the other hand, they meet with very little acceptance on the part of customers – illustrate the need to gain a deeper understanding of the factors determining acceptance, in order to initiate measures which increase the willingness to use an AIBS.
The results show that the three acceptance factors of expected benefit, social influence and involvement have the greatest direct positive influence on the intended use. In contrast, the other five determinants have only a weak to no (significant) direct influence and instead have an indirect effect on the intended use via other constructs, which is why these determinants cannot be neglected with regard to their significance in the acceptance model.
The consideration of experimental stimuli could show that a bank as a provider of AIBS and the testing of an AIBS can positively influence the determinants in the acceptance model. Many of the effects cited in the literature could not be confirmed with regard to the effect of sociodemographic characteristics (age, gender), so that it can be assumed that both the two stimuli and the social developments contribute to the fact that the differences are smaller.

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Mischel, Roman: Determinanten der Akzeptanz automatisierter Anlageberatungssysteme – Eine theoretische und empirisch-experimentelle Analyse. 2019.

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