Humankind is becoming increasingly dependent on algorithms in their everyday life. Algorithmic decision support has existed since the entrance of computers but are becoming more sophisticated with elements of Articial Intelligence (AI). Though many decision support systems outperform humans in many areas, e.g. in forecasting task, the willingness to trust and use algorithmic decision support is lower than in a corresponding human. Many factors have been investigated to why this algorithm aversion exists but there is a gap in research about the eects of scenario characteristics. Results provided by this study showed that people prefer recommendations from a human expert over algorithmic decision support. This was also re ected in the self-perceived likelihood of keeping a choice when the decision support recommended the other option, where the likelihood was lower for the group with human expert as the decision support. The results also showed that the decision supports, regardless of type, are more trusted by the user in an acquisition framed scenario than in a forfeiture framed. However, very limited support was found for the hypothesized interaction between decision support and scenario type, where it was expected that algorithm aversion would be stronger for forfeiture than acquisition scenarios. Moreover, the results showed that, independent of the experimental manipulations, participants with a positive general attitude towards AI had higher trust in algorithmic decision support. Together, these new results may be valuable for future research into algorithm aversion but must also to be extended and replicated using dierent scenarios and situations.