As a society, we put a lot of trust in technology. Advanced technology can offer cutting edge service that optimizes our performance. In terms of the financial industry, many AI and non-AI programs act as powerful investment tools. Robo-advisors, for example, calculate investment recommendations similarly to human advisors.
There are several factors that may impact the amount of trust an investor places in Robo-advisors. These measures are reflected in an investor’s likelihood of opting for the recommendation from the Robo-advisor rather than the human. “The Effect of Humanizing Robo‐Advisors on Investor Judgments” explores the phenomena of humanizing these programs and the impact it has with user interaction.
“Humanizing” was achieved by giving the Robo-advisor an actual name in two separate experiments. In the first experiment, Frank Hodge, Kim Mendoza, and Roshan Sinha predicted that investors would prefer the recommendation of an unnamed Robo-advisor. Conversely, they also predicted investors would prefer human advisors when they were named. They also explored investors’ judgments in terms of task complexity.
The data collected found their predictions in the first experiment to be correct. Further examining the perceived task complexity found a significant association between preference and the perceived intricacy.
When the advisor only needed to perform a simple task, investors judged named Robo-advisors more favorably. However, when tasks were considered complex, they were less likely to exhibit a preference for a named Robo-advisor. These results are consistent with previous findings and offer valuable insight in facilitating successful human-computer interactions.
Most importantly, this data highlights the potential downfall software developers may encounter when trying to develop friendly, more humanlike programs.
Full paper: Hodge, F., K. Mendoza, and R. Sinha. 2020. “The Effect of Humanizing Robo‐Advisors on Investor Judgments.” Contemporary Accounting Research https://doi.org/10.1111/1911-3846.12641