Human-Robot Interaction: When Investors Adjust the Usage of Robo-Advisors in Peer-to-Peer Lending | Semantic Scholar (2024)

The impact of anthropomorphized robo-advisors on investment decisions is considered, with a focus on their ability to mitigate investors’ behavioral biases, and the well-documented disposition effect is studied.

This study challenges the conventional linear and symmetric perspective adopted in prior research by examining the interplay among technology characteristics, human-like characteristics, and consumer characteristics to explain the acceptance of robo-advisory services.

Continuous advancements in fintech provide a somewhat customized service but the robot’s lack of ability to manage certain types of investments and integrate intangible yet critical human factors into the assessments limit the effectiveness of these tools, at least currently.

This study highlights capabilities that humans need to effectively work with AI and still be in control rather than just being directed.

Evidence is found that AI benefits workers with greater task-based experience, but senior workers gain less from AI than their junior colleagues, and the relatively lower productivity lift from AI is not a result of seniority per se but lower trust in AI, likely triggered by the senior workers’ broader job responsibilities.

It is argued that the future workplace may not belong exclusively to humans or machines, and it is better to use AI together with humans by combining their unique characteristics and abilities.

It is concluded that digital platforms increase financial inclusion, helping to foster investment and achieve a more egalitarian allocation of resources.

An overview of current research on XAI in Finance is provided with a systematic literature review screening 2,022 articles from leading Finance, Information Systems, and Computer Science outlets, and classify them according to the used XAI methods and goals that they aim to achieve.

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This work identifies requirements for robo-advisory, derive design principles and evaluate them in two iterations with a real roboservice in a controlled laboratory study, and results confirm the validity of the identified design principles.

It is found that the online friendships of borrowers act as signals of credit quality and increase the probability of successful funding, lower interest rates on funded loans, and are associated with lower ex post default rates.

The research broadens and deepens the understanding of the roles of information disclosure, social influence, information quality, and trust in economic exchanges in online settings and shows that not only the amount but also the content of such direct communication matters.

Giving participants the freedom to modify an imperfect algorithm made them feel more satisfied with the forecasting process, more likely to believe that the algorithm was superior, and morelikely to choose to use an algorithm to make subsequent forecasts.

It is shown that people are especially averse to algorithmic forecasters after seeing them perform, even when they see them outperform a human forecaster, and this phenomenon, which is called algorithm aversion, is costly, and it is important to understand its causes.

This paper considers labeling tasks and develops a comprehensive scheme for managing the quality of crowd labeling and introduces two novel metrics that can be used to objectively rank the performance of crowdsourced workers.

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