Distrust in Financial Professionals and Robo Advisor Adoption

Last registered on August 10, 2023


Trial Information

General Information

Distrust in Financial Professionals and Robo Advisor Adoption
Initial registration date
August 03, 2023

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
August 10, 2023, 1:22 PM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.



Primary Investigator

University of Hong Kong

Other Primary Investigator(s)

PI Affiliation
Lingnan University
PI Affiliation
University of Hong Kong

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Cases of financial professionals misappropriating client assets for personal gain are rampant. In personal finance, financial institutions have introduced AI-assisted robo-advisors to optimize investments for investors using AI technology. We are trying to understand if investors will convert their distrust in financial professionals into trust in objective algorithm-based investments. We plan to collaborate with a Chinese mobile financial app company, disseminating information to users regarding occupational misappropriation by financial professionals and observing whether users increase their preference for robo-advisor products in their choice of financial products.

We plan to replace the splash screen of the experimental group's mobile app with slogans related to losses caused by human errors of financial experts. On the other hand, the control group sample will use a splash screen with sayings unrelated to human errors of financial experts. By comparing the experimental and control groups, we can determine whether AI's objectivity or financial experts' subjectivity plays a positive role in user adoption of robo-advisor portfolios.
External Link(s)

Registration Citation

An, Jiafu, Wenzhi Ding and Xincheng Wang. 2023. "Distrust in Financial Professionals and Robo Advisor Adoption." AEA RCT Registry. August 10. https://doi.org/10.1257/rct.11886-1.0
Experimental Details


When the user opens the mobile application, we show the treatment on the splash screen.

The treatment consists of two parts: (1) Slogan; (2) loss amounts. Since our treated users are Chinese, we will describe the treatment in Chinese and English.

We have three types of slogans:
S1. No slogan
S2. "Research shows that financial markets have generated at least X billion human errors in the past ten years" (研究表明仅10年金融市场产生至少X0亿元人为错误)
S3. "Research shows that robo-advisors can avoid at least 10 billion yuan in human errors in the past ten years" (研究表明近10年智能投顾可避免至少X0亿元人为错误)

The treatment combinations to be tested are:

- S1 (1 group)
- S2 (2 groups, where X=1, 10)
- S3 (2 groups, where X=1, 10)

Therefore, there will be five groups in total.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
The net inflow investment amount into the robo-advisor portfolios.
Primary Outcomes (explanation)
We will obtain the change in investment for each user in each portfolio daily. Then we will classify the product into "robo-advisor portfolio" and "other portfolio" based on whether it is called an "All Weather Portfolio." The company defines "All Weather Portfolio" as a series of portfolios driven by AI algorithms and promotes them in this way.

We will summarize all inflow and outflow of the "robo-advisor portfolio" and "other portfolio" during the 14-day observation window. The outcome is our primary outcome.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We plan to show the users the intervented splash screen throughout the 14-day experimental period. We will randomly draw 5,000 users who logged into the mobile application within four weeks to one week before the experiment. Then we will randomize them into 5 groups. We repeat the randomization for several rounds to ensure the users are balanced regarding several pre-trial traits, such as age, gender, and asset amount, register date.

The treatments are described in the "Intervention" part.
Experimental Design Details
Randomization Method
Pre-treatment trait balanced sampling. The randomization is done in the office by a computer, and the group of users is pre-determined based on the randomization outcome.
Randomization Unit
Individual user-level.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
2500 active users.
Sample size: planned number of observations
2500 active users.
Sample size (or number of clusters) by treatment arms
500 active users for each treatment arm.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
Human Research Ethics Committee at the University of Hong Kong
IRB Approval Date
IRB Approval Number


Post Trial Information

Study Withdrawal

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information


Is the intervention completed?
Data Collection Complete
Data Publication

Data Publication

Is public data available?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials